Gastrointestinal Microbial Ecology With Perspectives on Health and Disease

32.1

Introduction

The human gut is one of the most diverse and rich ecosystems on Earth. It has been estimated that each person is home to over 100 trillion (10 ¹⁴ ) bacterial and archaeal cells. This number is impressive when you consider that the sum total of all human cells in an individual person is approximately 10 trillion (10 ¹³ ). The highest bacterial and archaeal densities found in the gastrointestinal tract (GIT) have been reported in the colon, where they approach 10 ¹¹ cells/mL. For comparison, the oceans and forest soils have estimated bacterial densities of 10 ⁵ cells/mL and 10 ⁷ cells/g, respectively. Thus, the GIT of a healthy human harbors one of the highest densities of bacteria on the planet, and this microbial consortium provides many benefits to its host.

Early estimates of the total number of bacterial species found in the GIT was 400–500. These early estimations were based upon culture-dependent techniques, and it was assumed that 90% of all bacteria present could be cultured. However, new data are suggesting that only 30%–40% of bacteria found in the human GIT can be cultured. It is evident that the preponderance of bacteria present in the gut are represented by species that have not yet been cultivated and new estimates, based upon culture-independent techniques, place the total number of “species” or operational taxonomic units (OTUs) in the human GIT at 15,000.

The human microbiome has been defined as the “ecological community of commensal, symbiotic, and pathogenic microorganisms that literally share our body space,” a key component of this definition is that it is not only the microorganisms, but the host or environment they occupy. The microbiota includes helminths (e.g., Cestoidea, Trematoda, and Nematoda), fungi, protozoa, viruses, bacteria, and archaea. For this chapter, the focus will be on the bacterial and archaeal members of the microbiota. Across the human population, there is tremendous diversity in the microbiota of individuals and there are many ecological, physiological, immunological, and toxicological benefits of the microbiome for the human host ( Table 32.1 ). In this chapter, the ecology and function of the gut microbiota will be presented. Key concepts such as the composition of the microbiota, microbial ecology, diversity, richness, evenness, gut microbial succession and colonization, metabolic niches, mechanisms that control diversity, bacterial metabolites, and the role of the microbiota in health and disease will be discussed.

The role the human microbiome plays in health and disease is just starting to be understood, a few of the examples include obese individuals can have a markedly different microbiome than lean individuals ; patients with inflammatory bowel disease (IBD) have altered microbiota, and they may have changes in their gut microbiota that precede a diagnosis, particularly in Roseburia spp., which have been shown to be decreased in those already diagnosed with IBD and as such, the manipulation of the microbiota using antibiotics, probiotics, and prebiotics might be useful in treating IBD ; and infants and adults with Celiac disease have a higher abundance of certain bacterial species in their GIT, that is, Bacteroides , Clostridium , and Staphylococcus , identified from the feces. These observations and many others have been the motivating force underpinning the National Institutes of Health (NIH) Human Microbiome Project (NIH HMP).

The NIH HMP roadmap for biomedical research has three main goals: (1) utilize new high-throughput screening technology to characterize the microbiome more completely by studying multiple body sites from 250 “normal” individuals; (2) determine if there are associations between changes in the microbiome and health and disease; and (3) standardize data resources and new technologies for the wider scientific community. Phase one of this project started in 2008 and landed two seminal papers in 2012, which examined a healthy or “normal” adult cohort. The collective findings from these papers were that (1) each body region was unique in its microbial community and (2) metabolic pathways were constant regardless of taxonomic composition. Furthermore, data from the HMP and other human microbiota studies can be added to epidemiological studies to better understand population health and human disease. Phase two of this project has begun, and using a multiomics approach (described later in this chapter), it aims to examine changes in three microbiome-associated conditions: (1) preterm birth, (2) IBD, and (3) type 2 diabetes.

To broaden the impact of microbiome research, in 2016, the White House Office of Science and Technology Policy announced a new National Microbiome Initiative (NMI). The goal of this project is to study different ecosystems, examining microbiome behavior under various conditions, and explore how microbial systems could impart protection or restore normal function after perturbation.

32.2

Overview of Culture-Independent Molecular Techniques for Characterizing the Human Gut Microbiome

Over 30 years ago, C.R. Woese pioneered the use of ribosomal RNA (rRNA) sequence comparisons for reconstructing the evolutionary history of microbes. With the introduction of improved sequencing technology, N.R. Pace and others developed culture-independent, molecular tools for assessing the ecology of microorganisms. The number of microbial phyla increased from Woese’s description of a dozen bacterial lineages to more than 100 major phyla; most of which do not include cultured representatives. Sequence analysis of polymerase chain reaction (PCR) amplicons for 16S rRNA gene genes has become the “gold standard” for assessing species richness in microbial communities.

The majority of culture-independent techniques are based upon the PCR amplification of the highly conserved 16S rRNA gene, which is a particularly useful molecular marker for identifying bacterial species. It is transmitted vertically without lateral gene transfer, highly conserved and not under natural selection. However, despite being relatively small (approximately 1.5 Kbp), it does have nine hypervariable regions that are useful for reconstructing evolutionary history (phylogeny). Conserved sequences allow for the design of broad bacterial kingdom primers that can create amplicons of individual 16S rRNA genes in a population. Differences in the numbers and sequences of 16S rRNA amplicons can be compared to identify and profile the myriad members of the human gut microbiome. Current molecular techniques can create microbial community fingerprints, locate and quantify bacteria geographically within the GIT, quantify and estimate relative bacterial abundance, and identify the members of the community. Also, with the advent of new high-throughput sequencing platforms, it is possible to perform metagenomic analysis of the entire community accurately, quickly, and relatively inexpensively.

Fluorescent in situ hybridization (FISH) is a technique that can be used to visualize and quantify bacteria abundance in their particular environments within the GIT. FISH uses fluorescent oligonucleotide probes that bind to a specific target sequence, that is, 16S rRNA, within the bacterial cell. Once the fluorescently labeled probe has hybridized with the target gene, a fluorescent microscope can be used to visualize the bacteria cells, allowing for the direct examination of spatial distribution and quantification. These techniques have also been adapted for flow cytometry, thereby allowing quantification of individual bacteria. Universal bacterial probes and probes for specific genera, for example, Bacteroides , Bifidobacterium , Streptococcus , Lactobacillus , and Clostridium , are available. It has been estimated that 90% of fecal bacteria can labeled by some type of taxon-specific FISH probes.

Even more widely used is the quantification of bacterial abundance using real-time quantitative PCR (Q-PCR). The primers for this assay can be kingdom-specific, phylum- specific, and even genus-specific. Targets for Q-PCR range from 16S rRNA or other highly conserved genes to taxon-specific gene targets. The data can be expressed (and normalized) as total bacterial gene copy number per unit sample or per single copy host gene, if biopsy or other tissue samples are analyzed.

Perhaps the most commonly used technique to determine community structure of the microbiota is to employ high-throughput sequencing techniques. Briefly, this approach requires the extraction and purification of genetic material, the preparation of a 16S-rRNA encoding gene amplicon library designed around the question or hypothesis being examined, sequencing of the material, and processing and analysis of the data. The Illumina platform is currently the platform of choice as it gives high-quality reads, is cost-effective (more sequence data per dollar), it generates longer sequence reads, and importantly, it supports data obtained on other platforms.

More recently, techniques, such as metagenomics, have been employed to study the functional role of gut microbe communities. This technique uses random sequencing of all the DNA extracted from a sample to predict polypeptides, which essentially catalogs all the genes of a given community without needing to identify individual community members. Metagenomics will be described in the context of health and disease later on in this chapter. However, a considerable portion (nearly half) of the microbial metagenome cannot be mapped to previously isolated genomes or even to annotated genes. A combined approach called meta-omics or multiomics circumvents this shortcoming by applying metagenomics, metatranscriptomics, metabolomics, and computational approaches to examine these functionally uncategorized microbial products such as proteins and metabolites. Fortunately, analytical computational tools such as Quantitative Insights Into Microbial Ecology (QIIME), the software package mothur, the web application METAREP, and others exist to help sort and make sense of data generated using these varied approaches.

Historically, other techniques were used to determine community structure and diversity. Commonly used techniques included denaturing and temperature gradient gel electrophoresis (DGGE and TGGE) and terminal restriction fragment length polymorphisms of the 16S rRNA gene (T-RFLP). Chromatograms showing the relative abundance of the individual fragments (T-RF’s) could then be constructed, and changes in the community were detected by the loss or gain of T-RFs. Additionally, constructing clone libraries of the 16S rRNA gene has been a useful method for community membership identification. Sequences can be compared using either classifier-based or OTU-based algorithms, to existing taxonomic databases, for example, the Ribosomal Database Project (RDP, http://rdp.cme.msu.edu/ ). While components of these previously used methods are still relevant, more precise and sophisticated techniques have been developed.

32.3

Applying the Principles of Microbial Ecology to Analyze the Human Gut Microbiome

Ecology is the study of the interplay between organisms, their physical environment, and other organisms that share the physical environment with them; hence, the study gut microbiome can draw many terms and analytical techniques from ecology. One of the key concepts in ecology is competition . Competition for resources, such as nutrients and space, can occur within a species or between species and is the major driving force behind natural selection and evolution. If one of the competitors is weaker or not as adept at utilizing a resource it will either die out or be excluded. Competitive exclusion is the end result of competition forcing an organism to be excluded from a habitat. In the case of gut microbes, this could be competition for the same nutrient, for example, carbon source. No two species will utilize the same resource with the same efficiency. Ultimately, one species will reach a higher population density and force the competitor out of that habitat. Resource partitioning or niche differentiation (a process of natural selection that will force competitors to use resources differently) is a way to avoid competition between species and allow for coexistence. The inhabitants of the GIT microbial community are adept at resource partitioning.

Another important ecological term is niche . An organism’s niche is not so much its physical placement within an ecosystem but its role in the ecosystem, often a metabolic role. No two species can ever occupy the same niche; competition will force one of the organisms out. Where and how bacteria get energy leads to the development of metabolic niches, for example, methane production, sulfate reduction, or carbohydrate fermentation.

Symbiosis is defined as the “living together” of two different organisms. Organisms can live with each other, on each other or within each other. The member within the relationship that has the other organism living in it or on it is the host . The member that is living in or on the host is the symbiont . If both members of the symbiotic union benefit from the relationship, it is termed mutualism . If one member benefits and the other is unaffected, it is called commensalism . The biomedical literature usually describes the members of the gut bacterial microbiome as commensals; however, we now understand much more about the complex relationship between humans and their microbiome and this symbiosis is clearly mutualistic. Iťs been suggested that because the health of humans is so intertwined with their microbiome (and vice-versa), that we are a superorganism. For example, the microbiota contains within its genome metabolic pathways that humans do not possess but that are necessary for our survival. Much of the microbiota cannot thrive outside of the human host. Given the extent of codependence between host and symbiont it seems fair to consider the idea of superorganism. Pathogenic species can be considered “cheaters” that benefit from the existing microbial community and at the same time can negatively impact the fitness of the host and microbial community.

Lastly, ecological succession is the process of changes in species structure within an ecological community. New habitats are first colonized by a pioneer species —this is autogenic succession . Pioneer species are able to withstand more extreme or less favorable living conditions. Their presence in the environment can change both biotic and abiotic components of that environment and over time new species that would not have normally been able to colonize that habitat can do so. If an existing habitat is perturbed (fire, flood, act of god, or antibiotics in the case of microbes), the succession that follows is called allogenic . Succession will be an important concept for understanding colonization of the gut by microbes. The indigenous microbiota that has taken up residence within our GIT is called autochthonous . Microbes that are not part of our resident microbiome and are transient in nature are called allochthonous . The allochthonous members may come from diet, water, or from other sources in our environment.

There are several ways to measure the membership and structure of a microbial community. First, a community is a group of different individual species that interact with each other in the same place at the same time. The most basic measure of a community is species richness . Richness is the total number of species present. Abundance is a measure of how many individuals of each species there are. With species richness and abundance, the species evenness can be calculated. Evenness is a measurement of individual variation in the abundance of the species present. For example: two communities exist (Com 1 and Com 2). Com1 has three species all with an abundance of 10. This community would be considered very even. That is, you have equal numbers of individuals for all three species ( n = 10). In Com 2, you also have three species; however, one species has an abundance of 25 while the remaining two have an abundance of 5. Both Com 1 and Com 2 have the same species richness ( n = 3), but the evenness is quite different. Com 2 two would have lower evenness because it is dominated by individuals of one species. With richness and evenness, you can calculate species diversity . Diversity measures the corresponding variation in richness and evenness. Communities with higher evenness are usually more diverse. In classical ecology, there are myriad ways to calculate diversity. Some of these diversity metrics, for example, Shannon’s Index or Simpson’s Index have been commonly used to describe the diversity of host-associated microbiota. The measurement of membership, richness, evenness, and diversity within a community is called the alpha diversity . Comparing the diversity between two different communities is called the beta diversity . Beta diversity measurements compare how similar or dissimilar the communities are based on changes in membership and structure, for example, Jaccard’s Coefficient of Community Similarity, Sorenson’s Similarity Index, Bray-Curtis Distance, and Morisita-Horn Index. However, one drawback to diversity calculations is that low abundance taxa are frequently not sufficiently quantified. One approach to better estimate diversity in low abundance taxa is to calculate the Tail statistic ( τ ) based on 16S data. Additionally, factors such as stability are being examined. It has been long suggested that healthy microbiomes are stable, and that although between individuals the microbes might be different, their individual microbiomes tend to keep the same key species over time. Recently, a theory to study stability has been proposed; this theory aims to identify key principles that underlie stability such as (1) the probability a community will return to its previous state after small perturbations, (2) the population dynamics during the return, and (3) how long the return takes. Altogether, these types of analyses facilitate discrete measurements and comparisons of highly complex microbial communities among sites and individuals.

In addition to taxonomic approaches, microbes can be analyzed at a molecular level using an OTU-based approach. An OTU can be defined as a collection of 16S rRNA sequences that have a certain percentage of sequence divergence. For example, as a rule of thumb for full-length 16S sequences, two OTUs with ~ 3% sequence divergence or ~ 97% similarity are often considered the equivalent of different “species.” At higher taxonomic levels, higher levels of sequence divergence can be used: genus (~ 5%); family/class (~ 10%); and phylum (~ 20%). It is important to understand how an OTU is created and how richness is estimated. Creating OTUs from raw sequence data can be a computational challenge; however, programs have been developed and are constantly being refined that can generate OTUs from large datasets and perform diversity and statistical analyses. One example is the previously mentioned open-source software program, mothur , which can be used to filter, trim, align, remove chimeras and group 16S rRNA sequences into OTUs. Several hundred thousand DNA sequences can be aligned and binned into OTUs using mothur in several hours.

With any community data, there will always be limitations to any sampling technique. Practically speaking, the total species richness is a function of sampling effort and no microbial community has ever been exhaustively sampled. However, there are methods available for estimating the species richness of microbial communities, for example, Chao1 estimator of species richness, which will utilize richness data, gathered for a particular community. Chao1 is based upon the idea that rarer species will carry the most information about the missing species. Chao1, therefore, places emphasis on those members that occur only once or twice in a community survey when calculating the estimate of species richness. In summary, the mathematical tools developed and utilized by environmental microbial ecologists are instrumental for studying the complex community of the human microbiome during health, perturbation, and disease.