PerspectiveMicrobiology

Exploring Microbial Diversity--A Vast Below

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Science  26 Aug 2005:
Vol. 309, Issue 5739, pp. 1331-1333
DOI: 10.1126/science.1118176

Exploring microbial diversity is becoming more like exploring outer space with soil representing a “final frontier” that harbors a largely unknown microbial universe. There are more than 1016 prokaryotes in a ton of soil compared to a mere 1011 stars in our galaxy. Astronomers have wisely inferred the population of celestial objects by mathematical inference. Now microbiologists are following suit, adopting a similar strategy to estimate the number of prokaryote taxa in soil. As shown by Gans et al. on page 1387 (1), the inferred diversity is staggering—higher than previously thought by almost three orders of magnitude.

The extent of prokaryote diversity has been hotly debated and rightly so. Microbial communities are central to health, sustainable cities, agriculture, and most of the planet's geochemical cycles. Prokaryote communities are also reservoirs for the discovery of new drugs and metabolic processes. As with any reservoir, its size is important.

Measuring the reservoir of prokaryotic diversity is not a trivial task. There is broad agreement that the key is to eschew the organisms themselves and to focus instead on their DNA. If DNA from a single organism is purified and heated, the strands of the double helix separate or “melt.” If you then slowly cool the DNA, the strands will reassociate or reanneal, and the rate at which this happens is affected by the size and complexity of the DNA. Big and complex DNA reanneals slowly. This fact has been used for the past four decades to estimate the size and complexity of genomes from individual organisms. Around 15 years ago, Torsvik et al. (2) reasoned that pooled genomic DNA from a microbial community might reanneal like the DNA from a large genome. Indeed, they showed that DNA extracted from soil reassociated slowly—so slowly that it resembled a genome that was 7000 times as large as the genome of a single bacterium. It follows that there could have been at least 7000 different prokaryote taxa in the sample of soil that they analyzed. At the time, this was considered a mind-boggling number. Even ecologist E.O. Wilson speculated that “microbial diversity was beyond practical calculation” (3).

There is, however, another way to estimate prokaryotic diversity in the environment. A biological community has a characteristic abundance distribution of its member species. The observation and contemplation of these distributions have a rich literature in conventional ecology that is helping rescue microbial ecology from the conundrum of how to estimate diversity. In principle, if you know the shape of the taxa abundance distribution curve, you know the diversity. But there is a catch: Typically, for large organisms, species abundance distributions have been determined by assessing the abundance of almost all of the species in a sample, which means that you must already know the number of species. In the absence of such information, one still can draw upon certain theoretical considerations (4), assume that a particular species distribution pertains, and then make an estimate (5). Alternatively, you can fit a curve to the data you have to make an estimate (6, 7). The latter approach has great merit, but gathering enough data to make a sensible decision about the underlying species distribution pattern is problematic. At present, most microbiologists attempt to estimate diversity by looking at a gene that occurs in all cellular life forms. They infer diversity from the number of different variants that can be cloned from a sample of environmental DNA. Unfortunately, the number of clones analyzed is typically small (tens to hundreds) compared to the number of individual microbes being analyzed (billions or trillions). This is like randomly sampling a bus load of people and then trying to infer the diversity of all people in the world. You would not expect to find many Lithuanians.

Gans et al.(1) and others (8) realized that the pattern of DNA reassociation kinetics reflects the underlying distribution of similar sequences, and hence likely reflects genomic diversity. However, the authors have gone further and show that there is probably enough data in published DNA reassociation curves for bacterial communities to allow discrimination between different possible species abundance curves. By applying new mathematical treatment of data, the authors generate abundance curves, the most plausible of which suggests that there could be 107 distinct prokaryote taxa in 10 grams of pristine (free of chemical contaminants) soil (see the figure). Moreover, rare organisms comprise most of this diversity. They further determine that most of these rare organisms could be wiped out by heavy metal pollution of the soil.

The jargon and mathematical notation of taxa abundance distributions can be obscure. However, when presented graphically the curves are as simple and as useful as an outline of an unexplored continent (see the figure). Thus, power law distributions (like the zipf distribution) simply describe exponentially increasing numbers of species at exponentially decreasing abundances. On the other hand, lognormal distributions suggest that at lower abundances, the number of rare species start to decrease. It is no mystery therefore that wiping out rare species, as in the case with heavy metal soil pollution, diminishes the ability to distinguish between the two different situations. Gans et al. (1) point out that the log-Laplace distribution (see the figure) is theoretically attractive because it derives from an ensemble of lognormal distributions but, as such, it can be made to look a little bit like either a lognormal or a zipf and therefore (unsurprisingly) fits well in all circumstances.

Mapping microbial diversity.

The relative abundance of microbial taxa can be described with abundance distributions. The total number of species is the area under the taxa plot line. The precise shape of any given curve will depend on the parameters selected. In all cases, the majority of the biomass that we can most readily observe (Terra Frequentata) make up a minority of the diversity. Most taxa are very hard to find by random sampling (Terra Incognita).

The work of Gans et al. (1) simply represents a rough “map” of the whole microbial community of a sample of soil, which may be far more useful right now than an exquisite description of just part of it. One could argue that the estimates might be affected by reassociation of sequences common to many taxa. And the curves presented by the authors certainly constitute tremendous extrapolations (though they are supported by simulations). However, such quibbles are immaterial if the overall picture is even approximately correct. An imperfect, simple map of an entire region can guide an explorer with more certainty than a perfect representation of one creek. The explorer, thus guided, will produce better maps that will better guide more explorers, and so on. Dispensing with the map is like “wildcatting” or “swashbuckling” for diversity: exciting and profitable if you make a strike, but ultimately subject to diminishing returns and the inevitable disinterest of your sponsors. (Sir Walter Raleigh, a 16th-century English swashbuckler, failed to find El Dorado on the Orinoco and was later beheaded. Today's sponsors are a little more understanding.) Rational plans and costs for exploring the microbial frontier require a proper mathematical framework for this task. Indeed, we cannot even resolve some very basic questions.

Thus, exponentially increasing numbers of taxa at exponentially decreasing abundances means that, in random samples of diversity, a few abundant taxa can turn up again and again. Resolving whether the recurrence of abundant taxa is an artifact of sampling (the equivalent of the mapless explorer going around in circles) or a genuine reflection of low diversity (9) is important in resolving the debate on the extent of prokaryotic diversity.

The Gans et al. (1) report is part of a wider shift in the study of microbial communities. At present, the science is primarily observational. This study, together with other work (4-7, 10), shows that our powers of observation can be vastly enhanced by sensible mathematical techniques. But observation per se will never be enough to explain the microbial world, any more than we can explain the universe by just looking at the stars. A recent flurry of papers on taxa-area relationships (11-14) may point to a way forward. These papers are also observational and cannot explain the microbial world either. However, the emergence of common patterns in data from a number of environments hints at deeper and perhaps universal, underlying processes that could be expressed formally as explicit mathematically supported theory.

In the world of microbial ecology, we need theory very badly. Almost any consequential microbial community will have 1010 to 1017 bacteria that could compose more than 107 differing taxonomic groups and countless functional groups. It seems remarkable that we should even contemplate trying to understand such vast systems without recourse to some form of theory. At the very least we may hope to not only “substitute one theory for many facts” (15) (and we are accruing facts at an astounding rate), but also quantitatively guide our exploration, facilitate the application of the methods we have, and develop new tools. We might ultimately hope to develop greater powers of prediction. This would be a revolutionary development, but not necessarily an easy one. The findings of Gans et al. place a special duty on theorists in this area of ecology. The challenge is to offer mathematical guidance in a world we can barely perceive and not to explore ultimate causes for patterns we can readily observe. We therefore need to develop simple (16) models that we can calibrate and then improve. Like the “map” outlined by Gans et al., simple theories will mark the beginning, not the end, of the rational exploration of this frontier. Exploring rationally might lead us to a healthier, cleaner, and more productive world and ensure that we don't lose our heads or our minds, walking in circles on the edge of a final frontier looking for an “El Dorado.”

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