Evolution, Energetics & Noise

Mitochondrial DNA (mtDNA) contains instructions for building important cellular machines. We have populations of mtDNA inside each of our cells -- almost like a population of animals in an ecosystem. Indeed, mitochondria were originally independent organisms, that billions of years ago were engulfed by our ancestor's cells and survived -- so the picture of mtDNA as a population of critters living inside our cells has evolutionary precedent! MtDNA molecules replicate and degrade in our cells in response to signals passed back and forth between mitochondria and the nucleus (the cell's "control tower"). Describing the behaviour of these population given the random, noisy environment of the cell, the fact that cells divide, and the complicated nuclear signals governing mtDNA populations, is challenging. At the same time, experiments looking in detail at mtDNA inside cells are difficult -- so predictive theoretical descriptions of these populations are highly valuable. 

Why should we care about these cellular populations? MtDNA can become mutated, wrecking the instructions for building machines. If a high enough proportion of mtDNAs in a cell are mutated, our cells struggle and we get diseases. It only takes a few cells exceeding this "threshold" to cause problems -- so understanding the cell-to-cell distribution of mtDNA is medically important (as well as biologically fascinating). Simple mathematical approaches typically describe only average behaviours -- we need to describe the variability in mtDNA populations too. And for that, we need to account for the random effects that influence them.  

​In our cells, signals from the "control tower" nucleus lead to the replication (orange) and degradation (purple) of mtDNA. These processes affect mtDNA populations that may contain normal (blue) and mutant (red) molecules. Our mathematical approach -- extending work addressing a similar but simpler system -- describes how the total number of machines, and the proportion of mutants, is likely to behave and change with time and as cells divide.

 
In the past, we have used a branch of maths called stochastic processes to answer questions about the random behaviour of mtDNA populations. But these previous approaches cannot account for the "control tower" -- the nucleus' control of mtDNA. To address this, we've developed a mathematical tradeoff -- we make a particular assumption (which we show not to be unreasonable) and in exchange are able to derive a wealth of results about mtDNA behaviour under all sorts of different nuclear control signals. Technically, we use a rather magical-sounding tool called "Van Kampen's system size expansion" to approximate mtDNA behaviour, then explore how the resulting equations behave as time progresses and cells divide.

Our approach shows that the cell-to-cell variability in heteroplasmy (the potentially damaging proportion of mutants in a cell) generally increases with time, and surprisingly does so in the same way regardless of how the control tower signals the population. We're able to update a decades-old and commonly-used expression (often called the Wright formula) for describing heteroplasmy variance, so that the formula, instead of being rather abstract and hard to interpret, is directly linked to real biological quantities. We also show that control tower attempts to decrease mutant mtDNA can induce more variability in the remaining "normal" mtDNA population. We link these and other results to biological applications, and show that our approach unifies and generalises many previous models and treatments of mtDNA -- providing a consistent and powerful theoretical platform with which to understand cellular mtDNA populations. The article is in the American Journal of Human Genetics here and a preprint version can be viewed here. Crossed from here.

The largest survey of opinions on vaccine confidence

Monitoring trust in immunisation programmes is essential if we are to identify areas and socioeconomic groups that are prone to vaccine-scepticism, and also if we are to forecast these levels of mistrust. Identification of vaccine-sceptic groups is especially important as clustering of non-vaccinators in social networks can serve to disproportionately lower the required vaccination levels for collective (or herd) immunity. To investigate these regions and socioeconomic groups, we performed a large-scale, data-driven study on attitudes towards vaccination. The survey — which we believe to be the largest on attitudes to vaccinations to date with responses from 67,000 people from 67 countries — was conducted by WIN Gallup International Association and probed respondents’ vaccine views by asking them to rate their agreement with the following statements: “vaccines are important for children to have”; “overall I think vaccines are safe”; “overall I think vaccines are effective”; and “vaccines are compatible with my religious beliefs”.

Our results show that attitudes vary by country, socioeconomic group, and between survey questions (where respondents are more likely to agree that vaccines are important than safe). Vaccine-safety related sentiment is particularly low in the European region, which has seven of the ten least confident countries, including France, where 41% of respondents disagree that vaccines are safe. Interestingly, the oldest age group — who may have been more exposed to the havoc that vaccine-preventable diseases can cause — hold more positive views on vaccines than the young, highlighting the association between perceived danger and pro-vaccine views. Education also plays a role. Individuals with higher levels of education are more likely to view vaccines as important and effective, but higher levels of education appear not to influence views on vaccine safety.

Vaccine World map of percentage negative ("tend to disagree" or "strongly agree") survey responses to the statement "overall I think vaccines are safe"

Our study, "The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey" can be read for free in the journal EBioMedicine here with a commentary here. You can find other treatments in Science magazine, New Scientist, Financial Times, Le Monde and Scientific American. Alex, Iain, and Nick.

Understanding the strength and correlates of immunisation programmes

Childhood vaccinations are vital for the protection of children against dreadful diseases such as measles, polio, and diphtheria. In addition to providing personal protection, vaccines can also suppress epidemic outbreaks if a sufficiently large proportion of the population has immunity status – this “herd immunity” is important for society as many individuals are unable to vaccinate for medical reasons. Over the past half a century, public health organisations have made concerted efforts to vaccinate every child worldwide. However, notwithstanding the substantial improvements to vaccine coverage rates across the globe over the past few decades, there are still millions of unvaccinated children worldwide. The majority of these children live in countries where large numbers of the populations live in deprived, rural regions with poor access to healthcare. However, a number of children are denied vaccines because of parental attitudes and beliefs (which are often influenced by the media, religious groups, or anti-vaccination groups) – such hesitancy has been responsible for recent outbreaks in developing (e.g. Nigeria, Pakistan, Afghanistan) and developed (e.g. USA, UK) countries alike. Monitoring vaccine coverage rates, summarising recent vaccination behaviours, and understanding the factors which drive vaccination behaviour are thus key to our understanding vaccine acceptance, and can allow immunisation programmes to be more effectively tailored.

To understand these pertinent issues, we used machine learning tools on publicly-available vaccination and socioeconomic data (which can be found here  and the World Health Organization’s websites). We used Gaussian process regression to forecast vaccine coverage rates and used the predictive distributions over forecasted coverage rates to introduce a quantitative marker summarising a country’s recent vaccination trends and variability:  this summary is termed the Vaccine Performance Index. Parameterisations of this index can then be used to identify countries which are likely (over next few years) to have vaccine coverage rates far from those required for herd immunity levels or that are displaying worrying declines in rates and to assess which countries will miss immunisation goals set by global public health bodies. We find that these poorly-performing countries were mostly located in South-East Asia and sub-Saharan Africa though, surprisingly, a handful of European countries also perform poorly.

To investigate the factors associated with vaccination coverage, we sought links between socioeconomic factors with vaccine coverage and found that countries with higher levels of births attended by skilled health staff, gross domestic product, government health spending, and higher education levels have higher vaccination coverage levels (though these results are region-dependent).

Our vaccine performance index could aid policy makers’ assessments of the strength and resilience of immunisation programmes. Further,  identification of socioeconomic correlates of vaccine coverage points to factors to address to improve vaccination coverage. You can read further in our freely available paper – which is in collaboration with the London School of Hygiene and Tropical Medicine (Heidi Larson and David Smith) and IIT Delhi (Sumeet Agarwal) – in the open-access journal Lancet Global Health under the title “Forecasted trends in vaccination coverage and correlations with socioeconomic factors: a global time-series analysis over 30 years” and there is another free article unpacking it under the title "Global Trends in Vaccination Coverage". Alex, Iain, Nick.

Energetic arguments constraining complex fungal systems

Fungi are ubiquitous and ecologically important organisms that grow over the resources they consume. Fungi decompose everything from dead trees to dung, but whatever substrate they consume, fungi are obliged to spend energy on growth, reproduction, and substrate digestion. Many fungi also recycle their own biomass to fuel further growth. Within this overall framework, each fungal species adopts a different strategy, depending on the relative investment in growth, recycling, digestion and reproduction. Collectively, these strategies determine ecologically critical rates of carbon and nutrient cycling, including rates of decomposition and CO2 release. Crucially, a given fungus will encounter more of a resource if it increases its growth rate, and it will obtain energy from that resource more rapidly if it increases its investment in transporters and digestive enzymes. However, any energy that is expended on growth or resource acquisition cannot be spent on spore production, so fungi necessarily confront trade-offs between these three essential processes.

An example of a foraging fungal network

To understand these trade-offs we developed an energy budget model which uses a common energy currency to systematically explore how different rates of growth, recycling, and investments in resource acquisition affect the amount of energy available for reproduction, and how those trade-offs are affected by characteristics of the resource environment. Our model helps to explain the complex range of strategies adopted by various fungi. In particular, it shows that recycling is only beneficial for fungi growing on recalcitrant, nutrient-poor substrates, and that when the timescale of reproduction is large compared to the time required for the fungus to double in size, the total energy available for reproduction will be maximal when a very small fraction of the energy budget is spent on reproduction. You can read about this free under the title "Energetic Constraints on Fungal Growth" and it appears in the glamorously titled American Naturalist. Luke, Mark and Nick

Generations of generating functions in dividing cells

Cell biology is a unpredictable world, as we've written about before. The important machines in our cells replicate and degrade in processes that can be described as random; and when cells divide, the partitioning of these machines between the resulting cells also looks random. The number of machines we have in our cells is important, but how can we work with numbers in this unpredictable environment?

In our cells, machines are produced (red), replicate (orange), and degrade (purple) randomly with time, as well as being randomly partitioned when cells split and divide (blue). Our mathematical approach describes how the total number of machines is likely to behave and change with time and as cells divide.

Tools called "generating functions" are useful in this situation. A generating function is a mathematical function (like G(z) = z², but generally more complicated) that encodes all the information about a random system. To find the generating function for a particular system, one needs to consider all the random things that can happen to change the state of that system, write them down in an equation (the "master equation") describing them all together, then use a mathematical trick to push that equation into a different mathematical space, where it is easier to solve. If that "transformed" equation can be solved, the result is the generating function, from which we can then get all the information we could want about a random system: the behaviour of its mean and variance, the probability of making any observation at any time, and so on.

We've gone through this mathematical process for a set of systems where individual cellular machines can be produced, replicated, and degraded randomly, and split at cell divisions in a variety of different ways. The generating functions we obtain allow us to follow this random cellular behaviour in new detail. We can make probabilistic statements about any aspect of the system at any time and after any number of cell divisions, instead of relying on assumptions that the system has somehow reached an equilibrium, or restricting ourselves to a single or small number of divisions. We've applied this tool to questions about the random dynamics of mitochondrial DNA (which we're very interested in! And this work connects explicitly with our recent eLife paper - blog article here) in cells that divide (like our cells) or "bud" (like yeast cells), but the approach is very general and we hope it will allow progress in many more biological situations. You can read about this, free, here under the title "Closed-form stochastic solutions for non-equilibrium dynamics and inheritance of cellular components over many cell divisions" in the Proceedings of the Royal Society A. Iain and Nick

How evolution deals with mitochondrial mutants (and how we can take advantage)

Our mitochondrial DNA (mtDNA) provides instructions for building vital machinery in our cells. MtDNA is inherited from our mothers, but the process of inheritance -- which is important in predicting and dealing with genetic disease -- is poorly understood. This is because mitochondrial behaviour during development (the process through which a fertilised egg becomes an independent organism) is rather complex. If a mother's egg cell begins with a mixed population of mtDNA -- say with some type A and some type B -- we usually observe hard-to-predict mtDNA differences between cells in the daughter. So if the mother's egg cell starts off with 20% type A, egg cells in the daughter could range (for example) from 10%-30% of type A, with each different cell having a different proportion of A. This increase in variability, referred to as the mtDNA bottleneck, is important for the inheritance of disease. It allows cells with higher proportions of mutant mtDNA to be removed; but also means that some cells in the next generation may contain a dangerous amount of mutant mtDNA. Crucially, how this increase in variability comes about during development is debated. Does variability increase because of random partitioning of mtDNAs at cell divisions? Is it due to the decreased number of mtDNAs per cell, increasing the magnitude of genetic drift? Or does something occur during later development to induce the variability? Without knowing this in detail, it is hard to propose therapies or make predictions addressing the inheritance of disease.

We set out to answer this question with maths! Several studies have provided data on this process by measuring the statistics of mixed mtDNA populations during development in mice. The different studies provided different interpretations of these results, proposing several different mechanisms for the bottleneck. We built a mathematical framework that was capable of modelling all the different mechanisms that had been proposed. We then used a statistical approach called approximate Bayesian computation to see which mechanism was most supported by the existing data. We identified a model where a combination of copy number reduction and random mtDNA duplications and deletions is responsible for the bottleneck. Exactly how much variability is due to each of these effects is flexible -- going some way towards explaining the existing debate in the literature.  We were also able to solve the equations describing the most likely model analytically. These solutions allow us to explore the behaviour of the bottleneck in detail, and we use this ability to propose several therapeutic approaches to increase the "power" of the bottleneck, and to increase the accuracy of sampling in IVF approaches.

A "bottleneck" acts to increase mtDNA variability between generations. But how is this bottleneck manifest? Our approach suggests that a combination of copy number reduction (pictured as a "true" copy number bottleneck), and later random turnover of mtDNA (pictured as replication and degradation), is responsible.

Our excellent experimental collaborators, led by Joerg Burgstaller, then tested our theory by taking mtDNA measurements from a model mouse that differed from those used previously and which, could in principle have shown different behaviour. The behaviour they observed agreed very well with the predictions of our theory, providing encouraging validation that we have identified a likely mechanism for the bottleneck. New measurements also showed, interestingly, that the behaviour of the bottleneck looks similar in genetically diverse systems, providing evidence for its generality. You can read about this in the free (open-access) journal eLife under the title "Stochastic modelling, Bayesian inference, and new in vivo measurements elucidate the debated mtDNA bottleneck mechanism"  Iain and Nick

The function of mitochondrial networks

Mitochondria are dynamic energy-producing organelles, and there can be hundreds or even thousands of them in one cell. Mitochondria (as we've blogged about before - e.g. here) do not exist independently of each other: sometimes they form giant fused networks across the cell, sometimes they are fragmented, and sometimes they take on intermediate shapes. Which state is preferred (fragmented, fused or in between) seems to depend on, for example, cell-division stage, age, nutrient availability and stress levels. But what is exactly the reason for the cell preferring one morphology over another?

Nonlinear phenomena -- like some percolation effects -- could help account for the functional advantage of mitochondrial networks

We recently wrote an open-access paper (free here in the journal BioEssays) in which we try to answer the question: what is it about fused mitochondrial networks that could make them preferable to fragmented mitochondria? Our paper differs from previous work in that we attempt to use a range of mathematical tools to gain insight into this complex biological system and we try to hit on the root physiological and physical roles. We use physical models, simulations, and numerical estimations to compare ideas, to reason about existing hypotheses, and to propose some new ones. Among the possibilities we consider are the effects of fusion on mitochondrial quality control, on the spread of important protein machinery throughout the cell, on the chemistry of important ions, and on the production and distribution of energy through the cell. The models we use are quite simple, but we propose ideas for improving them, and experiments that will lead to further progress.

Taking a mathematical perspective leads to a central idea: for fused mitochondria to be 'preferred' by the cell, there must be some nonlinear advantage to fusion. That's what the fuzzy line is representing in the figure above. A big mitochondrion formed by fusing two smaller ones must in some sense be 'better' than the sum of the two smaller ones, or there would be no reason why a fused state is preferred.

Mitochondria can fuse to form large continuous networks across the cell. From a mathematical and physical viewpoint, we evaluate existing and novel possible functions of mitochondrial fusion, and we suggest both experiments and modelling approaches to test hypotheses

What is the source of this nonlinearity? We find several physical and chemical possibilities. Large pieces of fused mitochondria are better at sharing their contents (e.g. proteins, enzymes, and possibly even DNA) than smaller pieces of fused mitochondria. If the 'fusedness' of the mitochondrial population increases by a factor of two, the efficiency with which they share their contents increases by more than two! Also, fusion can reduce damage. If a mitochondrion gets physically or chemically damaged, having some fused non-damaged neighbours can help to reduce the overall harm to the cell. Finally, fusion may increase energy production because of a nonlinear chemical dependence of energy production on mitochondrial membrane potential. Fusing more mitochondria may, under certain circumstances, have the effect of increasing energy production. Hanne, Iain and Nick

Turbocharging the back of the envelope

The numbers that we use to describe the world are rarely exact. How long will it take you to drive to work? Perhaps "between 20 and 30 minutes". It would be unwise (and unnecessary) to say "exactly 23.4 minutes".

This uncertainty means that "back-of-the-envelope" calculations are very valuable in estimating and reasoning about numerical problems, particularly in the sciences. The idea here is to perform a calculation using rough guesses of the quantities involved, to get an "order of magnitude" estimate of the answer you're after. Made famous in physics as "Fermi problems", attributed to Enrico Fermi (who used rough reasoning to deduce quantities from the power of an atomic bomb to the number of piano tuners in Chicago), this approach is integral in many current applications of maths and science. Cool books like "Street-fighting Mathematics", "Guesstimation", "Back of the envelope physics", the excellent "What If?" section of xkcd, and the lateral interview questions facing some job candidates: "how much of the world's water is contained in a cow?" are all examples.

Calculations in biology, such as the time it takes for a protein (foreground) to diffuse through an E. coli cell (background), are often subject to large uncertainties. Our approach and web tool allows us to track this uncertainty and obtain a probability distribution over possible answers (plotted).

We've built a free online calculator (Caladis -- calculate a distribution) that complements this approach by allowing one to take the uncertainty in one's estimates into account throughout a calculation. For example, what volume of CO2 is produced by our yearly driving? We could say that we cover 8000 miles per year "give or take" 1000 miles, and find that our car's CO2 emissions are between 100 and 150 grams per kilometre. Our calculator allows us to do the necessary conversions and sums while taking this possible variability into account -- doing maths with "probability distributions" describing our uncertainty. We no longer obtain a single (possibly inaccurate) answer, but a distribution telling us how likely any particular answer is -- in this case a rather concerning bell-shaped distribution between 1 and 2 tonnes which can be viewed here

In the sciences, particularly in biology, measurements often have substantial uncertainties -- due to experimental error, natural variability in the system of interest, or both -- and so using distributions rather than single numbers in calculations allows us to understand and process more about the question of interest. "Back-of-the-envelope" calculations are certainly useful in biology but, owing to the uncertainties involved, one can trust one's estimates better if one has a smart envelope that takes that uncertainty into account.  We've written an accompanying paper "Explicit tracking of uncertainty increases the power of quantitative rule-of-thumb reasoning in cell biology" (free to all in Biophysical Journal) showing how to use our calculator -- in conjunction with the excellent Bionumbers online database, a collection of (often uncertain) experimental measurements in biology -- to make real biological calculations more powerful. Do have a go at using our calculator at www.caladis.org : it's user-friendly and there are lots of examples showing how it works! Iain and Nick

Therapies for mtDNA disease: models and implications

Mitochondrial DNA (mtDNA) is a molecule in our cells that contains information about how to build important cellular machines that provide us with the energy required for life. Mutations in mtDNA can prevent our cells from producing these machines correctly, causing serious diseases. Mutant mtDNA can be passed from a carrier mother to her children, and as the amount of mutated mtDNA inherited can vary, children's symptoms can be much more severe (often deadly) than those in the mother.

Several therapies exist to prevent or minimise the inheritance of mutant mtDNA from mother to daughter. These range from simply using a donor mother's eggs (in which case the child inherits no genes from the "mother") to amazing new techniques where a mother's nucleus is transferred into a donor's egg cell which has had its nucleus removed (so that the child inherits nuclear DNA from the mother and father, and healthy mtDNA from the donor). The UK is currently debating whether to allow these new therapies: several potential scientific issues have been identified in their application.

If a mother carries an mtDNA mutation, (A) no clinical intervention can lead to her child inheriting that mutation and developing an mtDNA disease. Several "classical" (B-C) and modern (D-E) strategies exist to attempt to prevent the inheritance of mutant mtDNA, which we review (see paper link below)

As experiments with human embryos are heavily restricted, experiments in animals provide the bulk of our knowledge about how these therapies may work. We have previously written about our research in mice, highlighting a possible issue arising from mtDNA "segregation", where one type of mtDNA (possibly carrying a harmful mutation) may proliferate over another: this phenomenon could, in some circumstances, nullify the beneficial effects of mtDNA therapies. Another possible issue involves the effects of "mismatching" between the mother and father's nuclear DNA and the donor's mtDNA: current experimental evidence is conflicted regarding the strength of this effect. Finally, mismatch between donor mtDNA and any leftover mother mtDNA may also lead to biological complications.

We have recently written a paper explaining and reviewing the current state of knowledge of these effects, summarising the evidence from existing animal experiments. We are positive about implementing these therapies, which have the potential to prevent the inheritance of devastating diseases. However, we note cautions about this implementation, noting that several scientific questions remain debated or unanswered. We particularly highlight that "haplotype matching", a strategy to ensure that donor and mother mtDNA are as similar as possible, will largely remove these concerns. Iain

Mitochondrial motion in plants

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 "FRIENDLY regulates mitochondrial distribution, fusion, and quality control in Arabidopsis" (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.

Simulation of mitochondrial dynamics

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

'Mitoflashes' indicate acidity changes rather than free radical bursts

As we've written about before, mitochondria generate the energy required by our cells through respiration that involves using an "electrochemical gradient" as an energy store (a bit like pumping water up into a reservoir for energy storage to then harness it flowing down the gradient of a hill to turn a turbine), and produces superoxide (free oxygen radicals) as a by-product (a bit like sparks when the pumps are running hot). The fundamental importance of this machinery which not only delivers energy, but is also involved in disease and aging  has led to its investigation in great molecular detail (comparable to taking the turbines and generators apart to learn about their function). Much less is known about how mitochondria actually behave when they are fully functional in their natural environment inside our cells (comparable to looking at the fully intact and running turbine), and progress has been difficult since suitable `tools' are scarce.

A debate exists in the scientific literature about one of the key "tools" used in the investigation of living cells. A particular fluorescent sensor protein called cpYFP (circularly permuted yellow fluorescent protein) is used in biological experiments, ostensibly as a way of measuring the levels of superoxide/free oxygen radicals  in a mitochondrion. Our colleagues, however, have cast doubt on the ability of cpYFP to measure superoxide, providing evidence that it instead responds to pH, part of the above electrochemical gradient. This debate was complicated by the fact that in biology, pH and superoxide can vary together, as the amount of "driving" and amount of "sparks" might be expected to.

As another analogy: If we found an unknown measuring device and we did not know how it works, but we saw that it responds during sunny weather, we may conclude that it measures warm temperature. However, it may in fact measure high atmospheric pressure which is, like warm temperatures, often correlated with good weather.  

The protein cpYFP changes its fluorescence in response to pH changes, but is unaffected by superoxide changes.

A recent and fascinating paper in Nature observed that "flashes" of the cpYFP sensor during early development of worms (as a model for other animals and humans) were correlated with their eventual lifespan. However, despite the debate about what it is exactly that the  cpYFP sensor measures, the paper interpreted it as responding to superoxide: looking at the correlation in the light of the so called “free radical theory of aging". This long-standing and much debated theory hypothesizes that the cause of why we age and eventually die is related to the constant production of free oxygen radicals in our mitochondria causing a steady increase in damage to our cells weakening their energetic machinery more and more and making them prone to illnesses.

In response to this, our colleagues decided to settle the question about what the sensor actually measures chemically, removing biological complications from the system. In the analogy of the unknown measurement device, the device was now tested under controlled temperature and controlled pressure to clearly distinguish between the two. They produced an experimental setup where a mix of chemicals was used to generate superoxide in the absence of any pH change. cpYFP in this mix did not show any signal, showing that it remains unresponsive to superoxide. In concert, they showed that even small changes in pH produced a dramatic response in cpYFP signal. Finally, they investigated the physical structure of cpYFP, showing that a large opening in the barrel-like structure of the protein exposes a pH-sensitive chemical group to its environment (comparable to showing how exactly the inner mechanics of the unknown measurement device can pick up pressure changes). We thus concluded, in a recent publication "The ‘mitoflash’ probe cpYFP does not respond to superoxide" (in the journal Nature here) that the cpYFP sensor reports pH rather than superoxide, and that results using cpYFP (including the above Nature paper, which remains fascinating) should be interpreted as such. Iain, Markus and Nick