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The switch from culture-based enumeration to deep sequencing has enabled microbial community composition to be profiled en masse. In a new article, Maurice et al. (2013) report the use of fluorescence-activated cell sorting (FACS) to perform a high-throughput analysis of gut microbiota community function.
The big advance that sets the latest wave of microbiome research apart from earlier studies is deep sequencing: the ability to enumerate all of the cells in a complex microbial community at once. The switch from a low-throughput technique, culture-based enumeration, to the high-throughput technology of deep sequencing enabled the “magic” of viewing community composition from 30,000 feet (Figure 1). Not surprisingly, many of the key insights from the last few years of microbiome research—spatiotemporal variation in the microbiome (Costello et al., 2009), the effect of diet on the gut community (Turnbaugh et al., 2009), development of the infant microbiome (Dominguez-Bello et al., 2010; Koenig et al., 2011; Yatsunenko et al., 2012), and the response of the gut community to antibiotic treatment (Dethlefsen and Relman, 2011)—would have been difficult to glean from culture-based studies.
There is a great deal yet to learn about the microbiome from deep sequencing; many of the key questions that remain unanswered concern the temporal dynamics of the microbiota and disease-specific changes in community composition. Nevertheless, a consensus is emerging in the microbiome research community that questions about community composition—which are addressed by deep sequencing—should be accompanied by new lines of inquiry into community function (http://grants.nih.gov/grants/guide/rfa-files/RFA-RM-12-021.html). Taking microbiome insights from bench to bedside, the argument goes, will require a molecular-level understanding of function: metabolism of dietary inputs, synthesis of diffusible molecules and surface antigens, and modulation of host signaling pathways. Such a detailed description of host-microbiota interactions would reveal how the composition and function of the gut community relate to disease; how they can be modulated by small molecule drugs, probiotics, and prebiotics; and what the goals of those perturbations should be.
Classically, the study of microbial function has been a low-throughput endeavor. Notable papers have explored the biological role of a single molecule produced by an individual microbial species (Mazmanian et al., 2005; Shin et al., 2011). An important exception has been a series of metabolomic studies of the microbiota from Nicholson and coworkers, which have highlighted key microbial metabolites and unexpected similarities and differences in function among gut microbial communities (Nicholson et al., 2012).
In an exciting new manuscript, Turnbaugh and colleagues adapt a high-throughput technique pioneered for the analysis of aquatic microbial communities to study the metabolic state of the gut microbiota en masse (Figure 1) (Maurice et al., 2013). This technique consists of treating an intact microbial community (e.g., a human fecal sample) with the fluorescent dyes SYBR Green, propidium iodide (Pi), and DiBAC and using fluorescence-activated cell sorting (FACS) to determine the proportion of dye-positive versus dye-negative cells. SYBR Green binds to DNA and reports on the total quantity of DNA in a cell. The level of SYBR Green fluorescence can therefore distinguish between cells with high and low nucleic acid content (HNA and LNA, respectively); HNA cells are presumed to be actively dividing and/or to have an increased metabolic activity, while LNA cells are not. Propidium iodide is excluded by cells with an intact membrane, and DiBAC can enter depolarized cells, so cells that are Pi+ or DiBAC+ are presumed “damaged.” The authors used this FACS-based assay to profile the metabolic activity of tens of thousands of cells from each of 21 fecal samples from three individuals, both fresh and after treatment with antibiotics or other drugs.
Three of their findings are particularly notable. First, they show that while more than half of the cells in the community are active, 17% of cells are Pi+ and 27% are DiBAC+, indicating a sizable minority of damaged cells. By combining FACS with 16S rRNA sequencing, they could show that the active and damaged subsets are both dominated by members of the Firmicutes order Clostridiales, although different genera dominate the active and damaged subpopulations. These results imply a key functional difference between Firmicutes and the other major gut phylum, Bacteroidetes: different subsets of the Firmicutes are more likely to be metabolically active and dying, indicating a greater level of cell turnover among them than the Bacteroidetes. They also suggest the tantalizing finding that, in the authors’ words, “members of the gut microbiota may inhabit distinct ecological niches, defined not only by physical location and resource utilization but also by their level of metabolic activity.”
Second, antibiotics have a direct and rapid physiological effect on the gut microbiota. Notably, the Pi+ subset doubled from 12% to 23%, and the DiBAC+ subpopulation increased from 33% to 44%. This finding, which had been suspected but not demonstrated, shows the uneven manner in which antibiotic treatment affects cells in the population over time. A panel of six host-targeted drugs did not increase the proportion of Pi+ and DiBAC+ cells, showing that this effect is specific to compounds that target bacteria. Surprisingly, the proportion of HNA cells is not significantly affected by antibiotic treatment, indicating that even broad-spectrum antibiotics do not have an immediate effect on a substantial portion of actively dividing bacteria: more than half of the population.
For the third notable finding, Turnbaugh and coworkers turned from their FACS-based analysis of metabolic activity to meta-transcriptomics, a community-wide profiling technique with more precedent. They show that treatment with antibiotics and other xenobiotics increases the expression of genes involved in drug resistance and metabolism; for example, multiple antibiotics increased the expression of drug transporters, while the histamine H2-receptor antagonist nizatidine induced the expression of genes that might be involved in the reduction of its terminal nitro group.
Three important questions remain. First, how general are the findings? If nearly all unperturbed gut communities—independent of composition—show similar proportions of HNA and damaged cells, then Turnbaugh and coworkers’ results will be a true milestone, but their technique will be of limited use in assaying functional differences among communities. In contrast, if communities differ dramatically in their proportions of HNA and damaged cells, then this technique could be of broad utility for distinguishing among functionally distinct communities.
Second, can the proportion of active and damaged cells be linked to an important phenotype or disease? FACS-seq will prove to be especially useful, both as a diagnostic and as a tool to study the molecular underpinnings of disease etiology, if a microbiome-related disorder is characterized by differences in community metabolic activity. Importantly, the authors show that xenobiotic-perturbed communities exhibit interindividual and temporal differences in their active and damaged subsets, so it is likely that significant differences will be seen in any disease state that results in a similar perturbation (e.g., the oxidative stress of inflammation).
Third, can the authors’ approach be extended to other high-throughput, community-wide measurements of function? Fluorophore-labeled metabolites might allow the metabolism of a specific dietary molecule to be read out, while anaerobic fluorescent proteins could enable reporter assays of signal transduction or metabolic gene function. However this new field unfolds, one thing is certain: dyeing is just the beginning.
Work in the authors’ laboratory is supported by a Medical Research Program Grant from the W.M. Keck Foundation, a Fellowship for Science and Engineering from the David and Lucile Packard Foundation, and grants from the NIH (OD007290, AI101018, and AI101722).