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: 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.

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: Time marches on -- mitochondria, ageing, and disease

Burgstaller, J.P., Kolbe, T., Havlicek, V., Hembach, S., Poulton, J., Piálek, J., Steinborn, R., Rülicke, T., Brem, G., Jones, N.S. and Johnston, I.G. Large-scale genetic analysis reveals mammalian mtDNA heteroplasmy dynamics and variance increase through lifetimes and generations. Nature communications, 9 2488 (2018)

DNA in mitochondria, the powerhouses of the cell, is passed down from mother to child. But there are many mitochondria in each cell, and these mitochondria may have different genetic features. If a mother carries a mixture of mitochondrial DNA (mtDNA) types, this can make it hard to say which features their children will inherit. For mothers carrying a disease-causing mtDNA mutation, this makes family planning and clinical therapies challenging.

In particular, the role of a mother's age has long been a mystery. Is the probability of a child inheriting a particular mtDNA feature higher when mothers are younger or older? An answer to this question could help plan clinical strategies to improve fertility and prevent the inheritance of deadly mitochondrial disease.

To address this, we worked with our excellent collaborators with a combination of maths, statistics, and experiment. Our collaborators used cutting-edge technology to reveal the mixtures of mtDNA in the egg cells of mother mice at a wide range of ages, and in the litters of offspring the mothers produced. This experimental work was the largest-scale study of mammalian mtDNA that we're aware of, involving thousands of observations throughout lifetimes and between generations. In concert, we developed a mathematical model describing the changes to, and inheritance of, mtDNA from mother to offspring. We combined the model and data to learn how different biological processes affect mtDNA through and between generations.

Cells contain populations of mitochondria, and these populations change over time. In European mice, we observed how variability in these populations evolves as mammals age and reproduce. We found that older mother have more varied mitochondria and pass this variance on to their offspring -- of central importance in the inheritance of genetic disease. 

We found that the variability of mtDNA dramatically increased as mothers aged. This means that the probability of inheriting more extreme -- both lower and higher -- levels of a genetic feature increases for older mothers. We also found that different mtDNA mixtures were inherited in different ways - with some mtDNA types favoured for inheritance and some disfavoured. We used our findings to create a way to predict how the risk that offspring would inherit disease-causing mtDNA features changes over time. Moving forward, we're aiming to harness these powerful ways of using large datasets to describe and predict the dynamics of mtDNA inheritance in humans, and to learn what it is about these mtDNA types that predicts their evolution across generations. You can read the article for free in Nature Communications here.

ARTICLE: A healthy dose of mathematics

Toward Precision Healthcare: Context and Mathematical Challenges

C Colijn, N Jones, IG Johnston, S Yaliraki, M Barahona

Frontiers in Physiology 8 136 (2017)

• The continuing explosion of available biomedical data will help us tailor and optimise therapies for individual patients; we are designing new maths and statistics to help this process and to include social and other data into an overarching "precision healthcare" approach.

Our research combines tools from maths and statistics with biological data to learn more about the biological world. An exciting, growing, and much-discussed branch of science -- precision medicine -- is a specific instance of this idea. The vision of precision medicine is to use the expanding volume of data that's emerging from medicine and biology to tailor and optimise medical therapies for individual patients, making the therapies as effective as possible. This idea isn't new -- we are well aware, for example, that an individual's blood type dictates which blood transfusions they can successfully receive. But precision medicine is a much bigger picture, potentially taking into account large amounts of genetic, environmental, dietary, and other features to identify the optimal treatment for a disease -- for example, tailoring chemotherapy treatments to match the genetic specifics of a particular cancer case.

Dealing with these large and diverse datasets will need new mathematical and statistical approaches, built with an ongoing link to clinical practice. At the same time, we're interested in expanding the idea of precision medicine to include the "big data" that's increasingly available about individuals' social and logistic contexts. Social networks can dictate how diseases spread -- and how knowledge and views about therapies, vaccines, and other medically pertinent ideas are transmitted and shaped from person to person. A person's home region determines the genetic structure of local people who may act as donors. We're looking at the idea of "precision healthcare" -- using new maths and statistics to optimise healthcare strategy, not just individual therapies, in the light of large-scale datasets.

One aspect of precision healthcare we'll be exploring is exploring how progressive diseases -- those that involve the accumulation of symptoms over time -- develop in patients, using transition networks like those above to model "disease spaces" and find pathways in those spaces.

We're excited to be part of a new initiative -- the Centre for the Mathematics of Precision Healthcare -- involving six parallel and related research projects that align with this goal. Some of our previous work -- for example, estimating social structures of big UK cities to explore the challenges that genetic diversity poses to gene therapies for mtDNA disease -- already has a precision healthcare feel. In a new review paper (available for free) we discuss this and other examples of past and future work that we hope will contribute to the precision healthcare goal, along with some key ideas and context for the initiative. Iain

ARTICLE: European region is the most sceptical on vaccine safety

The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey
Heidi J Larson, Alexandre de Figueiredo, Zhao Xiahong, William S Schulz, Pierre Verger, Iain G Johnston, Alex R Cook, Nick S Jones
EBioMedicine 12, 295-301 (2016)

• How people view vaccines has a direct influence on the spread and impact of diseases; we use the largest-ever global survey of vaccine opinions to explore where and why people have issues with immunisation programmes.

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.

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

ARTICLE: Migration, mothers, mitochondria, and medicine

mtDNA diversity in human populations highlights the merit of haplotype matching in gene therapies

EC Røyrvik, JP Burgstaller, IG Johnston

Molecular Human Reproduction 22 (11), 809-817 (2016)

• The diversity of mtDNA in modern human populations may pose a challenge to gene therapies that aim to prevent the inheritance of deadly mtDNA disease; we use population and census data, and large-scale mtDNA sequence data, to assess this risk and suggest strategies to combat it.

Some mothers carry disease-causing mutations in their mitochondrial DNA (mtDNA), which can be passed on to their children. Amazing cutting-edge therapies are designed to avoid the inheritance of mutant mtDNA, by endowing a child with mtDNA from another woman (let's say Wilma) -- with no dangerous mutations -- instead of the mother's (let's say Miranda's) mtDNA. However, due to technical challenges in the implementation of these therapies, a small amount of the mother's mtDNA may remain in the child. If that initially small amount can become amplified -- say Miranda's mtDNA proliferates more quickly than Wilma's -- it may come to dominate cells in the child. Then the disease which the therapy attempted to avoid may become manifest -- as we've written about before. 

We have previously found, in mice, that the more different two mtDNA types are, the more likely one is to dominate over another. So if Miranda and Wilma have very different mtDNA, there's a good chance Miranda's might become amplified. But, although these effects are dramatic in natural mouse populations, we don't really know how likely this "winning" and "losing" was between human mtDNAs (as we'd see in the above therapies). Say Matilda and Wilma both come from London. How different will their mtDNA types likely be? And so, what is the risk that Matilda's mtDNA will beat Wilma's, potentially complicating therapies?

Human mtDNA varies by geography -- women from different parts of the world belong to different mtDNA "haplogroups". Some haplogroups are themselves very diverse, and some less so; haplogroups also differ from each other by varying degrees. So we needed to address two questions: (1) what are the likely mtDNA groups of women taken from a given region (say, Birmingham); and (2) how genetically different are two mtDNAs taken from these groups?

(left) Due to the history and evolution of human populations, some mtDNA types -- denoted here by letters -- are historically more common in different world regions. (right) Our analysis of large-scale sequence data tells us how genetically different two mtDNAs from randomly-sampled women from different ancestral backgrounds are likely to be (circle size). The more different, the more likely the therapies involving that pair of women will experience difficulties.

To answer these, we retrieved (from the NCBI database) over 7000 human mtDNA sequences, as well as information about the mtDNA makeup of pre-industrial different regions around the world, and census information about the UK's, London's, and Birmingham's ethnic makeup. We used this information to estimate the mtDNA makeup of modern human populations -- which have become highly mixed through migration in recent times. Using these estimates, we then simulated thousands of Matilda-Wilma pairings in specific regions around the world (including the UK, London, and Birmingham). We recorded the genetic differences between these simulated pairs of mtDNAs to see how different we may expect women from different regions to be. The results have just appeared in Molecular Human Reproduction here; a similar, pre-peer-review version can be viewed for free here.

We found that the size of genetic differences likely to arise when sampling pairs women from modern populations was around 20-80 SNPs (single nucleotide polymorphisms -- specific molecular differences in mtDNA). This level of difference was enough to lead to substantial segregation bias in mouse models, suggesting that unprincipled choice of Wilmas from the general population could be problematic. These large differences are in large part due to modern population mixing, with substantial mixing of African and Asian mtDNA in modern UK cities contributing to the diversity. We showed that "haplotype matching" -- checking that Wilma is genetically similar to Matilda -- decreases these differences and so decreases the likelihood of problems with therapies. We also created a preliminary chart to help this process, showing which human haplotypes are genetically similar to others -- hopefully this will both help scientific understanding and therapeutic implementation in this field. Iain and Ellen

ARTICLE: Controlling the control of our cellular power stations

Modulating mitochondrial quality in disease transmission: towards enabling mitochondrial DNA disease carriers to have healthy children

Alan Diot, Eszter Dombi, Tiffany Lodge, Chunyan Liao, Karl Morten, Janet Carver, Dagan Wells, Tim Child, Iain G Johnston, Suzannah Williams, Joanna Poulton

Biochem Soc Trans (in press) (2016)

• Dysfunctional mitochondria are recycled by the cell in a process that helps avoid disease; we summarise extending and provide new information about this process, and show -- agreeing with our mathematical theory -- that it can be modulated with drug treatments, providing potentially new therapeutic avenues.

Mitochondria -- a focus of our research -- are "power stations" in our cells that produce the energy we need to live. Like the power stations we build, mitochondria contain machines that work to produce this energy. They also contain the genetic "instructions" on how to build these machines, in the form of mitochondrial DNA (mtDNA). MtDNA can become mutated, spoiling these instructions, giving rise to dysfunctional machines and causing problems in our cells. Thankfully, our cells have systems that helps remove these mutant mtDNAs and recycle the bad machines that they've produced. One example is "mitophagy" (from mito-(chondria) and -phagy (eating)), as we've written about before.

Mitophagy uses "autophagosomes" to remove mtDNA from the cell, but it's hard to observe and measure: our understanding of the process, and how we may influence it to address diseases, is limited. In a recent paper, we summarise current understanding of mitophagy, particularly during early development (of importance for the inheritance of mtDNA diseases). As experiments and models explore the process in more detail, different types of mitophagy (progressing through different pathways) have been identified, as have fascinating "surges" of mitophagy at different developmental stages. In a new paper in Biochemical Society Transactions we discuss how these individual results are helping to build an overall picture of how mtDNA populations are controlled by cells.

Figure: single-cell microscopy determines how many autophagosomes (green), potentially recycling dysfunctional mitochondria, exist in cells during development. Drug treatments (lower row) can influence this number, potentially allowing us to control cellular mtDNA populations.

We also present some interesting preliminary results that may help us better understand, and control, mitophagy. Very soon after fertilisation, as an egg cell starts to divide, it seems that the amount of mtDNA in the growing embryo may decrease, rather more than previously reported. The experimental team, centred on Alan Diot, explored how many autophagosomes existed within cells during this process, and also showed that post-fertilisation treatment with drugs can affect the number of autophagosomes and hence the mtDNA populations in dividing cells (see figure). We've previously shown using mathematical modelling that decreasing mtDNA content may help avoid the inheritance of mtDNA diseases -- these new results highlight the feasibility of these potential new therapeutic strategies to address mtDNA disease inheritance. Iain

ARTICLE: Understanding the strength and correlates of immunisation programmes

Forecasted trends in vaccination coverage and correlations with socioeconomic factors: a global time-series analysis over 30 years

•  Lack of trust in vaccines results in preventable illness and death all over the world; we use tools from statistics and large-scale socio-economic data to explore which features of a country "prime" it for weakened vaccine coverage, identifying factors which may help policymakers address vaccine confidence issues.

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 on 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.

ARTICLE: Go green -- recycle mitochondria

A novel quantitative assay of mitophagy: Combining high content fluorescence microscopy and mitochondrial DNA load to quantify mitophagy and identify novel pharmacological tools against pathogenic heteroplasmic mtDNA

• Mitophagy degrades mitochondria, and likely plays important roles in the cell's responses to mitochondrial disease, but is hard to measure and thus poorly understood: we propose new ways of measuring mitophagy and use them to explore drugs that may help change damaged mitochondrial populations

Mitochondria, as we've written about before, are important entities in our cells that produce energy and take part in many other vital processes. Mitochondrial DNA (mtDNA), inherited from our mothers, contains instructions on how to build important mitochondrial machinery. MtDNA is sometimes mutated, leading to problems with our mitochondria. How do our cells cope?

Mitophagy (from mito-(chondria) and -phagy (eating)) is a process by which cells degrade and recycle mitochondria, allowing dysfunctional mitochondria to be removed and replaced. Mitophagy is one of a number of cellular mechanisms that maintain a healthy population of mitochondria, and appears to play a central role in determining the inheritance and evolution of mtDNA over our lifetimes. However, our understanding of mitophagy is limited because it is hard to observe.

In a recent and epically-titled paper in Pharmacological Research here, we explore two different approaches for measuring mitophagy in cells. The first is physical. We used chemicals to make mitochondria glow red, and autophagosomes (the cellular machines responsible for the degradation of mitochondria) glow green. We then used a microscope to examine large numbers of cells and recorded how often red (mitochondria) and green (autophagosomes) were seen together, which we took to imply that mitophagy may be occurring. We confirmed that various drugs and chemicals known to affect mitophagy had the expected effects on this estimate of mitophagy, and that perturbing ATG7 (an essential part of the autophagic machinery) sustantially reduced our observed mitophagy levels.

We also subjected cells to stress by growing them with a less plentiful supply of energy. We found that this energy stress increased the amount of mitophagy (perhaps as cells struggle to make the very best of their mitochondrial populations). We also found that mitophagy broadly decreased in cells from older people, and was increased in cells from people carrying an mtDNA disease (negatively affecting mitochondrial functionality).

The second approach is genetic. In cells from patients with mtDNA disease, some mtDNA is normal and some is mutated -- we used genetic tools to measure the proportion of mutant mtDNA in cells. We observed that when we stressed patients' cells, levels of mutant mtDNA decreased while our physically observed measure of mitophagy increased, supporting a picture in which mitophagy removes dysfunctional mitochondria when energy output is of central importance. We also found evidence for undirected mitophagy, where mtDNA copy number is depleted with no preference for mutant or wildtype.

Observing the colocalisation of autophagosomes (green) and mitochondria (red), as well as the proportion of mutant mtDNA (white stars), allows a bilateral characterisation of mitophagy. The patterns of changes in these observations tell us about how drug treatments and different environments change mitochondrial populations.

The physical and genetic approaches give us two largely independent means to estimate mitophagy, placing our understanding of this vital process on a solid analytical foundation. We used these tools to assess the effects of various drugs on mitophagy, allowing us to characterise the effects of drugs like metformin (inhibiting mitophagy) and phenanthroline (inducing undirected mitophagy) in unprecedented detail and facilitating more precise statements about their utility in clinical contexts. Iain

ARTICLE: Warburg Ensemble

Monitoring Intracellular Oxygen Concentration: Implications for Hypoxia Studies and Real-time Oxygen Monitoring

• Cancer cells vary in how they produce their energy: we make progress understanding this variability, which may eventually help scientists design better therapies.

Cells can produce energy through several processes. We'll consider two – process "O" (for "oxidative phosphorylation"), and process "G" (for "glycolysis"). "O" uses oxygen, and harnesses the cell's mitochondria to produce energy. "G" does not use oxygen and produces energy without directly using mitochondria.

Healthy cells use both “O” and “G”, but cancer cells are often observed to rely on "G" much more. The shift away from "O+G" towards just "G" in cancer is often called the "Warburg effect", after Otto Warburg, who wrote about the shift in the 1950s. It remains unclear, however, whether the Warburg effect applies to all cancer cells under all conditions, or if different cells and different environments experience different shifts. This is important because understanding how cancer cells get their energy -- and, more generally, what changes occur in cancer cells compared to healthy cells -- may allow us to design therapies that challenge cancer cells while leaving healthy cells undamaged.

We used some fancy modern technology (focussed around the MitoXpress-Intra probe) to measure the difference between oxygen levels within a cell and oxygen levels in the cell's environment. We developed a mathematical way of producing "calibration curves", directly linking the observed MitoXpress behaviour to oxygen concentrations. If cells are using "G" alone, these levels are similar, as no oxygen is being consumed by the cells. If cells are also using "O", oxygen levels within cells should be rather lower than in their environment.

We found that two different cancer cell lines (with the rather jargon-y names "RD" and "U87MG") behaved surprisingly differently. When grown on glucose, U87MG looks quite "G", with oxygen levels within cells similar to those in the environment (e.g. 17.1% in cells, 18% outside). RD looks much more "O+G", with substantial differences between in-cell and outside-cell oxygen levels (e.g 13.2% in cells, 18% outside). Importantly, these findings were reproduced across a range of environmental oxygen levels (18% to 5%), modelling the range of conditions that cancer cells experience in tumours in the body. The two cancer cell lines thus seem to produce their energy in rather different ways, underlining that the Warburg effect is not an invariant across all cancers, and that treatments may be improved by taking this into account. We also showed that treating a different cancer cell line ("786-0") with phenformin, a drug inhibiting mitochondria, shifts cells away from "O+G" to "G", and that this shift can be monitored in real time with MitoXpress.

Different cancer cell lines (U87MG and RD) produce energy through different pathways, engaging more “G” (glycolysis) or “O” (oxidative phosphorylation). “O” uses oxygen (O2), lowering oxygen levels in cells compared to their environment. The different balance of “G” and “O” in different cases is important for understanding the heterogeneity of cancer.

Our paper appears in a book with the catchy title "Oxygen Transport to Tissue XXXVII", associated with the journal Advances in Experimental Medicine and Biology. You can get a sneak peek here and we'll update with a link when possible. Iain

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

Stochastic modelling, Bayesian inference, and new in vivo measurements elucidate the debated mtDNA bottleneck mechanism

• Disease-causing mutant mtDNA is inherited through a complicated process: we use maths and statistics to shed light on this process and suggest possible therapeutic strategies to address disease inheritance and onset

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, lead 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 here. Iain and Nick [blog article also here]

ARTICLE: Great technological power, great statistical responsibility

Multiple hypothesis correction is vital and undermines reported mtDNA links to diseases including AIDS, cancer, and Huntingdon’s

• Several papers perform incorrect and misleading statistical analyses in seeking links between mtDNA and cancer: these statistical issues must be corrected before scientific and policy progress can be made from these investigations

Biologists often report a result as a "significant" sign of exciting new science if there is less than a 1-in-20 chance that the result they observe could have emerged by chance from boring old science. This is silly (although we do it too!) -- by contrast, for example, physicists require less than a 1-in-3,500,000 chance. But this post won't discuss too many problems with this state of affairs -- that is done admirably elsewhere.

The problem can be compounded when scientists take lots of measurements. Say we take 50 measurements of a boring old system, and every time we see something that has less than a 1-in-20 chance of appearing in a boring old system, we call it "significant". We're playing the odds 50 times, so we expect to see 1-in-20 results appear around 2 or 3 times; just as if we roll a dice 50 times, we'd expect to roll a good few sixes. If we call every 1-in-20 result "significant" without accounting for the fact that we've looked at lots of measurements (and are thus more likely to see 1-in-20s by chance), we are in danger of reporting exciting new science when in fact the boring old science has been true all along.

There are lots of ways of doing this accounting, but a series of papers that have been recently published linking mtDNA to diseases have made no attempt to do it. Generally, these papers look at the mtDNA of people without the disease and the mtDNA of people with the disease. If any mtDNA features appear more in the people with the disease, the paper calculates the chance of that difference occurring in the boring old picture (in which there is no link between the mtDNA feature and the disease). If they drop below the 1-in-20 mark, they report an exciting new link between that feature and the disease. But they test dozens of features and never account for this multiple testing -- so, as above, we'd expect them to see "significant" results emerging just by chance. In a paper in Mitochondrial DNA here (free here) I show, by creating artificial data, that this problem is rife, that most of these reported links are spurious, and that scientists really need to be more responsible, before their flawed analysis starts to misguide health policy and medicine.

The top graph shows how the probability of seeing a 1-in-20 occurrence (p < 0.05 in the jargon), when in fact there is nothing new and exciting to report, increases as a scientist investigates more things. If an experiment consists of one test, then a 1-in-20 occurrence indeed has a 1-in-20 probability (0.05). But as soon as we do more tests, the chance of seeing at least one 1-in-20 occurrence starts to increase, as we are "playing the game" more times. If we do 6 tests there is a 0.27 probability -- between a 1-in-4 and 1-in-3 chance -- that we will see at least one 1-in-20 event. This is illustrated below, where we have six dice and think some of them may be unfair. We roll each one five times and count the number of 6s. One of them comes up 6 three times -- the chance of this happening for one fair die is less than 1-in-20. But because we've looked at six dice, we should be less surprised to see this rare event, because we've looked at more events in total. We need more evidence to claim that this die is unfair.

This quick note only represents the tip of the iceberg. MtDNA studies are often statistically unsound; statistical misdemeanours in biomedical studies are so common that most published research is wrong; scientists increasingly focus on the 1-in-20 chance as opposed to the size and importance of the effect they're measuring; the majority of hallmark papers in vital fields like cancer science are unreproducible (though this last point may have other causes than statistical problems). The 1-in-20 idea was only ever meant to be a step in identifying interesting scientific avenues, not the final measure of scientific truth. This is a big, and growing, problem! Iain

(For accessibility I have used "exciting", "boring", and "1-in-20" instead of their usual, more technical labels; they of course are usually called the "alternative hypothesis", "null hypothesis", and "p < 0.05" respectively).

ARTICLE: Therapies for mtDNA disease: models and implications

Mitochondrial DNA disease and developmental implications for reproductive strategies

• The inheritance of mutant mtDNA can cause devastating diseases: we review how this inheritance occurs and the ways modern medicine can help (and how some therapies may be improved)

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 (free here) 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 [blog article also here]

ARTICLE: Evolutionary competition within our cells: the maths of mitochondrial DNA

mtDNA Segregation in Heteroplasmic Tissues Is Common In Vivo and Modulated by Haplotype Differences and Developmental Stage

• MtDNA mixtures in cells arise through mutation and gene therapies: we show that different types of mtDNA usually proliferate at different rates, which suggests ways that therapies could be made more efficient.

Women may carry mutated copies of mitochondrial DNA (mtDNA) -- a molecule that describes how to build important cellular machinery relating to cellular energy supply. If this mutant mtDNA is passed on to that woman's child, the child may develop a mitochondrial disease, which are often degenerative, fatal, and incurable.

Joerg created mice that contained two types of mtDNA -- here illustrated as blue (lab mouse mtDNA) and yellow (mtDNA from a mouse from a wild population). We used several different wild mice from across Europe to represent the mtDNA diversity one may find in a human population. We found that throughout a mouse's lifetime, one mtDNA type often outcompetes another (here, yellow beats blue), with different patterns across different tissues.

Amazing new therapies potentially allow a carrier mother A and a father B to use another woman C's egg cells to conceive a baby without much of mother A's mtDNA being present. The approach involves taking nuclear DNA content from A and B (so that most of the child's features are inherited from the true mother and father), and placing it into C's egg cells, which contain a background of healthy mtDNA. You can read about, what are misleadingly called, three-parent babies here.

Something that is less discussed is that, in this process, a small amount of A's mutant mtDNA can be "carried over" into C's cell. If this small amount remains small through the child's life, there is no danger of disease, as the larger amount of healthy C mtDNA will allow the child's cell to function normally. We can think of the resulting situation as a competition between A and C -- if A and C are evenly matched, the small amount of A will remain small; if C beats A, the small amount of A will disappear with time; and if A beats C, the small amount of A will increase and may eventually come to dominate over C.

Until recently it has been fair to assume that A and C are always about evenly matched (unless something is drastically different between A or C). However, evidence for this idea was based on model organisms in laboratories, which do not have the same amount of genetic diversity as found in human populations. Our collaborator Joerg addressed this by capturing wild mice from across central Europe, selecting a set that showed a comparable degree of genetic diversity to that expected in a human population. He used these, with our modelling and mathematical analysis, to show that pronounced differences between A and C often exist, and are more likely in more diverse populations. The possibility that A beats C, and mutant mtDNA comes to dominate the child's cells, therefore cannot be immediately discounted in a diverse population. We propose "haplotype matching" -- ensuring that A and C are as similar as possible -- to ameliorate this potential risk. It's open as to whether one can generalize from observations in mice to people and it's also open as to whether our conclusions, which used lab-mice as parent A (which are not entirely typical creatures) of necessity generalize to other non-lab mouse types.

Our mathematical approach also allowed us to explore, in detail, the dynamics by which this competition within cells occurs. We were able to use our data rather effectively by having a statistical model that allowed us to reason jointly about a range of data sets. We found that the degree to which one population of mtDNA beat the other depended on how genetically different they were.  We found that different tissues were like different environments: some favouring C over A and some vice-versa. This is perhaps surprising to some as this evolution in the proportions of different genetic species is not something we imagine occurring inside us, during our lives, and as something that might differ between our organs. We found several different regimes, where the strength of competition changes with time and as the organism develops: when our cells are multiplying faster they show a more marked preference for one of the species. We've shown our results to the UK HFEA in its ongoing assessment of these therapies, and you can read, for free, about our work in the journal Cell Reports here. Iain, Joerg, Nick [blog article also here].