69 results found
Chustecki JM, Gibbs DJ, Bassel GW, et al., 2021, Network analysis of Arabidopsis mitochondrial dynamics reveals a resolved tradeoff between physical distribution and social connectivity, CELL SYSTEMS, Vol: 12, Pages: 419-+, ISSN: 2405-4712
Radzvilavicius A, Layh S, Hall MD, et al., 2021, Sexually antagonistic evolution of mitochondrial and nuclear linkage, JOURNAL OF EVOLUTIONARY BIOLOGY, Vol: 34, Pages: 757-766, ISSN: 1010-061X
Ziegler C, Kulawska A, Kourmouli A, et al., 2021, Quantification and uncertainty of root growth stimulation by elevated CO2 in mature temperate deciduous forest
<jats:title>Abstract</jats:title><jats:p>Increasing CO<jats:sub>2</jats:sub> levels are a major global challenge, and the extent to which increasing anthropogenic CO<jats:sub>2</jats:sub> emissions can be mitigated by natural carbon sinks remains poorly understood. The uptake of elevated CO<jats:sub>2</jats:sub> (eCO<jats:sub>2</jats:sub>) by the terrestrial biosphere, and subsequent sequestration as biomass in ecosystems, may act as a negative feedback in the carbon budget, but remains hard to quantify in natural ecosystems. Here, we combine large-scale field observations of fine root stocks and flows, derived from belowground imaging and soil cores, with image analysis, stochastic modelling, and statistical inference, to elucidate belowground root dynamics in a mature temperate deciduous forest under free-air CO<jats:sub>2</jats:sub> enrichment to 150ppm above ambient levels. Using over 67<jats:italic>k</jats:italic> frames of belowground observation, we observe that eCO<jats:sub>2</jats:sub> leads to relatively faster root production (a peak volume fold change of 4.52 ± 0.44 eCO<jats:sub>2</jats:sub> versus 2.58 ± 0.21 control). We identify an increase in existing root elongation relative to root mass decay as the likely causal mechanism for this acceleration. Direct physical analysis of biomass and width measurements from 552 root systems recovered from soil cores support this picture, with lengths and widths of fine roots significantly increasing under eCO<jats:sub>2</jats:sub>. We use dynamic measurements to estimate fine root contributions to net primary productivity, finding an increase under eCO<jats:sub>2</jats:sub>, with an estimated mean annual 204 ± 93 g dw m<jats:sup>−2</jats:sup>yr<jats:sup>−1</jats:sup> eCO<jats:sub>2</jats:sub> versus 140 ±
Edwards DM, Royrvik EC, Chustecki JM, et al., 2021, Avoiding organelle mutational meltdown across eukaryotes with or without a germline bottleneck, PLOS BIOLOGY, Vol: 19, ISSN: 1544-9173
Peach R, Greenbury S, Johnston I, et al., 2021, Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation, Scientific Reports, Vol: 11, ISSN: 2045-2322
The intrinsic temporality of learning demands the adoption of methodologies capable of exploiting time-series information. In this study we leverage the sequence data framework and show how data-driven analysis of temporal sequences of task completion in online courses can be used to characterise personal and group learners’ behaviors, and to identify critical tasks and course sessions in a given course design. We also introduce a recently developed probabilistic Bayesian model to learn sequential behaviours of students and predict student performance. The application of our data-driven sequence-based analyses to data from learners undertaking an on-line Business Management course reveals distinct behaviors within the cohort of learners, identifying learners or groups of learners that deviate from the nominal order expected in the course. Using course grades a posteriori, we explore differences in behavior between high and low performing learners. We find that high performing learners follow the progression between weekly sessions more regularly than low performing learners, yet within each weekly session high performing learners are less tied to the nominal task order. We then model the sequences of high and low performance students using the probablistic Bayesian model and show that we can learn engagement behaviors associated with performance. We also show that the data sequence framework can be used for task-centric analysis; we identify critical junctures and differences among types of tasks within the course design. We find that non-rote learning tasks, such as interactive tasks or discussion posts, are correlated with higher performance. We discuss the application of such analytical techniques as an aid to course design, intervention, and student supervision.
Radzvilavicius AL, Johnston IG, 2021, Organelle bottlenecks facilitate evolvability by traversing heteroplasmic fitness valleys
<jats:title>Abstract</jats:title><jats:p>Bioenergetic organelles – mitochondria and plastids – retain their own genomes, and these organelle DNA (oDNA) molecules are vital for eukaryotic life. Like all genomes, oDNA must be able to evolve to suit new environmental challenges. However, mixed oDNA populations can challenge cellular bioenergetics, providing a penalty to the appearance and adaptation of new mutations. Here we show that organelle ‘bottlenecks’, mechanisms increasing cell-to-cell oDNA variability during development, can overcome this mixture penalty and facilitate the adaptation of beneficial mutations. We show that oDNA heteroplasmy and bottlenecks naturally emerge in evolutionary simulations subjected to fluctuating environments, demonstrating that this evolvability is itself evolvable. Usually thought of as a mechanism to clear damaging mutations, organelle bottlenecks therefore also resolve the tension between intracellular selection for pure oDNA populations and the ‘bet-hedging’ need for evolvability and adaptation to new environments. This general theory suggests a reason for the maintenance of organelle heteroplasmy in cells, and may explain some of the observed diversity in organelle maintenance and inheritance across taxa.</jats:p>
Radzvilavicius A, Johnston IG, 2020, Paternal leakage of organelles can improve adaptation to changing environments
<jats:title>Abstract</jats:title><jats:p>Sexual eukaryotes have diverse mechanisms preventing the biparental inheritance of mitochondria and plastids, and reducing the coexistence of dissimilar organelle DNA (heteroplasmy). Nevertheless, paternal leakage often occurs in plants, fungi, protists and animals, and this leaves the possibility that heteroplasmy can in some contexts be advantageous. Theoretical models developed in the past revealed that maternal inheritance improves selection against deleterious mitochondrial mutations, but none of them have explained the observed variation in the extent of paternal leakage. Here we show that paternal leakage regulated by nuclear loci can evolve to maintain advantageous organelle diversity in fluctuating environments. Strict maternal inheritance reduces organelle variance within the cell, but this loss of diversity can be detrimental when environments are shifting rapidly. Our model reveals that high levels of paternal leakage can evolve in these types of rapidly changing environments and that strict maternal inheritance evolves only when the environment is changing slowly.</jats:p><jats:sec><jats:title>Data</jats:title><jats:p>Matlab/Octave implementation of the model is available at <jats:italic><jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/StochasticBiology/PaternalLeakageEvolution">Https://github.com/StochasticBiology/PaternalLeakageEvolution</jats:ext-link></jats:italic>.</jats:p></jats:sec>
Royrvik EC, Johnston IG, 2020, MtDNA sequence features associated with 'selfish genomes' predict tissue-specific segregation and reversion, NUCLEIC ACIDS RESEARCH, Vol: 48, Pages: 8290-8301, ISSN: 0305-1048
Lechuga-Vieco AV, Latorre-Pellicer A, Johnston IG, et al., 2020, Cell identity and nucleo-mitochondrial genetic context modulate OXPHOS performance and determine somatic heteroplasmy dynamics, Science Advances, Vol: 6, Pages: eaba5345-eaba5345, ISSN: 2375-2548
Heteroplasmy, multiple variants of mitochondrial DNA (mtDNA) in the same cytoplasm, may be naturally generated by mutations but is counteracted by a genetic mtDNA bottleneck during oocyte development. Engineered heteroplasmic mice with nonpathological mtDNA variants reveal a nonrandom tissue-specific mtDNA segregation pattern, with few tissues that do not show segregation. The driving force for this dynamic complex pattern has remained unexplained for decades, challenging our understanding of this fundamental biological problem and hindering clinical planning for inherited diseases. Here, we demonstrate that the nonrandom mtDNA segregation is an intracellular process based on organelle selection. This cell type–specific decision arises jointly from the impact of mtDNA haplotypes on the oxidative phosphorylation (OXPHOS) system and the cell metabolic requirements and is strongly sensitive to the nuclear context and to environmental cues.
Johnston IG, Royrvik EC, 2020, Data-Driven Inference Reveals Distinct and Conserved Dynamic Pathways of Tool Use Emergence across Animal Taxa, ISCIENCE, Vol: 23
Duran-Nebreda S, Johnston IG, Bassel GW, 2020, Efficient vasculature investment in tissues can be determined without global information, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 17, ISSN: 1742-5689
Athanasiou A-T, Nussbaumer T, Kummer S, et al., 2020, S100A4 mRNA-protein relationship uncovered by measurement noise reduction, JOURNAL OF MOLECULAR MEDICINE-JMM, Vol: 98, Pages: 735-749, ISSN: 0946-2716
Greenbury S, Barahona M, Johnston I, 2020, HyperTraPS: Inferring probabilistic patterns of trait acquisition in evolutionary and disease progression pathways, Cell Systems, Vol: 10, Pages: 39-51, ISSN: 2405-4712
The explosion of data throughout the biomedical sciences provides unprecedented opportunities to learn about the dynamics of evolution and disease progression, but harnessing these large and diverse datasets remains challenging. Here, we describe a highly generalisable statistical platform to infer the dynamic pathways by which many, potentially interacting, discrete traits are acquired or lost over time in biomedical systems. The platform uses HyperTraPS (hypercubic transition path sampling) to learn progression pathways from cross-sectional, longitudinal, or phylogenetically-linked data with unprecedented efficiency, readily distinguishing multiple competing pathways, and identifying the most parsimonious mechanisms underlying given observations. Its Bayesian structure quantifies uncertainty in pathway structure and allows interpretable predictions of behaviours, such as which symptom a patient will acquire next. We exploit the model’s topology to provide visualisation tools for intuitive assessment of multiple, variable pathways. We apply the method to ovarian cancer progression and the evolution of multidrug resistance in tuberculosis, demonstrating its power to reveal previously undetected dynamic pathways.
Kerr R, Jabbari S, Johnston IG, 2019, Intracellular Energy Variability Modulates Cellular Decision-Making Capacity, SCIENTIFIC REPORTS, Vol: 9, ISSN: 2045-2322
Latorre-Pellicer A, Lechuga-Vieco AV, Johnston IG, et al., 2019, Regulation of mother-to-offspring transmission of mtDNA heteroplasmy, Cell Metabolism, Vol: 30, Pages: 1120-1130.e5, ISSN: 1550-4131
mtDNA is present in multiple copies in each cell derived from the expansions of those in the oocyte. Heteroplasmy, more than one mtDNA variant, may be generated by mutagenesis, paternal mtDNA leakage, and novel medical technologies aiming to prevent inheritance of mtDNA-linked diseases. Heteroplasmy phenotypic impact remains poorly understood. Mouse studies led to contradictory models of random drift or haplotype selection for mother-to-offspring transmission of mtDNA heteroplasmy. Here, we show that mtDNA heteroplasmy affects embryo metabolism, cell fitness, and induced pluripotent stem cell (iPSC) generation. Thus, genetic and pharmacological interventions affecting oxidative phosphorylation (OXPHOS) modify competition among mtDNA haplotypes during oocyte development and/or at early embryonic stages. We show that heteroplasmy behavior can fall on a spectrum from random drift to strong selection, depending on mito-nuclear interactions and metabolic factors. Understanding heteroplasmy dynamics and its mechanisms provide novel knowledge of a fundamental biological process and enhance our ability to mitigate risks in clinical applications affecting mtDNA transmission.
Johnston IG, 2019, Varied Mechanisms and Models for the Varying Mitochondrial Bottleneck, FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY, Vol: 7, ISSN: 2296-634X
Johnston IG, Burgstaller JP, 2019, Evolving mtDNA populations within cells, BIOCHEMICAL SOCIETY TRANSACTIONS, Vol: 47, Pages: 1367-1382, ISSN: 0300-5127
Aryaman J, Bowles C, Jones NS, et al., 2019, Mitochondrial network state scales mtDNA genetic dynamics, Genetics, Vol: 212, Pages: 1429-1443, ISSN: 0016-6731
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.
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.
Ziegler C, Dyson RJ, Johnston IG, 2019, Model selection and parameter estimation for root architecture models using likelihood-free inference, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 16, ISSN: 1742-5689
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.
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