enables a comprehensive analysis of the content and relative abundance of low molecular weight (5–1,500 Da) molecules or metabolites within a sample, which provides a better assessment of which metabolic pathways are active and what the relative flux through those pathways is. (A) The samples are processed using solvent extraction methods with various polarities that are specific for a particular type of molecule, such as lipids, sugars, or small organic molecules. (B) The extracted compounds are then separated from each other via chromatographic methods such as high-performance liquid chromatography or gas chromatography either directly or after chemical derivatization (to enhance solubility). This is followed by structural analysis using detection technologies, such as mass spectrometry or nuclear magnetic resonance spectroscopy. The data are then processed and analyzed using bioinformatics tools and comparison with existing data from known compounds in available databases to determine the structure and identity of the components and their relative abundance in the sample. The metabolites identified are then mapped onto cellular signaling and biosynthetic pathways. A comprehensive list of metabolomics databases and resources can be found at the website for the Metabolomics Society (http://metabolomicssociety.org).
Currently, comprehensive proteomic and metabolic profiling is sometimes a daunting undertaking, even in monocultures of one kind of bacterium. Each of the previously mentioned approaches add an extra level of technological and computational scale and complexity to the profile analyses, which make applications toward characterization of populations of bacteria in the microbiota very challenging. For example, while proteomics is one of the fastest growing areas of research today, the sheer number of protein variants possible for each gene product and its homologs in many bacteria from multiple phyla places a considerable burden on the bioinformatics required to appropriately annotate and classify the proteins evolutionarily based on sequence similarity.
Proteomic analysis is highly valuable in establishing whether a protein encoded by a gene is expressed at any given time. More sophisticated proteomic approaches (Figure 5-13) can determine relative amounts of proteins in a bacterium or population; therefore, proteomics can provide information regarding whether certain proteins have the potential to be functionally active in cells. However, this does not necessarily mean that the biochemical and cellular function of different variants have been established to be the same in all bacteria that harbor a homolog of a given gene, nor does it definitively establish the function of the expressed protein or proteins encoded by that gene. Moreover, the presence of a protein does not necessarily mean that it is functionally active, since it does not provide any information about whether its activity has been modulated through interactions with or modifications by other proteins or ligands in regulatory signaling networks or biosynthetic pathways.
Metabolomics also presents experimental challenges. It is not possible with today’s technologies to analyze all of the metabolites in a given sample by any single analytical method. In any given biological sample there are a wide range of primary and secondary metabolites involved in essential and nonessential metabolic and signaling pathways, including peptides, oligonucleotides, sugars, nucleic acids, ketones, aldehydes, alcohols, amino acids, amines, lipids, steroids, alkaloids, and other endogenously generated small molecules. In addition, numerous xenobiotics, such as drugs and antibiotics, are also often present in varying amounts in microbiota samples. All of these molecules have very different chemical and structural properties, including different functional features, hydrophobicity, acidity, redox potential, and reactivity. Consequently, multiple and different separation and detection methods must be applied in combination to comprehensively identify and quantify the enormously diverse metabolites in a single bacterium or combinations of bacteria.
Moving to the next level of complexity, a few intrepid researchers have begun to explore the application of these genomic and functional approaches toward the characterization of microbiota within the context of the host response and the interplay of host-microbe interactions. Importantly, an integrated multi-omics approach is critical for interpretation of the data obtained from each of the separate approaches to gain deeper biological insights. For instance, inclusion of gene copy number in a sample (obtained from genomic data) can be used to normalize the functional activity observed for a particular set of expressed genes with similar enzyme activities (obtained from transcriptomic, proteomic, or metabolomic data). A multi-omics approach also allows the researcher to confirm conclusions or hypotheses made from one set of data, such as the presence of a gene that has weak homology to a known gene in a known biosynthetic pathway that makes a particular metabolite (obtained from comparison of metagenomic data with databases) with correlative functional data obtained through other methods, such as the presence of the expressed protein (obtained from proteomic data) and/or the presence of the predicted metabolite (obtained from metabolomic data). Again, a comprehensive discussion of this complex topic is beyond the scope of this textbook, but we provide a few examples later and in the selected readings for those who are curious.
Overview of the Human Microbiota
A human fetus is devoid of microorganisms. Passage through the vaginal tract during birth begins the colonization process. Shortly after birth, the microbiota profiles of vaginally delivered infants resemble that of their mother’s vagina, while the microbiota profiles of infants delivered via Cesarean section more closely resemble that found on the mother’s skin (Figure 5-15). This process is further influenced by exposure to early environmental factors and continues as the infant grows. The final microbiota composition is not achieved until the child is about 2 or 3 years old. Once the microbiota of an area assumes its stabilized, steady-state form, different areas of the body harbor very different microbial populations. Even within a single site, such as the mouth, different areas may contain different sets of microbes (Figure 5-16). This diversity is not surprising in view of differences between conditions that microbes encounter in any particular site. Nevertheless, despite these site-to-site differences within an individual, microbial community compositions at particular sites are generally more similar among cohabiting family members, less similar among distant relatives, and vary considerably among unrelated individuals.
Figure 5-15. Birth delivery mode influences the microbiomes of newborn infants. Shown are pie charts of the genus-level bacterial compositions derived from 16S rRNA gene sequence analysis at different body sites for a group of mothers and their babies shortly after birth, grouped according to the delivery method of the babies: vaginal versus Cesarean section (C-section). Adapted from Reid G, Younes JA, Van der Mei HC, Gloor GB, Knight R, Busscher HJ. 2011. Nature Rev Microbiol 9(1):27–38, with permission; based on data from Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, Knight R. 2010. Proc Natl Acad Sci USA 107(26):11971–11975.
Figure 5-16. Phylogenetic relationships of microbiome profiles among human body sites. Phylogenetic comparison of 16S rRNA gene-based bacterial community profiles among different human body sites revealed strong clustering by body site, meaning community compositions varied significantly less within a particular body site than between sites. Each point in the principal-component analysis (PCA) plot in (A) and the dendrogram (tree) in (B) corresponds to the profile of a sample, colored according to particular body site. In these clustering analyses, 16S rRNA gene-based bacterial community profiles that cluster closer together are more similar to each other. Adapted from Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI, Knight R. 2009. Science 326(5960):1694–1697, with permission.
There are, however, some features of the microbiota that are common to all sites of the human body colonized by microbes. First, the numerically predominant microbes are bacteria. Archaea, fungi, and viruses (including bacteriophage) are also present, but archaea
