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How to understand complexity: harness the power of simplicity

One of the most powerful tools in science is the use of simple models that can represent a wide range of other similar systems. 

The power of single-cell organisms

A prime example of this is the way that investigation of the biology of the bacterium Escherichia coli has informed much of our understanding of both microbiology and molecular biology. 

Despite being a single-celled, easy-to-grow and relatively easy to manipulate bacterium, E. coli has taught us many things about how the human body works. It has enabled the development of tools that we have subsequently used to further our understanding of the biology of our own bodies. 

Similarly, baker’s yeast (Saccharomyces cerevisiae) has told us much about the differences between higher organisms and bacteria, despite being a single-celled species itself. Similarly, the worm C. elegans has shown us how muscles and nerves develop and function.

The remarkable amount of information we can learn from such simplistic model systems also tells us something fundamental about how the biological world is organized. Thanks to the evolutionary processes that took life to where it is today, there are symmetries and similarities which we can utilize to better understand the living world around us ­– and in us. 

The problem of human microbiome complexity

Strikingly, there are more bacterial cells than human cells in our bodies and they encode orders of magnitude more different gene functions than the human body does on its own. That means that, together, the bacteria in us can produce many more different compounds than our bodies can on their own.

As Jonathan Eisen has said concerning our interplay with human-associated bacteria: we are them, and they are us.

All of these bacteria impact us in a number of different ways. Bacteria don’t only cause infectious diseases, but also perform functions important for human well-being. Disruptions to the bacteria we carry – our microbiome – have been associated with several human diseases, including colon cancer, depression, IBS and IBD.

The mechanisms behind these diseases have largely eluded scientists, who have struggled to identify a clear causative factor. However, this could be because these diseases may not have a single cause – instead, they might be induced by lost or altered interactions between the microbes we carry in our bodies.

Unfortunately, finding out the mechanisms behind all the interactions between the thousands of different bacteria present in the human microbiome is almost impossible. Large-scale analyses largely end up identifying associations between pairs of microorganisms that appear (or disappear) together across large groups of human individuals. 

However, that information on its own does not help much in understanding why and how these bacteria interact and is a rather blunt predictor of whether loss of that potential interaction would cause disease.

Reducing complexity with model communities

The key to starting to unravel these complex interactions between microbes is the same as it has been for understanding the complex molecular biology and biochemistry of our bodies: a careful examination of simpler model systems where we can control (most of) the conditions.

This requires us to construct model communities built from a smaller subset of relevant human-associated bacteria that we know are interacting with each other in one way or another. These interactions can be identified in the large-scale screens mentioned earlier. By reducing the complexity of these model communities and making sure that we can genetically manipulate at least some of their members, we can start teasing apart what genetic and environmental factors that enable interactions and which factors that break the ability to interact.

Similar to how models such as E. coli have driven our understanding of biology also outside of these specific model organisms, simplified model communities can help us decipher the genetic basis of microbial interactions. Recent model community development and rapid advancement of large-scale analytical techniques for studying the genetic content of these model communities are now finally lending us insights into interactions between bacteria and the importance of that microbes can “talk” to each other.

Two examples of model communities

One example of this development is the model community for the human gut developed in Dr Ophelia Venturelli’s lab at the University of Wisconsin-Madison, comprising twelve interacting species that can be relatively easily grown in the lab. That system enables us to start teasing apart the genetic mechanisms that govern interactions in the gut and make predictions that can be tested in full-scale real gut communities. 

A model community for soil bacteria from the Bengtsson-Palme lab.

Another example is the work we have been doing in my lab, together with Prof. Jo Handelsman (also at the University of Wisconsin-Madison), on a three-species model community for soil bacteria. Using this model, we have been able to pinpoint genetic mechanisms that enable community responses to antibiotic stress as well as new invading species. By combining model community systems and large-scale sequencing approaches, we are finally starting to understand the genetic basis of community stability and the ability to invade microbial communities. 

The use of community model systems for human microbiomes will bring us closer to understanding human dysbiosis and how it is linked to various diffuse diseases, such as colon cancer, depression, atopic dermatitis, IBS and IBD.

Johan Bengtsson-Palme is an Assistant Professor at the Sahlgrenska Academy at the University of Gothenburg, Sweden.

Join Johan and hundreds of other leaders in the global microbiome community to discover the latest developments in research and industry at the 7th Microbiome and Probiotics R&D and Business Collaboration Forum. Find out more here.

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