Biology subjects

Johnston, I.

Publications and source records attributed to Johnston, I..

At least 19 recordsLinked to original sources

bioRxiv2026

Mitochondrial dynamics can resolve stoichiometric challenges in plant mtDNA repair

Although mitochondrial DNA (mtDNA) is subject to damage and high mutation rates in many eukaryotes, plants have a variety of mtDNA repair pathways that somehow result in exceptionally low mutation rates. However, the stoichiometry of biomolecules in plant mitochondria raises challenges for the DNA repair process. The protein complexes responsible for repair pathways contain subunits present at low copy numbers per mitochondrion. Mitochondria on average contain less than a full genome, limiting the capacity for templated repair, and raising the risk that repair machinery is localised to a DNA-empty mitochondrion where it cannot be used. Here we build on recent experimental characterisation of the copy numbers and physical processes involved to model the capacity for DNA repair in plant mitochondria. We find that targeted import of protein machinery, biased fusion of mitochondria bearing DNA, and exchange of biomolecules upon fusion can improve repair capacity through different mechanisms. Specifically, our model indicates that base excision repair can keep ~95% of mtDNA intact even without targetting or exchange rules and that targetting protein import to DNA-bearing mitochondria or exchanging protein content drives this to effectively 100%. In contrast, template repair cannot keep a majority of mtDNA intact unless mtDNA is shared across mitochondria through fusion, in which case its efficacy increases. Exchanging of metabolons (repair protein complexes) increases repair efficiency across all circumstances. We discuss the implications of our model for plant development and organelle inheritance.

plant biology↗

bioRxiv2026

Mitochondrial dynamics resolve physical-social tensions and challenges through adaptable multi-objective optimisation

Complex biological behaviours often emerge when a system is faced with mutually incompatible priorities. In such situations, the field of multi-objective optimisation can provide an informative and predictive theoretical foundation for biology. Here we adopt this paradigm in exploring the rich dynamic behaviour of mitochondria inside cells. Using plant cells as a model system, we use physical modelling to characterise the "morphospace" of possible mitochondrial behaviours, and single-cell microscopy with video analysis and network modelling to characterise collective mitochondrial dynamics. We show that wildtype Arabidopsis mitochondrial dynamics near-optimally resolve a tradeoff between maintaining physical spacing and supporting biomolecular exchange. With existing and new experimental data, we show that these dynamics adapt under mutational and chemical challenges to support a rebalanced, but still near-optimal, resolution to this tradeoff under different densities of the mitochondrial population. We also show how an assumption of multi-objective optimisation supports inference of biological mechanisms before any data are observed, and discuss the potential of this multi-objective optimisation paradigm to form a broader theoretical framework of spatial cell biology.

cell biology↗

bioRxiv2026

Learning and forecasting shared evolutionary pathways to multi-drug resistance across global pathogens

Infections with bacteria which have evolved multi-drug resistance (MDR) cause millions of deaths worldwide. Large-scale efforts are gathering genotypic and phenotypic data on MDR bacteria, but methods for learning the structure, diversity, and predictors of evolutionary pathways to MDR have yet to take full advantage of these data. Here, we use evolutionary accumulation modelling (EvAM), an emerging class of machine learning methods with roots in cancer progression, to infer these evolutionary pathways across ESKAPEE pathogens (seven bacterial species that dominate health burdens), using a database of over 635k genotyped phenotypic observations from around the world. We identify global patterns in MDR evolutionary pathways, remarkably shared across multiple ESKAPEE species. Species-specific deviations from these stereotypical pathways are connected with geographical and demographic covariates, facilitating predictions of future MDR evolution. We verify these predictions with several hundred new phenotypes from ESKAPEE samples spanning decades of clinical infections in sub-Saharan Africa, demonstrating the capacity to forecast future MDR evolution from these inferred shared pathways.

evolutionary biology↗

bioRxiv2026

The hypercubic Mk model in reduced state space for the coupled, reversible coevolution of multiple binary characters

Many scientific questions involve the coevolution of coupled, binary features over time - from phenotypes in evolutionary biology to mutations in cancer development. Evolutionary accumulation models (EvAMs) often neglect reversibility in these systems, uncertainty in observations, and/or phylogenetic connections between observations. By contrast, the Mk model from phylogenetic comparative methods supports reversibility, uncertainty, and relatedness, but compute time scales like O(4L) in number of features L, making it challenging to apply to more than about six coupled, coevolving binary characters. Here, we introduce HyperMk2, a method using output from a Fitch-like parsimony algorithm to reduce the state space associated with many coevolving characters while retaining flexibility, reversibility, and phylogenetic information. This approach, while approximate, scales linearly in the number of distinct observations rather than exponentially in the number of characters, supporting the investigation of much larger systems than previously possible. We demonstrate how this method allows the inference of evolutionary dynamics of anti-microbial resistance in bacteria, including the identification of potential influences between characters, and discuss its broader application.

evolutionary biology↗

bioRxiv2026

Epithelial NCAPD3 expression protects against stress-induced intestinal injury in mice

Intestinal epithelial cells are central players in mucosal barrier integrity and host-microbe interactions. Genetic studies have revealed that epithelial dysfunction is a key contributor to the pathogenesis of inflammatory bowel disease. Non-SMC condensin II complex subunit D3 (NCAPD3) is essential for chromatin organization and stability. NCAPD3 also promotes antimicrobial defense and autophagy responses in vitro. NCAPD3 expression is decreased in intestinal epithelial cells from patients with ulcerative colitis; however, it is not known whether loss of NCAPD3 expression drives intestinal barrier dysfunction or is a result of disease-associated inflammation. To investigate this relationship in vivo, a tissue-specific approach was required, as global constitutive knockout of NCAPD3 is embryonic lethal. Therefore, a transgenic mouse line with doxycycline-inducible expression of a short hairpin RNA targeting NCAPD3 restricted to villin-expressing cells was generated (NCAPD3KD mice) to enable the study of NCAPD3 function in the intestinal epithelium. Treatment of NCAPD3KD mice with 9-tert-butyl doxycycline resulted in [~]75% reduction of NCAPD3 protein in EpCAM+ intestinal cells. Short-term epithelial NCAPD3 knockdown did not induce spontaneous colitis but was associated with increased serum amyloid A and a trend towards increased intestinal permeability. Upon dextran sodium sulfate or Salmonella enterica serovar Typhimurium {Delta}AroA challenge, NCAPD3KD mice exhibited exacerbated weight loss, higher disease activity, increased histopathological damage, abnormal colonic cytokines and chemokines, and significantly increased intestinal permeability. These results indicate that NCAPD3 expression in the intestinal epithelium is required for optimal barrier maintenance and antimicrobial defense under chemical or microbial stress. These findings support prior in vitro observations and solidify NCAPD3 as a regulator of intestinal epithelial barrier function and mucosal host defense. Author SummaryNCAPD3 is a multifunctional protein with established roles in chromatin organization, genome stability, mitochondrial function, and antimicrobial defense. Dysregulated NCAPD3 is implicated in human diseases, such as inflammatory bowel disease (IBD) and microcephaly; however, due to its essential role in cellular division, determination of whether NCAPD3 loss drives these pathologies in vivo has been lacking. Using a new transgenic mouse model that selectively reduces NCAPD3 expression in intestinal epithelial cells, our study establishes NCAPD3 as an epithelial regulator of the mammalian intestine that enhances epithelial barrier resilience and antimicrobial defense during stress. Although dispensable for short-term basal homeostasis, NCAPD3 function becomes critical during epithelial injury and enteric infection. Reduced NCAPD3 expression may therefore lower the threshold for inflammatory disease by weakening barrier integrity, amplifying inflammatory cascades, and impairing antimicrobial defenses. These findings position NCAPD3 as a potential modulator of IBD susceptibility and highlight chromatin organization as an important, previously underappreciated layer of intestinal epithelial regulation.

cell biology↗

bioRxiv2026

Oxidative stress induces E. coli aryl polyene expression, sensitizing the bacterial stress response and modulating the redox environment of innate immune cells

Aryl polyenes (APEs) are specialized polyunsaturated outer membrane lipids that protect their producers from oxidative stress and contribute to biofilm formation. APEs are produced by an abundant biosynthetic gene cluster (BGC) family conserved across Gram-negative bacterial clades. The APE biosynthesis pathway involves 11 different enzymes and cumulates in the attachment of APEs to an anchor molecule in the Gram-negative outer membrane. Unlike most other small molecule BGCs, the APE BGC does not contain a dedicated regulatory gene that controls production of its metabolically costly compounds. Building from our prior observations of APEs role in acute oxidative stress protection, we here use a uropathogenic Escherichia coli (UPEC) strain to show that APE expression conveys a potential competitive advantage characterized by increased early-stage growth, sensitization of the bacterial oxidative stress response, and dampening of the redox stress of innate immune cells after in vitro infection. Our data indicate that APEs could act as a UPEC fitness factor, and in future work we aim to study their contribution to overall bacterial pathogenicity and survival, as well as how APEs could facilitate the transition from an oxygen poor environment such as the gut to the oxygen rich environment of the urinary tract. ImportanceBacterial pathogens use various mechanisms to achieve a competitive advantage under harsh conditions such as upon interaction with their host. We studied the function of aryl polyenes (APEs), specialized polyunsaturated fatty acids in the outer membrane, in the context of a uropathogenic E. coli strain. APE expression is induced by an oxidative environment and contributes to early-stage growth and sensitization of the oxidative stress response. Furthermore, APE-expressing E. coli dampens the intracellular oxidative milieu of target host phagocytes. These findings suggest a role for APEs as a fitness factor, and create opportunities to study their in vivo function, and explore them as a potential drug target.

microbiology↗

bioRxiv2025

Global diversity, predictors, and predictions of AMR evolutionary pathways in Klebsiella pneumoniae

Antimicrobial resistance (AMR) is a substantial and growing global health burden. Understanding, and predicting, its evolution in specific pathogens will help responses across scales from individual patient cases to large-scale policy. Here, we use global data on AMR features, predicted from 47k Klebsiella pneumoniae genomes, with hypercubic transition path sampling to infer the evolutionary pathways by which AMR features in K. pneumoniae (KpAMR) are acquired across 102 countries, territories and areas. We identify "globally consistent" evolutionary behaviours that hold across countries, and "globally divergent" behaviours including carbapenem and fluoroquinolone resistance that vary across countries. We show how these divergent dynamics covary both with public health superregion and drug use policy, and reveal competing evolutionary pathways within and between countries. Using newly-sequenced data across several decades from sub-Saharan Africa, we show that this inferred global roadmap of KpAMR evolution successfully predicts prospective evolutionary dynamics. Together, we hope that the ability to characterize and predict evolutionary dynamics of AMR acquisition, connected to socio-economic and drug policy predictors, will help strengthen our understanding of AMR evolution worldwide. SignificanceAntimicrobial resistance (AMR) occurs when microbial pathogens evolve resistance to the drugs we use to treat them. Our understanding of bacterial genomes and how they confer AMR is constantly expanding through beautiful and powerful work establishing large-scale global datasets. Here, we use emerging machine learning approaches with this genomic data to reveal the evolutionary dynamics that have generated AMR characters in a particular pathogen, Klebsiella pneumoniae (Kp), and how these dynamics are influenced by geography and drug use across the globe. This "natural history" of AMR in Kp makes predictions about which characters will evolve next for a given bacterium, and we validate these predictions with newly-sequenced data from clinical isolates from Africa, providing both past and prospective descriptions of AMR in Kp.

evolutionary biology↗

bioRxiv2025

Anti-PD-1/PD-L1 Therapy Triggers Cognitive Deficits and Anxiety-Like Behaviors Through Tumor-Initiated Neuroinflammatory Niches in Male Mice

Checkpoint inhibitors are promising immunotherapy to treat cancer patients, but their cognitive impact has not been evaluated despite several neurological adverse events. We studied the impact of immune desert or inflamed cancers when combined with immune checkpoint inhibitors (ICI) anti-PD-1/anti-PD-L1 on mouse behaviors and brain immune cells infiltration/homeostasis, and neuroinflammation in male mice. We showed that systemic inflammation, brain-barriers permeability accompanying meningeal infiltration of peripheral macrophages and neuroinflammation as well as deficits in cognition or emotional reactivity, depending on immuno-inflammatory or immune-desert cancer type. Combined with cancers, anti-PD-1 and PD-L1 treatments exacerbated the decline in executive functions and hippocampal vascular inflammation. PD-L1 specifically relayed the infiltration of the T{gamma}{delta} lymphocytes subpopulation in choroid plexus and leptomeninges implicated, whose systemic neutralization counteracted anti-PDL1-induced cognitive deficits and anxiety in mice bearing immune-inflamed cancer. Our findings highlight new systemic biomarkers of cold or hot cancer, treated with anti-PD-1/anti-PDL-1, and associated with cognitive and emotional alterations in mice; guiding ways of intervention to secure the cancer curation and improve patients quality of life under ICI treatment. Competing Interest StatementThe authors have declared no competing interest. One Sentence SummaryImpact of cancer and checkpoint inhibitors on cognitive functions

cancer biology↗

bioRxiv2025

Convergent pathways of reductive mitochondrial evolution characterised with hypercubic inference

For a striking example of mitochondrial behaviour beyond ATP generation, consider mitochondrion-related organelles (MROs). Hydrogenosomes, mitosomes, and other reduced mitochondrial forms have evolved through the loss of physical and functional features, from individual ETC complexes to oxidative phosphorylaytion and the very ability to produce ATP (and further). Reduction of mitochondria is a dramatic example of convergent evolution, occuring in every eukaryotic kingdom and many parallel times. Here, we use hypercubic inference, a class of methods from evolutionary accumulation modelling (EvAM), to explore the pathways of convergent mitochondrial reduction across eukaryotes. We find that most MRO diversity can be explained by small variations on two distinct pathways, starting with either the loss of Complex I or the loss of Complexes III/IV, which tend to proceed over different characteristic timescales. We show that different clades, including ciliates and apicomplexans, reflect particular instances of these pathways. Using metabolic modelling, we connect the structure of these evolutionary pathways to the metabolic impact of the changes involved, suggesting a plausible explanation for the dramatically convergent nature of reductive evolution. We discuss this approach in connection with related theory on the genetic and functional reduction of mitochondria across organisms.

evolutionary biology↗

bioRxiv2025

Estimating physical conditions supporting gradients of ATP concentration in the eukaryotic cell

The ATP molecule serves as an energy currency in eukaryotes (and all life), providing the energy needed for many essential cellular processes. But the extent to which substantial spatial differences exist in ATP concentration in the cell remains incompletely known. It is variously argued that ATP diffuses too quickly for large gradients to be established, or that the high rates of ATP production and use (sources and sinks) can support large gradients even with rapid diffusion - and microscopic models and detailed experiments in different specific cases support both pictures. Here we attempt a mesoscopic investigation, using reaction-diffusion modelling in a simple biophysical picture of the cell to attempt to ask, generally, which conditions cause substantial ATP gradients to emerge within eukaryotic cells. If ATP sources (like mitochondria) or sinks (like the nucleus) are spatially clustered, large fold changes in concentration can exist across the cell; if sources and sinks are more spread then rapid diffusion indeed prevents large gradients being established. This dependence holds in model cells of different sizes, suggesting its generality across cell types. Our theoretical work will complement developing intracellular approaches exploring ATP concentration inside eukaryotic cells.

cell biology↗

bioRxiv2024

Modelling benefits and costs of decision making and feedback control for organisms in changing environments

Cells (and organisms) make decisions in response to their environments. These decisions may help organism survival in environments with limited resource, but also constitute a cost to the organism in terms of the energy involved in sensing, processing, and responding to environmental change. Here, we explore the tradeoffs involved in a cost-benefit analysis of model organisms facing challenging deterministic or stochastic environments. The benefits of tunable versions of proportional-integral-derivative (PID) control are computed under different environmental behaviours; the model reflects both the ability to use this control to decide on cellular strategy and the potential cost associated with this feedback control. We quantify the circumstances under which control is most and least beneficial and the different weightings of the PID terms that perform best in specific and general situations. While our model is very simple, these results provide potential insight into the benefits of different control mechanisms, particularly at the single-cell level.

systems biology↗

bioRxiv2024

Identifying parsimonious pathways of accumulation and convergent evolution from binary data

How stereotypical, and hence predictable, are evolutionary and accumulation dynamics? Here we consider processes - from genome evolution to cancer progression - involving the irreversible accumulation of binary features (characters), which can be modelled as Markov processes on a hypercubic transition network. We seek subgraphs of such networks that can generate a given set of paired before-after observations and minimize a topological cost function, involving criteria on out-branching which are interpretable in terms of biological parsimony. A transition network supporting a single, deterministic dynamic pathway is maximally simple and lowest cost, and branches (corresponding to possibly different next steps) increase cost, particularly if these branches are "deep", occurring at early stages in the dynamics. In this sense, the lowest-cost subgraph measures how stereotypical the evolutionary or accumulation process is, and also identifies good start points for likelihood-based inference. The problem is solvable in polynomial time for cross-sectional observations by building on an existing method due to Gutin, and we provide a polynomial-time estimate in the more general case of pairs of observed states. We use this approach to define a "stereotypy index" reflecting the extent of evolutionary predictability. We demonstrate use cases in the evolution of antimicrobial resistance, organelle genomes, and cancer progression, and provide a software implementation at https://github.com/StochasticBiology/hyperDAGs.

evolutionary biology↗

bioRxiv2024

Clustering large-scale biomedical data to model dynamic accumulation processes in disease progression and anti-microbial resistance evolution

Accumulation modelling uses machine learning to discover the dynamics by which systems acquire discrete features over time. Many systems of biomedical interest show such dynamics: from bacteria acquiring resistances to sets of drugs, to patients acquiring symptoms during the course of progressive disease. Existing approaches for accumulation modelling are typically limited either in the number of features they consider or their ability to characterise interactions between these features - a limitation for the large-scale genetic and/or phenotypic datasets often found in modern biomedical applications. Here, we demonstrate how clustering can make such large-scale datasets tractable for powerful accumulation modelling approaches. Clustering resolves issues of sparsity and high dimensionality in datasets but complicates the intepretation of the inferred dynamics, especially if observations are not independent. Focussing on hypercubic hidden Markov models (HyperHMM), we introduce several approaches for interpreting, estimating, and bounding the results of the dynamics in these cases and show how biomedical insight can be gained in such cases. We demonstrate this Cluster-based HyperHMM (CHyperHMM) pipeline for synthetic data, clinical data on disease progression in severe malaria, and genomic data for anti-microbial resistance evolution in Klebsiella pneumoniae, reflecting two global health threats.

bioinformatics↗

bioRxiv2024

Optimal organelle inheritance strategies under different changing environments and mutational pressures

Mitochondrial and chloroplast DNA (mtDNA and cpDNA) encode essential cellular apparatus. This organelle DNA (oDNA) exists at high copy number (ploidy) in eukaryotic cells, which must both mitigate mutational damage and allow adaptation to changing demands. Across eukaryotes, a range of inheritance strategies are used for oDNA, including uniparental and doubly uniparental inheritance (DUI), paternal leakage, recombination-mediated repair and gene conversion, and an effective "genetic bottleneck" imposed between generations. Here, we use modelling and simulation to investigate how these different strategies support the robustness and evolvability of oDNA populations under different challenges of mutation and changes in selection imposed by the environment. We find a general tradeoff between maintaining heterozygosity for flexible adaptation and supporting purifying selection against dysfunctional mutants. Different combinations of leakage and bottleneck size provide optimal resolutions to this tradeoff under different sets of challenges. The model explains many observed behaviours, including the appearance of non-minimal bottleneck sizes, a tradeoff between high ploidy for heterozygosity and repair and tight bottlenecks for segregation, and environmental dependence of the benefits of leakage and DUI. We connect different strategies observed across eukaryotes with the ecology of the organisms involved to explore support for the predictions of this theory.

evolutionary biology↗

bioRxiv2024

A hypercubic Mk model framework for capturing reversibility in disease, cancer, and evolutionary accumulation modelling

Accumulation models, where a system progressively acquires binary features over time, are common in the study of cancer progression, evolutionary biology, and other fields. Many approaches have been developed to infer the accumulation pathways by which features (for example, mutations) are acquired over time. However, most of these approaches do not support reversibility: the loss of a feature once it has been acquired (for example, the clearing of a mutation from a tumour or population). Here, we demonstrate how the well-established Mk model from evolutionary biology, embedded on a hypercubic transition graph, can be used to infer the dynamics of accumulation processes, including the possibility of reversible transitions, from data which may be uncertain and cross-sectional, longitudinal, or phylogenetically / phylogenomically embedded. Positive and negative interactions between arbitrary sets of features (not limited to pairwise interactions) are supported. We demonstrate this approach with synthetic datasets and real data on bacterial drug resistance and cancer progression. While this implementation is limited in the number of features that can be considered, we discuss how this limitation may be relaxed to deal with larger systems.

bioinformatics↗

bioRxiv2024

HyperTraPS-CT: Inference and prediction for accumulation pathways with flexible data and model structures

Accumulation processes, where many potentially coupled features are acquired over time, occur throughout the sciences, from evolutionary biology to disease progression, and particularly in the study of cancer progression. Existing methods for learning the dynamics of such systems typically assume limited (often pairwise) relationships between feature subsets, cross-sectional or untimed observations, small feature sets, or discrete orderings of events. Here we introduce HyperTraPS-CT (Hypercubic Transition Path Sampling in Continuous Time) to compute posterior distributions on continuous-time dynamics of many, arbitrarily coupled, traits in unrestricted state spaces, accounting for uncertainty in observations and their timings. We demonstrate the capacity of HyperTraPS-CT to deal with cross-sectional, longitudinal, and phylogenetic data, which may have no, uncertain, or precisely specified sampling times. HyperTraPS-CT allows positive and negative interactions between arbitrary subsets of features (not limited to pairwise interactions), supporting Bayesian and maximum-likelihood inference approaches to identify these interactions, consequent pathways, and predictions of future and unobserved features. We also introduce a range of visualisations for the inferred outputs of these processes and demonstrate model selection and regularisation for feature interactions. We apply this approach to case studies on the accumulation of mutations in cancer progression and the acquisition of anti-microbial resistance genes in tuberculosis, demonstrating its flexibility and capacity to produce predictions aligned with applied priorities.

bioinformatics↗

bioRxiv2024

Comparing structure and dynamics of transition graphs by the symmetric difference metric over an edge-filtration

Transition graphs or transition diagrams, describing the rates and probabilities with which a system changes between discrete states, are common throughout the sciences. In many cases, parameterisations of transition graphs are inferred from different datasets, for example in the context of Markov or hidden Markov models. An important task for followup analysis is to find efficient and effective ways to compare transition graphs with different parameterisations. Here, we introduce the Weight-Filtration Comparison Curve (WFCC), an approach by which the differences between two or more parameterisations of a transition graph can be quantified and compared. Borrowing from topological data analysis, the WFCC allows graphs learned from different datasets and/or null models to be systematically compared, and differences in both the fine- and coarse-grained structure and dynamics of transition graphs to be quantitatively assessed. We demonstrate WFCC with simple illustrative cases and real-world cases of transition graphs inferred from global data on the evolution of antimicrobial resistance in different countries, showing how different inferred dynamics, and different levels of uncertainty, are reported by structural aspects of these comparison curves.

bioinformatics↗

bioRxiv2023

Connecting species-specific extents of genome reduction in mitochondria and plastids

Mitochondria and plastids have both dramatically reduced their genomes since the endosymbiotic events that created them. The similarities and differences in the evolution of the two organelle genome types has been the target of discussion and investigation for decades. Ongoing work has suggested that similar mechanisms may modulate the reductive evolution of the two organelles in a given species, but quantitative data and statistical analyses exploring this picture remain limited outside of some specific cases like parasitism. Here, we use cross-eukaryote organelle genome data to explore evidence for coevolution of mitochondrial and chloroplast genome reduction. Controlling for differences between clades and pseudoreplication due to relatedness, we find that mtDNA and ptDNA gene retention are related across taxa, in a generally positive correlation that appears to differ quantitatively across eukaryotes, for example, between algal and non-algal species. We find limited evidence for coevolution of specific mtDNA and ptDNA gene pairs, suggesting that the similarities between the two organelle types may be due mainly to independent responses to consistent evolutionary drivers.

evolutionary biology↗