The human microbiome consists of the total microbial community (or microbiota) and the associated biomolecules. It is composed of 10–100 trillion microbial cells (10× more than human cells containing genetic material), and it contains >1,000 bacterial species and 100-fold more genes than the human genome.Reference Ley, Peterson and Gordon1–Reference Lloyd-Price, Abu-Ali and Huttenhower3 A study in the early 2000s by Eckburg et alReference Eckburg, Bik and Bernstein4 analyzed the microbial composition of human fecal samples using 16S rRNA gene sequencing. They showed that 60% of the identified bacteria corresponded to novel organisms and 80% of sequences belonged to previously uncultivated bacterial species.Reference Eckburg, Bik and Bernstein4 These striking findings, of a previously underrecognized and immense diversity of the intestinal microflora, have led to a tremendous focus on the microbiota. Culture-independent techniques, which identify microorganisms based on DNA sequences directly from the sample, have begun to elucidate the complex composition of microbial communities. Functional metagenomic and metabolomic techniques have also begun to describe the biological tasks of the microbiome.
The microbiome participates in numerous aspects of human physiology including the development of the immune system, energy metabolism, and intestinal endocrine functions.Reference Marchesi, Adams and Fava5, Reference Lynch and Pederson6 It performs other essential functions such as the production of vitamin B and K groups and the degradation of complex carbohydrates from ingested plant-derived fibers.Reference Brestoff and Artis7 Imbalances in the human microbiome, often referred as dysbiosis, induced by lifestyle factors, diet, and antimicrobials, have been implicated in obesity, cardiovascular and autoimmune diseases, malignancies, and infections.Reference Marchesi, Adams and Fava5, Reference Gilbert, Quinn and Debelius8 Given its potential role in disease states, interventions to restore the microbiome, such as fecal transplantation and the development of consortia of beneficial bacteria, are under investigation as potential therapeutic options.Reference Lynch and Pederson6
The objective of this paper is to provide an overview of the current approaches for assessing the microbiome and the implications that altered, or dysbiotic, microbiomes may have in infection prevention. The review also focuses on methodological principles to promote understanding of the complexities of microbiome research to facilitate more complete interpretation of the literature. Common terms used in microbiome studies are shown in Table 1.
Table 1. Common Terms in Microbiota or With Particular Relevance to Infection Prevention
Note. MDRO, multidrug-resistant organism.
Composition of the microbiome
The tree of life consists of 3 domains: Archea, Eukarya, and Bacteria. The Bacteria domain includes 29 phyla, of which 6 predominate in the human microbiome: Bacteroidetes, Firmicutes, Actinobacteria, Proteobacteria, Cyanobacteria, and Fusobacteria.Reference Parte9 The type of bacteria within each of these phyla are shown in Table 2. The relative abundance of the members of these phyla varies among different sites in the body (Figure 1).Reference Cho and Blaser10 In the healthy gut, Firmicutes and Bacteroidetes represent >90% of the bacterial community. At lower phylogenetic levels, such as the genus or species level, the gut microbiota is vastly diverse among individuals. This high interindividual variability disproved the initial hypothesis that postulated the existence of a taxonomical core shared by most individuals and has posed a significant challenge for defining what we understand to be a healthy human microbiome. Although the human microbiome varies over time within individuals, the extent of its longitudinal variation is significantly lower than the variability observed between hosts, indicating that the human microbiome is individualized.2 In contrast to the taxonomical variability observed between subjects, the functional metagenomic prediction of the metabolic pathways present in the human microbiome showed that most individuals share the same gene-associated functions, suggesting the existence of a functional core among the human microbiomes.Reference Turnbaugh, Hamady and Yatsunenko11 The microbiome individuality and functional stability are thought to be key features of the healthy human microbiome and are the focus of intense investigation.Reference Lloyd-Price, Abu-Ali and Huttenhower3, Reference Eckburg, Bik and Bernstein4
Fig. 1. Compositional differences in the microbiome by anatomic site.Reference Cho and Blaser10
Table 2. Composition of Phyla Present in the Human Microbiota
One of the approaches most commonly used to study the microbiome is based on the amplification and sequencing of the 16S rRNA gene of Archaea and Bacteria. The 16S gene has regions that are highly conserved adjacent to regions that are highly variable among prokaryotes. These characteristics make the 16S gene an ideal marker for cataloging microorganisms. First, based on its conserved regions, it is possible to use universal primers to detect and amplify bacterial DNA from almost any sample. Second, by sequencing its variable regions, it is possible to group the sequences obtained into molecular operational taxonomic units (OTU) based on a predefined sequence similarity threshold, usually 97%. Thus, these OTUs refer to clusters of organisms, grouped by DNA similarity, and they are usually taken as a species surrogate for diversity analyses.Reference Morgan and Huttenhower12
Some limitations of the 16S targeted approach have been described. First, the 16S gene is subject to variation in the number of copies per cell, which may affect accuracy, particularly when estimating microbial abundances. Second, the amplification step is susceptible to biases introduced by the propensity of primers to hybridize more efficiently and of amplification to proceed for some bacterial 16S sequences over others, which may also lead to misrepresentation of the relative abundances of community members. Third, the 16S gene does not provide information regarding whole genomes; therefore, inferring functional roles from the microbial community, although possible, is limited. Finally, 16S gene sequencing approaches only identify Bacteria and Archaea, leaving other microorganisms that are part of the human microbiome, such as viruses and eukaryotes, out of the analysis.Reference Morgan and Huttenhower13, Reference Kuczynski, Lauber and Walters14
Whole-metagenome shotgun sequencing
In addition to 16S rRNA sequencing, another approach to study microbial communities is whole-metagenome shotgun (WMS) sequencing. This term refers to the untargeted process of sequencing the entire pool of DNA extracted directly from a sample (the mixture of genomes, or metagenome).Reference Quince, Walker, Simpson, Loman and Segata15 Concerning taxonomic profiling studies, WMS sequencing is not subject to PCR-related biases, it is not affected by the variable copy number of the 16S gene, and compared to the 16S targeted approach, it provides higher biological resolution even at the species and strain level. Additionally, WMS sequencing can provide meaningful data about the functional potential of microbial communities, such as antimicrobial resistance genes and biochemical compounds they produce.Reference Morgan, Langille and Zaneveld16, Reference Kaminski, Gibson and Franzosa17 Higher costs compared to the 16S approach, and significant computational and analytical challenges, however, are still major limitations of this sequencing method. Current efforts to integrate WMS sequencing with other techniques, including metatranscriptomics (the activity of present genes) and metabolomics (the metabolic products), will be key for linking metagenomic data with the terminal bioactive products of the microbial community.Reference Morgan and Huttenhower13
Biodiversity of the microbiota
One of the main goals of microbial community analyses is to determine not only its composition but its community structure, or diversity. Two important parameters are commonly used for describing microbial diversity: alpha diversity, or within-sample diversity, and beta diversity, or between sample diversity. Alpha diversity can be described regarding its richness (ie, the total number of taxa observed in a sample), its evenness (ie, how balanced are the relative abundances of the community members), and its phylogenetic relationships. These characteristics are complementary and show different aspects of the community assembly. Commonly used alpha diversity measures include directly counting the number of taxa present in a sample (richness), and the use of parameters that consider both richness and the distribution of the relative abundances of community members, such as the Shannon and inverse Simpson indices. These metrics are accurate at estimating microbial diversity based on the most abundant taxa, but their performance decreases when addressing the contribution of the less abundant members of a community. Despite its inherent limitations, alpha diversity estimation is useful for quantifying changes in microbial diversity associated with the different situation of interest, such as antimicrobial exposure, or a particular disease.Reference Morgan and Huttenhower12
In addition to alpha diversity, microbiome analyses also examine the “between sample” diversity or beta diversity. Beta diversity estimates the degree of similarity or difference in the taxonomical composition between samples or group of samples. Beta diversity metrics are diverse and inform different aspects of community composition when comparing samples. Qualitative measures consider the presence or absence of features, and quantitative estimators consider the relative abundance of community members. The third class of beta diversity measures includes phylogenetic information coupled either with qualitative or quantitative data. Beta diversity data are frequently summarized using ordination techniques, such as principal coordinates analysis for visualizing and exploring sample clustering according to metadata of interest. Samples that cluster together are more similar than samples that cluster apart.Reference Morgan and Huttenhower12
A key concept that affects both 16S rRNA amplicon sequencing and WMS sequencing is the sequencing depth, which refers to the total number of DNA sequences per sample obtained after completing the sequencing process. Similar to the sampling effort in ecology, the sequencing depth significantly impacts the biodiversity observed in a sample; more deep sequencing efforts have a higher probability for detecting the less abundant members of a community. In other words, “the more you sample, the more you find.” Microbiome studies usually report a sample’s sequencing depths using summary statistics and rarefaction curves. The latter put summary statistics into context by plotting curves that show the association between the number of sequences retrieved from each sample and the expected diversity of the sample based on the observed abundances. Diversity analysis should be conducted at a sequencing depth approaching the saturation point for new species discovery to provide meaningful data. Furthermore, samples usually yield variable number of reads, posing a challenge to differentiate between true biologic variation versus dissimilar sequencing efficiency. Different analytic approaches (ie, normalization methods) are commonly used to account for variable library sizes before comparing diversity metrics between samples or groups of samples.Reference Weiss, Xu and Peddada18
Microbiome dysbiosis and its impact on colonization and infections caused by multidrug-resistant organisms and other pathogens
Age, diet, and geographical distribution are important factors that shape the microbiome and explain in part its compositional variability.Reference Cho and Blaser10, Reference Yatsunenko, Rey and Manary19 Similarly, exposure to some drugs has been associated with significant changes in the structure and composition of the human microbiome. Antimicrobial exposure profoundly affects the microbiome structure, leading to a decrease in bacterial diversity and to both decreases and blooms of specific taxa.Reference Tosh and McDonald20, Reference Kim, Covington and Pamer21 The effects can be long-lasting. Dethlefsen et alReference Dethlefsen, Huse, Sogin and Relman22 showed that a 5-day course of ciprofloxacin could cause microbiome dysbiosis for up to 5 months. Substantial changes in microbiome composition for up to 4 years have also been found after a 7-day course of clarithromycin, metronidazole, and omeprazole.Reference Jakobsson, Jernberg and Anderson23
The key concept pertaining to antimicrobial exposure and the microbiome is a decrease in “colonization resistance.” This term refers to protective taxa within the microbiome that reduce the risk colonization with a pathogen, mediated either through functions that directly inhibit growth of the organism, for example, competition for nutrients or the direct expression of inhibitory or toxic substances, or through functions that interact with the host to indirectly inhibit growth of a pathogen, for example, stimulation of the host’s innate immunity.
Several protective taxa have been identified. Caballero et al showed that Blautia producta restores colonization resistance against vancomycin-resistant Enterococcus (VRE) and directly inhibits VRE growth in murine models.Reference Caballero, Kim and Carter24 In vitro studies have shown that the commensal bacterium C. scindens converts primary bile acids to secondary ones, and mathematical models have shown that the absence of C. scindens in the gut promotes C. difficile infections, since secondary bile acids inhibit the germination of C. difficle spores.Reference Buffie, Bucci and Stein25 Negative and positive correlations with C. difficile infections have also been shown with other taxa.Reference Seekatz and Young26, Reference Araos, Andreatos and Ugalde27 Thus, reconstitution to a healthy microbiome via fecal transplant has been associated with preventing C. difficile infections and is now recommended as a treatment option for patients with multiple recurrences.Reference McDonald, Gerding and Johnson28 Lactobacillus spp have also been implicated in colonization resistance. Comparison of the fecal microbiome among hospitalized patients exposed to antimicrobials, who acquired or did not acquire a multidrug-resistant organism (an MDRO), identified a greater abundance of Lactobacillus spp among those who did not acquire an MDRO, suggesting that these bacteria may have a protective role against MDRO colonization.Reference Araos, Tai, Snyder, Blaser and D’Agata29
Domination of a particular taxa can also lead to an increased risk of infection. Using 16S rRNA gene sequencing, Taur et alReference Taur, Xavier and Lipuma30 showed that intestinal domination (>30% of the microbiota) by Enterococcus spp and Proteobacteria (a phylum of gram-negative bacteria) increased the risk of VRE bacteremia by 9-fold and gram-negative rod bacteremia by 5-fold. In a study of long-term acute-care residents, an increased relative abundance of carbapenemase-producing Klebsiella pneumoniae (KPC-Kp) in the gut was associated with an increased risk of KPC-Kp bacteremia.Reference Shimasaki, Seekatz and Bassis31 Although likely correlated, further studies are needed to determine whether the ratio of the dominant taxa to other taxa in the microbiome or the actual bacterial load of the dominant taxa affects the risk of subsequent infection.
Antimicrobials also affect the gut resistome, defined as the pool of antimicrobial resistance genes within the microbiome. Increases in the abundance of antimicrobial resistance genes occur with antimicrobial exposure.Reference Van Schaik32 In a study of recurrent C. difficile infection among patients with repeated antimicrobial exposure, the abundance of β-lactam, fluoroquinolone and multidrug efflux-pump–resistant genes was higher than in healthy controls. Moreover, fecal microbiota transplantation reduced the load of these genes.Reference Millan, Park and Hotte33 Metagenomic analyses among antimicrobial exposed patients who acquired an MDRO also revealed a higher abundance of genes related to several pathways implicated in multidrug resistance, including the 2-component system, the ATP-binding cassette system, and the phosphotransferase system.Reference Araos, Montgomery, Ugalde and Snyder34
Importantly, nonantimicrobial medications also lead to microbiome dysbioisis and an increased risk of colonization with pathogens. A systematic review of medications associated with gut dysbiosis identified proton pump inhibitors, metformin, and nonsteroidal anti-inflammatory agents with changes in the structure of the microbial gut composition.Reference Le Bastard, Al-Ghalith and Grégoire35 Nonantimicrobial medications have also been associated with an increased risk of MDRO acquisition. In a nested case-control study of 137 nursing home residents who were not exposed to antimicrobials, of whom 32% acquired an MDRO, exposure to laxatives and acid reducers was significantly associated with a greater risk of acquisition compared to those who did not receive these medications.Reference D’Agata, Varu and Geffert36
Infection prevention strategies
Although antimicrobial stewardship and prevention of transmission through hand hygiene and contact precautions has decreased the spread of MDROs, the problem persists. Innovative strategies are needed. An intact microbiome is a host defense mechanism for preventing colonization and infection with MDRO and other pathogens. Dysbiosis leads to colonization and dominance of MDROs and other pathogens and is a risk factor for infection. Dominance of a particular taxa in the gut has also been associated with greater environmental contamination, which implies a greater risk of transmission.Reference Donskey, Chowdhry and Hecker37 Recently, the Centers for Disease Control and Prevention has begun to study “microbiome disruption indices” (MDI), characteristics of the microbiome structure and composition, its resistome, and the biochemicals it produces (metabolome), that can identify patients at high risk of colonization with, infection with, or transmission of MDROs and other pathogens (Figure 2).Reference Tosh and McDonald20, Reference Halpin, de Man and Kraft38 Studies have begun to characterize these MDIs, as mentioned above. Several key questions require further study: (1) What are the MDIs that promote colonization and infection with pathogens? (2) What are the cumulative MDIs that increase the risk of transmission? (3) What are the differences in MDIs induced by different antimicrobials? And (4) are there specific antimicrobials that have minimal effect on the microbiome or, of least duration?
Fig. 2. Causal pathway from health to disease: microbiome disruption indices (MDI). 1Antibiotic MDI indicates the potential an antibiotic has for disrupting the intestinal microbiome. 2Disrupted microbiome status MDI characterizes the degree and type of disruption in the intestinal microbiome and the susceptibility to colonization by a MDRO. 3MDRO colonization MDI indicates susceptibility to overgrowth and dominance by a MDRO. 4MDI characterizing overgrowth and dominance by a MDRO indicates susceptibility for infection with a MDRO and the potential for transmission to others through skin-environment contamination.Reference Dubberke, Lee and Orenstein39 Note. MDRO, multidrug-resistant organism.
Future directions
Despite the tremendous number of publications pertaining to the role of the microbiome in infections and other disease states in the last decade, and even journals dedicated only to microbiome research, the study of the microbiota, in the area of infection prevention, is still in its infancy. Considerable research is needed to meet Koch’s postulates for establishing a causative relationship between specific characteristic of the microbiome bacterial compositions. Other research areas include the role of taxa that may not be detected due to very low bacterial loads or insufficient sequencing depth, the resistome and metabolome components of the microbiome, and the role of a dysbiotic microbiome in pathogen transmission.
Recent clinical trials are focusing on restoring the microbiome to prevent infections. A randomized double-blinded, placebo-controlled phase 2B trial of a microbiota-based drug, RBX2660, showed promising results in the prevention of recurrent C. difficile infection.Reference Dubberke, Lee and Orenstein39 Looking forward, a similar “pill” could be developed to prevent colonization, infection or transmission of MDROs. The current research strongly suggests that this is a real possibility.
Author ORCIDs
Erika M.C. D’Agata, 0000-0001-5079-9998
Acknowledgments
This work was supported by the National Institute of Health’s K24 AI119158 (EMCD).
Financial support
This work was supported by the National Institute of Health’s K24 AI119158 (EMCD), and the Millennium Initiative for Collaborative Research on Bacterial Resistance (MICROB-R), Iniciativa Científica Milenio, Ministerio de Economía, Gobierno de Chile (RA).
Conflicts of interest
All authors report no conflicts of interest relevant to this article.
