Biology subjects

Rees, P.

Publications and source records attributed to Rees, P..

13 recordsLinked to original sources

bioRxiv2025

Open-science discovery of DNDI-6510, a compound that addresses genotoxic and metabolic liabilities of the COVID Moonshot SARS-CoV-2 Mpro lead inhibitor

The 2020 SARS-CoV-2 coronavirus pandemic highlighted the urgent need for novel small molecule antiviral drugs. (S)-x38 DNDI-6510 is a non-covalent SARS-CoV-2 main protease inhibitor developed by the open science collaboration COVID Moonshot. Here, we report on the metabolic and toxicologic optimization of the lead series previously disclosed by the COVID Moonshot Initiative, leading up to the selection of (S)-x38 DNDI-6510 as the preclinical candidate. We describe the thorough profiling of the series, identifying key risks such as formation of genotoxic metabolites and high clearance, which were successfully addressed during lead optimization. In addition, we disclose the in vitro and in vivo evaluation of (S)-x38 DNDI-6510 in pharmacokinetic and pharmacodynamic models, exploring multiple approaches to ameliorate rodent-specific metabolic clearance, and show that both co-dosing of (S)-x38 DNDI-6510 with an ABT inhibitor and utilizing a metabolically humanized mouse model (8HUM) achieve significant improvements in exposure. Through comparisons of ABT co-dosing and humanized mouse models in efficacy experiments, we demonstrate that continuous exposure over cellular EC90 is required for SARS-CoV-2 antiviral efficacy in vivo in an antiviral model using a mouse-adapted SARS-CoV-2 strain. Finally, (S)-x38 DNDI-6510 was assessed in maximum tolerated dose experiments in two species, demonstrating significant in vivo PXR-linked auto-induction of metabolism, leading to the discontinuation of this compound. In summary, we report the successful effort to overcome series-specific AMES liabilities in a lead development program. Downstream optimization of existing series will require in-depth optimization of rodent-specific liabilities and metabolic induction profile.

pharmacology and toxicology↗

bioRxiv2025

Nuclear phylogenomics reveals strong geographic patterns in the evolutionary history of Aloe and related genera (alooids)

Background and AimsWith >700 species, Aloe and its generic kin (alooids) are a morphologically diverse group of succulent plants with a wide range across Africa, Madagascar and the Arabian Peninsula. Species such as Aloe vera and A. ferox are cultivated at scale for natural products, whole foods, and cosmetics. Despite substantial -taxonomy contributions, infrageneric classification of Aloe has remained unresolved. Molecular systematics has been compromised by the lack of informative characters in standard markers and high costs of obtaining informative nuclear loci from large genomes (e.g. >15 Gbp); and the difficulty of obtaining quality DNA extractions from material of known provenance. Here these constraints are overcome with target capture sequencing that allows cost-effective sequencing of informative low-copy nuclear loci and unlocks genetic resources from preserved specimens in herbaria as well as silica-dried tissues. MethodsUsing a custom kit for alooids, 189 nuclear loci were sequenced in 294 species, including 50 herbarium specimens, to build a new phylogenomic framework for the big genus Aloe and 11 closely related genera in the alooid clade. Available taxonomic classifications for Aloe sensu stricto were contrasted against the obtained topology in an effort to stabilise taxonomy. Genus-level representations of the family Asphodelaceae were sequenced with the same tool. Key resultsThe new phylogenomic framework demonstrates the monophyly of the alooids and confirms recent classifications in which smaller genera (Aloidendron, Aloiampelos, Aristaloe, Gonialoe, Kumara) are separated. Strong geographic patterns in the Aloe phylogeny are contrasted by less obvious phylogenetic structure in habit (growth form), and vegetative or reproductive morphology that are mainstays of -taxonomy. ConclusionRepeated incidents of adaptive radiation and niche specialisation appear to underlie species diversity in Aloe. This study illustrates the power of combined (nuclear) phylogenomic and -taxonomic inference, including the utility of herbarium genomics, in resolving the systematics of big genera. Lay summaryThe alooids are a group of >700 succulent plants, such as Aloe vera. Testing their taxonomy with genetics is difficult as Aloe genomes are large and very similar between species. A targeted approach was used to classify alooids with highly variable nuclear genes in {+/-}300 species. The evolutionary relationships of alooids are determined principally by geographic distribution patterns, rather than plant morphology or flower shape.

evolutionary biology↗

bioRxiv2024

An updated DNA barcoding tool for Aloe vera and related CITES-regulated species

DNA barcoding has revolutionised the identification of illegally traded material of endangered species as it overcomes the lack of resolution encountered with morphological identification. Nonetheless, in recently evolved and highly diverse clades, such as the relatives of Aloe vera, the lack of interspecific sequence variation in standardised markers compromises the barcoding efficacy. We present a new DNA barcoding tool using 189 nuclear markers, optimised for aloes (Asphodelaceae, Alooideae). We built a comprehensive sequence reference dataset from taxonomically verified sources for >300 species and validated its reliability for identification using phylogenomic inference. Seven anonymised samples from verified botanical collections and ten plants seized at London Heathrow Airport were correctly identified to species level, including a critically endangered species from Madagascar. Commercially purchased samples were confirmed to be the species as advertised. An accurate, reliable DNA barcoding method for aloe identification introduces new assurance to regulatory processes for endangered plants in trade.

genomics↗

bioRxiv2024

Cell Targeting and Adjuvant Activity of Dietary Titanium Dioxide

Food-grade titanium dioxide (fgTiO2) is a bio-persistent particle under intense regulatory scrutiny. Paradoxically, meaningful in vivo cellular accumulation has never been demonstrated: the only known cell reservoirs for fgTiO2 are graveyard intestinal pigment cells which are metabolically and immunologically quiescent. Here we identify major new immunocompetent cell targets of fgTiO2 in humans, most notably in the subepithelial dome region of intestinal Peyers patches. Using multimodal microscopy techniques with single-particle detection and per-cell / vesicle image analysis we achieved correlative dosimetry, quantitatively recapitulating human cellular exposures in the terminal ileum of mice fed a fgTiO2-containing diet. Epithelial microfold cells selectively funneled fgTiO2 into LysoMac and LysoDC cells with ensuing accumulation. Notwithstanding, proximity extension analyses for 92 protein targets revealed no measureable perturbation of cell signalling pathways. When chased with oral {Delta}aroA-Salmonella, pro-inflammatory signalling was confirmed, but no augmentation by fgTiO2 was revealed despite marked same-cell loading. Interestingly, Salmonella caused the fgTiO2-recipient cells to migrate basolaterally in the patch and, sporadically, to the lamina propria, thereby fully recreating the intestinal tissue distribution of fgTiO2 in humans. Immunocompetent cells that accumulate fgTiO2 in vivo are now identified and we demonstrate a mouse model that finally enables human-relevant risk assessments of ingested, bio-persistent (nano)particles.

pharmacology and toxicology↗

bioRxiv2023

In the murine and bovine maternal mammary gland signal transducer and activator of transcription 3 is activated in clusters of epithelial cells around the day of birth

Signal Transducers and Activators of Transcription (STATs) regulate mammary gland development. Here we investigate the expression of pSTAT3 in the murine and bovine mammary gland around the day of birth. We identify polarisation of mammary alveoli towards either a low- or high-proportion of pSTAT3 positive alveolar epithelial cells. We present localised colocation analysis applicable to other mammary studies where identification, quantification and interrogation of significant, spatially congregated events is required. We demonstrate that pSTAT3-positive events are multifocally clustered in a non-random and statistically significant fashion. This finding represents a new facet of mammary STAT3 biology meriting further functional investigation.

cell biology↗

bioRxiv2023

OPTIMAL: An OPTimsed Imaging Mass cytometry AnaLysis framework for Segmentation and Data Exploration

Analysis of Imaging Mass Cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single cell segmentation and sub-optimal approaches for data visualisation and exploration. This can lead to inaccurate identification of cell phenotypes, states or spatial relationships compared to reference data from single cell suspension technologies. To this end we have developed the "OPTIMAL" framework to benchmark any approaches for cell segmentation, parameter transformation, batch effect correction, data visualisation/clustering and spatial neighbourhood analysis. Using a panel of 27 metal-tagged antibodies recognising well characterised phenotypic and functional markers to stain the same FFPE human tonsil sample Tissue Microarray (TMA) over 12 temporally distinct batches we tested several cell segmentation models, a range of different arcsinh cofactor parameter transformation values, five different dimensionality reduction algorithms and two clustering methods. Finally we assessed the optimal approach for performing neighbourhood analysis. We found that single cell segmentation was improved by the use of an Ilastik-derived probability map but that issues with poor segmentation were only really evident after clustering and cell type/state identification and not always evident when using "classical" bi-variate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximised the statistical separation between negative and positive signal distributions and a simple Z-score normalisation step after arcsinh transformation eliminated batch effects. Of the five different dimensionality reduction approaches tested, PacMap gave the best data structure with FLOWSOM clustering out-performing Phenograph in terms of cell type identification. We also found that neighbourhood analysis was influenced by the method used for finding neighbouring cells with a "disc" pixel expansion outperforming a "bounding box" approach combined with the need for filtering objects based on size and image-edge location. Importantly OPTIMAL can be used to assess and integrate with any existing approach to IMC data analysis and, as it creates .FCS files from the segmentation output, allows for single cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists.

bioinformatics↗

bioRxiv2022

A quantitative and spatial analysis of cell cycle regulators during the fission yeast cell cycle

We have carried out a systems-level analysis of the spatial and temporal dynamics of cell cycle regulators in the fission yeast Schizosaccharomyces pombe. In a comprehensive single cell analysis we have precisely quantified the levels of 38 proteins previously identified as regulators of the G2 to mitosis transition, and of 7 proteins acting at the G1 to S-phase transition. Only two of the 38 mitotic regulators exhibit changes in concentration at the whole cell level, the mitotic B-type cyclin Cdc13 which accumulates continually throughout the cell cycle, and the regulatory phosphatase Cdc25 which exhibits a complex cell cycle pattern. Both proteins show similar patterns of change within the nucleus as in the whole cell but at higher concentrations. In addition, the concentrations of the major fission yeast cyclin dependent kinase (CDK) Cdc2, the CDK regulator Suc1 and the inhibitory kinase Wee1 also increase in the nucleus peaking at mitotic onset but are constant in the whole cell. The significant increase in concentration with size for Cdc13 supports the model that mitotic B-type cyclin accumulation acts as a cell size sensor. We propose a two-step process for the control of mitosis. First, Cdc13 accumulates in a size-dependent manner which drives increasing CDK activity. Second, from mid G2 the increasing nuclear accumulation of Cdc25 and the counteracting Wee1 introduces a bistability switch that results in a rapid rise of CDK activity at the end of G2 and thus brings about an orderly progression into mitosis. Significance StatementAcross eukaryotes the increasing level of cyclin dependent kinase (CDK) activity drives progression through the cell cycle. As most cells divide at specific sizes, information responding to the size of the cell must feed into the regulation of CDK activity. In this study, we use fission yeast to precisely measure how proteins that have been previously identified in genome wide screens as cell cycle regulators change in their levels with cell cycle progression. We identify the mitotic B-type cyclin Cdc13 and mitotic inhibitory phosphatase Cdc25 as the only two proteins that change in both whole cell and nuclear concentration through the cell cycle, making them candidates for universal cell size sensors at the onset of mitosis and cell division.

cell biology↗

bioRxiv2021

Determining jumping performance from a single body-worn accelerometer using machine learning

External peak power in the countermovement jump is frequently used to monitor athlete training. The gold standard method uses force platforms, but they are unsuitable for field-based testing. However, alternatives based on jump flight time or Newtonian methods applied to inertial sensor data have not been sufficiently accurate for athlete monitoring. Instead, we developed a machine learning model based on characteristic features (functional principal components) extracted from a single body-worn accelerometer. Data were collected from 69 male and female athletes at recreational, club or national levels, who performed 696 jumps in total. We considered vertical countermovement jumps (with and without arm swing), sensor anatomical locations, machine learning models and whether to use resultant or triaxial signals. Using a novel surrogate model optimisation procedure, we obtained the lowest errors with a support vector machine when using the resultant signal from a lower back sensor in jumps without arm swing. This model had a peak power RMSE of 2.3 W{middle dot}kg-1 (5.1% of the mean), estimated using nested cross validation and supported by an independent holdout test (2.0 W{middle dot}kg-1). This error is lower than in previous studies, although it is not yet sufficiently accurate for a field-based method. Our results demonstrate that functional data representations work well in machine learning by reducing model complexity in applications where signals are aligned in time. Our optimisation procedure also was shown to be robust can be used in wider applications with low-cost, noisy objective functions.

bioinformatics↗

bioRxiv2021

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25 - 5.0 {micro}g/mL) and/or carbendazim (0.8 - 1.6 {micro}g/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the "DeepFlow" neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for mononucleates, binucleates, mononucleates with MN and binucleates with MN, respectively. Successful classifications of trinucleates (90%) and tetranucleates (88%) in addition to other or unscorable phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful ([≤] 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent dose regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks.

pharmacology and toxicology↗

bioRxiv2020

Synergistic CDK control pathways maintain cell size homeostasis

To coordinate cell size with cell division, cell size must be computed by the cyclin-CDK control network to trigger division appropriately. Here we dissect determinants of cyclin-CDK activity using a novel high-throughput single-cell in vivo system. We show that inhibitory phosphorylation of CDK encodes cell size information and works synergistically with PP2A to prevent division in smaller cells. However, even in the absence of all canonical regulators of cyclin-CDK, small cells with high cyclin-CDK levels are restricted from dividing. We find that diploid cells of equivalent size to haploid cells exhibit lower CDK activity in response to equal cyclin-CDK enzyme concentrations, suggesting that CDK activity is reduced by DNA concentration. Thus, multiple pathways directly regulate cyclin-CDK activity to maintain robust cell size homeostasis.

cell biology↗

bioRxiv2020

Developing mammary terminal duct lobular units have a dynamic mucosal and stromal immune microenvironment

The human breast and ovine mammary gland undergo a striking degree of postnatal development, leading to formation of terminal duct lobular units (TDLUs). In this study we interrogated aspects of sheep TDLU growth to increase understanding of ovine mammogenesis and as a model for the study of breast development. Mammary epithelial proliferation is significantly higher in lambs less than two months old than in peri-pubertal animals. Ki67 expression is polarized to the leading edge of the developing TDLUs. Intraepithelial ductal macrophages exhibit striking periodicity and significantly increased density in lambs approaching puberty. Stromal macrophages are more abundant centrally than peripherally. The developing ovine mammary gland is infiltrated by intraepithelial and stromal T lymphocytes that are significantly more numerous in older lambs. In the stroma, hotspots of Ki67 expression colocalize with large aggregates of lymphocytes and macrophages. Multifocally these aggregates exhibit distinct organization consistent with tertiary lymphoid structures. The lamb mammary gland thus exhibits a dynamic mucosal and stromal immune microenvironment and, as such, constitutes a valuable model system that provides new insights into postnatal breast development. Summary statementDevelopment of terminal duct lobular units in the sheep mammary gland involves distinct growth phases and macrophage and lymphocyte fluxes. Tertiary lymphoid structures are present subjacent to the mucosal epithelium.

developmental biology↗

bioRxiv2018

Label-free assessment of red blood cell storage lesions by deep learning

Blood transfusion is a life-saving clinical procedure. With millions of units needed globally each year, it is a growing concern to improve product quality and recipient outcomes.\n\nStored red blood cells (RBCs) undergo continuous degradation, leading to structural and biochemical changes. To analyze RBC storage lesions, complex biochemical and biophysical assays are often employed.\n\nWe demonstrate that label-free imaging flow cytometry and deep learning can characterize RBC morphologies during 42-day storage, replacing the current practice of manually quantifying a blood smear from stored blood units. Based only on bright field and dark field images, our model achieved 90% accuracy in classifying six different RBC morphologies associated with storage lesions versus human-curated manual examination. A model fitted to the deep learning-extracted features revealed a pattern of morphological changes within the aging blood unit that allowed predicting the expiration date of stored blood using solely morphological assessment.\n\nDeep learning and label-free imaging flow cytometry could therefore be applied to reduce complex laboratory procedures and facilitate robust and objective characterization of blood samples.

bioinformatics↗

bioRxiv2016

Deep Learning for Imaging Flow Cytometry: Cell Cycle Analysis of Jurkat Cells

We show that deep convolutional neural networks combined with non-linear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by recon-structing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a 6-fold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer.

cell biology↗