eLife Episode 36: Epilepsy and Sushi

The nerves or neurones that send messages from one end of the body to the other have fascinated anatomists for over a century. An outstanding question is how do these cells, which can be metres in length, keep all of the remote parts of the cell supplied with energy and raw materials, which are normally made in just one central region of the cell. One popular idea is that neurones contain the microscopic equivalent of a conveyor belt system which transports materials to where they need to go inside the cell. But, by building a mathematical model of how this happens, one scientist who has found that anyone waiting for their dinner to be delivered by a system like this would end up very hungry indeed, so something else must be going on, as he explained to Chris Smith...

Timothy: I'm Dr. Timothy O’Leary based at the University of Cambridge and I'm a lecturer in Information Engineering and Neuroscience. Today, we’re in a sushi restaurant in Cambridge and it’s one of those sushi restaurants that has a snazzy belt mechanism that allows all of the dishes to be delivered to the customers as they sit around the sushi belt.

Chris: What has this got to do with cell biology?

Timothy: The cells I'm interested in are neurones and neurones are the cells that essentially make your brain work. A typical person has around 86 billion neurones and one neuron can potentially connect thousands of other neurones and it’s this connectivity that gives your brain its power. So, how do neurones connect to each other? Well, in order to reach out and connect to their neighbours, they have this long, thin, branch-like processes called dendrites. So if we looked at a neuron under a microscope, it would look like a tree, a very bushy tree with lots of branches and some of them very long. If we were to zoom in to this neuron and look inside one of the branches, we’d see there are lots of things moving up and down the branch. And this is because neurones are composed of lots of proteins and small components that all need to be manufactured and moved around inside the cell. So sometimes material needs to be made in one part of the cell and then shuttled along to another part of a cell. And the analogy that we use is the sushi belt because there really is something inside the cell that moves this cargo along in a similar fashion to a sushi belt.

Chris: Effectively, you can imagine the analogy is the nucleus is the recipe book with the chef standing there, cooking stuff, putting it on the plates that then go on a sushi belt. They're carted around the cell and the customers, the parts of a cell that need them are going to be lifting dishes off the sushi belt at various points and using them.

Timothy: That’s just the picture that we have, yes.

Chris:: What's wrong with it?

Timothy: Life isn’t really like that. At the molecular level, the movements of these particles are stochastic, that means there's chance element in it. To explain what that would mean in the sushi restaurant analogy, let’s imagine that we’re waiting for a tuna roll and it’s a few feet away. But then randomly, the belt changes direction and starts moving the other way, and that would be very frustrating. But what would be even more frustrating is if a person next to us who doesn’t even want the tuna roll just took the tuna roll, sat it on their table for a while and then maybe decided to put it back on the belt. Those are the kinds of things that can occur at the molecular level by chance and it’s for this reason that we can expect long delays sometimes in this transport mechanism within a neuron.

Chris: Can cells tolerate not getting their tuna roll for ages or actually, are you saying that this is such a significant constraint. There must be something else going on because cells would not be able to put up with that?

Timothy: That was actually the motivation for this study. What we did was we took experimental data where scientists had measured the movements of these microscopic particles and then we simply took the measurements and we did the maths. We figured out how long it would take on average a collection of particles to move across a typical sized neuron. And a number turned out to be disappointingly large. It can take many hours or days to distribute cargo throughout a typical neuron. And this came as a surprise because many of us thought that cargo could be distributed on the order of minutes or hours at the very worst.

Chris: Is it not that the cell does something else which could be, it says, “Well, I’ll tolerate some constraints of the sushi belt but at the same time, I’ll also have my own local solution, so I’ll make some stuff myself locally so I'm not held up. So if it’s not available on the sushi belt right now, I’ll make my own.”?

Timothy: That’s absolutely true and in fact, in recent years, it’s been observed that neurones do have the capacity to make things that they need locally. However, the ingredients for the things that they make locally and the machinery for making them still need to be delivered to those sites. Our claim is that that may take a lot longer than is currently thought.

Chris:  I suppose what you have done is, a.) to highlight some of these inconsistencies, but you’ve also now generated with this model a bunch of testable hypotheses.

Timothy: That’s absolutely true. So, it’s now becoming possible to peer inside a living neuron and watch these components moving around. And the kinds of experiments people can do now can start to address some of the predictions of the model. We might find that actually, neurones are far cleverer than we think and they're able to distribute components far more efficiently than our calculations suggest. And that’s why our model made the minimal possible set of assumptions. Now, if further observations contradict those predictions, then we know that actually, there are parts missing to our understanding of this molecular sushi belt.

The Amazon rainforest covers an area of more than 5 and a half million square kilometres in South America. Some describe it as the “lungs of the planet” because, each year, the forest soaks up more than 2 billion tonnes of carbon dioxide. But the area is under threat from logging, including the practice of “selective logging” although how this affects the forest - and the carbon budget - isn’t known. Chris Smith hears from one researcher who has been finding out...

Camille: My name is Camille Piponiot and I work at the University of French Guiana. We’re looking here at ‘selective logging’ which is a widespread practice in the Amazon. It consists in selectively harvesting a few trees and leaving the rest of the forest to regenerate after logging. We know that it has big impact on the forest – the trees that you harvest and the trees that you damage by harvesting some trees will emit carbon to the atmosphere which means greenhouse gases. But what we also know is that the rest of the forest that is left to regrowth will store carbon back and that’s what we are interested in – how it stores carbon.

Chris: This is quite a complicated question though, isn’t it? So how did you approach studying that and working out what the impact of that process is across the Amazon and is it the same all over the Amazon, because the Amazon is a big place?

Camille: Yes. So, what we have is data from satellites that can tell you how the forest is growing or you can have data that has been measured on the ground with for example, measurement of tree growth. Each tree is measured and that’s what we have. We have some plots all over the Amazon that have been logged and then that have been measured for several years as much as 30 years. By using that data, we have a lot of data on all the processes that go on in the forest after logging.

Chris: So you can basically work out how much carbon must be being emitted when you look at the scale of the logging and the removal of certain trees, and damage of trees when big trees are felled. And then you can look at the long term – what grows back and therefore, how much carbon do you think it’s drawing down as it grows back and you can sort of do the carbon equation.

Camille: Exactly. You can measure very easily how much carbon was emitted in the first year. It’s a lot harder to have data on the – all the processes that go on after logging because it lasts for many decades.

Chris: So, on what sort of scale is this happening? Tell us how much of the Amazon is being exploited in this way?

Camille:  So, in the Amazon, 2 million hectares are exploited every year at least, and that means 1 per cent of the whole region every year.

Chris: Gosh! That’s a lot, isn’t it – 1%? And what do the numbers look like when you begin to do the sums for carbon? What does the equation look like?

Camille: We looked at the effect of climate and also soil properties, and we found that there were big differences between regions, and that the regions that have less rainfall and more seasonality of rainfall will have less carbon storage after logging. And so, the forest grows less after logging and stores less carbon than in the north of the Amazon where there is more rainfall and less seasonality of rainfall. And there, we saw that stored more carbon especially the big trees that were left after logging.

Chris: So what you're saying is that although you found this relationship, the Amazon isn’t made equal everywhere and there are some areas which are going to be more vulnerable to certain types of logging or agricultural practice than other places. And so, it’s not just a simple case of, we have one-size-fits-all as a policy for how to log the Amazon sustainably.

Camille: Yes, exactly. We saw that everywhere, the big trees were the ones that were storing more carbon. So, I guess that having logging practices that avoid damage on big trees will help the carbon recovery after logging, but not all regions are equal and the south of the Amazon is more vulnerable to  logging or maybe other practices and grows slower after logging.

Chris: So, what is the take home message and how does this change our opinion of the Amazon is managed?

Camille: The Amazon is a very diverse region and when we think about planning logging, you have to be very careful of where we are because it might affect some forests more than others. Also, if we want to improve carbon models, taking into account climatic or other environmental variables is very important because you could improve a lot the accuracy of the models.