Use of the Microbiome in the Practice of Epidemiology
Use of the Microbiome in the Practice of Epidemiology
Taxonomic screens, which are relatively inexpensive, permit characterization of colonization dynamics over time and space, the identification of microbial communities associated with health or disease, and the collection of information for addressing questions regarding selection, evolution, and succession. At the time of this writing, the most common method used for characterizing the bacteria present is a taxonomic screen based on variable portions of the genetic sequence that codes for the ribosome (16S ribosomal RNA (rRNA)).
All cells have ribosomes, and because ribosomes perform the essential function of translating messenger RNA into protein, the genetic sequence is highly conserved. This makes it possible to use primer sets that cross most of the bacterial species present (universal primers) and phylogenetics to classify bacteria into taxonomic groups. The 16S rRNA sequence data are compared with sequence databases to determine the genus and species of bacteria that are present. Relative quantities of different 16S rRNA sequences can also be obtained to establish the relative abundance of each species. These data provide a snapshot of who is present from the bacterial world and in what quantities, but they are unable to characterize the relationships between community members. However, the ability to resolve the obtained genetic sequence to the species level varies with the region chosen; depending on the body site and bacterial composition, different regions are preferred. Further, while 16S rRNA analysis can characterize the members of a bacterial community, its use of a single bacterial gene precludes the detection of potential members from other kingdoms, including viruses, fungi, and archaea, and it can also limit resolution to the species level for bacteria. For fungi, ribosomal analysis is also used, but 18S rRNA is sequenced. Other methods are required to capture the viruses and archaea present.
Broader strategies for sequencing the genetic material of microbiota allow investigators to describe all organisms present in a community, encompassing bacteria, viruses, fungi, and archaea. This set of all genomes from a diverse set of microbial sources (i.e., the "metagenome") can be viewed as the gene pool of the functioning of the microbial community at that particular body site (i.e., the "functional potential"). In addition, like taxonomic screens, metagenomics can provide a sense of the relative abundance of different organisms. The usual strategy for whole-microbiome sequencing is to randomly sequence genomic fragments and then compile them to represent whole genomes (shotgun sequencing). Because these methods do not target a single region, they require additional care to ensure that both RNA (which must be reverse-transcribed) and DNA present in small-sized genomes in small quantities are appropriately captured from the sample and that human DNA is not processed and mistaken for organism data. Microbial community structure can also be estimated with metagenomic data, using the ribosomal genes. Metagenomic data sets are very large and the analysis is challenging, but appropriate software is increasingly becoming available.
Measuring function is considerably more expensive than conducting taxonomic screens, and each of the different methods for assessing function has strengths and limitations. Measuring the metabolic products present (metabolomics) is the only way to directly assess the ongoing interactions among all of the microbes present and with the human host, but there is considerable technical variation. Transcriptomic studies require targeting transcripts from specific groups (e.g., bacteria) and do not directly correspond to functions. Human messenger RNA is much larger and more stable, and thus (since transcripts are sequenced for detection) can overwhelm the microbial transcriptome. Metagenomics enables characterization of gene potential but not ongoing functions.
Analytical software packages are available for analyzing the results of taxonomic screens (e.g., see Schloss et al. and Hamady et al.), and there is a software package that allows one to infer bacterial functions from taxonomy. Software with which to analyze the metagenome is also increasingly available. However, beyond the ability to process huge amounts of data from microbiomic studies, the real challenge lies in the best way to achieve the data reduction needed to use these data as an epidemiologic parameter. Epidemiologists can make an important contribution to microbiomic research by working to develop and evaluate methods of producing meaningful parameters from complex microbiomic data.
How Do You Measure the Microbiome?
Taxonomic screens, which are relatively inexpensive, permit characterization of colonization dynamics over time and space, the identification of microbial communities associated with health or disease, and the collection of information for addressing questions regarding selection, evolution, and succession. At the time of this writing, the most common method used for characterizing the bacteria present is a taxonomic screen based on variable portions of the genetic sequence that codes for the ribosome (16S ribosomal RNA (rRNA)).
All cells have ribosomes, and because ribosomes perform the essential function of translating messenger RNA into protein, the genetic sequence is highly conserved. This makes it possible to use primer sets that cross most of the bacterial species present (universal primers) and phylogenetics to classify bacteria into taxonomic groups. The 16S rRNA sequence data are compared with sequence databases to determine the genus and species of bacteria that are present. Relative quantities of different 16S rRNA sequences can also be obtained to establish the relative abundance of each species. These data provide a snapshot of who is present from the bacterial world and in what quantities, but they are unable to characterize the relationships between community members. However, the ability to resolve the obtained genetic sequence to the species level varies with the region chosen; depending on the body site and bacterial composition, different regions are preferred. Further, while 16S rRNA analysis can characterize the members of a bacterial community, its use of a single bacterial gene precludes the detection of potential members from other kingdoms, including viruses, fungi, and archaea, and it can also limit resolution to the species level for bacteria. For fungi, ribosomal analysis is also used, but 18S rRNA is sequenced. Other methods are required to capture the viruses and archaea present.
Broader strategies for sequencing the genetic material of microbiota allow investigators to describe all organisms present in a community, encompassing bacteria, viruses, fungi, and archaea. This set of all genomes from a diverse set of microbial sources (i.e., the "metagenome") can be viewed as the gene pool of the functioning of the microbial community at that particular body site (i.e., the "functional potential"). In addition, like taxonomic screens, metagenomics can provide a sense of the relative abundance of different organisms. The usual strategy for whole-microbiome sequencing is to randomly sequence genomic fragments and then compile them to represent whole genomes (shotgun sequencing). Because these methods do not target a single region, they require additional care to ensure that both RNA (which must be reverse-transcribed) and DNA present in small-sized genomes in small quantities are appropriately captured from the sample and that human DNA is not processed and mistaken for organism data. Microbial community structure can also be estimated with metagenomic data, using the ribosomal genes. Metagenomic data sets are very large and the analysis is challenging, but appropriate software is increasingly becoming available.
Measuring function is considerably more expensive than conducting taxonomic screens, and each of the different methods for assessing function has strengths and limitations. Measuring the metabolic products present (metabolomics) is the only way to directly assess the ongoing interactions among all of the microbes present and with the human host, but there is considerable technical variation. Transcriptomic studies require targeting transcripts from specific groups (e.g., bacteria) and do not directly correspond to functions. Human messenger RNA is much larger and more stable, and thus (since transcripts are sequenced for detection) can overwhelm the microbial transcriptome. Metagenomics enables characterization of gene potential but not ongoing functions.
Analytical software packages are available for analyzing the results of taxonomic screens (e.g., see Schloss et al. and Hamady et al.), and there is a software package that allows one to infer bacterial functions from taxonomy. Software with which to analyze the metagenome is also increasingly available. However, beyond the ability to process huge amounts of data from microbiomic studies, the real challenge lies in the best way to achieve the data reduction needed to use these data as an epidemiologic parameter. Epidemiologists can make an important contribution to microbiomic research by working to develop and evaluate methods of producing meaningful parameters from complex microbiomic data.