Biology subjects

MacDonald, S.

Publications and source records attributed to MacDonald, S..

11 recordsLinked to original sources

bioRxiv2026

A scalable in vitro cell competition model identifies preleukemic super competitor clones

Preleukemic and leukemic conditions are frequently characterized by the acquisition of recurrent genetic mutations in hematopoietic stem cells (HSCs). One of the most frequently mutated genes in both clonal hematopoiesis and myeloid malignancies is the ten-eleven translocation 2 (TET2) gene, which encodes an epigenetic enzyme with a critical role in the regulation of gene transcription. While substantial work has shown that TET2 loss-of-function mutations lead to a clonal advantage, the underpinning cellular mechanism remains unclear, especially with respect to the early interactions with other cells. To study these early cell interactions of mutant and non-mutant cells, we developed an in vitro clonal competition platform where single-cell derived cultures are split into subclonal fractions which can be simultaneously cross-competed against multiple different clones of an opposing genotype. We identify a novel preleukemic cell phenotype that parallels the developmental biology phenomenon of super-competition, whereby ~10% of TET2-mutant clones actively and selectively suppress wild type (WT) cell expansion and, in some cases, completely eliminate WT clones. Molecular profiling and functional assays point to RAC1-GEF signalling inhibition as a specific WT cell vulnerability that is promoted, and tolerated, by neighbouring TET2-mutated clones. In addition to pinpointing selective drivers of non-mutant cell suppression, this clonal competition platform offers a broadly applicable new approach to study cell competition dynamics at an unprecedented scale.

cancer biology↗

bioRxiv2026

Adapting the OpenFlexure Microscope for Affordable Live-Cell Imaging

Live-cell imaging (LCI) captures dynamic cellular behaviours missed in fixed samples. Researchers in resource-limited settings are underrepresented in the adoption of LCI due to the prohibitive cost of dedicated systems. We adapted the 3D-printed OpenFlexure Microscope (OFM) for LCI in humid tissue culture incubators. We relocated electronics to prevent corrosion, and printed in acrylonitrile styrene acrylate (ASA) to improve image stability in prolonged experiments. Current-limiting resistors reduced heat buildup inside sealed incubators, while our simple graphical user interface (GUI)-based application enabled rapid setup of timelapse experiments. Using a 48-hour docetaxel treatment of breast cancer cells, we validated how the LCI-OFM seamlessly fits into established bioimaging pipelines to generate biologically meaningful data. We further demonstrated broad applicability of the LCI-OFM by imaging Leishmania-infected macrophages in a containment level 3 laboratory and gut fungi in an anaerobic environment. This accessible platform expands opportunities for resource-limited researchers to study locally-relevant health challenges.

cell biology↗

bioRxiv2025

Pathogenicity and genome assembly of a Pythium aphanidermatum isolate causing damping-off in amaranth in controlled environment agriculture

Several species of Pythium are destructive soilborne pathogens, causing root rot and damping-off of seedlings and posing significant challenges in controlled environment agriculture (CEA). In this study, four isolates (PT2-1-1, PT2-1-2, PT5, and PT6) were obtained from infected amaranth seedlings and confirmed as P. aphanidermatum through morphology and ITS rDNA sequencing. These isolates dramatically reduced the root length of amaranth seedlings in plate-based pathogenicity assays. In soil-based post-emergence assays, damping-off symptoms were prevalent, with disease incidence reaching up to 100% in susceptible amaranth genotypes. Genome sequencing of isolate PT2-1-1 yielded a 51.55 Mb assembly consisting of 120 contigs, with 14,453 predicted protein-coding genes, including a diverse set of plant cell wall-degrading enzymes with a likely role in host invasion. Analysis of the predicted P. aphanidermatum secretome revealed potential intracellular and apoplastic effectors, including Crinkler and YxSL[RK] effectors; no RxLR effectors were detected. This work provides a second genome assembly for P. aphanidermatum as well as demonstrating variation in pathogenicity of this isolate on different amaranth accessions. Together these pave the way for investigation of pathogen-host interaction and identification of key virulence and defence strategies.

plant biology↗

bioRxiv2025

Selection of pre-leukemic hematopoietic stem cells driven by distinct extracellular matrix molecules

Despite rapid advances in mapping genetic drivers and gene expression changes in hematopoietic stem cells (HSCs), there is a relative paucity of studies at the protein level. Here, we perform a deep, multi-omic characterization (epigenome, transcriptome and proteome) of HSCs carrying a loss-of-function mutation in Tet2, a key driver of increased self-renewal in blood cancers. Using state-of-the-art, multiplexed, low-input mass spectrometry (MS)-based proteomics, we profile wildtype (WT) and TET2-deficient (Tet2-/-) HSCs and show that the proteome captures previously unrecognized molecular processes which define the pre-leukemic HSC molecular landscape. Specifically, we obtain more accurate stratification of WT and Tet2-/- HSCs than transcriptomic approaches and identify extracellular matrix (ECM) molecules as novel points of dysregulation upon TET2 loss. HSC expansion assays using ECM-functionalized hydrogels confirm a selective effect on the expansion of Tet2-mutant HSCs. Taken together, our study represents a comprehensive molecular characterization of Tet2-mutant HSCs and identifies a previously unanticipated role of ECM molecules in regulating self-renewal of disease-driving HSCs.

cell biology↗

bioRxiv2024

Killer Toxin K28 resistance in yeast relies on COG complex mediated trafficking of the defence factor Ktd1

A/B toxins are a diverse family of protein toxins that enter host cells via endocytosis and induce cell death. In yeast, the A/B toxin K28 is internalised to endosomes of susceptible yeast, before following the retrograde trafficking pathway and ultimately triggering cell cycle arrest. The endolysosomal defence factor Ktd1 protects against K28, but its regulation remains unclear. Cog7, a subunit of the conserved oligomeric Golgi (COG) tethering complex, has been implicated in K28 defence, though the mechanism is unknown. We developed a high throughput K28 sensitivity assay and bespoke analysis package to show that all lobe B COG subunits (Cog5 - 8) are required for K28 resistance. Although the COG complex modulates glycosylation of the surface molecules required to bind extracellular K28, our experiments reveal that the hypersensitivity of cog mutants is primarily explained by defects in Ktd1 trafficking. Ktd1 mis-localisation in cog mutants is reminiscent to disruptions in Snc1, a surface cargo that recycles multiple times via the Golgi. This work suggests not only that the COG complex is responsible for the precise trafficking Ktd1 required to mediate toxin defence, but that Ktd1 may survey endolysosomal compartments for internalised K28. This work underpins the importance of Ktd1 in defence against the A/B toxin K28, and implies various membrane trafficking regulators might influence toxin effects in other eukaryotic systems.

cell biology↗

bioRxiv2024

A rational approach for the targeted discovery and characterisation of microbiome-derived therapeutics

The human gut microbiome is intrinsically involved in health and disease, representing a wealth of untapped therapeutic potential. Here, we demonstrate the utility and potential of a metagenome guided, large cohort-based approach for the rational selection of live biotherapeutics from the human gut. We applied this approach to Inflammatory Bowel Disease (IBD), identifying several lead candidates that were significantly depleted in individuals with IBD compared to healthy controls. Their therapeutic potential was assessed in preclinical models of IBD where they improved markers of disease pathology by reducing inflammation and promoting mucosal healing and wound repair. All leads had excellent safety profiles in silico and in vitro, and several additionally presented favourable manufacturing properties, supporting their progression into clinical trials. We believe that this rational approach will be generalisable to any disease state with underlying microbiome aetiology and will expedite the development of novel microbiome-derived therapeutics to improve human health.

microbiology↗

bioRxiv2023

Lipids with negative spontaneous curvature decrease the solubility of the cancer drug paclitaxel in liposomes

Paclitaxel (PTX) is a hydrophobic small-molecule cancer drug that loads into the membrane (tail) region of lipid carriers such as liposomes and micelles. The development of improved lipid-based carriers of PTX is an important objective to generate chemotherapeutics with fewer side effects. The lipids 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE) and glyceryl monooleate (GMO) show propensity for fusion with other lipid membranes, which has led to their use in lipid vectors of nucleic acids. We hypothesized that DOPE and GMO could enhance PTX delivery to cells through a similar membrane fusion mechanism. As an important measure of drug carrier performance, we evaluated PTX solubility in cationic liposomes containing GMO or DOPE. Solubility was determined by time-dependent kinetic phase diagrams generated from direct observations of PTX crystal formation using differential-interference-contrast optical microscopy. Remarkably, PTX was much less soluble in these liposomes than in control cationic liposomes containing univalent cationic lipid 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP) and 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine (DOPC), which are not fusogenic. In particular, PTX was not substantially soluble in GMO-based cationic liposomes. The fusogenicity of DOPE and GMO is related to the negative spontaneous curvature of membranes containing these lipids, which drives formation of nonlamellar self-assembled phases (inverted hexagonal or gyroid cubic). We used synchrotron small-angle x-ray scattering to determine whether PTX solubility is governed by lipid membrane structure (condensed with DNA in pellet form) or by local intermolecular interactions. The results suggest that local intermolecular interactions are of greater importance and that the negative spontaneous curvature-inducing lipids DOPE and GMO are not suitable components of lipid carriers for PTX delivery regardless of carrier structure.

biophysics↗

bioRxiv2023

STimage:robust, confident and interpretable models for predicting gene markers from cancer histopathological images

Spatial transcriptomic (ST) imaging and sequencing data enable us to link tissue morphological features with thousands of previously unseen gene expression values, opening a new horizon for understanding tissue biology and achieving breakthroughs in digital pathology. Deep learning models are emerging to predict gene expression or classify cell types using images as the sole input. Such models hold significant potential for clinical applications, but require improvements in interpretability and robustness. We developed STimage as a comprehensive suite of models for both regression (predicting gene expression) and classification (mapping tissue regions and cell types) tasks. STimage is the first to thoroughly address robustness (uncertainty) and interpretability. For robustness, STimage predicts gene expression based on parameter distributions rather than fixed data points, allowing for generalisation at a population scale. STimage estimates uncertainty from the data (aleatoric) and from the model (epistemic) for each of thousands of imaging tiles. STimage achieves interpretability by analysing model attribution at a single-cell level, and in the context of histopathological annotation. While existing models focus on predicting highly variable genes, STimage predicts functional genes and identifies highly predictable genes. Using diverse datasets from three cancers and one chronic disease, we assessed the models performance on in-distribution and out-of-distribution samples. STimage is robust to technical variations across platforms, data types, sample preservation methods, and disease types. Further, we implemented an ensemble approach, incorporating pre-trained foundation models, to improve performance and reliability, especially in cases with small training datasets. With single-cell resolution Xenium data, STimage could classify cell types for millions of individual cells. Applying STimage to proteomics data such as CODEX, we found that STimage can predict gene expression consistent with protein expression patterns. Finally, we showed that using STimage-predicted values based solely on imaging input, we could stratify patient survival groups. Overall, STimage advances spatial transcriptomics by improving the prediction of gene expression from traditional histopathological images, making it more accessible for tissue biology research and digital pathology applications.

bioinformatics↗

bioRxiv2022

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology

Trust and transparency are critical for deploying deep learning (DL) models into the clinic. DL application poses generalisation obstacles since training/development datasets often have different data distributions to clinical/production datasets that can lead to incorrect predictions with underestimated uncertainty. To investigate this pitfall, we benchmarked one pointwise and three approximate Bayesian DL models used to predict cancer of unknown primary with three independent RNA-seq datasets covering 10,968 samples across 57 primary cancer types. Our results highlight simple and scalable Bayesian DL significantly improves the generalisation of uncertainty estimation (e.g., p-value = 0.0013 for calibration). Moreover, we demonstrate Bayesian DL substantially improves accuracy under data distributional shifts when utilising uncertainty thresholding by designing a prototypical metric that evaluates the expected (accuracy) loss when deploying models from development to production, which we call the Area between Development and Production curve (ADP). In summary, Bayesian DL is a hopeful avenue of research for generalising uncertainty, which improves performance, transparency, and therefore safety of DL models for deployment in real-world.

bioinformatics↗

bioRxiv2020

A workflow for accurate metabarcoding using nanopore MinION sequencing

Metabarcoding has become a common approach to the rapid identification of the species composition in a mixed sample. The majority of studies use established short-read high-throughput sequencing platforms. The Oxford Nanopore MinION, a portable sequencing platform, represents a low-cost alternative allowing researchers to generate sequence data in the field. However, a major drawback is the high raw read error rate that can range from 10% to 22%. To test if the MinION represents a viable alternative to other sequencing platforms we used rolling circle amplification (RCA) to generate full-length consensus DNA barcodes (658bp of cytochrome oxidase I - COI) for a bulk mock sample of 50 aquatic invertebrate species. By applying two different laboratory protocols, we generated two MinION runs that were used to build consensus sequences. We also developed a novel Python pipeline, ASHURE, for processing, consensus building, clustering, and taxonomic assignment of the resulting reads. We were able to show that it is possible to reduce error rates to a median accuracy of up to 99.3% for long RCA fragments (>45 barcodes). Our pipeline successfully identified all 50 species in the mock community and exhibited comparable sensitivity and accuracy to MiSeq. The use of RCA was integral for increasing consensus accuracy, but it was also the most time-consuming step during the laboratory workflow and most RCA reads were skewed towards a shorter read length range with a median RCA fragment length of up to 1262bp. Our study demonstrates that Nanopore sequencing can be used for metabarcoding but we recommend the exploration of other isothermal amplification procedures to improve consensus length.

bioinformatics↗

bioRxiv2020

Activation of Rab11a and endocytosis by Phosphatidylinositol 4-kinase III beta promotes oncogenic signaling in breast cancer

Endosomes are now recognized as important sites for regulating signal transduction. Here we show that the lipid kinase phosphatidylinositol 4-kinase III beta (PI4KIII{beta}) regulates both endocytic kinetics and receptor signaling in breast cancer cells. PI4KIII{beta} generates phosphatidylinositol 4-phosphate from phosphatidylinositol and is highly expressed in a subset of breast cancers. However, the molecular mechanism by which PI4KIII{beta} promotes breast cancer is unclear. We demonstrate that ectopic PI4KIII{beta} expression increases the rates of both endocytic internalization and recycling. PI4KIII{beta} deletion reduces endocytic kinetics accompanied by a concomitant decrease in activity of the Rab11a GTPase, a protein required for endocytic function. Finally, we find that PI4KIII{beta} activates IGF-IR{beta} signaling dependent on endosome function. Regulation of endocytic function by PI4KIII{beta} is independent of its kinase activity but requires interaction with the Rab11a. This suggests that PI4KIII{beta} controls endosomal kinetics and signaling by directly modulating Rab11a function. Our work suggests a novel regulatory role for PI4KIII{beta} in endosome function and plasma membrane receptor signaling.

cancer biology↗