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  • Journal article
    Aryaman J, Bowles C, Jones NS, Johnston IGet 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.

  • Journal article
    Kuntz Nussio J, Thomas P, Stan GB, Barahona Met al., 2019,

    Bounding the stationary distributions of the chemical master equation via mathematical programming

    , Journal of Chemical Physics, Vol: 151, ISSN: 0021-9606

    The stochastic dynamics of biochemical networks are usually modelled with the chemical master equation (CME). The stationary distributions of CMEs are seldom solvable analytically, and numerical methods typically produce estimates with uncontrolled errors. Here, we introduce mathematical programming approaches that yield approximations of these distributions with computable error bounds which enable the verification of their accuracy. First, we use semidefinite programming to compute increasingly tighter upper and lower bounds on the moments of the stationary distributions for networks with rational propensities. Second, we use these moment bounds to formulate linear programs that yield convergent upper and lower bounds on the stationary distributions themselves, their marginals and stationary averages. The bounds obtained also provide a computational test for the uniqueness of the distribution. In the unique case, the bounds form an approximation of the stationary distribution with a computable bound on its error. In the non unique case, our approach yields converging approximations of the ergodic distributions. We illustrate our methodology through several biochemical examples taken from the literature: Schl¨ogl’s model for a chemical bifurcation, a two-dimensional toggle switch, a model for bursty gene expression, and a dimerisation model with multiple stationary distributions.

  • Journal article
    Johnston I, Hoffmann T, Greenbury S, Cominetti O, Jallow M, Kwiatkowski D, Barahona M, Jones N, Casals-Pascual Cet 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.

  • Journal article
    Maes A, Barahona M, Clopath C, 2019,

    Learning spatiotemporal signals using a recurrent spiking network that discretizes time

    , PLOS Computational Biology, Vol: 16, Pages: e1007606-e1007606

    <jats:title>Abstract</jats:title><jats:p>Learning to produce spatiotemporal sequences is a common task the brain has to solve. The same neural substrate may be used by the brain to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory biophysical neurons drives a read-out layer: the dynamics of the recurrent network is constrained to encode time while the read-out neurons encode space. Space is then linked with time through plastic synapses that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on a timescale that is behaviourally relevant. Learned sequences are robustly replayed during a regime of spontaneous activity.</jats:p><jats:sec><jats:title>Author summary</jats:title><jats:p>The brain has the ability to learn flexible behaviours on a wide range of time scales. Previous studies have successfully build spiking network models that learn a variety of computational tasks. However, often the learning involved is not local. Here, we investigate a model using biological-plausible plasticity rules for a specific computational task: spatiotemporal sequence learning. The architecture separates time and space into two different parts and this allows learning to bind space to time. Importantly, the time component is encoded into a recurrent network which exhibits sequential dynamics on a behavioural time scale. This network is then used as an engine to drive spatial read-out neurons. We demonstrate that the model can learn complicated spatiotemporal spiking dynamics, such as the song of a bird, and replay the song robustly.</jats:p></jats:sec>

  • Conference paper
    Chrysostomou S, Roy R, Prischi F, Chapman K, Mufti U, Mauri F, Bellezza G, Abrahams J, Ottaviani S, Castellano L, Giamas G, Hrouda D, Winkler M, Klug D, Yaliraki S, Barahona M, Wang Y, Ali M, Seckl M, Pardo Oet al., 2019,

    Abstract 1775: Targeting RSK4 prevents both chemoresistance and metastasis in lung cancer

    , AACR Annual Meeting on Bioinformatics, Convergence Science, and Systems Biology, Publisher: American Association for Cancer Research, Pages: 1-2, ISSN: 0008-5472

    Lung cancer is the commonest cause of cancer death worldwide with a five-year survival rate of less than five percent for metastatic tumors. Non-small cell lung cancer (NSCLC) accounts for 80% of lung cancer cases of which adenocarcinoma prevails. Patients almost invariably develop metastatic drug-resistant disease and this is responsible for our failure to provide curative therapy. Hence, a better understanding of the mechanisms underlying these biological processes is urgently required to improve clinical outcome.The 90-kDa ribosomal S6 kinases (RSKs) are downstream effectors of the RAS/MAPK cascade. RSKs are highly conserved serine/threonine protein kinases implicated in diverse cellular processes, including cell survival, proliferation, migration and invasion. Four isoforms exist in humans (RSK1-4) and are uniquely characterized by the presence of two non-identical N- and C-terminal kinase domains. RSK isoforms are 73-80% identical at protein level and this has been thought to suggest overlapping functions.However, through functional genomic kinome screens, we show that RSK4, contrary to RSK1, promotes both drug resistance and metastasis in lung cancer. This kinase is overexpressed in the majority (57%) of NSCLC biopsies and this correlates with poor overall survival in lung adenocarcinoma patients. Genetic silencing of RSK4 sensitizes lung cancer cells to chemotherapy and prevents their migration and invasiveness in vitro and in vivo. RSK4 downregulation decreases the anti-apoptotic proteins Bcl2 and cIAP1/2 which correlates with increased apoptotic signalling, whilst it also induces mesenchymal-epithelial transition (MET) through inhibition of NFκB activity. A small-molecule inhibitor screen identified several floxacins, including trovafloxacin, as potent allosteric inhibitors of RSK4 activation. Trovafloxacin reproduced all biological and molecular effects of RSK4 silencing in vitro and in vivo, and is predicted to bind a novel allosteric site revealed

  • Conference paper
    Prischi F, Chrysostomou S, Roy R, Chapman K, Mufti U, Peach R, Ding L, Mauri F, Bellezza G, Cagini L, Barbareschi M, Ferrero S, Abrahams J, Ottaviani S, Castellano L, Giamas G, Pascoe J, Moonamale D, Billingham L, Cullen M, Hrouda D, Winkler M, Klug D, Yaliraki S, Barahona M, Wang Y, Ali M, Seckl M, Pardo Oet al., 2019,

    Targeting RSK4 prevents both chemoresistance and metastasis in lung and bladder cancer

    , FEBS Open Bio, Publisher: WILEY, Pages: 330-330, ISSN: 2211-5463
  • Journal article
    Burgstaller J, Kolbe T, Havlicek V, Hembach S, Poulton J, Piálek J, Steinborn R, Rulicke T, Brem G, Jones NS, Johnston Iet 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.

  • Journal article
    Hoitzing H, Gammage PA, Haute LV, Minczuk M, Johnston IG, Jones NSet 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.

  • Journal article
    Schaub MT, Delvenne JC, Lambiotte R, Barahona Met al., 2019,

    Multiscale dynamical embeddings of complex networks

    , Physical Review E, Vol: 99, Pages: 062308-1-062308-18, ISSN: 1539-3755

    Complex systems and relational data are often abstracted as dynamical processes on networks. To understand, predict, and control their behavior, a crucial step is to extract reduced descriptions of such networks. Inspired by notions from control theory, we propose a time-dependent dynamical similarity measure between nodes, which quantifies the effect a node-input has on the network. This dynamical similarity induces an embedding that can be employed for several analysis tasks. Here we focus on (i) dimensionality reduction, i.e., projecting nodes onto a low-dimensional space that captures dynamic similarity at different timescales, and (ii) how to exploit our embeddings to uncover functional modules. We exemplify our ideas through case studies focusing on directed networks without strong connectivity and signed networks. We further highlight how certain ideas from community detection can be generalized and linked to control theory, by using the here developed dynamical perspective.

  • Journal article
    Warren L, Clarke J, Arora S, Barahona M, Arebi N, Darzi Aet al., 2019,

    Transitions of care across hospital settings in patients with inflammatory bowel disease

    , World Journal of Gastroenterology, Vol: 25, Pages: 2122-2132, ISSN: 1007-9327

    BACKGROUNDInflammatory bowel disease (IBD) is a chronic, inflammatory disorder characterised by both intestinal and extra-intestinal pathology. Patients may receive both emergency and elective care from several providers, often in different hospital settings. Poorly managed transitions of care between providers can lead to inefficiencies in care and patient safety issues. To ensure that the sharing of patient information between providers is appropriate, timely, accurate and secure, effective data-sharing infrastructure needs to be developed. To optimise inter-hospital data-sharing for IBD patients, we need to better understand patterns of hospital encounters in this group.AIMTo determine the type and location of hospital services accessed by IBD patients in England.METHODSThis was a retrospective observational study using Hospital Episode Statistics, a large administrative patient data set from the National Health Service in England. Adult patients with a diagnosis of IBD following admission to hospital were followed over a 2-year period to determine the proportion of care accessed at the same hospital providing their outpatient IBD care, defined as their ‘home provider’. Secondary outcome measures included the geographic distribution of patient-sharing, regional and age-related differences in accessing services, and type and frequency of outpatient encounters.RESULTSOf 95055 patients accessed hospital services on 1760156 occasions over a 2-year follow-up period. The proportion of these encounters with their identified IBD ‘home provider’ was 73.3%, 87.8% and 83.1% for accident and emergency, inpatient and outpatient encounters respectively. Patients living in metropolitan centres and younger patients were less likely to attend their ‘home provider’ for hospital services. The most commonly attended specialty services were gastroenterology, general surgery and ophthalmology.CONCLUSIONTransitions of care between secondary care sett

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