Biology subjects

Johnston, I.

Publications and source records attributed to Johnston, I..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Fine-grained modelling of ATP dependence of decision-making capacity in genetic regulatory networks

Cellular decision-making is fundamental to life, from developmental biology to environmental responses and antimicrobial resistance. Many regulatory processes that drive cellular decisions rely on gene expression, which requires energy in the form of ATP. As even genetically identical cells can have dramatically different ATP levels, bioenergetic status can be an important source of variability in cellular decision-making. Existing studies have investigated this energy dependence but often use coarse-grained modelling approaches (which are not always readily connected to the underlying molecular processes of gene regulation). Here we use a fine-grained mathematical model of gene expression in a two-gene decision-making regulatory network to explore cellular decision-making capacity as energy availability varies. We simulate both a deterministic model, to explore the emergence of different cell fate attractors as ATP levels vary, and a stochastic case to explore how ATP influences the noisy dynamics of stochastic cell decision-making. Higher energy levels typically support increased decision-making capacity (higher numbers of, and more separated, cell states that can be selected), and the fine-grained modelling reveals some differences in behaviour from previous coarse-grained modelling approaches.

systems biology↗

bioRxiv2023

Ecological predictors of organelle genome evolution: Phylogenetic correlations with taxonomically broad, sparse, unsystematized data

Comparative analysis of variables across phylogenetically linked observations can reveal mechanisms and insights in evolutionary biology. As the taxonomic breadth of the sample of interest increases, challenges of data sparsity, poor phylogenetic resolution, and complicated evolutionary dynamics emerge. Here, we investigate a cross-eukaryotic question where all these problems exist: which organismal ecology features are correlated with gene retention in mitochondrial and chloroplast DNA (organelle DNA or oDNA). Through a wide palette of synthetic control studies, we characterize the specificity and sensitivity of a collection of parametric and non-parametric phylogenetic comparative approaches to identify relationships in the face of such sparse and awkward datasets. We combine and curate ecological data coupled to oDNA genome information across eukaryotes, including a new semi-automated approach for gathering data on organismal traits from less systematized open-access resources including encyclopedia articles on species and taxa. Combining this unique dataset with our benchmarked comparative approaches, we confirm support for several known links between organismal ecology and organelle gene retention, identify several previously unidentified relationships constituting possible ecological contributors to oDNA genome evolution, and provide support for a recently hypothesized link between environmental demand and oDNA retention. We, with caution, discuss the implications of these findings for organelle evolution and of this pipeline for broad comparative analyses in other fields.

evolutionary biology↗

bioRxiv2023

Stromule geometry allows optimal spatial regulation of organelle interactions in the quasi-2D cytoplasm

In plant cells, plastids form elongated extensions called stromules, the regulation and purposes of which remain unclear. Here we quantitatively explore how different stromule structures serve to enhance the ability of a plastid to interact with other organelles: increasing the effective space for interaction and biomolecular exchange between organelles. Strikingly, electron microscopy and confocal imaging showed that the cytoplasm in Arabidopsis thaliana and Nicotiana benthamiana epidermal cells is extremely thin (around 100 nm in regions without organelles), meaning that inter-organelle interactions effectively take place in 2D. We combine these imaging modalities with mathematical modelling and new in planta experiments to demonstrate how different the elongation of stromules (single or multiple, linear or branching) could be employed to optimise different aspects of inter-organelle interaction capacity in this 2D space. Stromule formation and branching is shown to provide a proportionally higher benefit to interaction capacity in 2D than in 3D. Additionally we find this benefit depends on optimal plastid spacing. We hypothesize that cells can promote the formation of different stromule architectures in the quasi-2D cytoplasm to optimise their interaction interface to meet specific requirements. These results provide new insight into the mechanisms underlying the transition from low to high stromule numbers during biotic stress, the consequences for interaction with smaller organelles, how plastid access and plastid to nucleus signalling is balanced, as well as the impact of plastid density on organelle interaction.

plant biology↗

bioRxiv2022

Dynamics of organelle DNA segregation in Arabidopsis development and reproduction revealed with tissue-specific heteroplasmy profiling and stochastic modelling

Organelle DNA (oDNA) in mitochondria and plastids is vital for plant (and eukaryotic) life. Selection against damaged oDNA is mediated in part by segregation - the sorting of different oDNA types into different cells in the germline. Plants segregate oDNA very rapidly, with oDNA recombination protein MutS Homolog 1 (MSH1), a key driver of this segregation, but in contrast to mammals, we have very limited knowledge of the dynamics of this segregation within plants and between generations. Here, we combine stochastic modelling with tissue-specific heteroplasmy measurements to reveal the trajectories of oDNA segregation in Arabidopsis thaliana development and reproduction. We obtain and use new experimental observations of oDNA through development to confirm and refine the predictions of the theory inferred from existing measurements. Ongoing segregation proceeds gradually but continually during plant development, with a more rapid increase between inflorescence formation and the establishment of the next generation. When MSH1 is compromised, we show that the majority of observed segregation could be achieved through partitioning at cell divisions. When MSH1 is functional, mtDNA segregation is far more rapid than can be achieved through cell divisions; we show that increased oDNA gene conversion is a plausible mechanism quantitatively explaining this acceleration. We also discuss the support for different models of the plant germline provided by these observations.

plant biology↗

bioRxiv2022

Cellular and environmental dynamics influence species-specific extents of organelle gene retention

Mitochondria and plastids rely on many nuclear-encoded genes, but retain small subsets of the genes they need to function in their own organelle DNA (oDNA). Different species retain different numbers of oDNA genes, and the reasons for these differences are not completely understood. Here we use a mathematical model to explore the hypothesis that the energetic demands imposed by an organisms changing environment influence how many oDNA genes it retains. The model couples the physical biology of cell processes of gene expression and transport to a supply-and-demand model for the environmental dynamics to which an organism is exposed. The tradeoff between fulfilling metabolic and bioenergetic environmental demands, and retaining genetic integrity, is quantified for a generic gene encoded either in oDNA or in nuclear DNA. Species in environments with high-amplitude, intermediate-frequency oscillations are predicted to retain the most organelle genes, whereas those in less dynamic or noisy environments the fewest. We discuss support for, and insight from, these predictions with oDNA data across eukaryotic taxa, including high oDNA gene counts in sessile organisms exposed to day-night and intertidal oscillations (including plants and algae) and low counts in parasites and fungi.

genetics↗

bioRxiv2022

Avoiding misleading estimates using mtDNA heteroplasmy statistics to study bottleneck size and selection

Mitochondrial DNA (mtDNA) heteroplasmy samples can shed light on vital developmental and genetic processes shaping mtDNA populations. The sample mean and sample variance of a set of heteroplasmy observations are typically used both to estimate bottleneck sizes and to perform fits to the theoretical "Kimura" distribution in seeking evidence for mtDNA selection. However, each of these applications raises problems. Sample statistics do not generally provide optimal fits to the Kimura distribution and so can give misleading results in hypothesis testing, including false positive signals of selection. Using sample variance can give misleading results for bottleneck size estimates, particularly for small samples. These issues can and do lead to false positive results for mtDNA mechanisms - all published experimental datasets we re-analysed, reported as displaying departures from the Kimura model, do not in fact give evidence for such departures. Here we outline a maximum likelihood approach that is simple to implement computationally and addresses all of these issues. We advocate the use of maximum likelihood fits and explicit hypothesis tests, not fits and Kolmogorov-Smirnov tests via summary statistics, for ongoing work with mtDNA heteroplasmy.

genetics↗

bioRxiv2022

Mitochondrial network structure controls cell-to-cell mtDNA variability generated by cell divisions

Mitochondria are highly dynamic organelles, containing vital populations of mitochondrial DNA (mtDNA) distributed throughout the cell. Mitochondria form diverse physical structures in different cells, from cell-wide reticulated networks to fragmented individual organelles. These physical structures are known to influence the genetic makeup of mtDNA populations between cell divisions, but their influence on the inheritance of mtDNA at divisions remains less understood. Here, we use statistical and computational models of mtDNA content inside and outside the reticulated network to quantify how mitochondrial network structure can control the variances of inherited mtDNA copy number and mutant load. We assess the use of moment-based approximations to describe heteroplasmy variance and identify several cases where such an approach has shortcomings. We show that biased inclusion of one mtDNA type in the network can substantially increase heteroplasmy variance (acting as a genetic bottleneck), and controlled distribution of network mass and mtDNA through the cell can conversely reduce heteroplasmy variance below a binomial inheritance picture. Network structure also allows the generation of heteroplasmy variance while controlling copy number inheritance to sub-binomial levels, reconciling several observations from the experimental literature. Overall, different network structures and mtDNA arrangements within them can control the variances of key variables to suit a palette of different inheritance priorities.

genetics↗

bioRxiv2022

Data science approaches provide a roadmap to understanding the role of abscisic acid in defence

Abscisic acid is a plant hormone well known to regulate abiotic stress responses. ABA is also recognised for its role in biotic defence, but there is currently a lack of consensus on whether it plays a positive or negative role. Here, we used supervised machine learning to analyse experimental observations on ABA to identify the most influential factors determining disease phenotypes. ABA concentration, plant age and pathogen lifestyle were identified in our computational predictions. We explored these predictions with new experiments in tomato, demonstrating that phenotypes after ABA treatment were highly dependent on plant age and pathogen lifestyle. Integration of these new results into the statistical framework refined the quantitative model of ABA influence, suggesting a framework for proposing and exploiting further research to make more progress on this complex question. Our approach provides a unifying road map to guide future studies involving the role of ABA in defence.

plant biology↗

bioRxiv2022

Efficient inference of evolutionary and progressive dynamics on hypercubic transition graphs

The progression of cancer and other diseases, the evolution of organismal features in biology, and a wide range of broader questions can often be viewed as the sequential stochastic acquisition of binary traits (for example, genetic changes, symptoms, or characters). Using potentially noisy or incomplete data to learn the sequences by which such traits are acquired is a problem of general interest. The problem is complicated for large numbers of traits which may, individually or synergistically, influence the probability of further acquisitions both positively and negatively. Hypercubic inference approaches, based on hidden Markov models on a hypercubic transition network, address these complications, but previous Bayesian instances can consume substantial time for converged results, limiting their practical use. Here we introduce HyperHMM, an adapted Baum-Welch (expectation maximisation) algorithm for hypercubic inference with resampling to quantify uncertainty, and show that it allows orders-of-magnitude faster inference while making few practical sacrifices compared to existing approaches. We apply this approach to synthetic and biological datasets and discuss its more general application in learning evolutionary and progressive pathways.

bioinformatics↗

bioRxiv2021

Dynamic Boolean modelling reveals the influence of energy supply on bacterial efflux pump expression

Antimicrobial resistance (AMR) is a global health issue. One key factor contributing to AMR is the ability of bacteria to export drugs through efflux pumps, which relies on the ATP-dependent expression and interaction of several controlling genes. Recent studies have shown significant cell-to-cell ATP variability exists within clonal bacterial populations, but the contribution of intrinsic cell-to-cell ATP heterogeneity is generally overlooked in understanding efflux pumps. Here, we consider how ATP variability influences gene regulatory networks controlling expression of efflux pump genes in two bacterial species. We develop and apply a generalisable Boolean modelling framework, developed to incorporate the dependence of gene expression dynamics on available cellular energy supply. Theoretical results show differences in energy availability can cause pronounced downstream heterogeneity in efflux gene expression. Cells with higher energy availability have a superior response to stressors. Further, in the absence of stress, model bacteria develop heterogeneous pulses of efflux pump gene expression which contribute to a sustained sub-population of cells with increased efflux expression activity, potentially conferring a continuous pool of intrinsically resistant bacteria. This modelling approach thus reveals an important source of heterogeneity in cell responses to antimicrobials and sheds light on potentially targetable aspects of efflux pump-related antimicrobial resistance.

systems biology↗

bioRxiv2021

Altered collective mitochondrial dynamics in an Arabidopsis msh1 mutant compromising organelle DNA maintenance

Mitochondria form highly dynamic populations in the cells of plants (and all eukaryotes). The characteristics of this collective behaviour, and how it is influenced by nuclear features, remain to be fully elucidated. Here, we use a recently-developed quantitative approach to reveal and analyse the physical and collective "social" dynamics of mitochondria in an Arabidopsis msh1 mutant where organelle DNA maintenance machinery is compromised. We use a newly-created line combining the msh1 mutant with mitochondrially-targeted GFP, and characterise mitochondrial dynamics with a combination of single-cell timelapse microscopy, computational tracking and network analysis. The collective physical behaviour of msh1 mitochondria is altered from wildtype in several ways: mitochondria become less evenly spread, and networks of inter-mitochondrial encounters become more connected with greater potential efficiency for inter-organelle exchange. We find that these changes are similar to those observed in friendly, where mitochondrial dynamics are altered by a physical perturbation, suggesting that this shift to higher connectivity may reflect a general response to mitochondrial challenges.

plant biology↗

bioRxiv2021

Encounter networks from collective mitochondrial dynamics support the emergence of effective mtDNA genomes in plant cells

Mitochondria in plant cells form strikingly dynamic populations of largely individual organelles. Each mitochondrion contains on average less than a full copy of the mitochondrial DNA (mtDNA) genome. Here, we asked whether mitochondrial dynamics may allow individual mitochondria to collect a full copy of the mtDNA genome over time, by facilitating exchange between individuals. Akin to trade on a social network, exchange of mtDNA fragments across organelles may lead to the emergence of full effective genomes in individuals over time. We characterise the collective dynamics of mitochondria in Arabidopsis thaliana hypocotyl cells using a recent approach combining single-cell timelapse microscopy, video analysis, and network science. We then use a quantitative model to predict the capacity for the sharing and accumulation of genetic information through the networks of encounters between mitochondria. We find that biological encounter networks are strikingly well predisposed to support the collection of full genomes over time, outperforming a range of other networks generated from theory and simulation. Using results from the coupon collectors problem, we show that the upper tail of the degree distribution is a key determinant of an encounter networks performance at this task and discuss how features of mitochondrial dynamics observed in biology facilitate the emergence of full effective genomes.

plant biology↗

bioRxiv2021

Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution

Engineers routinely design systems to be modular and symmetric in order to increase robustness to perturbations and to facilitate alterations at a later date. Biological structures also frequently exhibit modularity and symmetry, but the origin of such trends is much less well understood. It can be tempting to assume - by analogy to engineering design - that symmetry and modularity arise from natural selection. But evolution, unlike engineers, cannot plan ahead, and so these traits must also afford some immediate selective advantage which is hard to reconcile with the breadth of systems where symmetry is observed. Here we introduce an alternative non-adaptive hypothesis based on an algorithmic picture of evolution. It suggests that symmetric structures preferentially arise not just due to natural selection, but also because they require less specific information to encode, and are therefore much more likely to appear as phenotypic variation through random mutations. Arguments from algorithmic information theory can formalise this intuition, leading to the prediction that many genotype-phenotype maps are exponentially biased towards phenotypes with low descriptional complexity. A preference for symmetry is a special case of this bias towards compressible descriptions. We test these predictions with extensive biological data, showing that that protein complexes, RNA secondary structures, and a model gene-regulatory network all exhibit the expected exponential bias towards simpler (and more symmetric) phenotypes. Lower descriptional complexity also correlates with higher mutational robustness, which may aid the evolution of complex modular assemblies of multiple components.

evolutionary biology↗

bioRxiv2021

Quantification and uncertainty of root growth stimulation by elevated CO2 in mature temperate deciduous forest

Increasing CO2 levels are a major global challenge, and the extent to which increasing anthropogenic CO2 emissions can be mitigated by natural carbon sinks remains poorly understood. The uptake of elevated CO2 (eCO2) 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 CO2 enrichment to 150ppm above ambient levels. Using over 67k frames of belowground observation, we observe that eCO2 leads to relatively faster root production (a peak volume fold change of 4.52 {+/-} 0.44 eCO2 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 eCO2. We use dynamic measurements to estimate fine root contributions to net primary productivity, finding an increase under eCO2, with an estimated mean annual 204 {+/-} 93 g dw m-2yr-1 eCO2 versus 140 {+/-} 60 g dw m-2 yr-1 control. We also quantify and discuss the uncertainties in such productivity measurements. This multi-faceted approach thus sheds quantitative light on the challenging characterisation of the eCO2 response of root biomass in mature temperate forests.

plant biology↗

bioRxiv2021

Sexually antagonistic evolution of mitochondrial and nuclear linkage

Across eukaryotes, genes encoding bioenergetic machinery are located in both mitochondrial and nuclear DNA, and incompatibilities between the two genomes can be devastating. Mitochondria are often inherited maternally, and theory predicts sex-specific fitness effects of mitochondrial mutational diversity. Yet how evolution acts on linkage patterns between mitochondrial and nuclear genomes is poorly understood. Using novel mito-nuclear population genetic models, we show that the interplay between nuclear and mitochondrial genes maintains mitochondrial haplotype diversity within populations, and it selects both for sex-independent segregation of mitochondrion-interacting genes and for paternal leakage. These effects of genetic linkage evolution can eliminate male-harming fitness effects of mtDNA mutational diversity. With maternal mitochondrial inheritance, females maintain a tight mitochondrial-nuclear match, but males accumulate mismatch mutations because of the weak statistical associations between the two genomic components. Sex-independent segregation of mitochondria-interacting loci improves the mito-nuclear match. In a sexually antagonistic evolutionary process, male nuclear alleles evolve to increase the rate of recombination, while females evolve to suppress it. Paternal leakage of mitochondria can evolve as an alternative mechanism to improve the mito-nuclear linkage. Our modelling framework provides an evolutionary explanation for the observed paucity of mitochondrion-interacting genes on mammalian sex chromosomes and for paternal leakage in protists, plants, fungi, and some animals.

evolutionary biology↗

bioRxiv2021

Organelle bottlenecks facilitate evolvability by traversing heteroplasmic fitness valleys

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.

genetics↗

bioRxiv2020

Patterns of within-host genetic diversity in SARS-CoV-2

Monitoring the spread of SARS-CoV-2 and reconstructing transmission chains has become a major public health focus for many governments around the world. The modest mutation rate and rapid transmission of SARS-CoV-2 prevents the reconstruction of transmission chains from consensus genome sequences, but within-host genetic diversity could theoretically help identify close contacts. Here we describe the patterns of within-host diversity in 1,181 SARS-CoV-2 samples sequenced to high depth in duplicate. 95% of samples show within-host mutations at detectable allele frequencies. Analyses of the mutational spectra revealed strong strand asymmetries suggestive of damage or RNA editing of the plus strand, rather than replication errors, dominating the accumulation of mutations during the SARS-CoV-2 pandemic. Within and between host diversity show strong purifying selection, particularly against nonsense mutations. Recurrent within-host mutations, many of which coincide with known phylogenetic homoplasies, display a spectrum and patterns of purifying selection more suggestive of mutational hotspots than recombination or convergent evolution. While allele frequencies suggest that most samples result from infection by a single lineage, we identify multiple putative examples of co-infection. Integrating these results into an epidemiological inference framework, we find that while sharing of within-host variants between samples could help the reconstruction of transmission chains, mutational hotspots and rare cases of superinfection can confound these analyses.

genomics↗

bioRxiv2020

Paternal leakage of organelles can improve adaptation to changing environments

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. DataMatlab/Octave implementation of the model is available at Https://github.com/StochasticBiology/PaternalLeakageEvolution.

ecology↗