The social networks of plant mitochondria

Together with publishers Cambridge University Press, we collaborate on an open access journal. Quantitative Plant Biology (QPB), which provides an interdisciplinary forum for high-quality research on groundbreaking discoveries and predictions in botany.

QPB is a home dedicated to research that applies technologies such as data mining and analysis, mathematical modeling, and machine learning to plant biology.

The journal welcomes research from across the spectrum of basic, applied, and societal plant research. Across all biological levels, from molecular to cellular, organic to population; It should be based on data from laboratories, field work, and citizen sciences

With that in mind, this week we asked University of Birmingham PhD student Joanna Chosticky to guest blog about her work using computational analysis to map the social networks of plant mitochondria.

We started by asking Joanna, why she became interested in mitochondria and what it means through their social networks.

“Mitochondria are amazing organelles. They are so dynamic, watching their movement under the microscope is incredible – I remember clearly the first time I saw them.

Where are they going? What do they do talking to all those other organelles? How do they move? We are getting close to the answers.

Our work investigates mitochondrial populations in plant cells as a whole, and there is much information that can be gained with a broad systemic approach.

Networks have previously been used to characterize all kinds of systems—cellular organization in plants, endoplasmic reticulum structure, fungal networks, and even mitochondrial reticulum networks in yeast.

But we take a different approach because plant mitochondria spend most of their time alone, as separate individuals. But they do interact, and communicate closely with each other – we build encounter networks out of these events, and use them to define communication across all individuals.

This gave us insight into not only when they communicate, but why.

We discovered that the plant cell has a constant trade-off to work with; How to keep mitochondria together to exchange proteins, mtDNA, metabolites, etc., and how to separate them to avoid accumulation of local mutations, get energy delivered around the cell quickly and converge with other organelles.

We used modeling and mutagenesis methods to explore this relationship and showed that the social networks of these mitochondria are more than an easy analogy.

It can be difficult to monitor and measure the interaction of something so small and you need the right tools. Fortunately, imaging and tracking technologies are improving all the time.

We use a confocal microscope, and the seedlings we use are very small, about 5 mm in length.

Our plants contain genetically modified genes, which place a green luminous protein in the mitochondria, so individual organelles glow brightly when imaged with the correct laser power. This makes them visible in real time, so you can monitor them as they move around the cell, doing their very important work of saving energy.

From these videos, we use a tracking software that locates these individual organelles over time, and from this you get a quantitative estimate of how many mitochondria there are, where they are at any one time, and how close they are to each other.

We measure interaction as affinity, so if one mitochondria is in close proximity to another, we record that.

When you measure all the interactions within the video, you can create a social network at any point in the video.

Each interaction is an edge between the ganglia that represents the mitochondria. Borrowing lessons from computer science and graph theory, we can identify connectivity between small groups or groups and across the entire cell.

The movement of these organelles is dictated by all kinds of cellular signals, as well as the flow of cytoplasm into the cell, and the crowded cellular environment.

It’s hard not to personify them, because they often look like people walking around a party – few stick to only a certain area, very popular ones, those who don’t talk to anyone, and those who are too busy just shoot through the room without wasting time.

We’re trying to figure out why the hive benefits from these diverse behaviors and connections (and what makes a good party…).

Using social networks to determine how these mitochondria communicate and communicate has allowed us to know what the cell prioritizes.

When you have this system of dynamic organelles, there has to be a way to display the connection between all of them, and social networks have allowed us to do that.

It’s also a tool that can be used across other species, and even other organelles.

We’re currently using it to investigate how mitochondrial movement and connectivity are affected when the genes of these individual organelles are perturbed – it’s a direct application of this technology born out of a scope project – so it’s surprising to see.

This new project began with the strange difference between plant and animal mitochondria.

Animal mitochondria are formed in long spaghetti-like networks, forming a somatic attached network of organelles.

However, they remain mostly pointy in plants, moving on their own and merging only transiently.

This could be motivated by the fact that mitochondrial DNA (inherited from bacteria ancestors) is reassembled in plants, not in animals.

This division allows the cell to control, through its movement, which mitochondria it can talk to and thus have the opportunity to recombine its DNA.

This recombination also helps plants have very flexible responses to changing environments that they can’t get away from, something animals have no problem with.

Our current work delves into the movement of mitochondria in plants, focusing on how they enable DNA sharing and recombination—and applying our networks to a new problem.

Looking ahead, I’m also very excited about the current research being done on organelle placement in plant cells, and how this can help us design more efficient connections between organelles such as mitochondria/chloroplasts/peroxisomes and apply this to crop plants and engineer C4 capabilities.

In short, mitochondria are amazing because they are so dynamic – great to watch and assume what they are – while trying not to materialize too much…

I grew up in Worcestershire, out in the countryside, and always love to be outside exploring.

I was always curious, and still am, about everything around me. My favorite things when I was little were treasure hunts, and I had trouble pulling keys off my mom’s old computer keyboard trying to figure out what’s underneath.

When I first started studying biology, I found something that I really liked, which for me was more intuitive than other subjects and I believe that being a scientist became a real goal.

My first undergraduate cell biology lectures showed me how incredible these biological processes are – eg the formation of a clathrin layer around a vesicle – how can something so small be so well-coordinated, so clear in its purpose, while also being so real and so measurable?

I was so amazed that we could find out so much about this new world (for me) – but I didn’t really focus on plants until it came to my thesis.

George Basil took me as a summer project student and his team taught me how we can use computational tools to reveal communication patterns in plants, how to mix experimental and theoretical data, wet lab work and computational analysis.

I followed this philosophy until my Ph.D. with Iain Johnston and the Stochastic Biology team, now based in Bergen (I also work with Gibbs Lab here at UoB – keep me ‘plant-based’), and it’s been a great journey so far – quantitative methods are the pillar The backbone of our work and allows us to ask new questions about the system, and to test hypotheses that might otherwise be out of reach.

One thing I think our group does well (though I’m biased) is to take a very broad look at the questions we’re dealing with – what these processes look like across the entire tree of life; What can we learn from other disciplines?

I plan to take this philosophy to where my scientific journey takes me…”

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