ARTICLE: Social networks of plant mitochondria

Network analysis of Arabidopsis mitochondrial dynamics reveals a resolved tradeoff between physical distribution and social connectivity
Joanna M Chustecki, Daniel J Gibbs, George W Bassel, Iain G Johnston
Cell Systems 12 419 (2021)

We recently spent some time looking at a long-standing question in plant cell biology -- why do mitochondria move the way they do? Plant mitos look for all the world like cars in a city, moving along highways and speedily getting from place to place. We combined laser microscopy, video analysis, physical modelling, and network science to explore what benefits this motion might bring to the cell. It turns out, it allows mitochondria to have social lives! Through the "social network" of encounters between moving mitochondria, beneficial exchange of contents can occur, while their motion allows the cell to keep its population well spread. Here's a blog article from Jo explaining things more!

Read more...

... and also check out Jo's beautiful site!

Mitochondria are in yellow in the microscopy image; their "social network", describing their encounters, is overlaid in white. Cover of the month's Cell Systems issue.

ARTICLE: Corals to crops -- how life protects the plans for its cellular power stations

Avoiding organelle mutational meltdown across eukaryotes with or without a germline bottleneck
David M Edwards, Ellen C Røyrvik, Joanna M Chustecki, Konstantinos Giannakis, Robert C Glastad, Arunas L Radzvilavicius, Iain G Johnston
PLoS Biology 19 e3001153 (2021)

(this text is from a press release about the article)

An international team of researchers led by the University of Bergen has uncovered how organisms from crops to corals may avoid deadly DNA damage during evolution.

Our cells, and those of animals, plants and fungi, contain compartments that produce chemical fuel. These compartments contain their own DNA, which stores instructions for important cellular machinery. But this so-called oDNA (organelle DNA) can become mutated, corrupting the instructions and preventing cells making enough energy.

In humans and some other animals, a process called the “bottleneck” allows some offspring to inherit less mutated oDNA. This process needs mothers’ egg cells to develop early, like in humans, where a human girl is born with all her egg cells already formed. But other organisms, from plants to fungi, don’t develop these cells early – their flexible body plans mean that eggs are not “set aside” early in development.<\p>

“We wanted to know how these organisms might avoid inheriting mutations without a human-like bottleneck,” said Ellen Røyrvik, a geneticist on the research team, based at UiB.

The scientists used mathematical modelling to show that a process called gene conversion – the controlled overwriting of DNA – could in theory allow some offspring to inherit less mutant oDNA without requiring a bottleneck. Using genome data, they found machinery controlling this process in plants and fungi, but also in soft corals, sponges, and algae – all organisms without fixed body plans. They also found that this machinery was most active in the parts of plants that will end up producing the seeds of the next generation, suggesting that it is indeed used to allow some offspring to inherit fewer mutations.

Organisms without fixed body plans (including octocorals, sea pens, sponges, plants, and fungi) and with fixed body plans (including humans and many animals) may use different strategies to avoid the buildup of damage in their cellular "power stations." CREDIT: Gemma Lofthouse

“Taken together, it looks like organisms without a fixed body plan – plants, fungi, corals, sponges, algae – may have adopted gene conversion to deal with oDNA mutations,” said Iain Johnston, an associate professor in the Mathematics Institute at UiB, who led the research. “Humans and other animals can develop egg cells early and use a bottleneck; other organisms can use gene conversion instead.”

Going forward, the team plans to explore how this overwriting of oDNA causes other issues in the organisms that use it – including crop plants, where it can cause sterility. They are also exploring the broader question of why these compartments contain oDNA at all, given the risk of mutational damage.

ARTICLE: Transport planning in biology

Efficient vasculature investment in tissues can be determined without global information
S Duran-Nebreda, IG Johnston, GW Bassel
Journal of the Royal Society Interface 17 20200137 (2020)

We need roads. Roads link up different parts of our society, allowing us to send messages and supplies from one region to another. But they come at a cost. If we lay down a road across the country, we can't use that land to farm or build houses, and maintaining roads costs a lot of tax money.

Multicellular organisms have the same issue. They also need to send supplies (e.g. nutrients) and messages (e.g. chemical signals) from one place to another. So they build roads. Our blood vessels are one example, transporting oxygen and hormonal messages throughout our bodies. So-called vasculature -- our blood vessels are one example, as are xylem and phloem in plants -- is used to allow transport around an organism. But again, if some parts of the organism are being used for transport, they can't be used for doing other useful things.

Given this cost of producing "roads", organisms would presumably like to be efficient as possible when laying out their transport systems. This may involve, for example, making journey lengths as short as possible while using as little land as possible for roads. But while city planners and engineers can look at maps and run simulations to work out how best to place roads, organisms lack a top-down "planner" with a large-scale map. How then do organisms efficiently resolve this tradeoff? Specifically, how is it decided where best to place vasculature to minimise the effective distance between cells?

We took a look at this using a theoretical model where an organism's tissue is modelled as a collection of cells in a 2D layer, a 3D block, or an intermediate case involving a set of layers, or a more realistic structure taken from experimental characterisation of plant tissues. We considered different ways that an organism might produce vasculature by fusing together cells in this model tissue to make "roads". This method for making vasculature models the case in immobilised cells, like we find in plants. We considered different ways that cells might be chosen to fuse, based on the physical structure of the tissue, and allowing some randomness in this decision.

 How has this plant made efficient "roads" (vasculature, like the veins seen here) without having a map of the whole leaf? We found that it can do a pretty good job without a global map, just using local sensing.

We found that using a "top-down" planner (with a map of all cells – which organisms don't have!) to choose which cells to fuse is usually the best way of producing an efficient transport network. But, we found that "bottom-up" approaches, where cells fuse based on purely local information (as opposed to a global map of the whole tissue) can actually do almost as well as the top-down planner. Strikingly, we found that these bottom-up approaches can provide "scale-free" improvements in transport. This means that the amount by which having more roads decreases journey lengths doesn't depend on the overall size of the system. The transport improvements from vasculature were more pronounced in 3D than in 2D, and the best approach for vasculature production varied in the different plant tissues we looked at. This suggests that there may be some evolutionary back-and-forth between the rules that plants use to create vasculature and the form of their tissues, which we plan to explore further in future!

ARTICLE: Tension and Resolution

Tension and resolution: dynamic, evolving populations of organelle genomes within plant cells
IG Johnston
Molecular Plant 12 764 (2019)

Mitochondria and chloroplasts are compartments in cells that power complex life. Both started out billions of years ago as independent organisms with complete genomes, that were acquired by ancestral cells. Since these endosymbioses, the genomes of mitochondria (mt) and chloroplasts (cp) have become stripped down. Modern mt and cp have lost lots of genes either completely or the “host” cell nucleus. Mt and cp now exist in dynamic populations within the cells of modern organisms. In plants and algae, the two co-exist, sharing responsibility for the energy balance of the organism – and hence ultimately powering and feeding life, including the human population.

Plant mt and cp populations are weird. Different plants and algae have very different mt and cp genomes – some huge (many megabases, several chromosomes in the case of some mt) and some tiny. Unlike the more familiar animal (and human) case, plant mt genomes readily recombine, mixing up their structures and genetic content within the cell. Both mt and cp move around plant cells rapidly – we’re not sure why, particular for mt. Again, unlike animal mt, neither plant mt not cp are particularly prone to meet up and fuse into big networks – they usually stay as individual compartments, except for short interactions. We do know that if we perturb the physical or genetic dynamics of organelles, the plant suffers – which we can sometimes exploit in breeding efficient crops.

 Populations of mitochondria (A green, B) and chloroplasts (A blue, C) moving in the plant cell

In a recent review article here in Molecular Plant, we reviewed current knowledge about these dynamics and speculated about what principles these populations of mt and cp may be responding to. We first asked why mt and cp may retain different sets of genes in different species – a question we’ve touched upon before here (blog). Retaining more genes in organelles may have the “pro” of making individual organelles more independent, and better at responding to demands (see John Allen’s CoRR hypothesis, e.g. here). But there’s the “con” that organelles are dangerous places, and genes retained there may be more subject to damage than in the safe haven of the nucleus. So individual plants may choose to retain mt and cp genes for dynamism, or shift them to the nucleus for robustness. Neither extreme is perfect – there are always pros and cons – leading to a tension to which different plants have selected different resolutions.

Pursuing this line, we next speculated that because plants are immobile (and hence unable to move away from challenging conditions), they may favour the “dynamism” side over the “robustness” side. This would explain why they often retain more organelle genes than motile organisms, but would also predict that they face a double challenge: (i) more organelle genes and (ii) exposure to more challenging environments, both of which may lead to genetic damage. This could be a reason why plant organelles undergo recombination – as a way of ameliorating genetic damage. But again, there are pros and cons: the “pro” of fixing genetic damage is balanced by the “con” of recombination mixing and confusing genetic structure. Perhaps this is why the physical behaviour of plant organelles is different to that in animals – keeping mt and cp separate may limit the amount of recombination that can take place, allowing the plant to control this second pro-con tradeoff.

 

(left) the proposed tension between robustness (i) and dynamism (ii). Perhaps plants are more (ii)-like because they need to respond to fluctuating conditions... because of their immobility (right) with hypothesised knock-on consequences.

All of these ideas are presented as hypotheses, and we proposed some ways that a combination of new experiment and theory can help make progress understanding these complex, vital systems in future. Watch this space! Iain

ARTICLE: Getting to the root of the problem

Model selection and parameter estimation for root architecture models using likelihood-free inference
Clare Ziegler, Rosemary J. Dyson, Iain G. Johnston
J Roy Soc Interface (online, doi.org/10.1098/rsif.2019.0293 , 2019)

Roots bridge plants and soil, making vital contributions to crops, the environment, and fundamental biology. Because of this importance, understanding how roots grow under different conditions is a key scientific target. Experimental approaches to study roots can be challenging: being underground, it’s hard to observe root systems without perturbing them. Computer models can help here: we can simulate root growth and the “architecture” of root systems under lots of different conditions, without having to dig up and destroy real plants.

 
Observing roots growing underground is hard, but not impossible: here's a shot from our "minirhizotron" experiments using underground cameras to watch roots grow in an experimental woodland facility (see article here, and 3D version here!)

As computers have become more powerful, more and more sophisticated models for root growth and architecture have emerged. These simulation approaches typically take as input a set of parameters, and produce as output a model root system. These parameters are numbers describing, for example, the rates of root elongation, distances between lateral root branches, and so on – there may be dozens, or hundreds, of parameters in a sophisticated root model.

The output of a model depends strongly on these parameter values. So how can we choose the “right” ones? We may know some from experiments – for example, the widths of roots can be readily measured. But others may be less easy to observe. It is quite common to make educated guesses at these parameters, and see if the resulting root system “looks right”. This approach has a few issues – it can be subjective, and doesn’t give us information on how flexible our guesses are. For example, is a growth rate of 0.1cm per day just as likely as 0.5cm per day, or 0.02cm per day? And how can we tell if one version of a model does “better” than another, and is more supported by real observations?

In a new paper here in Journal of the Royal Society Interface, we propose a platform to provide answers to these questions, using so-called “approximate Bayesian computation” or ABC. This is a way of learning which parameter values and models are most compatible with observed data, by running many simulations with many different choices, and comparing the output of each choice to our observations using specific criteria. This replaces the subjective “looks right” and explores a wide set of parameterisations, allowing us to learn what ranges of values are most likely given our data. We can also use ABC to compare different mechanisms for root growth, finding which is most supported by observation. This helps us gain scientific insight and ensures that the outputs of our models can be more reliably intepreted.

Overview of our approach. Using ABC allows us to identify governing parameters and mechanisms for root growth that are most supported by real observations.

We tested our ABC approach with synthetic observations from models of thale cress and narrowleaf lupin, confirming that we can recover the parameter values we put in. We then used real thale cress plants (wild and mutant) to show that our platform distinguishes genetically different plants and identifies most-likely parameters and model structures for real root growth. We used the platform to select models for growth and branching, showing how it can be used to compare existing models from the literature. We hope that this approach can be used to further help improve the interpretability and rigour of plant modelling and simulation! Iain and Clare

ARTICLE: Plant stem cells strive towards equality

Jackson, Matthew DB, et al. "Global Topological Order Emerges through Local Mechanical Control of Cell Divisions in the Arabidopsis Shoot Apical Meristem." Cell Systems 8 53 (2019).

 
We recently wrote this paper (available in Cell Systems here -- and featured on the journal's front cover below!) about how cells are globally organised through local behaviour in an important plant organ. There's a blog article about the paper on "The Node", a developmental biology blog, here:

http://thenode.biologists.com/plant-stem-cells-strive-towards-equality/research/

ARTICLE: How do plants roll dice?

Johnston, I.G. and Bassel, G.W. Identification of a bet-hedging network motif generating noise in hormone concentrations and germination propensity in Arabidopsis. Journal of the Royal Society Interface, 15 141 (2018)

Seeds feed the world, and uniform, reliable harvests of seeds and grains is essential for food security. However, there's a fundamental tension between the evolutionary priorities of plants and the agricultural priorities of humans. Evolutionarily, it is good for plants to "hedge their bets" by having seeds germinate at different times. A plant whose seeds all germinate in March will be susceptible to a frost in April, potentially leading to the loss of a generation of offspring. By contrast, a plant whose seeds germinate throughout March and April will have a subset of its offspring survive that frost, and its genes will be passed on to the next generation.

This bet-hedging poses a challenge for agriculture. In agricultural settings, we have more control over plant environments, and so plants have less need to withstand unpredictable environmental fluctuations. At the same time, non-uniform germination decreases crop yields, makes harvesting harder, and makes crops more susceptible to pest invasion. If we can learn how plants generate this evolved germination variability, we can design engineering and/or breeding strategies to reduce this and improve crop yields.

Plants have evolved to "hedge their bets" by having seeds germinate at different times -- this makes generations of plants more robust to environmental fluctuations. Our work reveals a mechanism that "rolls dice" within plant cells, acting like a random number generator to produce variability in germination propensity. 

In a previous paper (blog post here), we looked at how germination is controlled by an interaction between two hormones known as ABA and GA. During that project, we noticed a surprising feature of the cellular pathways affecting ABA. Oddly, it seemed that ABA both activated a pathway that increased its own production, and at the same time (and in the same place) activated a pathways that increased its own degradation. These two pathways seemed to be competitive -- one increases levels of ABA, the other decreases them. Why would cells spend energy in this "futile" way?

We hypothesised that these competitive pathways might have the effect of generating variability in ABA levels. The pathways are fundamentally "noisy", involving random interactions in the chaotic environment of the cell. Consider increasing the activity of both pathways simultaneously. One pathway would act to increase levels of ABA, the other would act to decrease it. The increased "push and pull" of these noisy pathways would increase the spread of levels of ABA in different cells, even if average levels stayed the same.

Because it's hard to measure the levels of hormones in individual cells over time, we initially took a theoretical approach. We showed, with maths, that the competing pathways did indeed have this variability-inducing effect. By varying the activity through these pathways, the cell can increase variability in ABA levels, and hence increase variability in germination propensity. We showed that the theory we developed was compatible with some experiments where the ABA circuitry was artificially manipulated. The theory went on to reveal various aspects of cellular machinery that we could conceivably target through synthetic approaches, in order to reduce germination variability. Put together, our quantitative theory, supported by experiment, explained the mysterious competitive pathways and revealed several new interventions with the potential to improve food security. You can read about it for free in the Journal of the Royal Society Interface here. Iain  

ARTICLE: How plants decide when to germinate

Topham, A.T., Taylor, R.E., Yan, D., Nambara, E., Johnston, I.G. and Bassel, G.W. Temperature variability is integrated by a spatially embedded decision-making center to break dormancy in Arabidopsis seeds. PNAS 114 6629 (2017)

A plant's choice to germinate is one of the most important decisions in the world. If it is made too soon, the plant may be damaged by harsh winter conditions; if too late, the plant may be outcompeted, and crop yields may be lower. If crops in a field make the decision at different times, there is more room for weeds to grow and pests to take over. 

In a recent study, we combined mathematical modelling with several neat experiments to identify sets of cells that make this germination choice in a much-studied plant called thale cress (Arabidopsis thaliana), and have learned how it makes decisions based on the plant's environment.

Two views of the plant embryo from laser microscopy, highlighting cells where different components of the germination control machinery are expressed. The background shows the "attractor basins" in a mathematical description of the germination decision: horizontal and vertical axes give the levels of two hormones ABA and GA, the blue region corresponds to dormant seeds and the red region to germination. 

This germination circuitry functions through a circuit of chemical stimuli and responses. Using laser microscopy, we found that different parts of this circuit exist in different parts of the plant embryo -- and that the separation of these parts is central to how the brain functions. We used mathematical modelling to show that communication between separated elements of the germination circuitry controls the plant's sensitivity to its environment. Following this theory, we used a mutant plant where cells were more chemically linked -- essentially enhancing communication between circuit elements -- to show that germination depends on these intra-cellular signals.

The separation of circuit elements allows a wider palette of responses to stimuli. It's like the difference between reading one critic's review of a film four times over, or amalgamating four different critics' views before deciding to go to the cinema. Our mathematical theory predicted that more plants would germinate when exposed to varying environments -- like three short pulses of cold -- than constant environments -- like one long cold period. We tested this theory in the lab and found exactly this behaviour.

Next, the hope is to learn about the germination brain in other plants and crops, and to show how our new knowledge of the germination machinery can be used to enhance and synchronise germination in crops. You can read the paper for free in the journal PNAS here. Iain

The Algorithmic Beauty of Plants

(title taken from a wonderful book here)

Last year we ran a second year undergraduate computer practical in Biosciences introducing students to the ways in which computer simulations can be used to model plant growth. There are several neat scientific and practical ideas here. The students find that the diverse and complex range of beautiful plant forms can be mimicked by simulating simple, iterated rules (as in the pic). But the deeper idea is that these forms are not just mimicked by iterated rules – they genuinely emerge from such rules, not represented in a computer but in the biological language of the genome. This is the first time many students have met the idea that computational modelling allows a scientist to "play god" – they can make whatever changes they like to the rules and explore the effect on the simulated plants they grow. They also get to grips with algorithmic thinking – a highly transferrable skill given the expansion of coding and computational approaches across sectors.

Figure 1: L-Studio, simulating plant growth in computers from simple iterated rules. (top) An introductory exercise modelling a highly reduced model plant using a small number of growth rules. (bottom) A more involved simulation, based again around repeated application of simple rules, giving a fairly natural-looking plant structure.

The class is about 50 students, working in pairs or small groups with a computer. They are given some introductory exercises on “L-systems” (have a play e.g. here!), then given increasingly complicated structures to play with – including some famous fractals – and finally meet the translation to plant forms. In this way they learn to think algorithmically in a more abstract sense before the connection to biology is driven home. The exercises start fairly prescriptive, reproducing given structures. They then progress to a more investigative mode – given a particular plant form, how would you change its growth rules to, for example, outcompete tall neighbours, or disperse seeds more broadly? The final, exploratory, mode is the most interesting, where the students are given free rein, and design, adapt, and compare their own plant “designs”.

There are typically two members of staff and a handful of PhD students or postdocs acting in a TA capacity. We help with the initial setup – smoothing the way to this (unnatural for some) way of working with biological model systems in a computer. We then engage in a more scientific way with the groups, posing extension questions, guiding through questioning (careful not to just recite the solution to a given problem). In the final exploratory mode, we reduce the formality and jointly discuss scientific extensions and applications with the students and encourage the social aspect of the comparison and collaboration.

This class is an interesting one from a pedagogical point of view. The mode of learning shifts through the course of the two-hour session, from prescriptive to exploratory. Typically the class is very split in their uptake of this unfamiliar way of thinking. Some students love it, particularly the open-ended parts, and stick around to the end taking pictures of their plants and sending them to friends. Some frequently question the “point” of the class – a common question because logistics often mean they meet this class before lectures that naturally set up the modelling perspective. However, it usually just takes a few minutes of one-on-one discussion – illustrated with examples of our own research using computational modelling – to convince students of the utility both of the science and of the transferrable skill acquisition. One of my most personally rewarding experiences was with a student who started out almost aggressively sceptical about the point of these models – and whether the class would get him a better degree. After a discussion of the science and the transferrable value of computational modelling, he completely switched around and was interested in further pursuit of these topics. Other students are less polarised – they view the class as a box to be ticked. The final section of the class has an interesting influence here, where the enthusiasm of some groups rubs off onto the box-tickers. As such, there’s an interesting dynamic of teaching staff acting to catalyse the spread of enthusiasm – and of information – that emerges from the students’ own exploration.  

ARTICLE: Random number seed

Variability in seeds: biological, ecological, and agricultural implications 

J Mitchell, IG Johnston, GW Bassel

Journal of Experimental Botany, erw397 (2016) 

• Natural variability across scales, from the molecular to the environmental, means that individual seeds behave differently; we explore the challenges this variability poses for agriculture and food security, and how modern science can help address these challenges.

Seeds feed the world. Whether eaten themselves, or allowed to develop into crop plants which are then consumed by humans or livestock, seeds are the fundamental starting point for agriculture. But each seed has a different story. Throughout millions of years of evolution, plants have evolved to -- forgive the pun -- "hedge" their bets from one generation to the next. A parent plant cannot completely predict the environmental conditions that its offspring will face, so it induces variability in the seeds it produces. If some seeds are better at surviving in environment A and some are better in environment B, the plant has a way of ensuring its genes will survive regardless of whether the environment is A-like or B-like in future.

This bet-hedging is a sensible evolutionary strategy when environments are unpredictable. But modern agriculture makes environments much more predictable than the wild situations plants have been exposed to throughout evolutionary history. Now bet-hedging becomes a bad thing -- if we know the environment will always be C, energy spent ensuring that seeds survive in environments A and B is wasted, reducing potential yields.

Understanding and controlling the variability within populations of seeds thus has huge implications for agriculture. Variability inherent within populations of seeds, in addition to differences in the environments that seeds experience, means that, for example, seed lots germinate asynchronously (some quickly, some slowly or not at all). This leads to non-uniform and sub-optimal crop production, allows pests to enter fields, and challenges our ability to plan agricultural strategies. If we could control seed variability, these problems would be diminished, with a host of positive consequences for food security.

A given set of seeds will vary in their behaviour due to influences on many scales, from random molecular processes within cells to large-scale environmental stimuli. As a result, important features like germination propensity vary across seed lots (perhaps taking a broad distribution like that illustrated here), posing a challenge to agriculture and food security, which scientific understanding can mitigate.

In a new review, we survey our current understanding of the sources of variability in seeds, and its biological and agricultural implications. Processes across many scales induce variability in seed behaviour, from random cell biological interactions (like we've written about before!), through seed position in a parent plant, to large-scale environmental differences. We particularly focus on germination, an aspect of seed behaviour of crucial biological and agronomic importance, which takes place when a "developmental switch" in a seed is flipped. We discuss the genetic and molecular players that modern science has discovered to influence this decision to germinate in seeds, and describe the challenges in furthering our understanding of this vital question -- and how cool new tech, and maths, can help us make new progress! The review is in the Journal of Experimental Botany here. Iain

ARTICLE: Mitochondrial motion in plants

FRIENDLY regulates mitochondrial distribution, fusion, and quality control in Arabidopsis

• Plant mitochondria play central roles in carbon metabolism, photosynthesis, and plant growth, but the genes controlling their structure are poorly understood: we make progress understanding how one important gene influences mitochondria and plant structure

Mitochondria are often likened to the power stations of the cell, producing energy that fuels life's processes. However, compared to traditional power stations, they're very dynamic: mitochondria move through the cell, and fuse together and break apart (among other things). Interestingly, their ability to move and undergo fusion and fission affects their functionality, and so has powerful implications for understanding disease and cellular energy supplies.

Because of this central role, it is important to understand the fundamental biological mechanisms that govern mitochondrial dynamics. Several important genes controlling mitochondrial dynamics are known in humans (and other organisms), but plant mitochondria (despite the fundamental importance of plant bioenergetics for our society) are less well understood.

Our collaborators, David Logan and his team, working with a plant called Arabidopsis, observed that a particular gene, entertainingly called "FRIENDLY", affected mitochondrial dynamics when it was artificially perturbed. (This approach, artificially interfering with a gene to explore the effects that it has on the cell and the overall organism, is a common one in cell biology.) We've just written a paper with them (free here) exploring these effects. Plants with disrupted FRIENDLY had unusual clusters of mitochondria in their cells, their mitochondria were stressed, and cell death and poor plant growth resulted.

A simulation of mitochondrial dynamics in plant cells under our simple mathematical model, which we compared to observations in real plants.

We used a 3D computational and mathematical model of randomly-moving mitochondria within the cell to show that an increased "association time" (the friendly mitochondria stick around each other for longer) was sufficient to explain the experimental observations of clustered mitochondria. Our paper thus identifies an important genetic player in determining mitochondrial dynamics in plants; and explores in substantial detail the intra-cellular, bioenergetic, and physiological implications of perturbation to this important gene. Iain and Nick [blog article also here]

ARTICLE: Inferring the evolutionary history of photosynthesis : C 4 yourself

Phenotypic landscape inference reveals multiple evolutionary paths to C4 photosynthesis

• Some plants have evolved efficient photosynthesis, but important crops including rice have not: we use maths and statistics in conjunction with biological data to understand this evolution, with a view to repeating it artificially in crops to increase food production

Biological evolution is a complex, stochastic process which dictates fundamental properties of life. Our understanding of evolutionary history is severely limited by the sparsity of the fossil record: we only have a handful of fossilised snapshots to infer how evolution may have progressed throughout the history of life. Many physicists and mathematicians have attempted theoretical treatments of the process of evolution, using varying degrees of abstraction, in order to provide a more solid quantitative foundation with which to study this complex and important phenomenon, but the predictive power of these theoretical models, and their ability to answer specific biological questions, is often questioned.

We recently focussed on one remarkable product of evolution in plants: so-called "C4 photosynthesis". C4 consists of a complex set of changes to the genetic and physiological features which have evolved in some plants and act to increase the efficiency of photosynthesis. This complex set of changes has evolved over 60 times convergently: that is, plants from many different lineages independently "discover" C4 photosynthesis through evolution. We were interested in the evolutionary history of how these discoveries occurred -- both motivated by fundamental biology and the possibility of "learning from evolution" and using information about the evolution of C4 to design more efficient crop plants.

C3 and C4 plants differ in several physical and genetic ways (leaves and cells either side). We picture evolution as progressing along paths over a hypercube connecting these states (grey lines) -- some paths will give rise to intermediate species matching those we really observe (red and blue points). We can calculate how likely each path is and thus reconstruct evolutionary history.

 
To this end, we modelled the evolution of C4 as a pathway through a space containing many different possible plant features. The pathway starts at C3 -- the precursor to C4 -- and progressively takes steps in different directions, acquiring one-by-one the features that sum up to C4 photosynthesis. Using a survey of plant properties from across the wide scientific literature, we identified which intermediate states these pathways were likely to pass through, given observed properties of plants that currently possess some, but not all, C4 features. We were then able to use a new inference technique to predict the ordering in which these likely pathways traverse the evolutionary space. We showed that this approach worked by both successfully inferring the known evolutionary steps in synthetic datasets and correctly predicting previously unknown properties of several plants, which we verified experimentally. Our (open access) paper is here and there's a less technical summary and commentary here. Our approach showed that C4 photosynthesis can evolve through a range of distinct evolutionary pathways, providing a potential explanation for its striking convergence. Several of these different pathways were made explicitly visible when we examined the inferred evolutionary histories of different plant lineages -- different families are likely to have converged on C4 through different evolutionary routes. Furthermore, the most likely initial steps towards C4 photosynthesis are surprisingly not directly related to photosynthesis, being solutions to different biological challenges, but also providing evolutionary "foundations" upon which the machinery of C4 can evolve further. We hope that the recipes for C4 photosynthesis that we have inferred find use in efficient crop design, and anticipate our inference procedure being of use in the study of other specific biological questions regarding evolutionary histories. Iain [blog article also here]

ARTICLE: Taking the pulse of cellular power stations

Pulsing of Membrane Potential in Individual Mitochondria: A Stress-Induced Mechanism to Regulate Respiratory Bioenergetics in Arabidopsis

• Mitochondria in plants sometimes switch off their membrane potential, which contributes to their ability to make energy for the cell: we characterise this "pulsing" and explore how it can be beneficial to plants under stress

We've just written an article in the journal Plant Cell about pulsing cellular power-stations and will motivate it by an analogy. Imagine we have a reservoir of water, and this water flows downhill through an outlet pipe, turning a turbine and producing energy. In this thought experiment, we're faced with a problem: the only way we can get water into our reservoir is by pumping it into the bottom of the reservoir. The higher up a reservoir is, the harder it is to pump water up there and the higher the risk of pumps overheating and getting damaged.

The problem can be solved by allowing the height of our reservoir to vary. If we lower our reservoir, it will become easier to fill, and the higher water pressure that arises from an increasingly filled reservoir will partly compensate for the fact that turbine-turning water will flow downhill from a lowered height, while allowing the pumps to relax and cool.

This model is a crude representation of mitochondria, the power stations of the cell, which use energy from respiration to create an energetic gradient across their membranes -- like a natural version of an AA battery. In our picture, this corresponds to the pumps feeding into our reservoir -- and in the cell, these pumps produce dangerous chemicals when they are overworked. The gradient they produce imbues protons with energy that is part electrical -- which we picture as the height of our reservoir -- and part chemical -- which we picture as the amount of water in our reservoir. These protons then flow through a protein complex -- the turbine -- to produce ATP, the universal cellular fuel.

An abstract representation (acrylic on canvas) of a single mitochondrion undergoing a 'pulse'. Its change in energy status is shown by the change in colour that we have also observed by microscopy using fluorescent sensors. Artist: Markus S

When mitochondria pump many protons, their "reservoirs" rise, with the increase in height forcing the pumps to work harder to pump water into the reservoir. This work produces dangerous chemicals which can damage the cell and the mitochondria themselves (called reactive oxygen species - they're what antioxidants try to combat). We have found a new mechanism by which this risk is decreased: if mitochondria are having to work hard, they "pulse", spontaneously lowering the height of their reservoir. This decreases the amount of work that the mitochondrial pumps have to do to fill the reservoir. The amount of turbine-turning energy per unit of water decreases, but as it becomes easier to fill the reservoir, more water gets pumped into it, partly compensating for the loss of height by an increase in volume. The pulsing process thus lowers the reservoir but fills it with more water, allowing the mitochondrial pumps to relax and reducing the production of dangerous chemicals.

We observed these pulses, spontaneous decreases of mitochondrial membrane potential, in Arabidopsis thaliana, a model plant species used in many biological contexts. Treating plant mitochondria with a variety of chemicals and observing the effects on pulsing, we deduced a biochemical mechanism by which pulsing occurs: a controlled influx of cations such as calcium ions into the mitochondrial matrix decreases membrane potential. We also found that pulsing is increased when plants face stressful environments: if they are suddenly heated, for example, or exposed to toxic chemicals. This novel mechanism may help explain some of the variability that our cellular engines exhibit and may be an important discovery in considering how mitochondria react to dangerous cellular conditions. You'll find the article here. Iain, Markus & Nick [blog article also here].