ERC panel and grant links

Grant awards

2021

https://erc.europa.eu/sites/default/files/2022-12/erc-2021-stg-results-reserve-list-all-domains.pdf

2022

https://erc.europa.eu/sites/default/files/2022-11/erc_2022_stg_results_all_domains.pdf

 

 

Panels

2021

https://erc.europa.eu/sites/default/files/document/file/Panel_Members_ERC_Starting_Grant_2021.pdf

2022

https://erc.europa.eu/sites/default/files/2023-01/Panel_Members_ERC_Starting_Grant_2022.pdf

ARTICLE: Cellular energy budgets and antimicrobial resistance

Dynamic Boolean modelling reveals the influence of energy supply on bacterial efflux pump expression
Ryan Kerr, Sara Jabbari, Jessica MA Blair, Iain G Johnston
Journal of the Royal Society Interface 19 20210771 (2021)

Antimicrobial resistance or AMR is a major global health issue, with disease-causing organisms like bacteria acquiring resistance to the drugs we use to kill them. One way that bacteria acquire this resistance is through so-called efflux pumps -- cellular machinery that removes chemicals like drugs from inside the bacterium. Bacteria produce these pumps when faced with drug treatments, but not all cells produce the same amount or at the same time. Understanding this variability could help the theory behind future treatments.

After finding that the available "energy budget" influences the behaviour of cellular programs, we asked whether energy variability could be a cause of these differences. Using lots of diverse experimental observations, we built a theoretical model of the signals that tell a bacterium to produce efflux pumps in response to sensing a drug, with a new and simple way of accounting for how energy affects these signals. We then simulated this model in a computer to see how model cells with different amounts of available energy (as we see in real bacterial populations) behaved.

We found that differences in cellular energy budgets can have a profound effect on when, and how much, efflux machinery is produced. This variability builds on the natural randomness of the system, leading to several interesting results: energy changes the dynamics of how signalling programs work in the cell, alters timescales, and affects the "priming" of a population of cells to anticipate future stress. The approach we developed is quite general and can be used to explore energy influence on any other cell programs and signals too.

Including ATP, an important cellular energy currency, in models of how bacteria express efflux machinery helps us understand how cell-to-cell differences in energy budget may influence AMR.

ARTICLE: Removing mutant mtDNA from cells

2-Deoxy-D-glucose couples mitochondrial DNA replication with mitochondrial fitness and promotes the selection of wild-type over mutant mitochondrial DNA
Boris Pantic, Daniel Ives, Mara Mennuni, Diego Perez-Rodriguez, Uxoa Fernandez-Pelayo, Amaia Lopez de Arbina, Mikel Muñoz-Oreja, Marina Villar-Fernandez, Thanh-mai Julie Dang, Lodovica Vergani, Iain G Johnston, Robert DS Pitceathly, Robert McFarland, Michael G Hanna, Robert W Taylor, Ian J Holt, Antonella Spinazzola
Nature Communications 12 1 (2021)

This is an exciting one! As we've discussed before, mitochondrial DNA (mtDNA) molecules exist in large populations in our cells, encoding vital machinery. Devastating diseases can result when a high proportion of a cell's mtDNA molecules are mutated, but cells can deal with a low proportion of mutant mtDNA. So, it'd be great if we had a way to decrease the proportion of mutant mtDNA in cells -- below the threshold for disease.

Perhaps we do! We recently played a supporting role in a project with Antonella Spinazzola and Ian Holt, looking at what happens when cells containing a mixture of mutant and normal mtDNA are treated with chemicals. They found that a molecule called 2DG (for short) slows down the replication of mutant mtDNA in cells. As mtDNA is constantly replicating, this preferential inhibition of mutant means that normal mtDNA comes to dominate the cellular population over time. We showed this population shifting over time in a variety of human cell lines and growth media, including several chosen to model in vivo behaviour.

The project showed that 2DG compromises mitochondrial respiration much more in mutant than in wildtype mitochondria. This is likely why mutant replication was so challenged -- poorly functioning mitochondria are less likely to replicate. Restricting glutamine and glucose together had the same effect (though is perhaps harder to achieve in a therapeutic context). 2DG is in trials for epilepsy treatment, so may represent a path to new therapies addressing mtDNA disease. There's a press release here with some more commentary.

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: Stochastic fantasy combat!

Optimal strategies in the Fighting Fantasy gaming system: influencing stochastic dynamics by gambling with limited resource
Iain G Johnston
European Journal of Operational Research DOI 10.1016/j.ejor.2022.01.039 (2022)

Here's a more unusual one. Fighting Fantasy gamebooks, hugely popular in the 80s and resurgent now, are "games in a book". The reader/player chooses their path through the book's many sections, overcoming challenges, fighting monsters, and hopefully succeeding in their quest.

The combat system is quite interesting. The player and their opponent both have "stamina" -- like the health bar in a video game. The player rolls dice to determine the strength of one of their attacks, and rolls again for the opponent. Whoever has the higher strength inflicts some stamina loss on the other. The combat proceeds through rounds like this until someone's stamina reaches zero.

Phrased like this, the player has no agency and the system is quite easy to solve (ie, work out the probability of winning a given fight). But there's another factor. The player (not the opponent) also has some "luck", describing how lucky they are. Testing luck involves rolling two dice: if the sum is less than or equal to the player's luck score, they are luck, otherwise they're unlucky. If they win an attack round, they can choose to test their luck, and if luck they deal more damage to their opponent. If they lose an attack round, they can also test their luck, and will take less damage if lucky. If they're unlucky, the outcome is negative: they do less, and take more, damage than if they'd not tested.

A bit more complicated, but still possible to solve. But here's the rub. Every time you test your luck, your luck score decreases -- whether you're lucky or unlucky. So as you test your luck more and more, you are less and less likely to get a positive outcome. The question is -- when is it a good idea to use a luck test in combat?

To answer this we used an approach called dynamic programming. We first considered all the ways combat can end -- with someone having no stamina left. We next considered every state of the combat that could lead to one of these end states, and calculated the probability of each possible outcome in the case where the player chose to test their luck and the case where they didn't. We then considered all states of combat that led to these states, and so on, multiplying probabilities as we went to calculate the overall probability of victory from any state given any luck strategy.

We found that judicious use of luck can dramatically increase victory probability in some cases, particularly when player and opponent statistics are unbalanced. There are some general rules -- for example, no matter how low your luck, you should always test if you are otherwise about to die. We used some tools from statistics to distil the complex set of detailed optimal strategies into more general principles to follow. We also connect to more real-world questions, like cheating and espionage, where a "lucky" outcome can be beneficial -- but an unlucky one can be disastrous, and the more you test your luck the more likely you are to be unlucky!

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: How does tool use evolve in animals?

Data-Driven Inference Reveals Distinct and Conserved Dynamic Pathways of Tool Use Emergence across Animal Taxa, iScience 23 101245 (2020)

There are some wonderful examples of animals using tools. Octopuses block oyster shells open with coral; boxer crabs wave captive anemones for defense and food capture; captive dolphins use feather to wipe clean their aquarium windows. A while ago, we saw this excellent infographic in National Geographic, and got interested in these data. Do different families of animals learn to use tools in completely different ways? Or are there some general ("universal") principles behind how animals learn to use tools?

There is, of course, a big and fascinating literature on tool use, but we found rather few studies attempting a quantitative comparative analysis across bilaterian animals. To address other evolutionary questions, we've developed HyperTraPS (hypercubic transition path sampling), a statistical approach for learning the "pathways" of evolutionary processes. That is, which events occur before and after which other events in an evolving system? Does feature A always evolve before feature B? We used HyperTraPS to ask about the orderings with which different types of tool use appeared in animals. For example, do animals always learn to "poke" before they learn to "dig"? Do all animals learn tool use in the same order, first A then B then C..., or does it vary across species?

We found some answers that we think are quite interesting. There seem to be some quite deep similarities across animal species in how tool use evolves. Types of tool use like "affixing" and "throwing" are almost universally acquired early; types like "cutting" and "symbolising" are acquired late and rarely, only by primates. The environment and animal family influences the structure of these pathways: aquatic organisms seem to discover "waving" tool use relatively early, for example, and primates discover tools that "block" relatively late.

(A) The inferred pathways of tool use emergence across animals. The size of a blob gives the probability that that mode of tool use (on the horizontal axis) is acquired at that stage (on the vertical axis) of a species' discovery of tool use types. (B) Sample evolutionary pathways of tool use, with individual animal lineages illustrated at the positions corresponding to the modes of tool use they have discovered.

Of course, there's a lot of uncertainty about any analysis like this. Are we talking about wild or captured animals? What if we just haven't observed some types of tool use? We attempted to address several such questions with our analysis and showed that our overall results were quite robust with respect to these uncertainties. HyperTraPS fully describes the uncertainty in its outcomes, helping interpretability. We hope that our results help at least to suggest some possible principles and points for further investigation in this fascinating topic. You can read more in iScience here.

ARTICLE: What makes mitochondria selfish, and when do selfish ones win?

MtDNA sequence features associated with ‘selfish genomes’ predict tissue-specific segregation and reversion, Nucleic Acids Research 48 8290 (202)

Mitochondria, the power stations of the cell, are in some senses like people in a company. The company needs several people to contribute if it is to survive. Some people may work hard and contribute lots to the company. Others may selfishly slack off and rely on others doing the work.

The cell needs mitochondria to produce ATP, the chemical that powers many important processes. But there is evidence that some mitochondria are more selfish, and some less so, than others. Unselfish mitochondria produce machinery which helps produce ATP. Selfish mitochondria prefer to replicate, copying their DNA and contributing less to the cell. Interestingly, at the molecular level, there is something like a "switch": a mitochondrion either takes steps to produce useful machinery, or takes steps that will help it replicate.

We were interested in why different mitochondria choose different positions of this switch, and what is a good "strategy" for mitochondria under different conditions. We built a simple model of this behaviour to understand it. Unsurprisingly, we found that selfish mitochondria -- favouring replication, and contributing less to the cell -- profilerate over unselfish ones in cells where there's little pressure to co-operate. As they replicate more, selfish mitochondria eventually come to dominate such cells. Where there is cellular pressure, however, unselfish mitochondria may win out. This is because cells full of selfish mitochondria won't perform adequately, and the whole cell and all its mitochondria will die -- leaving those cells with more unselfish mitochondria remaining.

What do we mean by "cellular pressure"? Well, if some type of cells never die, clearly the latter event can't happen, and we'd expect selfish mitochondria to win. If cells die regularly, perhaps there's more capacity to select those filled with unselfish mitochondria. We looked at different tissues where cells die with different rates, in mice where cells had two different types of mitochondrial DNA (mtDNA). We found a consistent pattern where one type of mtDNA proliferated in slow-dying cells and the other proliferated in fast-dying cells. Why different mtDNA types "win" in different tissues is a big question (which we've looked at before!), and it looks like this might help explain some of these differences.

(A) Sequence features may make different mtDNA types more "selfish" (favouring replication) or "unselfish" (favouring the production of useful machinery). (B) Our theory shows how, depending on cellular pressures, one or the other strategy can be favoured, leading to selection for one or the other mtDNA type.

We also asked what it is about a particular mtDNA sequence that might make it more or less selfish. Based on how mtDNA produces useful machinery, and how it replicates, we hypothesised that some features in the so-called "control region" of mtDNA may influence selfishness. Using sequence information, we found that these features tied quite neatly in with the observations in these mouse models, and also in (more limited) observations from human cells. While certainly not resolved, this picture suggests a link between sequence features of mtDNA, cellular selfishness, and proliferation differences across different tissues. You can read more (for free) in Nucleic Acids Research here.

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: Powering cellular decision-making

Intracellular energy Variability Modulates cellular Decision-Making capacity
Ryan Kerr, Sara Jabbari, Iain G Johnston
Scientific Reports 9 1 (2019)

The ability to process information and make decisions is fundamental to life. Intelligent organisms use their brains to do this, but individual cells are also constantly making decisions, changing their behaviour in response to microscopic stimuli. Examples of this cellular decision-making abound in biology: stem cells decide which type of cell to become; some bacteria decide to become robust "persister" cells that can survive drug treatments; cells in plant seeds decide when to germinate.

Often, the "decisions" that cells make involve which genes to express. Genes contain information on how to build cellular machinery, and "expressing" a gene in a sense means turning it on so that its machinery gets built in the cell. We often see that two genes, say A and B, build proteins that switch each other's genes off. So if we have lots of A, it's very hard to produce B, and vice versa. These genes can determine the type of cell we have -- for example, cells with lots of A might be white blood cells, and cells with lots of B might be red blood cells. A blood stem cell could then become a white or a red cell depending on how the interaction between A and B plays out.

All this is reasonably common knowledge (though rather simplified!). But we got interested in how energy plays a role in these decisions. Gene expression requires energy, which in the cell is provided by a molecule called ATP. Different cells have different amounts of ATP, so the processes involved in the interaction of our genes A and B can take place at different rates. Following some ideas we laid out here, we asked, using maths, how this energy dependence might affect the decisions that cells make.

We found, in a new paper free to read in Scientific Reports, that ATP levels strongly influence the decision-making capacity of a cell. Consider the simple A-B case above. Four states are possible: no A or B (state 0), more A than B (state A), more B than A (state B), and high A and B (state AB). We found that, at low ATP, only state 0 is possible (the cell can't make any decisions). As ATP increases, states A and B become possible, and for high ATP the state AB also appears. So, the number of states a cell can choose between (for example, white, red, or stem blood cell) depends strongly on how much energy that cell has available to power these genetic interactions.

We also found that more energy stabilised the decisions that could be made (cells are noisy, so decisions can be randomly "overturned" by gene expression fluctuations), and mapped out the "landscape" of decisions that can be made as the biochemical features of the genes involved change. We're now going to the lab to explore these mathematical predictions in real cells -- particularly in bacterial persister cells -- and developing the theory further for more complicated decision-making circuits.

ARTICLE: The inheritance of mtDNA

Regulation of mother-to-offspring transmission of mtDNA heteroplasmy
Ana Latorre-Pellicer, Ana Victoria Lechuga-Vieco, Iain G Johnston, Riikka H Hämäläinen, Juan Pellico, Raquel Justo-Méndez, Jose María Fernández-Toro, Cristina Clavería, Adela Guaras, Rocío Sierra, Jordi Llop, Miguel Torres, Luis Miguel Criado, Anu Suomalainen, Nick S Jones, Jesús Ruíz-Cabello, José Antonio Enríquez
Cell Metabolism 30 1120 (2019)

Mitochondrial DNA (mtDNA) is inherited from mothers to children. If two or more types of mtDNA exist in a cell, the cell is called "heteroplasmic". Mothers may carry a heteroplasmic mixture of different mtDNA types in each of their oocytes (egg cells), so a mixture of different types may be passed on to children. Different oocytes may have different mixtures -- for example, one cel may have 50% type A and 50% type B, and another may have 70% A and 30% B. 

The inheritance of heteroplasmy depends both on a complicated "bottleneck" (see here and here) and whether either type has some advantage over the other -- a question that is hotly debated. The mechanisms that shape the inheritance of mtDNA populations remain poorly understood, so it's hard to predict which offspring will inherit which mixture. It's often the case that a disease is caused by a particular mixture -- for example, over 60% of type B -- so this complex inheritance makes it hard to plan fertility treatments too.

In a new paper in Cell Metabolism, we looked at the inheritance and consequences of heteroplasmy in mice. Strikingly, we found that the presence of any heteroplasmy has generally negative consequences for the cell. This is perhaps surprising, given the above view that we normally need a certain amount of a dangerous mtDNA type to cause disease. But it does match a prediction that we recently made by mathematically considering how a cell must invest energy in controlling mixed mtDNA populations. Correspondingly, we found that regardless of how much type A and type B there is, having a heteroplasmic mixture challenges metabolism in the embryo, and affects how readily induced pluripotent stem cells (iPSCs) can be produced from cells.

Given that heteroplasmy is a challenge, it seems that cells have evolved mechanisms to sense and address the inheritance of heteroplasmy. In addition to the bottleneck, we found (as in our previous work) that cell-to-cell variance of heteroplasmy increased in oocytes with age -- which will have the eventual effect of reducing heteroplasmy. We also found that particular mtDNA types had a selective advantage through inheritance, and identified a set of genes that shape this advantage. Variability in the expression of these genes, and variability in metabolic factors, led to differences in the strength of selection.

Some key findings from this paper, and links to our previous work.

This work was exciting because it provided some insights into the mechanisms that shape mtDNA populations between generations -- but also because it validated several predictions that our theoretical work had proposed in the past:

• Increasing heteroplasmy variance with age (predicted here, observed here)
• MtDNA selection occurs at different developmental stages (as we found here and here)
• Mixed mtDNA populations challenge the cell (predicted here)
• Genes related to mitochondrial dynamics shape mtDNA genetic makeup (predicted here)

We're continuing this exciting collaboration and looking in more depth at the behaviour of mtDNA over time.

ARTICLE: Learning pathways of disease progression

HyperTraPS: Inferring probabilistic patterns of trait acquisition in evolutionary and disease progression pathways
Sam F Greenbury, Mauricio Barahona, Iain G Johnston
Cell Systems (2019)

Many diseases that take a substantial human toll can be viewed as “progressive”. That is, a patient starts out healthy, then disease-related problems and/or symptoms develop over time. For example, a given case of cancer may begin with a patient acquiring a particular mutation, then other mutations building up in their genome over time.

How the same disease progresses in different patients often varies widely. Understanding this variability is important for precision medicine, where detailed knowledge of individual patients is used to design the best targeted treatments. However, learning the varied pathways of diseases and using them to predict future outcomes is challenging. Human researchers usually cannot hope to remember or analyse enough examples of patient data to provide the most reliable picture.

We previously developed an algorithm called HyperTraPS (hypercubic transition path sampling) to explore how biological systems evolve over time. We reasoned that HyperTraPS could also be used to learn the pathways of disease progression. In a new study in Cell Systems (free preprint available here) we used HyperTraPS to analyse biomedical data from many patients – hundreds, or thousands of individuals – to build a ‘road map’ of the different pathways that a disease takes over time.

Picture a river that branches out into a wide delta. Patients start out healthy – upstream in the river – and different patients go down different branches as the disease progresses and they acquire more symptoms. HyperTraPS learns the structure of the river delta from data, and predicts which river branches are more or less likely – and, importantly, where you'll end up if you're currently at a particular point.

By learning these branching patterns of disease progression, HyperTraPS has helped provide a refined risk assessment for malaria, based on data from thousands of Gambian children – as we’ve written about before. The approach also revealed diverse pathways of ovarian cancer progression, where the first mutation to occur appears to play a large role in determining subsequent mutations.

The "waterfall" in the foreground shows paths from one stage of a disease to the next, learnt by HyperTraPS using data from a high number of patients. Each dot of the illustration represents different stages of disease, for example a specific set of symptoms or a given set of mutations. The thickness of the lines indicate the probability of moving from one specific stage of disease to the next.

HyperTraPS is very generalisable and can be used to learn pathways by which mutations, symptoms, or other features develop over time from an initial state. We further used this generalisability to understand a biomedically important example of evolution – specifically, how tuberculosis evolves to become resistant to antibiotics.

Tuberculosis acquires resistance through mutations, and HyperTraPS has revealed the patterns of these mutations in TB bacteria reported from a group of 1000 Russian patients. These patterns help predict which mutation a bacterium will acquire next, and hence which drugs may be more effective for a given case. We’re following up with other applications of HyperTraPS, to learn about other progressive diseases, ageing, and evolution, and even to analyse how students complete tasks in online courses.

ARTICLES: Evolving cellular populations of mtDNA

Evolving mtDNA populations within cells
Iain G Johnston, Joerg P Burgstaller
Biochemical Society Transactions 47 1367 (2019)

and

Varied mechanisms and models for the varying mitochondrial bottleneck
Iain G Johnston
Frontiers in Cell and Developmental Biology 7 294 (2019)

We've recently written two review papers looking at the dynamics of mitochondrial DNA (mtDNA) in cells. As we've written about before, cells contain populations of hundreds or thousands of mtDNA molecules. These molecules replicate and degrade, so that over time, cellular populations of mtDNA change and evolve. The amount of disease-causing mutations, and the number and structure of mtDNA molecules, may all change as organisms develop and age, with different consequences.

The first article, in Biochemical Society Transactions, takes a broad look at how cellular mtDNA populations change over time, considering a range of organisms from humans and other animals to plants and fungi. We look at the different processes that change mtDNA populations, which include replication and degradation but may also include recombination (particularly in plants) and cell-to-cell exchange of mitochondria. The review particularly highlights the importance of understanding cell-to-cell variability in mtDNA populations -- as it only takes a few cells with lots of mutant mtDNA to cause disease, it's important to understand the statistics of mtDNA populations across cells. We review experiments and theory aiming to do so, including our recent work showing that cell-to-cell variability of mtDNA mutant load increases over time in a wide variety of circumstances.

The second article, in Frontiers in Cell and Developmental Biology, focuses on the so-called "mtDNA bottleneck", a process that shapes mtDNA populations in early mammalian development, and helps prevent the inheritance of mutant mtDNA. Specifically, mtDNA undergoes a "genetic bottleneck" between generations, meaning that mothers' egg cells, and offspring, often have dramatically different mtDNA populations. The review emphasises that this "genetic bottleneck" is an effective quantity, not a directly measurable observation, that arises from several physical processes, including but not limited to a "physical bottleneck" or mtDNA depletion during development. Different ways of modelling, analysing, and explaining the "genetic bottleneck" are reviewed, from human populations to mouse egg cells and Adélie penguins. We invest some time in trying to explain the different assumptions, symbols, and methods that researchers have used to quantify the bottleneck over the years. Again, the importance of understanding cell-to-cell variance in mtDNA populations is a core theme.

A. Different processes shaping mixed mtDNA populations inside cells. B. The "genetic bottleneck", increasing mtDNA variance between egg cells and offspring.  

Like all reviews, these articles don't have new results, but attempt to summarise existing knowledge and thinking on these topics. We hope that both papers provide some interesting insights, references, and (in the case of the bottleneck paper) visualisations that may help understand these often confusing topics.

ARTICLE: Phenotypes and progression pathways in severe malaria

Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data
Iain G Johnston, Till Hoffmann, Sam F Greenbury, Ornella Cominetti, Muminatou Jallow, Dominic Kwiatkowski, Mauricio Barahona, Nick S Jones, Climent Casals-Pascual
npj Digital Medicine 2 63 (2019)

We recently published an article here in npj Digital Medicine using maths (including HyperTraPS) to learn more about severe malaria, a disease that kills over 400 000 people (mainly African children) a year. Severe malaria is challenging in the clinic because its symptoms and progress vary a lot from patient to patient. Our approach helps learn about this variability and identify high-risk patients and pathways. You can read our blog article about the paper on the npj Digital Medicine community blog here!

ARTICLE: The cell's power station policies

Energetic costs of cellular and therapeutic control of stochastic mitochondrial DNA populations

Hanne Hoitzing, Payam A Gammage, Lindsey van Haute, Michal Minczuk, Iain G Johnston, Nick S Jones

PLoS Computational Biology 15 e1007023 (2019)

(Hanne's also written a post about this paper, you can read it here)

Our cells are filled with populations of mitochondrial DNA (mtDNA) molecules, which encode vital cellular machinery that supports our energy requirements. The cell invests energy in maintaining its mtDNA population, like us using electricity-powered tools to help maintain our power stations. Our cellular power stations can vary in quality (for example, mutations can damage mtDNA), and are subject to random influences. How should the cell best invest energy in controlling and maintaining its power stations? And can we use this answer to design better therapies to address damaged mtDNA?

In a new paper here in PLoS Computational Biology, we attempt to answer this question using mathematical modelling, linking with genetic experiments done by our excellent collaborators at Cambridge (Payam Gammage, Lindsey Van Haute and Michal Minczuk). We first expand a mathematical model for how diverse mtDNA populations within cells change over time – building new power stations and decommissioning old ones, under the “governance” of the cell. We then produce an “energy budget” for the cellular “society” – describing the costs of building, decommissioning, and maintaining different power stations, and the corresponding profits of energy generation.

We find some surprising results. First, it can get harder to maintain a good energy budget in a tissue (a collection of individual cellular “societies”) over time, even if demands stay the same and average mtDNA quality doesn’t change. This is because the cell-to-cell variability in mtDNA quality does increase, carrying with it an added energetic challenge. This increased challenge could be a contributing factor to the collection of problems involved in ageing.

An overview of our approach. A mathematical model for the processes and "budget" involved in controlling mtDNA populations makes a general set of biological predictions and explains gene-therapy observations

Next, we found that cells with only low-quality mtDNA can perform worse than cells with a mix of low- and high-quality mtDNA. This is because low-quality mtDNA may consume less cellular resource, although global efficiency is decreased. Linked to this, removal of low-quality mtDNA (decommissioning bad power stations) alone is not always the best strategy to improve performance. Instead, jointly elevating low- and high-quality mtDNA levels, avoiding this detrimental mixed regime, is the best strategy for some situations. These insights may help explain some of the negative effects recently observed in cells with mixed mtDNA populations.

Our theory suggests that mixed mtDNA populations may do worse than pure ones, even if the pure population is a low-functionality mutant. Image from Hanne's post here 

We identified how best to control cellular mtDNA populations across the full range of possible populations, and used this insight to link with exciting gene therapies where low-quality mtDNA is preferentially removed through an experimental intervention (using so-called “endonucleases” to cut particular mtDNA molecules). We found that strong, single treatments will be outperformed by weaker, longer-term treatments, and identified how the mtDNA variability we know is present can practically effect the outcome of these therapies. We hope that the principles found in this work both add to our basic understanding of ageing and mixed (“heteroplasmic”) mitochondrial populations, and may inform more efficient therapeutic approaches in the future. Iain, Hanne, Nick

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: Coupling mitochondrial physics and genetics

Mitochondrial Network State Scales mtDNA Genetic Dynamics
Juvid Aryaman, Charlotte Bowles, Nick S. Jones and Iain G. Johnston
Genetics Early online July 10, 2019; https://doi.org/10.1534/genetics.119.302423

Mitochondrial DNA (mtDNA) populations within our cells encode vital energetic machinery. MtDNA is housed within mitochondria, cellular compartments lined by two membranes, that lead a very dynamic life. Individual mitochondria can fuse when they meet, and fused mitochondria can fragment to become individual smaller mitochondria, all the while moving throughout the cell. The reasons for this dynamic activity remain unclear (we’ve compared hypotheses about them before here and here, with blog articles here). But what influence do these physical mitochondrial dynamics have on the genetic composition of mtDNA populations?

MtDNA populations can, naturally or as a result of gene therapies, consist of a mixture of different mtDNA types. Typically, different cells will have different proportions of, say, type A and type B. For example, one cell may be 20% type A, another cell may be 40% type A, and a third may be 70% type A. This variability matters because when a certain threshold (often around 60%) is crossed for some mtDNA types, we get devastating diseases.

We previously showed mathematically (blog) and experimentally (blog) that this cell-to-cell variability in mtDNA proportions (often called “heteroplasmy variance” and sometimes referred to via the “mtDNA bottleneck”) is expected to increase linearly over time. However, this analysis pictured mtDNAs as individual molecules, outside of their mitochondrial compartments. When mitochondria fuse to form larger compartments, their mtDNA is more protected: smaller mitochondria (and their internal mtDNA) are subject to greater degradation. More degradation means more replication, and more opportunities for the fraction of a particular type of mtDNA to change per unit time. In a new paper here in Genetics, we show (using a mathematical tour de force by Juvid) that this protection can dramatically influence cell-to-cell mtDNA variability. Specifically, the rate of heteroplasmy variance increase is scaled by the proportion of mitochondria that exist in a fragmented state. (It turns out that it's the proportion of itochondria that are fragmented that's important -- not whether the rate of fission-fusion is fast or slow).

This has knock-on effects for how the cell can best get rid of low-quality mutant mtDNA. In particular, if mitochondria are allowed to fuse based on their quality (“selective fusion”), we show that intermediate rates of fusion are best for removing mutants. Too much fusion, and all mtDNA is protected; too little, and good mtDNA cannot be sorted from bad mtDNA using the mitochondrial network. This mechanism could help explain why we see different levels of mitochondrial fusion in different conditions. More broadly, this link between mitochondrial physics and genetics (which we’ve also speculated about here (blog) and here) suggests one way that selective pressures and tradeoffs could influence mitochondrial dynamics, giving rise to the wide variety of behaviours that remain unexplained. Juvid, Nick, and 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 mitochondria can vary, and consequences for human health

(cross-posted from Imperial Mitochondriacs)

Mitochondria are components of the cell which are involved in generating “energy currency” molecules called ATP across much of complex life. Since many mitochondria exist within single cells (often hundreds or thousands), it is possible for the characteristics of individual mitochondria to vary within cells, and within tissues. This variation of mitochondrial characteristics can affect biological function and human health.

Since mitochondria possess their own, small, circular, DNA molecules (mtDNA), we can split mitochondrial characteristics into two categories: genetic and non-genetic. In our review, we discuss a number of aspects in which mitochondria vary, from both genetic and non-genetic perspectives. 

In terms of mitochondrial genetics, the amount of mtDNA per cell is variable. When a cell divides, its daughters receive a share of its parents mtDNA, but the split isn’t precisely 50/50, so cell division can cause variability in the number of mtDNAs per cell. As mtDNAs are replicated and degraded over time, errors in the copying process may give rise to mtDNA mutations, which may spread throughout a cell. Factors such as: the total amount, the rate of degradation/replication, the mean fraction of mutants, and the extent of fragmentation in the mitochondrial network, can all influence how variable the fraction of mutated mtDNAs becomes through time (see here for a preview of some upcoming work on this topic). The total amount, and mutated fraction of mtDNAs, are implicated in diseases such as neurodegeneration, as well as the ageing process.

Apart from genetic variations, there are many non-genetic features of mitochondria which also vary within and between cells. Changes in mtDNA sequence can change the amino-acid sequence of the proteins encoded by mtDNA, causing structural changes in the molecular machines which generate ATP. The shape of the membranes of mitochondria are also highly variable, and respond to mitochondrial activity through quantities such as pH, where mitochondrial activity itself may depend on mtDNA sequence. The previous two examples (mitochondrial protein and membrane structure) demonstrate how the genetic state of mitochondria may influence their non-genetic characteristics. Mitochondrial non-genetic characteristics may also influence the genetic state: for instance, mitochondrial membrane potential can influence the probability of a mitochondria being degraded, along with its mtDNA.

The inter-dependence of genetic and non-genetic characteristics demonstrate the complex feedback loops linking these two aspects of mitochondrial physiology. We suggest here that, since changes in mitochondrial genetics occur more slowly than most physical aspects of mitochondrial physiology, understanding mitochondrial genetics may be especially important in explaining phenomena such as ageing, which appears to be closely related to mitochondrial heterogeneity. You can freely access our work, which has recently been published in Frontiers in Genetics, as “Mitochondrial Heterogeneity” https://www.frontiersin.org/articles/10.3389/fgene.2018.00718/full Juvid, Iain and Nick.