Classical microbiology and germ theory provided us with powerful techniques and a theoretical framework to identify diseases caused by single microbial species. Koch’s postulates conclusively indict one specific pathogen as the cause of a disease. The contemporary challenge is to develop similarly powerful techniques and theories for diseases that are caused by microbial communities of multiple species, such as bacterial vaginosis (Forney et al. 2006
). We cannot apply Koch’s postulates to bacterial vaginosis because there is no single microbial community that is present in all bacterial vaginosis patients, even when the composition of the microbial community is clearly correlated with symptoms of the disease (Zhou et al. 2007
). We face a similar challenge when we try to describe, predict, or control microbial community dynamics in industrial or ecological settings (Brenner et al. 2008
). These are technical and conceptual challenges difficult enough to justify revisiting our most basic assumptions, the most basic of which is our degree of reliance on microbial species concepts. What are the limitations of microbial species concepts, and what are the practical alternatives?
We advocate for a novel experimental approach called artificial ecosystem selection, an approach validated by only one group to date (Swenson et al. 2000a
). Artificial ecosystem selection experiments treat the microbial community as the unit of analysis and do not require recourse to any species concept. Naturally, we may wish to use a species concept to explain a community’s response to selection, but this is not mandatory. Selection upon a community-level phenotype is an experimental answer to a compatible philosophical shift advocated by Doolittle and Zhaxybayeva (2010)
in a recent issue of this journal. The most appropriate response to a call for such a change in thought is experiments that test the value of the change.
How are microbial species concepts misleading? The molecular species concept is the most commonly employed idea in this era of cheap DNA sequencing. Essentially, a single highly conserved sequence called the small subunit ribosomal RNA (rRNA) gene (16S) is used as a proxy for eubacterial and archaeal species; other marker sequences are used for eukaryotes. This ribo-species concept and its associated methodologies revolutionized our understanding of the diversity of living things, but it is not without limitations (Woese et al. 1990
). There is a one-to-many mapping between a particular ribo-species and the sets of genes found within representative members of that group. Several different research groups have demonstrated that this pangenome—the total collection of genes found in all the different members of a single ribo-species—is very large, perhaps even practically unbounded (Streptococcus agalactiae
, Tettelin et al. 2005
; Escherichia coli
, Rasko et al. 2008
; Staphylococcus aureus
, Gerrish et al. 2010
). The immensity of the pangeome indicates that microbes that are considered part of the same species can have vastly different properties. For instance, the typical strains of E. coli
in the human gut are beneficial to digestion, but the colonization of the gut with a pathogenic E. coli
strain can have deadly consequences. The apparently vast extent of the pangenome leads many to wonder whether a robust and universal microbial species concept is inherently elusive (Gevers et al. 2005
This suspicion is also supported by recent discoveries made using high-throughput sequencing (Margulies et al. 2005
) to produce metagenomes—the collection of sequences randomly sampled from the genetic material from a microbial community (Vieites et al. 2009
). A recent study by the Human Microbiome Project (Peterson et al. 2009
) of the communities in the distal gut of human twin pairs is especially telling (Turnbaugh et al. 2009
). This study collected a metagenomic sequence using pyrosequencing as well as ribo-species information from 16S rRNA sequences (Margulies et al. 2005
). The researchers concluded there was no conserved core of ribo-species present in all patients, but there was a conserved core of specific gene functions: “It appears that a core gut microbiome exists at the level of shared genes” (p. 483, Turnbaugh et al. 2009
). Turnbaugh and colleagues (2009)
described a parallel between the microbiome of the human gut and neutral theories of island biogeography, wherein different communities form to fulfill similar ecological functions in an idiosyncratic manner dominated by random colonization events. Another metagenomic study found that functional categories of genes derived from metagenomic data were highly predictive of environmental parameters in nine different nonhost associated biomes; that is, “each environment has a distinguishing metabolic profile” (Dinsdale et al. 2008
). Yet another study found ribo-species to be relatively poor predictors of the ecotype from which they were derived (Lozupone and Knight 2007
If information is “the difference that makes a difference” (Bateson 1979
), these discoveries resulting from pangenomics and metagenomics demonstrate that knowing only the ribo-species composition may not be enough to fully explain the differences—and similarities—between any two communities. As in our previous example of pangenomic diversity, possession of a 16S sequence that matches that of E. coli
is just not enough information to distinguish between health and disease. In the case of pathogenic E. coli
we can and have devised other tests to rapidly obtain this information for clinical purposes. However, if we expect to transfer this general process to highly diverse microbial communities, we are accepting the Sisyphean task of enumerating every possible mapping between a ribo-sequence and genomic contents. In short, we cannot simply transfer the epistemology and attendant methodologies of classical microbiology to a general framework for studying microbial communities.
Doolittle and Zhaxybayeva (2010)
argued that we might avoid some of this difficulty by framing microbial communities as the unit of study, which requires us to establish that microbial communities are units of selection, that they constitute lineages that occupy stable niches, and that they migrate collectively to new environments to reestablish their niches. We will revisit this point to show how several different host-associated communities have already demonstrated inheritance, a necessary property of a lineage.
Microbial communities might be units of selection under specific conditions; that is, selection might act on properties produced by a community. This means that selection may be acting on community-level properties at the same time that it acts on individual organisms. We who argue that microbial communities are valid “units for evolutionary and ecological study” (Doolittle and Zhaxybayeva 2010
) need to demonstrate the logical validity and the experimental utility of this viewpoint. The units-of-selection debate turns on a number of subtle points and suffers from a surfeit of semantic confusions because of the complexity of the subject. Readers interested in the broader unit-of-selection debate will find the encyclopedic review by Lloyd (2008)
an excellent starting point.
Artificial ecosystem selection (Swenson et al. 2000a
) as an experimental technique is species agnostic. This means simply that we do not need to make any decision about the validity of species concepts in order to perform the experiments and interpret the results. These concepts just aren’t a necessary part of the logical framework of the experiments. We may seem to overreach by advocating artificial ecosystem selection, the concept of microbial communities as experimental units, and the demotion of microbial species concepts—all in a single pass. We present these ideas as an integral package because emerging patterns from microbial community studies suggest significant shift of perspective is warranted. The shift we suggest is logically consistent, supported by data and enabled by technology. Artificial ecosystem selection provides a clear and specific path to move from philosophical proposal to experimental design. Selection upon a diverse microbial community trapped in replicate microcosms changes the function of the community and maintains parallel experimental controls that are not subject to directional selection—usually dubbed “the random line.” A randomly selected line gives an experimental control against stochasticity within communities and uncontrollable environmental variation. This level of experimental control is not possible when we sample wild-type communities from hosts or from the environment. We can describe most Human Microbiome Project (Peterson et al. 2009
) studies as using a strategy of induction by enumeration. Their exploratory comparisons of ribo-species found within wild-type communities from demonstrably similar environments (specific habitats within or upon human or other animal hosts) do identify useful patterns of connection to disease states (e.g., Zhou et al. 2007
). The relative strength of an artificial ecosystem selection experiment is that similarities between replicate microcosms are a result of true homology, and we gain statistical power to distinguish whether differences are due to stochastic variation or to the force of our imposed selection. We can apply induction by elimination when rigorous controls are available; this type of formal hypothesis testing is sometimes described as strong inference
In three of the four experiments reported by Swenson and colleagues (2000a
, there was a significant response to artificial ecosystem selection. Because of the degree of replication (at least 15 microcosms per line depending on the experiment) and the presence of experimental controls, we can reject their null hypothesis that community function simply drifted according to stochastic responses to the disturbance inherent in the experiment. A response to artificial selection demonstrates that the community is an evolvable system; it also hints that natural selection on microbial communities as units and the inheritance of community traits is a possibility. A new generation of a microbial community lineage is established with each round of artificial selection, and inheritance is inferred from the degree to which offspring resemble their parents. In the case of artificially selected communities, this family resemblance is judged by the degree to which the selected function is changed in offspring microcosms relative to the random line.
Our species agnosticism is not dogmatic; we adopt it as a heuristic to guide exploration of new experimental designs that follow from this perspective. It remains to be seen what changes in the microbial communities produced the response to selection in the artificial ecosystem selection experiments (Swenson et al. 2000a
), and ribo-species composition is one obvious and convenient type of data to collect. Cheap, high-throughput sequencing and new bioinformatic techniques now allow the use of culture-free microbial community analysis to determine ribo-type composition and functional gene content. For example, the technique of comparative metatranscriptomics seems well suited to discovering causal mechanisms behind a response to community-level selection (Poretsky et al. 2009
). Hamady and Knight (2009)
discuss a number of other applicable techniques.