Tag: Microbial community systems biology

This week, in a stroke of luck coinciding with my conference presentation on the same topic, my review paper on microbial model communities came out in Computational and Structural Biotechnology Journal. The paper (1) provides an overview of the existing microbial model communities that have been developed for different purposes and makes some recommendations on when to use what kind of community. I also make a deep-dive into community intrinsic-properties and how to capture and understand how microbes growing together interact in a way that is not predictable from how they grow in isolation.

The main take-home messages of the paper are that 1) there already exists a quite diverse range of microbial model communities – we probably don’t need a wealth of additional model systems, 2) there need to be better standardization and description of the exact protocols used – this is more important in multi-species communities than when species are grown in isolation, and 3) the researchers working with microbial model communities need to settle on a ‘gold standard’ set of model communities, as well as common definitions, terms and frameworks, or the complexity of the universe of model systems itself may throw a wrench into the research made using these model systems.

The paper was inspired by the work I did in Jo Handelsman‘s lab on the THOR model community (2), which I then have brought with me to the University of Gothenburg. In the lab, we are also setting up other model systems for microbial interactions, and in this process I thought it would be useful to make an overview of what is already out there. And that overview then became this review paper.

The paper is fully open-access, so there is really not much need to go into the details here. Go and read the entire thing instead (or just get baffled by Table 1, listing the communities that are already out there!)

References

1. Bengtsson-Palme J: Microbial model communities: To understand complexity, harness the power of simplicity. Computational and Structural Biotechnology Journal, in press (2020). doi: 10.1016/j.csbj.2020.11.043
2. Lozano GL, Bravo JI, Garavito Diago MF, Park HB, Hurley A, Peterson SB, Stabb EV, Crawford JM, Broderick NA, Handelsman J: Introducing THOR, a Model Microbiome for Genetic Dissection of Community Behavior. mBio, 10, 2, e02846-18 (2019). doi: 10.1128/mBio.02846-18

As I have been indicating before, I will be presenting at the Microbiome & Probiotics Collaboration Forum in Rotterdam on May 18-20. In relation to this, I was asked to write a shorter blog post on (or, if you will, some type of extended abstract) what I will talk about, which is how simple model systems for microbial communities can be used to understand complex systems with loads of interactions, similar to how E. coli and yeast have enabled a much more wide-reaching understanding of molecular biology than just about those two single-celled organisms themselves. The entire post can be read here, and I hope that I will see you in Rotterdam in May!

Phil Goetz at JCVI recently posted his reflections from the Summit of Systems Biology. I was not there, but I read his summary with interest. Now, what strikes me as interesting is the notion that “there were no talks on metagenomics.  This also struck me as odd; bacterial communities seem like a natural systems biology problem.” Having been working with microbial communities for a while, I am surprised that the modeling perspective that is so prevalent in macro-organism ecosystems ecology have not yet really come to fruition in microbial ecology. With the tremendous amounts of sequences that are pouring over us from microbial communities, and with the plethora of functional metagenomics annotation that is made, how come that there has been so little research in the actual interactions between microorganisms within e.g. biofilms?

The problem is also connected to the lack of time-series data from community research. To be able to understand how a system behaves under changing conditions, we need to measure its reactions to various parameter changes over time. Instead of pooling metagenomes to reduce temporal “noise” we need to be better at identifying the changing parameters and then use the temporal differences to look for responses to the parameter changes. By applying a functional metagenomics perspective at each sample point, combining this with measured changes in community species structure (as measured e.g. by 16S or some other marker gene), and correlating this with changes in the parameters, we should be able to build a model of how the ecosystem responds to changing environments. With the large-scale sequencing technologies available today, and the possibilities given by metatranscriptomics, these ideas should be challenging but not impossible.

I am not saying that any of these things have not been done. But it has been done to a surprisingly small extent. I would highly appreciate reading a paper trying to build a mathematical model of how the ecosystem functions in bacterial communities shift in response to an environmental stressor. Because when someone builds such a model we suddenly have a tool to take microbial community research from an explorative perspective to an applied one. The applied perspective will be useful for actually protecting environments and ecosystem services, as well as for understanding how to manipulate microbial ecosystems to maximize the outtake beneficial to society. Also, the understanding the ecosystem dynamics of microbial systems could be carried over to macro-ecosystems and provide a small-scale ecosystem laboratory for all ecosystem research. Such a shift towards applied microbial community systems biology will be more or less necessary to be able to argue for more resources and time being spent on e.g. metagenomics. And I believe that we will soon be there, because the step is shorter than we might imagine.