Some EEN science news from 2016!

Our paper on mitochondrial gene loss (paper; free preprint; blog article) was, excitingly, Science magazine's #1 favourite news story of 2016! The other stories in the top 10 are all fascinating too -- highly worth a read! http://www.sciencemag.org/news/2016/12/our-10-favorite-science-news-stories-2016

The Science news coverage of the paper is here
http://www.sciencemag.org/news/2016/02/why-do-our-cells-power-plants-have-their-own-dna
and the story appeared in the print journal too
http://science.sciencemag.org/content/351/6276/903.long 

We were also involved in some news coverage in Nature of some exciting bits of science and policy from outside the group:
Mitochondrial behaviour may dictate whether or not organisms evolve germlines: http://www.nature.com/news/why-humans-develop-sex-cells-as-embryos-but-corals-don-t-1.21218
Mitochondrial gene therapy approval in the UK: http://www.nature.com/news/uk-moves-closer-to-allowing-three-parent-babies-1.21067
Potential issues with mitochondrial gene therapies: http://www.nature.com/news/three-person-embryos-may-fail-to-vanquish-mutant-mitochondria-1.19948

Our work on mtDNA dynamics and human population diversity (paper; free preprint; blog article) was included in the latest policy document on UK implementation of mitochondrial gene therapies
http://www.hfea.gov.uk/10557.html

And our other bits of work -- particular our work on vaccine confidence (free paper; blog article) -- appeared in various national and international news outlets too, including Scientific American, New Scientist, Le Monde, Daily Mail, Daily Mirror, Fox News and others; for some appearances see http://mitomaths.blogspot.co.uk/2016/02/evolution-energetics-and-noise-in-press.html

ARTICLE: The maths of mitochondrial DNA

Evolution of Cell-to-Cell Variability in Stochastic, Controlled, Heteroplasmic mtDNA Populations

IG Johnston, NS Jones

The American Journal of Human Genetics 99 (5), 1150-1162 (2016)

• Vital populations of mtDNA are constantly evolving in our cells in response to random influences and control from the nucleus: we build a general mathematical theory describing this poorly-understood process and show that it predicts a wide range of existing experimental outcomes and gives us lots of new insights into biology and disease

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. Iain

ARTICLE: Random number seed

Variability in seeds: biological, ecological, and agricultural implications 

J Mitchell, IG Johnston, GW Bassel

Journal of Experimental Botany, erw397 (2016) 

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

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

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

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

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

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

ARTICLE: 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.