The Emerging Role of the Microbiota in the ICU

Nora Suzanne Wolff; Floor Hugenholtz; Willem Joost Wiersinga


Crit Care. 2018;22(78) 

In This Article

Microbiota Analyses

The composition of the microbiota in the gut has been studied extensively using 16S ribosomal RNA (rRNA) gene-targeted approaches. The use of this single genetic marker has revolutionized microbial ecology.[13,14] It has become relatively easy to amplify the 16S rRNA encoding genes from environmental DNA. Nowadays, with next-generation sequencing techniques, many microbial environments can be studied in depth and at high resolution in space and time, using relatively straightforward procedures.

To address not only microbiota composition, but rather focus on the metabolic potential and actual activity of the intestinal microbiota, "meta'omic" approaches have emerged during the last decade and are now widely used.[15–17] Each of the meta'omic approaches provides different information about the functional potential or activity profiles of a microbial community.

Metagenomics is used to determine the collective genomes of members present in a microbial community as well as their functional capacity. Metagenomics was used in the Meta HIT (Metagenomics of the Human Intestinal Tract) project and provided a human microbiome-derived gene catalogue with over 3 million genes, indicating a community of over 150 species in an individual and a 100-fold larger non-redundant gene set compared to the human gene complement.[16]

Metatranscriptomics and metaproteomics are able to provide information about the functions expressed by the members of the community. For example, metaproteomics analysis in healthy humans revealed a difference in the amount of proteins expressed and the proteins predicted by metagenomics. Moreover, metaproteomics in fecal material not only gives information on the bacterial proteins, but also on the major human proteins, giving insights into the main host responses.[18] However, it is difficult and sometimes not possible to use conserved proteins to distinguish between species or even higher taxonomic levels (e.g., genus or family). Consequently, by using this technique, you lose some of the taxonomic information otherwise gained by using metagenomics, metatranscriptomics or targeted 16S rRNA sequencing.

Metatranscriptome analysis of the gastrointestinal tract microbiota enables elucidation of the specific functional roles microbes have in this complex community. Although initial studies on the human large intestine revealed that different functions are expressed among individuals, core functions of the microbiota appear to be consistently expressed in different individuals.[19,20] Moreover, metatranscriptome analyses of the small intestinal microbiota underpinned the cross-feeding between two dominant members of the small intestinal microbiota, i.e., Streptococcus spp. and Veilonella spp., in which the lactate produced by Streptococcus spp. is used as a carbon and energy source by the Veillonella spp..[17] Metatranscriptome analysis of the microbiota in humanized mice revealed that mice colonized with the microbiota obtained from a lean human donor had greater expression of genes involved in polysaccharide breakdown and in propionate and butyrate production as compared to those colonized with the microbiota of an obese human donor.[21] These findings imply that metatranscriptomics can provide insight into the differential activity profiles in the intestinal microbiota, and enables reconstruction of the metabolic activity profile of microbial communities.

Metabolomic approaches are used to detect and quantify the metabolites that are produced by the microbial community. This approach has been suggested to be applicable as a diagnostic tool in diseases that involve aberrations of the intestinal microbiota composition and activity.[22] However, as with metaproteomics, you also lose information on the specific bacteria producing these metabolites. To overcome the loss of taxonomic information in metaproteomics and metabolomics, these techniques are often combined with 16S rRNA sequencing, metagenomics or metatranscriptomics.

These tools often generate very complex datasets with many different measurements (e.g., species or function) under various conditions. Therefore, these multivariate meta'omics datasets need tools to simplify the datasets and focus on correlations between points of interest, such as dietary interventions and the bacterial community or the bacterial community and host responses. Multivariate statistics are used to handle these large datasets and enable a relatively quick focus on data of importance.[23] An overview of available techniques to analyze the microbiome is provided in Figure 1.

Figure 1.

Overview of techniques to detect bacterial microbiota. To detect which bacterial species there are in a sample, there are three options: 1) use quantitative polymerase chain reaction (qPCR) to detect total bacterial 16S rRNA gene and/or in combination with a specific qPCR for a bacterial group or species; 2) use 16S rRNA gene for amplicon multiplex sequencing to get information on the taxonomic distribution in a sample; 3) use total DNA of a sample for shot-gun sequencing of the metagenomics content to get information on taxonomic distribution and functions, examples of which are indicated in the box, to show that you have functional information within a taxonomic group, in this case butyrate kinase and glycoside hydrolase. The dollar sign below the different techniques is an indicator of the price: depending on the sample type and information depth, prices are variable, but, roughly, a one-dollar sign is around 5–10 dollars per sample; the two-dollar sign is 50–150 dollars per sample, and the three-dollar sign is 300–1000 dollars per sample