How cyanobacteria define their middle point
The Ducat lab in direct collaboration with the Osteryoung lab has shown, for the first time, how cyanobacterial cells define their middle point, in order to promote cell division that ensures the daughter cells are of equal size. The results have been published as the cover article in the journal, Molecular Microbiology.
Josh MacCready, the first author on the study, is a PhD student in the Department of Microbiology and Molecular Genetics, and is co-advised by Drs. Katherine Osteryoung and Danny Ducat. Josh says, “Cell division in most bacteria occurs at the middle of the cell. If division deviates away from this plane, daughter cells might not inherit important cellular material, compromising their ability to survive and reproduce.”
The first mechanism found to allow bacteria to “identify” their middle was first discovered in the bacterium Escherichia coli. Known as the Min system, the mechanism involves three proteins that oscillate across the cell from pole-to-pole, and by doing so help to define a zone at the middle of the cell.
“Basically, because these Min proteins concentrate onto one pole then the other, when you consider them over time, they reside the least in the center of the cell. This helps to designate a zone at the center where conditions are right to assemble the division machinery. A variety of factors, including another protein called FtsZ, are then able to assemble into a ring-like structure that gradually constrict, leading to the pinching of the cell in two equal halves.”
Yet, when the Min mechanism was first discovered, it solved one problem while generating a whole slew of other mysteries. For example: how do Min proteins “know” to go to the poles? What “directs” them to move back and forth? In the past 30 years, many researchers have tackled these problems, determining that this behavior is an example of a complex pattern that emerges out of properties of self-organization from the Min proteins.
“The behavior of Min proteins isn’t very intuitive and can be difficult to grasp. But we see emergence of complex behaviors from very simple components all over nature,” says Dr, Danny Ducat, Assistant Professor at the PRL.
“A good example is an ant colony: no individual ant “knows” the master plan, but through a set of simple rules and repeated interactions between ants, a complicated behavior emerges that allows ants to appear much more “directed” at the level of the colony. The emergence of complicated oscillations of Min proteins within the cell is a similar phenomenon: rigid rules of interaction between Min proteins within the cell creates a surprisingly complicated pattern, seemingly spontaneously!”
Studying cell division in cyanobacteria
Despite many years of study, most direct research on the Min system has been focused on in E. coli and a small handful of similar bacteria. “From this research, we know that Min protein oscillation is highly dependent upon them attaching to accessible membranes inside the bacteria,” according to Josh.
“Yet, this is a problem when you think of some other forms of bacteria that possess additional internal membranes: Would Min proteins still “know” how to navigate these more complicated cells?"
The Ducat and Osteryoung labs wanted to look at this problem directly in cyanobacteria, given that their internal makeup is more complex than that of E. coli. Josh adds, “Cyanobacteria contain a network of thylakoid membranes not found in E. coli. These membranes are found throughout the entire cell and are used to generate energy from sunlight. We thought the presence of these additional membranes might “confuse” the Min system or even just present a physical barrier for Min proteins seeking to find the cell pole.”
But despite these spatial constraints in cyanobacteria, the lab observed beautiful pole-to-pole oscillation of Min system proteins.
To examine how the Min proteins navigated the more complicated innards of cyanobacteria, the Ducat and Osteryoung labs used computational models – with help from Jory Schossau from Chris Adami’s lab here at MSU – to simulate Min proteins interacting within an artificial cell.
The simulations showed that it is likely that Min proteins in cyanobacteria have an additional talent (Video 2). “Cyanobacterial Min proteins must somehow be able to identify and avoid the thylakoid membranes. Instead they likely squeeze through small gaps in the stacks of thylakoids in order to reach the cell membrane – our model tells us it’s less likely they bind to the thylakoids at all.”
Another mystery emerges
“We still don’t know how Min proteins can “identify” one membrane from another," Josh says. "But regardless, the fact this system works at all in cyanobacteria is incredible! If oscillations can occur in such a geometrically-complex organism, chances are Min oscillations might be widespread across other bacteria, even those with unusual cell shapes. One of the most tantalizing implications is that this may even be true in some organelles, like the chloroplast of plants or the mitochondria of animals. We’ll have to do more work to verify this idea.”
Indeed, the Osteryoung laboratory has expertise in cell division within the plant chloroplast, and believes that it is likely that the methods cyanobacteria use to define their division plane are likely conserved in higher plants.
Moving forward, Josh is interested in the behavior of this system in the context of circadian rhythms. But the work also has implications for controlling the size of cyanobacterial cells – a useful trick for improving the biotechnological potential for cyanobacteria.
Researchers are integrating their work into undergraduate cell and molecular biology laboratory courses at Michigan State University through the use of Arabidopsis mutant screenings.
MSU-DOE Plant Research Laboratory (PRL) scientists have published a new study that furthers our understanding of how plants make membranes in chloroplasts, the photosynthesis powerhouse
A new AI system, called DeepLearnMOR, can identify organelles and classify hundreds of microscopy images in a matter of seconds and with an accuracy rate of over 97%. The study illustrates the potential of AI to significantly increase the scope, speed, and accuracy of screening tools in plant biology.