Biology subjects

Goodwin, R. J. A.

Publications and source records attributed to Goodwin, R. J. A..

5 recordsLinked to original sources

bioRxiv2026

Hypoxia-activated prodrug and chemotherapy disrupt resistance-associated metabolism in osteosarcoma

Chemotherapy resistance remains a critical barrier in treating osteosarcoma. Hypoxia-activated prodrugs (HAPs) target oxygen-deprived tumor regions that evade conventional chemotherapy. Here, we applied integrated spatial multimodal mass spectrometry imaging of metabolites (DESI-MSI, MALDI-MSI), targeted proteomics (IMC), and metallomics (LA-ICP-MSI) to naive and newly developed doxorubicin-resistant osteosarcoma spheroids treated with a novel HAP tirapazamine analogue, TPZ-A-X, and doxorubicin. Combination treatment selectively downregulated GLUT1 and suppressed pro-survival pAkt in doxorubicin-resistant spheroids whilst inducing comparable DNA damage ({gamma}H2AX) across both phenotypes. Metabolomics imaging identified ferroptosis pathway suppression in doxorubicin resistance, which combination treatment reversed, whilst simultaneously depleting glycolytic fuels. Integrative protein-metabolite correlation analysis uncovered functional couplings between glucose transport and CoA-dependent metabolism and spatially revealed anabolic signaling at spheroid peripheries. Combination treatment induced endogenous copper, zinc and magnesium depletion, independent of ATP/ADP collapse reflecting an adaptive survival remodeling of the metalloproteome. HAP/chemotherapy combinations exploit metabolic vulnerabilities via coordinated disruption of ferroptosis suppression, glycolytic dependence, and survival pathways underlying apoptotic resistance. These findings demonstrate a framework for informing mechanistic reasoning and combination strategy design in chemotherapy-resistant tumors.

cancer biology↗

bioRxiv2025

Mapping gene expression across the microbiome-gut-brain axis of germ- and specific pathogen-free mice

Perturbation of the gut microbiota has been implicated in neurological diseases via communication across the microbiome-gut-brain axis. As a result, the discovery of mechanisms underlying interaction across this axis are becoming increasingly important. The germ-free (GF) mouse model has enabled an improved understanding of the influence of the gut microbiota on brain development and function. By utilising an advanced spatial profiling approach, we determined transcriptional changes in the brain, improving our understanding of how brain cells function and interact within their microenvironment in the absence of microbiome influence. Targeted regions of interest were selected based on brain regions implicated in neurological disease or reported structural differences between GF mouse brains and those of colonized mice. In the hippocampus 276 differentially expressed genes (DEGs) were identified, 345 DEGs in the thalamus, and 21 DEGs in pons. Contrastingly we identified only 2 DEGs in the midbrain and 4 in the medulla oblongata, with no DEGs in the cerebellum or corpus callosum. This data provides an overview of gut microbiota influence on gene expression in the brain, highlighting multiple genes of interest for further investigation in the context of microbiome influence on brain function and their potential relevance to neurological disease.

microbiology↗

bioRxiv2023

Metabolic profiling stratifies colorectal cancer and reveals adenosylhomocysteinase as a therapeutic target

With colorectal cancer (CRC) being the second most common cause of cancer-related deaths worldwide1, there is an urgent need for better diagnostic tools and new, more targeted therapies. Here we used genetically engineered mouse models (GEMMs), and multimodal mass spectrometry-based metabolomics to study the impact of common genetic drivers of CRC on the metabolic landscape of the intestine. We show that unsupervised metabolic profiling can stratify intestinal tissues according to underlying genetic alterations, and use mass spectrometry imaging (MSI) to identify tumour, stromal and normal adjacent tissues. By identifying ions that drive variation between normal and transformed tissues, we found dysregulation of the methionine cycle to be a hallmark of APC-mutant CRC, and propose one of its enzymes, i.e. adenosylhomocysteinase (AHCY), as a new therapeutic target. Collectively, we show that the profound genotype-dependent alterations in both lipid and small molecule metabolism in CRC may be exploited for tissue classification with no need for ion identification, and we applied further data analysis to expose a novel metabolic vulnerability of CRC.

cancer biology↗

bioRxiv2020

Training a neural network to learn other dimensionality reduction removes data size restrictions in bioinformatics and provides a new route to exploring data representations

High dimensionality omics and hyperspectral imaging datasets present difficult challenges for feature extraction and data mining due to huge numbers of features that cannot be simultaneously examined. The sample numbers and variables of these methods are constantly growing as new technologies are developed, and computational analysis needs to evolve to keep up with growing demand. Current state of the art algorithms can handle some routine datasets but struggle when datasets grow above a certain size. We present a training deep learning via neural networks on non-linear dimensionality reduction, in particular t-distributed stochastic neighbour embedding (t-SNE), to overcome prior limitations of these methods. One Sentence SummaryAnalysis of prohibitively large datasets by combining deep learning via neural networks with non-linear dimensionality reduction.

bioinformatics↗

bioRxiv2020

Microbiome-derived metabolites reproduce the mitochondrial dysfunction and decreased insulin sensitivity observed in type 2 diabetes

Diabetes is a global health problem that was estimated to be the 7th leading cause of death worldwide in 2016. Type 2 diabetes mellitus (T2DM) is classically associated with genetic and environmental factors, however recent studies have demonstrated that the gut microbiome, which is altered in T2DM patients, is also likely to play a significant role in disease development. Despite this, the identity of microbiome-derived metabolites that influence T2DM onset and/or progression remain elusive. Here we demonstrate that a serum biomarker for T2DM, previously of unknown structure and origin, is actually two microbiome-derived metabolites, 3-methyl-4-(trimethylammonio)butanoate (3M-4-TMAB) and 4-(trimethylammonio)pentanoate (4-TMAP). These metabolites are produced by the Lachnospiraceae family of bacteria, which are highly prevalent in the gut microbiome of T2DM patients and are associated with high dietary fat intake. Treatment of human liver cells with 3M-4-TMAB and 4-TMAP results in a distinct change in the acylcarnitine profile in these cells and significantly reduced their insulin sensitivity; both indicators of T2DM. These results provide evidence of a mechanistic link between gut microbiome derived metabolites and T2DM.

microbiology↗