Biology subjects

Wheadon, H.

Publications and source records attributed to Wheadon, H..

3 recordsLinked to original sources

bioRxiv2026

LIMK Inhibition and Metformin Block Mitochondrial Transfer Overcoming Macrophage Driven Therapy Resistance in Acute Myeloid Leukaemia

Chemoresistance is a major contributor to poor clinical outcomes in AML patients and can arise from interactions between AML cells and the bone marrow microenvironment (BME). How immune cells, particularly macrophages (M{varphi}s), facilitate this process requires better clarification. This study shows that M2-like M{varphi}s protect AML cells from apoptosis induced by daunorubicin (DNR) and cytarabine (Ara-C). This protection occurs via co-culture and is linked to enhanced mitochondrial transfer from M{varphi}s to AML cells. M{varphi}s interacted with AML cells via tunneling nanotube (TNT)-like structures. Furthermore, inhibition of mitochondrial transfer using cytochalasin B reduced the protective effect, indicating that mitochondria mediate this process. M{varphi}s transferred functional mitochondria to AML cells as evidenced by enhanced metabolic capacity and reduced reactive oxygen species levels in AML cells under chemotherapy stress. TH-257 (LIMK inhibitor) and metformin blocked mitochondrial transfer and M{varphi}-driven chemoprotection. Moreover, increased transcript expression levels of RhoC and cofilin correlate with inferior overall survival in AML patients. These findings suggest that M2-like M{varphi}s contribute to chemoresistance through TNT-mediated mitochondrial transfer and the LIMK-Cofilin pathway, identifying potential therapeutic targets to circumvent chemoresistance in AML.

cancer biology↗

bioRxiv2025

Targeting MDM2 homodimer and heterodimer disruption with DRx-098D in TP53 wild-type and mutant cancer cells

Novel pharmacological strategies capable of inhibiting pro-oncogenic MDM2 beyond its p53-dependent functions represents an increasingly attractive therapeutic strategy to treating solid and haematological cancers that are depdendent upon MDM2/MDMX, regardless of TP53 mutational status. Utilising a novel first-in-class cell-penetrating peptide disruptor of MDM2 homo- and heterodimerisation (DRx-098D), we demonstrate the anti-proliferative effect of blocking MDM2 dimerisation against a a panel of human cancer cell lines that are TP53 wild-type, mutant or null. DRx-098D elicits its anti-cancer activity via a differentiated mechanism vs. Idasanutlin (a Phase III clinical candidate MDM2-p53 small molecule inhibitor), inducing significantly superior growth inhibition against TP53 null HCT116 cells. Our preliminary data highlight, for the first time, the potential therapeutic utility of exploiting MDM2 dimerisation in TP53 wild-type and mutant cancers.

cancer biology↗

bioRxiv2021

Using topic modeling to detect cellular crosstalk in scRNA-seq

Cell-cell interactions are vital for numerous biological processes including development, differentiation, and response to inflammation. Currently most methods for studying interactions on scRNA-seq level are based on curated databases of ligands and receptors. While those methods are useful, they are limited to our current biological knowledge. Recent advances in single cell protocols have allowed for physically interacting cells to be captured and as such we have the potential to study interactions in a complimentary way without relying on prior knowledge. We introduce a new method for detecting genes that change as a result of interaction based on Latent Dirichlet Allocation (LDA). We apply our method to synthetic datasets to demonstrate its ability to detect genes that change in an interacting population compared to a reference population. Next, we apply our approach to two datasets of physically interacting cells and identify genes that change as a result of interaction, examples include adhesion and co-stimulatory molecules which confirm physical interaction between cells. For each dataset we produce a ranking of genes that are changing in subpopulations of the interacting cells. In addition to genes, discussed in the original publications we highlight further candidates for interaction in the top 100 and 300 ranked genes. Lastly, we apply our method to a dataset generated by a standard droplet based protocol, not designed to capture interacting cells and discuss its suitability for analysing interactions. We present a method that streamlines the detection of interactions and does not require prior clustering and generation of synthetic reference profiles to detect changes in expression. Author summaryWhile scRNA-seq research is a dynamic area, progress is lacking when it comes to developing methods that allow analysis of interaction that is independent of curated resources of known interacting pairs. Recent advances of sequencing protocols have allowed for interacting cells to be captured. We propose a novel method based on LDA that captures changes in gene expression as a result of interaction. Our method does not require prior information in the form of clustering or generation of synthetic reference profiles. We demonstrate the suitability of our approach by applying it to synthetic and real datasets and manage to capture biologically interesting candidates of interaction.

bioinformatics↗