Biology subjects

Osborne, M.

Publications and source records attributed to Osborne, M..

11 recordsLinked to original sources

bioRxiv2026

Molecular arms race in WHO elements, a category of homing genetic elements distinct from inteins and introns

Homing genetic elements are selfish elements that insert themselves into a specific site in a host gene without disrupting its function. They spread through the population because the element codes for an endonuclease that cleaves alleles of the host gene that do not contain the element, leading to DNA repair by gene conversion that increases the elements frequency. Most homing genetic elements in eukaryotes are either self-splicing introns or inteins but we recently discovered a third category, called WHO elements, in the budding yeast genus Torulaspora. WHO elements code for endonuclease proteins with LAGLIDADG motifs and a zinc finger domain, and are related to the mating-type switching endonuclease HO. Their host gene is the aldolase gene FBA1, which is essential. Clusters of up to 9 diverse WHO endonuclease genes are found downstream of FBA1 in different isolates of Torulaspora. Here, we show that there is a genetic conflict between WHO endonucleases and their target site in FBA1. Different alleles of FBA1 vary in their sensitivity or resistance to cleavage by individual WHO endonucleases. We show that a WHO endonuclease recognizes a 28-bp sequence in FBA1 and does not tolerate much sequence variation, but also that this region of FBA1 has experienced positive selection for sequence diversification to evade cleavage. WHO endonucleases and their target site in FBA1 are therefore engaged in an arms race in which each WHO element is under selection to home into other elements, while avoiding being homed into. Significance StatementWHO elements are a recently discovered type of homing genetic element in yeasts, targeting the aldolase gene FBA1. Rather than disrupting FBA1 when they integrate, WHO elements instead replace the 3 half of the gene with an alternative FBA1 3 half. Each WHO element consists of an endonuclease gene and a version of the 3 half of FBA1, and there is high sequence diversity in both genes. We show that there is an evolutionary arms race between WHO endonucleases and their target site in FBA1, which has resulted in rapid evolution of both genes and the formation of clusters of WHO elements at the FBA1 locus.

evolutionary biology↗

bioRxiv2026

Organizing principles in the Nitrogen-Carbon Landscape of Marine Heterotrophic Bacteria

Marine microbes metabolize a wide range of carbon and nitrogen sources, shaping global biogeochemical cycles. Despite being crucial at the global scale, the coupling between carbon and nitrogen remains poorly understood at the level of individual metabolites and bacteria. By phenotyping a library of marine heterotrophic bacteria across increasingly complex carbon and nitrogen sources, we generated a snapshot of this coupling. Growth phenotypes were weakly explained by phylogeny, but could be organized around substrate properties, including C:N stoichiometry and degree of reduction. Beyond explainable average trends along these axes, different strains displayed significant variability. For some bacteria, nitrogen use efficiency was associated with ammonium secretion, consistent with their role in ecological interactions. Furthermore, nonlinearities emerged in how yields depend on combinations of resources, such as mixtures of amino acids and dipeptides. These organizing principles may help understand the ecological role of marine heterotrophic bacteria and parametrize models of biogeochemical cycles.

systems biology↗

bioRxiv2026

Mechanistic machine learning enables interpretable and generalizable prediction of prime editing outcomes

Although prime editing (PE) can effect virtually any specified local change to genomic DNA in living systems, its efficient application currently requires extensive optimization of prime editing guide RNA (pegRNA) sequences. We present OptiPrime, a machine learning model of PE efficiency based on our current understanding of the mechanism of prime editing. OptiPrime achieves state-of-the-art accuracy on PE efficiency prediction and also enables prediction of nicking guide RNA (PE3) and dual pegRNA (twinPE) outcomes. We validated that OptiPrime has learned the determinants of mammalian mismatch repair (MMR), and is therefore well suited for nominating MMR-evasive silent edits that improve PE efficiency. We demonstrate the utility of OptiPrime in a variety of prospective therapeutic contexts, including in primary human and mouse cells. Finally, we show how OptiPrime can be used to achieve highly streamlined and efficient in vivo correction of a pathogenic mutation in the brain of a mouse model of KIF1A-associated neurological disorder.

biochemistry↗

bioRxiv2025

Biological insights and methodological challenges learned from working with a diverse heterotrophic marine bacterial library

BackgroundOrganized collections of bacterial strains can help bridge the gap between studying model organisms and communities, through comparative experiments between genetically and phenotypically diverse strains. We describe the establishment and initial characterization of a library of 62 marine heterotrophic bacteria, selected to represent a significant fraction of the genome-encoded functional diversity and a wide range of known phytoplankton-bacteria interactions. We focus on important but often undiscussed aspects of collecting and maintaining such a library, verifying strain identity, and applying classical microbiological methods across diverse strains. ResultsCultured strains contain up to hundreds of mutations compared with the reference genomes, with non-synonymous mutations in rpoB and/or rpoC genes observed in [~]15% of the cultures. Most strains grow well at 25{degrees}C, but the dependence of growth rate on temperature and the width of the temperature niche vary between strains in a systematic manner. We describe steps towards designing a universal, defined, minimal media for marine bacteria, revealing that growth inhibition on amino acids and peptides by carbohydrates is widespread. Cell counts obtained from flow cytometry and colony plating differ systematically, as do different methods to assess motility. Finally, we discuss traits potentially related to microbial interactions such as hemolysis, biofilm formation, and antibiotic resistance. Gammaproteobacteria such as Alteromonas, Pseudoalteromonas, and Vibrio reveal consistently robust growth, and activity, perhaps explaining why these clades are well-explored. ConclusionExplicitly discussing the insights and challenges of working with strain libraries will pave the way to robust, reproducible, and generalizable mapping of bacterial traits across diversity.

microbiology↗

bioRxiv2025

Exploring chemotaxis in spatio-temporal stoichiometric models of metabolism: Pseudomonas simiae as a case study

Summary/AbstractChemotaxis is the movement of cells or organisms in the direction of an increasing or decreasing gradient of a chemical, i.e. a chemoattractant or chemorepellent, respectively. In bacteria, this movement is typically manifested through the presence of bias in the random walk generated by run-and-tumble motility. Often, the chemoattractant is consumed by the organism, thus coupling the biophysical, directed motion with the biochemical, metabolic activity in the growing bacterial colony. In this chapter, we describe our approach to simulating chemotaxis, coupled with detailed metabolic activity, within the framework of our modeling software COMETS (Computation of Microbial Ecosystem in Time and Space). By combining genome-scale metabolic models, dynamic flux balance analysis and the newly implemented biophysical chemotaxis model, we built and qualitatively tested a chemotaxis module for COMETS. We applied this model to an automatically constructed genome-scale model of the plant commensal bacterium Pseudomonas simiae WCS417, which we hypothesized would perform chemotaxis on amino acids similar to its close relative Pseudomonas putida. COMETS simulations recapitulated the typical ring colony pattern, which is due to propagation at the colony front, indicative of bacteria growing on a chemoattractant-filled plate. By allowing for detailed examination of spatial distributions and time dynamics of the biomass, as well as concentration profiles of the metabolites involved in the growth and chemotaxis in bacterial colonies, the software method described here enables simulations of complex microbial patterns involving metabolism and directed motility. Beyond testing the appearance of the ring, future model validation could include comparisons of local growth rates and metabolic fluxes, including rates of metabolite uptake and secretion. The newly added COMETS chemotaxis capability integrates flux balance modeling with gradient-induced biomass propagation, paving the way for more realistic simulations of microbial ecosystem dynamics in complex environments, such as plant roots, where chemotaxis may play a fundamental role in microbiome-host dynamics.

systems biology↗

bioRxiv2024

Axenisation of oleaginous microalgal cultures via anoxic photosensitisation.

Growing interest in sustainable biofuel research has necessitated high quality axenic oleaginous microalgal strains. Unfortunately, most strains available in culture banks contain commensal microbes such as bacteria and the default decontamination method involves antibiotic treatment which has begun to exacerbate the emergence of antibiotic resistance. To overcome this problem, anoxic photosensitisation was investigated as an alternate approach. Four oleaginous microalgal species (Tetradesmus obliquus, Desmodesmus armatus, Chlorella vulgaris and Nannochloropsis limnetica) were incubated in varying concentrations of Rose Bengal (0 {micro}M, 1 {micro}M, 3 {micro}M and 9 {micro}M) either in normal (oxic) or anoxic conditions, for 72 h under light (8.85 {+/-} 0.40 W m-2) in a specially designed heterotrophic growth complex (HGC) medium, followed by 72 h in standard Bolds Basal Medium (BBM). Commonly used antibiotics-based protocol was used as the control method. Post treatment, cell numbers and percentage populations were counted with Flow cytometry, and viability was tested using standard plating methods using BBM and LB. Additionally, the contaminating microbes in the cultures were profiled using 16s rRNA sequencing. Anoxic conditions were able to significantly decrease bacterial content, albeit with an equally detrimental effect on the microalgal population. Although the responses differed between the microalgae, anoxic incubation along with Rose Bengal at 3 {micro}M was able to completely decontaminate N. limnetica and C. vulgaris, while D. armatus and T. obliquus could be decontaminated with an additional streak-plating step. None of the cultures could be decontaminated using antibiotics treatment owing to the presence of gram-negative, multi-drug resistant bacteria such as S. maltophilia, M. foliorum and S. chilensis. These results suggest that decontamination of xenic microalgal cultures was largely due to anoxy, that was synergistically enhanced by Rose Bengal at a concentration of [≥]3 {micro}M. HighlightsO_LIRose Bengal at 3 {micro}M in anoxic conditions can help achieve axenic microalgal cultures. C_LIO_LIStandard antibiotics were unable to decontaminate any of the cultures owing to the presence of multi-drug resistant bacteria. C_LIO_LIStandard antibiotics treatment was deleterious to D. armatus cultures. C_LIO_LIStreak plating may be required for D. armatus and T. obliquus after anoxic Rose Bengal treatment to obtain individual colonies for further inoculation. C_LI

microbiology↗

bioRxiv2024

Reading Outside the Lines: A Systematic Approach for Detecting Bias in Scientific Communication

Consciousness of the social impact of science and the potential biases of its authors is critical to understanding, interpreting, and using scientific findings responsibly. This is especially true for sciences concerned with human health and behavior, where societal and unconscious biases may reinforce existing inequities and discriminatory practices. Considering backgrounds and biases, we may notice bias influencing scientists methodological choices and conclusions, even when a work is otherwise scientifically sound. To this end, we created the pedagogical tool Finding inEquity in LIterature and eXperimentation (FELIX). FELIX is a tool that systematizes the detection of bias and subjectivity in scientific communications by using a three-phase progression of (i) Annotation, (ii) Analysis and (iii) Synthesis, where students form a unified argument about the text with a focus on its relationship to social or ethical context. Results from a mixed methods approach indicated the efficacy of our approach in supporting student learning related to reading comprehension, critical thinking skills, and in understanding the social and ethical implications of the research they were reading. We put forward FELIX as a universal method for training students in the reading of scientific communications and as a tool for addressing systemic inequities in science and science education.

scientific communication and education↗

bioRxiv2024

Biophysical metabolic modeling of complex bacterial colony morphology

Microbial colony growth is shaped by the physics of biomass propagation and nutrient diffusion, and by the metabolic reactions that organisms activate as a function of the surrounding environment. While microbial colonies have been explored using minimal models of growth and motility, full integration of biomass propagation and metabolism is still lacking. Here, building upon our framework for Computation of Microbial Ecosystems in Time and Space (COMETS), we combine dynamic flux balance modeling of metabolism with collective biomass propagation and demographic fluctuations to provide nuanced simulations of E. coli colonies. Simulations produced realistic colony morphology, consistent with our experiments. They characterize the transition between smooth and furcated colonies and the decay of genetic diversity. Furthermore, we demonstrate that under certain conditions, biomass can accumulate along "metabolic rings" that are reminiscent of coffee-stain rings, but have a completely different origin. Our approach is a key step towards predictive microbial ecosystems modeling.

biophysics↗

bioRxiv2022

Phenotypic Variation from Waterlogging in Multiple Perennial Ryegrass Varieties under Climate Change Conditions

Identifying how various components of climate change will influence ecosystems and vegetation subsistence will be fundamental to mitigate negative effects. Climate change-induced waterlogging is understudied in comparison to temperature and CO2. Grasslands are especially vulnerable through the connection with global food security, with perennial ryegrass dominating many flood-prone pasturelands in North-western Europe. We investigated the effect of long-term waterlogging on phenotypic responses of perennial ryegrass using four varieties grown in atmospherically controlled growth chambers (ambient vs 2050, +2{degrees}C and eCO2) during two months of peak growth. Using image analysis and PCA methodologies, we assess how multiple predictors (phenotypic, environmental, genetic and temporal) influence overall plant performance and productivity. Long-term waterlogging was found to reduce leaf-colour intensity, with younger plants having purple hues indicative of anthocyanins. Plant performance and yield was lower in waterlogged plants, with tetraploid varieties coping better than diploid ones. The climate change treatment was found to reduce colour intensities further. Flooding was found to reduce plant productivity via reductions in colour pigments and root proliferation. These effects will have negative consequences for global food security from facing extreme weather events and flooding. Our approach can be adapted as plant health diagnostics tools via remote sensing and drone-technology.

ecology↗

bioRxiv2021

Personalized Closed-Loop Brain Stimulation for Effective Neurointervention Across Participants

Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants--personalized Bayesian optimization (pBO)--that searches available parameter combinations to optimize an intervention as a function of an individuals ability. This novel technique was utilized to identify transcranial alternating current stimulation frequency and current strength combinations most likely to improve arithmetic performance, based on a subjects baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, stimulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.

neuroscience↗

bioRxiv2019

Independence of HIF1a and androgen signaling pathways in prostate cancer

Androgen signaling drives prostate cancer progression and is a therapeutic target. Hypoxia/HIF1a signaling is associated with resistance to hormone therapy and a poor prognosis in patients treated with surgery or radiotherapy. It is not known whether the pathways operate in cooperation or independently. Using LNCaP cells with and without stable transfection of a HIF1a expression vector, we show that combined AR and HIF1a signaling promotes tumor growth in vitro and in vivo, and the capacity of HIF1a to promote tumor growth in the absence of endogenous androgen in vivo. Gene expression analysis identified 7 genes that were upregulated by both androgen and HIF1a. ChIP-Seq analysis showed that the AR and HIF/hypoxia signaling pathways function independently regulating the transcription of different genes with few shared targets. In clinical datasets elevated expression of 5 of the 7 genes was associated with a poor prognosis. Our findings suggest that simultaneous therapeutic inhibition of AR and HIF1a signaling pathways should be explored as a potential therapeutic strategy.

cancer biology↗