50 results found
Ziegler C, Dyson RJ, Johnston IG, 2019, Model selection and parameter estimation for root architecture models using likelihood-free inference., J R Soc Interface, Vol: 16
Plant root systems play vital roles in the biosphere, environment and agriculture, but the quantitative principles governing their growth and architecture remain poorly understood. The 'forward problem' of what root forms can arise from given models and parameters has been well studied through modelling and simulation, but comparatively little attention has been given to the 'inverse problem': what models and parameters are responsible for producing an experimentally observed root system? Here, we propose the use of approximate Bayesian computation (ABC) to infer mechanistic parameters governing root growth and architecture, allowing us to learn and quantify uncertainty in parameters and model structures using observed root architectures. We demonstrate the use of this platform on synthetic and experimental root data and show how it may be used to identify growth mechanisms and characterize growth parameters in different mutants. Our highly adaptable framework can be used to gain mechanistic insight into the generation of observed root system architectures.
Johnston I, Hoffmann T, Greenbury S, et al., 2019, Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data, npj Digital Medicine, Vol: 2, ISSN: 2398-6352
More than 400,000 deaths from severe malaria (SM) are reported every year, mainly in African children. The diversity of clinical presentations associated with SM indicates important differences in disease pathogenesis that require specific treatment, and this clinical heterogeneity of SM remains poorly understood. Here, we apply tools from machine learning and model-based inference to harness large-scale data and dissect the heterogeneity in patterns of clinical features associated with SM in 2904 Gambian children admitted to hospital with malaria. This quantitative analysis reveals features predicting the severity of individual patient outcomes, and the dynamic pathways of SM progression, notably inferred without requiring longitudinal observations. Bayesian inference of these pathways allows us assign quantitative mortality risks to individual patients. By independently surveying expert practitioners, we show that this data-driven approach agrees with and expands the current state of knowledge on malaria progression, while simultaneously providing a data-supported framework for predicting clinical risk.
Aryaman J, Bowles C, Jones NS, et al., 2019, Mitochondrial Network State Scales mtDNA Genetic Dynamics., Genetics
Mitochondrial DNA (mtDNA) mutations cause severe congenital diseases but may also be associated with healthy aging. MtDNA is stochastically replicated and degraded, and exists within organelles which undergo dynamic fusion and fission. The role of the resulting mitochondrial networks in the time evolution of the cellular proportion of mutated mtDNA molecules (heteroplasmy), and cell-to-cell variability in heteroplasmy (heteroplasmy variance), remains incompletely understood. Heteroplasmy variance is particularly important since it modulates the number of pathological cells in a tissue. Here, we provide the first wide-reaching theoretical framework which bridges mitochondrial network and genetic states. We show that, under a range of conditions, the (genetic) rate of increase in heteroplasmy variance and de novo mutation are proportionally modulated by the (physical) fraction of unfused mitochondria, independently of the absolute fission-fusion rate. In the context of selective fusion, we show that intermediate fusion/fission ratios are optimal for the clearance of mtDNA mutants. Our findings imply that modulating network state, mitophagy rate and copy number to slow down heteroplasmy dynamics when mean heteroplasmy is low could have therapeutic advantages for mitochondrial disease and healthy aging.
Burgstaller J, Kolbe T, Havlicek V, et al., 2019, Large-scale genetic analysis reveals mammalian mtDNA heteroplasmy dynamics and variance increase through lifetimes and generations, Nature Communications, Vol: 9, ISSN: 2041-1723
Vital mitochondrial DNA (mtDNA) populations exist in cells and may consist of heteroplasmic mixtures of mtDNA types. The evolution of these heteroplasmic populations through development, ageing, and generations is central to genetic diseases, but is poorly understood in mammals. Here we dissect these population dynamics using a dataset of unprecedented size and temporal span, comprising 1947 single-cell oocyte and 899 somatic measurements of heteroplasmy change throughout lifetimes and generations in two genetically distinct mouse models. We provide a novel and detailed quantitative characterisation of the linear increase in heteroplasmy variance throughout mammalian life courses in oocytes and pups. We find that differences in mean heteroplasmy are induced between generations, and the heteroplasmy of germline and somatic precursors diverge early in development, with a haplotype-specific direction of segregation. We develop stochastic theory predicting the implications of these dynamics for ageing and disease manifestation and discuss its application to human mtDNA dynamics.
Hoitzing H, Gammage PA, Haute LV, et al., 2019, Energetic costs of cellular and therapeutic control of stochastic mitochondrial DNA populations, PLoS Computational Biology, Vol: 15, ISSN: 1553-734X
The dynamics of the cellular proportion of mutant mtDNA molecules is crucial for mitochondrial diseases. Cellular populations of mitochondria are under homeostatic control, but the details of the control mechanisms involved remain elusive. Here, we use stochastic modelling to derive general results for the impact of cellular control on mtDNA populations, the cost to the cell of different mtDNA states, and the optimisation of therapeutic control of mtDNA populations. This formalism yields a wealth of biological results, including that an increasing mtDNA variance can increase the energetic cost of maintaining a tissue, that intermediate levels of heteroplasmy can be more detrimental than homoplasmy even for a dysfunctional mutant, that heteroplasmy distribution (not mean alone) is crucial for the success of gene therapies, and that long-term rather than short intense gene therapies are more likely to beneficially impact mtDNA populations.
Johnston IG, 2019, Tension and Resolution: Dynamic, Evolving Populations of Organelle Genomes within Plant Cells, MOLECULAR PLANT, Vol: 12, Pages: 764-783, ISSN: 1674-2052
Aryaman J, Johnston I, Jones N, 2019, Mitochondrial heterogeneity, Frontiers in Genetics, Vol: 9, ISSN: 1664-8021
Cell-to-cell heterogeneity drives a range of (patho)physiologically important phenomena, such as cell fate and chemotherapeutic resistance. The role of metabolism, and particularly of mitochondria, is increasingly being recognized as an important explanatory factor in cell-to-cell heterogeneity. Most eukaryotic cells possess a population of mitochondria, in the sense that mitochondrial DNA (mtDNA) is held in multiple copies per cell, where the sequence of each molecule can vary. Hence, intra-cellular mitochondrial heterogeneity is possible, which can induce inter-cellular mitochondrial heterogeneity, and may drive aspects of cellular noise. In this review, we discuss sources of mitochondrial heterogeneity (variations between mitochondria in the same cell, and mitochondrial variations between supposedly identical cells) from both genetic and non-genetic perspectives, and mitochondrial genotype-phenotype links. We discuss the apparent homeostasis of mtDNA copy number, the observation of pervasive intra-cellular mtDNA mutation (which is termed “microheteroplasmy”), and developments in the understanding of inter-cellular mtDNA mutation (“macroheteroplasmy”). We point to the relationship between mitochondrial supercomplexes, cristal structure, pH, and cardiolipin as a potential amplifier of the mitochondrial genotype-phenotype link. We also discuss mitochondrial membrane potential and networks as sources of mitochondrial heterogeneity, and their influence upon the mitochondrial genome. Finally, we revisit the idea of mitochondrial complementation as a means of dampening mitochondrial genotype-phenotype links in light of recent experimental developments. The diverse sources of mitochondrial heterogeneity, as well as their increasingly recognized role in contributing to cellular heterogeneity, highlights the need for future single-cell mitochondrial measurements in the context of cellular noise studies.
Jackson MDB, Duran-Nebreda S, Kierzkowski D, et al., 2019, Global Topological Order Emerges through Local Mechanical Control of Cell Divisions in the Arabidopsis Shoot Apical Meristem, CELL SYSTEMS, Vol: 8, Pages: 53-+, ISSN: 2405-4712
Johnston IG, Bassel GW, 2018, Identification of a bet-hedging network motif generating noise in hormone concentrations and germination propensity in Arabidopsis, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 15, ISSN: 1742-5689
Aryaman J, Johnston IG, Jones NS, 2017, Mitochondrial DNA Density Homeostasis Accounts for a Threshold Effect in a Cybrid Model of a Human Mitochondrial Disease, Biochemical Journal, Vol: 474, Pages: 4019-4034, ISSN: 1470-8728
Mitochondrial dysfunction is involved in a wide array of devastating diseases, but the heterogeneity and complexity of the symptoms of these diseases challenges theoretical understanding of their causation. With the explosion of omics data, we have the unprecedented opportunity to gain deep understanding of the biochemical mechanisms of mitochondrial dysfunction. This goal raises the outstanding need to make these complex datasets interpretable. Quantitative modelling allows us to translate such datasets into intuition and suggest rational biomedical treatments. Taking an interdisciplinary approach, we use a recently published large-scale dataset and develop a descriptive and predictive mathematical model of progressive increase in mutant load of the MELAS 3243A>G mtDNA mutation. The experimentally observed behaviour is surprisingly rich, but we find that our simple, biophysically motivated model intuitively accounts for this heterogeneity and yields a wealth of biological predictions. Our findings suggest that cells attempt to maintain wild-type mtDNA density through cell volume reduction, and thus power demand reduction, until a minimum cell volume is reached. Thereafter, cells toggle from demand reduction to supply increase, up-regulating energy production pathways. Our analysis provides further evidence for the physiological significance of mtDNA density and emphasizes the need for performing single-cell volume measurements jointly with mtDNA quantification. We propose novel experiments to verify the hypotheses made here to further develop our understanding of the threshold effect and connect with rational choices for mtDNA disease therapies.
Topham AT, Taylor RE, Yan D, et al., 2017, Temperature variability is integrated by a spatially embedded decision-making center to break dormancy in Arabidopsis seeds, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 114, Pages: 6629-6634, ISSN: 0027-8424
Aryaman J, hoitzing H, burgstaller J, et al., 2017, Mitochondrial heterogeneity, metabolic scaling and cell death, Bioessays, Vol: 39, ISSN: 1521-1878
Heterogeneity in mitochondrial content has been previously suggested as a major contributor to cellular noise, with multiple studies indicating its direct involvement in biomedically important cellular phenomena. A recently published dataset explored the connection between mitochondrial functionality and cell physiology, where a non-linearity between mitochondrial functionality and cell size was found. Using mathematical models, we suggest that a combination of metabolic scaling and a simple model of cell death may account for these observations. However, our findings also suggest the existence of alternative competing hypotheses, such as a non-linearity between cell death and cell size. While we find that the proposed non-linear coupling between mitochondrial functionality and cell size provides a compelling alternative to previous attempts to link mitochondrial heterogeneity and cell physiology, we emphasise the need to account for alternative causal variables, including cell cycle, size, mitochondrial density and death, in future studies of mitochondrial physiology.
Colijn C, Jones N, Johnston I, et al., 2017, Towards precision healthcare: context and mathematical challenges, Frontiers in Physiology, Vol: 8, ISSN: 1664-042X
Precision medicine refers to the idea of delivering the right treatment to the right patient at the right time, usually with a focus on a data-centred approach to this task. In this perspective piece, we use the term "precision healthcare" to describe the development of precision approaches that bridge from the individual to the population, taking advantage of individual-level data, but also taking the social context into account. These problems give rise to a broad spectrum of technical, scientific, policy, ethical and social challenges, and new mathematical techniques will be required to meet them. To ensure that the science underpin-ning "precision" is robust, interpretable and well-suited to meet the policy, ethical and social questions that such approaches raise, the mathematical methods for data analysis should be transparent, robust and able to adapt to errors and uncertainties. In particular, precision methodologies should capture the complexity of data, yet produce tractable descriptions at the relevant resolution while preserving intelligibility and traceability, so that they can be used by practitioners to aid decision-making. Through several case studies in this domain of precision healthcare, we argue that this vision requires the development of new mathematical frameworks, both in modelling and in data analysis and interpretation.
Mitchell J, Johnston IG, Bassel GW, 2017, Variability in seeds: biological, ecological, and agricultural implications, JOURNAL OF EXPERIMENTAL BOTANY, Vol: 68, Pages: 809-817, ISSN: 0022-0957
Hoitzing H, Johnston IG, Jones NS, 2017, Stochastic models for evolving cellular populations of mitochondria: Disease, development, and ageing, Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology, Pages: 287-314, ISBN: 9783319626260
© Springer International Publishing AG 2017. Mitochondria are essential cellular organelles whose dysfunction is associated with ageing, cancer, mitochondrial diseases, and many other disorders. They contain their own genomes (mtDNA), of which thousands can be present in a single cell. These genomes are repeatedly replicated and degraded over time, and are prone to mutations. If the fraction of mutated genomes (heteroplasmy) exceeds a certain threshold, cellular defects can arise. The dynamics of mtDNAs over time and the accumulation of mutant genomes form a rich and vital stochastic process, the understanding of which provides important insights into disease progression. Numerous mathematical models have been constructed to provide a better understanding of how mitochondrial dysfunctions arise and, importantly, how clinical interventions can alleviate disease symptoms. For a given mean heteroplasmy, an increased variance-and thus a wider cell-to-cell heteroplasmy distribution-implies a higher probability of exceeding a given threshold value, meaning that stochastic models are essential to describe mtDNA disease. Mitochondria can undergo fusion and fission events with each other making the mitochondrial population a dynamic network that continuously changes its morphology, and allowing for the possibility of exchange of mtDNA molecules: coupled stochastic physical and genetic dynamics thus govern cellular mtDNA populations. Here, an overview is given of the kinds of stochastic mathematical models constructed describing mitochondria, their implications, and currently existing open problems.
Royrvik EC, Burgstaller JP, Johnston IG, 2016, mtDNA diversity in human populations highlights the merit of haplotype matching in gene therapies, MOLECULAR HUMAN REPRODUCTION, Vol: 22, Pages: 809-817
Johnston IG, Jones NS, 2016, Evolution of cell-to-cell variability in stochastic, controlled, heteroplasmic mtDNA populations, American Journal of Human Genetics, Vol: 99, Pages: 1150-1162, ISSN: 1537-6605
Populations of physiologically vital mitochondrial DNA (mtDNA) molecules evolve in cells under control from the nucleus. The evolution of populations of mixed mtDNA types is complicated and poorly understood, and variability of these controlled admixtures plays a central role in the inheritance and onset of genetic disease. Here, we develop a mathematical theory describing the evolution of, and variability in, these stochastic populations for any type of cellular control, showing that cell-to-cell variability in mtDNA and mutant load inevitably increases with time, according to rates that we derive and which are notably independent of the mechanistic details of feedback signaling. We show with a set of experimental case studies that this theory explains disparate quantitative results from classical and modern experimental and computational research on heteroplasmy variance in different species. We demonstrate that our general model provides a host of specific insights, including a modification of the often-used but hard-to-interpret Wright formula to correspond directly to biological observables, the ability to quantify selective and mutational pressure in mtDNA populations, and characterization of the pronounced variability inevitably arising from the action of possible mtDNA quality-control mechanisms. Our general theoretical framework, supported by existing experimental results, thus helps us to understand and predict the evolution of stochastic mtDNA populations in cell biology.
Larson HJ, de Figueiredo A, Xiahong Z, et al., 2016, The state of vaccine confidence 2016: global insights through a 67-country survey, EBioMedicine, Vol: 12, Pages: 295-301, ISSN: 2352-3964
BackgroundPublic trust in immunization is an increasingly important global health issue. Losses in confidence in vaccines and immunization programmes can lead to vaccine reluctance and refusal, risking disease outbreaks and challenging immunization goals in high- and low-income settings. National and international immunization stakeholders have called for better monitoring of vaccine confidence to identify emerging concerns before they evolve into vaccine confidence crises.MethodsWe perform a large-scale, data-driven study on worldwide attitudes to immunizations. This survey – which we believe represents the largest survey on confidence in immunization to date – examines perceptions of vaccine importance, safety, effectiveness, and religious compatibility among 65,819 individuals across 67 countries. Hierarchical models are employed to probe relationships between individual- and country-level socio-economic factors and vaccine attitudes obtained through the four-question, Likert-scale survey.FindingsOverall sentiment towards vaccinations is positive across all 67 countries, however there is wide variability between countries and across world regions. Vaccine-safety related sentiment is particularly negative in the European region, which has seven of the ten least confident countries, with 41% of respondents in France and 36% of respondents in Bosnia & Herzegovina reporting that they disagree that vaccines are safe (compared to a global average of 13%). The oldest age group (65 +) and Roman Catholics (amongst all faiths surveyed) are associated with positive views on vaccine sentiment, while the Western Pacific region reported the highest level of religious incompatibility with vaccines. Countries with high levels of schooling and good access to health services are associated with lower rates of positive sentiment, pointing to an emerging inverse relationship between vaccine sentiments and socio-economic status.ConclusionsRegular monitoring of vaccine
de Figueiredo A, Johnston IG, Smith DM, et al., 2016, Forecasted trends in vaccination coverage and correlations with socioeconomic factors: a global time-series analysis over 30 years., Lancet Global Health, Vol: 4, Pages: e726-e735, ISSN: 2214-109X
BACKGROUND: Incomplete immunisation coverage causes preventable illness and death in both developing and developed countries. Identification of factors that might modulate coverage could inform effective immunisation programmes and policies. We constructed a performance indicator that could quantitatively approximate measures of the susceptibility of immunisation programmes to coverage losses, with an aim to identify correlations between trends in vaccine coverage and socioeconomic factors. METHODS: We undertook a data-driven time-series analysis to examine trends in coverage of diphtheria, tetanus, and pertussis (DTP) vaccination across 190 countries over the past 30 years. We grouped countries into six world regions according to WHO classifications. We used Gaussian process regression to forecast future coverage rates and provide a vaccine performance index: a summary measure of the strength of immunisation coverage in a country. FINDINGS: Overall vaccine coverage increased in all six world regions between 1980 and 2010, with variation in volatility and trends. Our vaccine performance index identified that 53 countries had more than a 50% chance of missing the Global Vaccine Action Plan (GVAP) target of 90% worldwide coverage with three doses of DTP (DTP3) by 2015. These countries were mostly in sub-Saharan Africa and south Asia, but Austria and Ukraine also featured. Factors associated with DTP3 immunisation coverage varied by world region: personal income (Spearman's ρ=0·66, p=0·0011) and government health spending (0·66, p<0·0001) were informative of immunisation coverage in the Eastern Mediterranean between 1980 and 2010, whereas primary school completion was informative of coverage in Africa (0·56, p<0·0001) over the same period. The proportion of births attended by skilled health staff correlated significantly with immunisation coverage across many world regions. INTERPRETATION: Our vaccine performance inde
Diot A, Dombi E, Lodge T, et al., 2016, Modulating mitochondrial quality in disease transmission: towards enabling mitochondrial DNA disease carriers to have healthy children, BIOCHEMICAL SOCIETY TRANSACTIONS, Vol: 44, Pages: 1091-1100, ISSN: 0300-5127
de Figueiredo A, Johnston IG, Smith DMD, et al., 2016, Forecasting time-series trends in vaccination coverage and their links with socio-economic factors: A global analysis over 30 years, Lancet Global Health, ISSN: 2214-109X
Background Incomplete immunisation coverage causes preventable illness and death in both the developing anddeveloped world. Identifying factors that may modulate coverage can inform effective immunisation programmes andpolicies.Methods We perform a data-driven analysis of unprecedented scale, examining time-varying trends in Diphtheriatetanus-pertussiscoverage across 190 countries over the past three decades. Gaussian process regression is employedto forecast future coverage rates and provide a Vaccine Performance Index: a summary measure of the strength ofimmunisation coverage in a country.Findings Overall vaccine coverage has increased in all five world regions between 1980 and 2010, with markedvariation in volatility and trends. Our Vaccine Performance Index identifies 53 countries with a less than 50% chanceof missing the Global Vaccine Action Plan (GVAP) target of 90% worldwide DTP3 coverage by 2015, in agreementwith recent immunisation data. These countries are mostly sub-Saharan and South Asian, but Austria and Ukraine inEurope also feature. Factors associated with DTP3 immunisation coverage vary by world-region: personal income(! = 0.66, ' < 0.001) and government health spending (! = 0.66, ' < 0.01) are particularly informative in theEastern Mediterranean between 1980 and 2010, whilst primary school completion is informative in Africa (! =0.56, ' < 0.001) over the same time. The fraction of births attended by skilled health staff is significantly informativeacross many world regionsInterpretation A Vaccine Performance Index can highlight countries at risk identifying the strength and resilience ofimmunisation programmes. Weakening correlations with socio-economic factors indicate a need to tackle vaccineconfidence whereas strengthening correlations points to clear factors to address.
Johnston I, Williams B, 2016, The Shrinking Mitochondrion, SCIENTIST, Vol: 30, Pages: 22-23, ISSN: 0890-3670
Johnston IG, Williams BP, 2016, Evolutionary Inference across Eukaryotes Identifies Specific Pressures Favoring Mitochondrial Gene Retention, CELL SYSTEMS, Vol: 2, Pages: 101-111, ISSN: 2405-4712
Potter M, Badder L, Hoade Y, et al., 2016, Monitoring Intracellular Oxygen Concentration: Implications for Hypoxia Studies and Real-Time Oxygen Monitoring, 42nd Annual Meeting of International-Society-on-Oxygen-Transport-to-Tissue, Publisher: SPRINGER, Pages: 257-263, ISSN: 0065-2598
The metabolic properties of cancer cells have been widely accepted as a hallmark of cancer for a number of years and have shown to be of critical importance in tumour development. It is generally accepted that tumour cells exhibit a more glycolytic phenotype than normal cells. In this study, we investigate the bioenergetic phenotype of two widely used cancer cell lines, RD and U87MG, by monitoring intracellular oxygen concentrations using phosphorescent Pt-porphyrin based intracellular probes. Our study demonstrates that cancer cell lines do not always exhibit an exclusively glycolytic phenotype. RD demonstrates a reliance on oxidative phosphorylation whilst U87MG display a more glycolytic phenotype. Using the intracellular oxygen sensing probe we generate an immediate readout of intracellular oxygen levels, with the glycolytic lines reflecting the oxygen concentration of the environment, and cells with an oxidative phenotype having significantly lower levels of intracellular oxygen. Inhibition of oxygen consumption in lines with high oxygen consumption increases intracellular oxygen levels towards environmental levels. We conclude that the use of intracellular oxygen probes provides a quantitative assessment of intracellular oxygen levels, allowing the manipulation of cellular bioenergetics to be studied in real time.
Diot A, Hinks-Roberts A, Lodge T, et al., 2015, 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, PHARMACOLOGICAL RESEARCH, Vol: 100, Pages: 24-35, ISSN: 1043-6618
Johnston IG, Jones NS, 2015, Closed-form stochastic solutions for non-equilibrium dynamics and inheritance of cellular components over many cell divisions, Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences, Vol: 471, ISSN: 1364-5021
Stochastic dynamics govern many important processes in cellular biology, and an underlying theoretical approach describing these dynamics is desirable to address a wealth of questions in biology and medicine. Mathematical tools exist for treating several important examples of these stochastic processes, most notably gene expression and random partitioning at single-cell divisions or after a steady state has been reached. Comparatively little work exists exploring different and specific ways that repeated cell divisions can lead to stochastic inheritance of unequilibrated cellular populations. Here we introduce a mathematical formalism to describe cellular agents that are subject to random creation, replication and/or degradation, and are inherited according to a range of random dynamics at cell divisions. We obtain closed-form generating functions describing systems at any time after any number of cell divisions for binomial partitioning and divisions provoking a deterministic or random, subtractive or additive change in copy number, and show that these solutions agree exactly with stochastic simulation. We apply this general formalism to several example problems involving the dynamics of mitochondrial DNA during development and organismal lifetimes.
Johnston IG, Burgstaller JP, Havlicek V, et al., 2015, Stochastic modelling, Bayesian inference, and new in vivo measurements elucidate the debated mtDNA bottleneck mechanism, eLife, Vol: 4, ISSN: 2050-084X
Dangerous damage to mitochondrial DNA (mtDNA) can be ameliorated during mammalian development through a highly debated mechanism called the mtDNA bottleneck. Uncertainty surrounding this process limits our ability to address inherited mtDNA diseases. We produce a new, physically motivated, generalisable theoretical model for mtDNA populations during development, allowing the first statistical comparison of proposed bottleneck mechanisms. Using approximate Bayesian computation and mouse data, we find most statistical support for a combination of binomial partitioning of mtDNAs at cell divisions and random mtDNA turnover, meaning that the debated exact magnitude of mtDNA copy number depletion is flexible. New experimental measurements from a wild-derived mtDNA pairing in mice confirm the theoretical predictions of this model. We analytically solve a mathematical description of this mechanism, computing probabilities of mtDNA disease onset, efficacy of clinical sampling strategies, and effects of potential dynamic interventions, thus developing a quantitative and experimentally-supported stochastic theory of the bottleneck.
Johnston IG, 2015, Multiple hypothesis correction is vital and undermines reported mtDNA links to diseases including AIDS, cancer, and Huntingdon's, Mitochondrial DNA, Vol: 27, Pages: 3423-3427, ISSN: 1940-1736
The ability to sequence mitochondrial genomes quickly and cheaply has led to an explosion in available mtDNA data. As a result, an expanding literature is exploring links between mtDNA features and susceptibility to, or prevalence of, a range of diseases. Unfortunately, this great technological power has not always been accompanied by great statistical responsibility. I will focus on one aspect of statistical analysis, multiple hypothesis correction, that is absolutely required, yet often absolutely ignored, for responsible interpretation of this literature. Many existing studies perform comparisons between incidences of a large number (N) of different mtDNA features and a given disease, reporting all those yielding p values under 0.05 as significant links. But when many comparisons are performed, it is highly likely that several p values under 0.05 will emerge, by chance, in the absence of any underlying link. A suitable correction (for example, Bonferroni correction, requiring p < 0.05/N) must, therefore, be employed to avoid reporting false positive results. The absence of such corrections means that there is good reason to believe that many links reported between mtDNA features and various diseases are false; a state of affairs that is profoundly negative both for fundamental biology and for public health. I will show that statistics matching those claimed to illustrate significant links can arise, with a high probability, when no such link exists, and that these claims should thus be discarded until results of suitable statistical reliability are provided. I also discuss some strategies for responsible analysis and interpretation of this literature.
Hoitzing H, Johnston IG, Jones NS, 2015, What is the function of mitochondrial networks? A theoretical assessment of hypotheses and proposal for future research, Bioessays, Vol: 37, Pages: 687-700, ISSN: 1521-1878
Mitochondria can change their shape from discrete isolated organelles to a large continuous reticulum. The cellular advantages underlying these fused networks are still incompletely understood. In this paper, we describe and compare hypotheses regarding the function of mitochondrial networks. We use mathematical and physical tools both to investigate existing hypotheses and to generate new ones, and we suggest experimental and modelling strategies. Among the novel insights we underline from this work are the possibilities that (i) selective mitophagy is not required for quality control because selective fusion is sufficient; (ii) increased connectivity may have non-linear effects on the diffusion rate of proteins; and (iii) fused networks can act to dampen biochemical fluctuations. We hope to convey to the reader that quantitative approaches can drive advances in the understanding of the physiological advantage of these morphological changes.
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