Still learning from Myxococcus

Myxococcus xanthus has been a model for motility, multicellular development and quorum sensing for decades. Just when you thought we had learned all we can, big data sheds new light on this fascinating organism.

Go to the profile of Ben Libberton
May 26, 2017
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The more we research, the more we realise that things aren't as simple as they seem. A common finding in modern studies is that, 

actually this populations of cells/receptors/molecules that we thought were all the same are incredibly diverse. 

As tools develop such as Next-Gen sequencing and Super-Resolution microscopy we have to opportunity to look at the details of populations that were previously beyond our grasp. Now we see that previous observations were recording the net result of hundreds of different individual actions and interactions that all play an important role in biological system that we study.

Another important tool that is not only developing fast but is also being rapidly placed into the hands of researchers is data analysis. By carefully scrutinising large amounts of data, it is possible to understand the underlying heterogeneity of complex systems. 

A collaboration between the University of Georgia and Rice University have used a data driven approach to try and understand the complexity of Myxococcus xanthus. In their paper titled "Data-driven modeling reveals cell behaviors controlling self-organization during Myxococcus xanthus development", they explain that most previous data driven studies focus on collective migration patternsWhile this is important, they state that many molecular studies have shown that collective traits are not the result of many bacterial cells doing the same thing, rather individual bacteria behaving differently but ultimately contributing to a net outcome.

In their study, instead of looking at molecular traits of individual M. xanthus cells, they look at motility data from individual cells and feed this into models to understand the behaviour. Perhaps unsurprisingly, they observe that:

"Our modeling method indicated that decreased cell motility inside the aggregates, a biased walk toward aggregate centroids, and alignment among neighboring cells in a radial direction to the nearest aggregate are behaviors that enhance aggregation dynamics."

However one of the most interesting finds from this study is that:

"Our modeling method also revealed that aggregation is generally robust to perturbations in these behaviors and identified possible compensatory mechanisms"

This means that by using data modelling techniques and measuring motility, the research team were able to determine that the motility patterns oberseved in M. xanthus are resistant to changes, and that a number of different motility phenotypes will give rise to the same collective traits. This makes sense for behaviours that are critical for the survival such as the production of fruiting bodies in Myxococcus. While some of the observations may seem obvious, this is important for a proof of concept that can now be applied to other fields such as neurodegenerative disorders and tumor development.

For more information check out the publication in PNAS.

Publication: 

Data-driven modeling reveals cell behaviors controlling self-organization during Myxococcus xanthus development

Significance

Coordinated cell movement is critical for a broad range of multicellular phenomena, including microbial self-organization, embryogenesis, wound healing, and cancer metastasis. Elucidating how these complex behaviors emerge within cell populations is frequently obscured by randomness in individual cell behavior and the multitude of internal and external factors coordinating cells. This work describes a technique of combining fluorescent cell tracking with computational simulations driven by the tracking data to identify cell behaviors contributing to an emergent phenomenon. Application of this technique to the model social bacterium Myxococcus xanthus suggested key aspects of cell coordination during aggregation without complete knowledge of the underlying signaling mechanisms.

Abstract

Collective cell movement is critical to the emergent properties of many multicellular systems, including microbial self-organization in biofilms, embryogenesis, wound healing, and cancer metastasis. However, even the best-studied systems lack a complete picture of how diverse physical and chemical cues act upon individual cells to ensure coordinated multicellular behavior. Known for its social developmental cycle, the bacterium Myxococcus xanthus uses coordinated movement to generate three-dimensional aggregates called fruiting bodies. Despite extensive progress in identifying genes controlling fruiting body development, cell behaviors and cell–cell communication mechanisms that mediate aggregation are largely unknown. We developed an approach to examine emergent behaviors that couples fluorescent cell tracking with data-driven models. A unique feature of this approach is the ability to identify cell behaviors affecting the observed aggregation dynamics without full knowledge of the underlying biological mechanisms. The fluorescent cell tracking revealed large deviations in the behavior of individual cells. Our modeling method indicated that decreased cell motility inside the aggregates, a biased walk toward aggregate centroids, and alignment among neighboring cells in a radial direction to the nearest aggregate are behaviors that enhance aggregation dynamics. Our modeling method also revealed that aggregation is generally robust to perturbations in these behaviors and identified possible compensatory mechanisms. The resulting approach of directly combining behavior quantification with data-driven simulations can be applied to more complex systems of collective cell movement without prior knowledge of the cellular machinery and behavioral cues.

Reference 

Proc Natl Acad Sci U S A. 2017 May 22. pii: 201620981. doi: 10.1073/pnas.1620981114. [Epub ahead of print]. Data-driven modeling reveals cell behaviors controlling self-organization during Myxococcus xanthus development.

Cotter CR, Schüttler HB, Igoshin OA, Shimkets LJ.

Go to the profile of Ben Libberton

Ben Libberton

Postdoc and Public Information Officer, Karolinska Institute

I'm a researcher at the Swedish Medical Nanoscience Center in Stockholm and the Community Editor for npj Biofilms and Microbiomes. I'm interested in how bacteria cause disease and look to technology to produce novel tools to study and ultimately prevent infection. My research spans different disciplines from basic microbiology to surface chemistry and organic bioelectronics.

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