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 Huttenhower^3, 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 Huttenhower^13, 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 Zaneveld^16, 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 Blaser^10, 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 McDonald^20, 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 Young^26, 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 McDonald^20, 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.