Biology subjects

Nelson, C. C.

Publications and source records attributed to Nelson, C. C..

8 recordsLinked to original sources

bioRxiv2024

Valine Catabolism Drives Bioenergetic and Lipogenic Fuel Plasticity in Prostate Cancer

Metabolic reprogramming is a hallmark of cancer and fundamental for disease progression. The remodelling of oxidative phosphorylation and enhanced lipogenesis are key characteristics of prostate cancer (PCa). Recently, succinate-dependent mitochondrial reprogramming was identified in high-grade prostate tumours with upregulation of enzymes associated with branched-chain amino acid (BCAA) catabolism. We hypothesised that the degradation of BCAAs, particularly valine may play a critical role in anapleurotic refuelling of the mitochondrial succinate pool. Through suppression of valine availability, we report strongly reduced lipid content despite compensatory upregulation of fatty acid uptake, indicating valine is an important lipogenic fuel in PCa. Inhibition of the enzyme 3-hydroxyisobutyryl-CoA hydrolase (HIBCH) also resulted in selective inhibition of cellular proliferation of malignant but not benign prostate cells and impaired succinate production. In combination with a comprehensive multi-omic investigation of patient and cell line data, our work highlights a therapeutic target for selective inhibition of metabolic reprogramming in PCa.

cancer biology↗

bioRxiv2022

Latent representation of single-cell transcriptomes enables algebraic operations on cellular phenotypes

Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of intra-tumoral heterogeneity, it is challenging to assess the risk associated with individual cell subpopulations, primarily due to the complexity of the cancer phenotype space and the lack of clinical annotations associated with tumor scRNA-seq studies. To this end, we introduce SCellBOW, a scRNA-seq analysis framework inspired by document embedding techniques from the domain of Natural Language Processing (NLP). SCellBOW is a novel computational approach that facilitates effective identification and high-quality visualization of single-cell subpopulations. We compared SCellBOW with existing best practice methods for its ability to precisely represent phenotypically divergent cell types across multiple scRNA-seq datasets, including our in-house generated human splenocyte and matched peripheral blood mononuclear cell (PBMC) dataset. For tumor cells, SCellBOW estimates the relative risk associated with each cluster and stratifies them based on their aggressiveness. This is achieved by simulating how the presence or absence of a specific cell subpopulation influences disease prognosis. Using SCellBOW, we identified a hitherto unknown and pervasive AR-/NElow (androgen-receptor-negative, neuroendocrine-low) malignant subpopulation in metastatic prostate cancer with conspicuously high aggressiveness. Overall, the risk-stratification capabilities of SCellBOW hold promise for formulating tailored therapeutic interventions by identifying clinically relevant tumor subpopulations and their impact on prognosis.

bioinformatics↗

bioRxiv2021

Gene expression based inference of drug resistance in cancer

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of large-scale drug screening datasets has provided an opportunity for predicting appropriate patient-tailored therapies by employing machine learning approaches. In this study, we report a predictive modeling approach to infer treatment response in cancers using gene expression data. In particular, we demonstrate the benefits of considering integrated chemogenomics approach, utilizing the molecular drug descriptors and pathway activity information as opposed to gene expression levels. We performed extensive validation of our approach on tissue-derived single-cell and bulk expression data. Further, we constructed several prostate cancer cell lines and xenografts, exposed to differential treatment conditions to assess the predictability of the outcomes. Our approach was further assessed on pan-cancer RNA-sequencing data from The Cancer Genome Atlas (TCGA) archives, as well as an independent clinical trial study describing the treatment journey of three melanoma patients. To summarise, we benchmarked the proposed approach on cancer RNA-seq data, obtained from cell lines, xenografts, as well as humans. We concluded that pathway-activity patterns in cancer cells are reasonably indicative of drug resistance, and therefore can be leveraged in personalized treatment recommendations.

cancer biology↗

bioRxiv2021

Isomeric lipid signatures reveal compartmentalised fatty acid metabolism in cancer

1.0Cellular energy and biomass demands of cancer drive a complex dynamic between uptake of extracellular fatty acids (FA) and de novo synthesis. Given that oxidation of de novo synthesised FAs for energy would result in net-energy loss, there is an implication that FAs from these two sources must have distinct metabolic fates - however hitherto FAs were considered part of a common pool. To probe FA metabolic partitioning, cancer cells were supplemented with stable-isotope labelled FAs. Structural analysis of the resulting glycerophospholipids revealed that labelled FAs from uptake were largely incorporated to canonical (sn-)positions on the glycerol backbone. Surprisingly, labelled FA uptake disrupted canonical isomer patterns of the unlabelled lipidome and induced repartitioning of n-3 and n-6 polyunsaturated-FAs into glycerophospholipid classes. These structural changes evidence differences in the metabolic fate of FAs derived from uptake or de novo sources and demonstrate unique signalling and remodelling behaviours usually hidden to conventional lipidomics. HighlightsO_LILipid isomers reveal discrete metabolic compartmentalisation in cancer C_LIO_LIFAs derived from uptake and de novo synthesis have different metabolic fates C_LIO_LIStearate uptake signals for PUFA (n-3 and n-6) repartitioning between lipid classes C_LIO_LIsn-positional isomers are a marker for aberrant lipid metabolism C_LI

cancer biology↗

bioRxiv2020

Metabolic plasticity in cancer activates apocryphal pathways for lipid desaturation

Fatty acid (FA) modifications, such as enzymatic desaturation and elongation, have long been thought to involve sequential and highly specific enzyme-substrate interactions, which result in canonical products that are well-defined in their chain lengths, degree of unsaturation and double bond positions.1 These products act as a supply of building blocks for the synthesis of complex lipids supporting a symphony of lipid signals and membrane macrostructure. Recently, it was brought to light that differences in substrate availability due to enzyme inhibition can activate alternative pathways in a range of cancers, potentially altering the total species repertoire of FA metabolism.2,3 We have used isomer-resolved lipidomics to analyse human prostate tumours and cancer cell lines and reveal, for the first-time, the full extent of metabolic plasticity in cancer. Assigning the double bond position(s) in simple and complex lipids allows mapping of fatty acid desaturation and elongation via hitherto apocryphal metabolic pathways that generate FAs with unusual sites of unsaturation. Downstream utilisation of these FAs is demonstrated by their incorporation into complex structural lipids. The unsaturation profiles of different phospholipids reveal substantive structural variation between classes that will, necessarily, modulate lipid-centred biological processes in cancer cells including membrane fluidity3-5 and signal transduction.6-8

biochemistry↗

bioRxiv2020

Therapy-induced lipid uptake and remodeling underpin ferroptosis hypersensitivity in prostate cancer

BackgroundMetabolic reprograming, non-mutational epigenetic changes, increased cell plasticity and multidrug tolerance are early hallmarks of therapy resistance in cancer. In this temporary, therapy-tolerant state, cancer cells are highly sensitive to ferroptosis, a form of regulated cell death that is caused by oxidative stress through excess levels of iron-dependent peroxidation of polyunsaturated fatty acids (PUFA). However, mechanisms underpinning therapy-induced ferroptosis hypersensitivity remain to be elucidated. MethodsWe used quantitative single cell imaging of fluorescent metabolic probes, transcriptomics, proteomics and lipidomics to perform a longitudinal analysis of the adaptive response to androgen receptor-targeted therapies (androgen deprivation and enzalutamide) in prostate cancer (PCa). ResultsWe discovered that cessation of cell proliferation and a robust reduction in bioenergetic processes were associated with multidrug tolerance and a strong accumulation of lipids. The gain in lipid biomass was fueled by enhanced lipid uptake through cargo non-selective (macropinocytosis, tunneling nanotubes) and cargo-selective mechanisms (lipid transporters), whereas de novo lipid synthesis was strongly reduced. Enzalutamide induced extensive lipid remodeling of all major phospholipid classes at the expense of storage lipids, leading to increased desaturation and acyl chain length of membrane lipids. The rise in membrane PUFA levels enhanced membrane fluidity and lipid peroxidation, causing hypersensitivity to glutathione peroxidase (GPX4) inhibition and ferroptosis. Combination treatments against AR and fatty acid desaturation, lipase activities or growth medium supplementation with antioxidants or PUFAs altered GPX4 dependence. Despite multidrug tolerance, PCa cells displayed an enhanced sensitivity to inhibition of lysosomal processing of exogenous lipids, highlighting an increased dependence on lipid uptake in the therapy-tolerant state. ConclusionsOur work provides mechanistic insight into processes of lipid metabolism that underpin the acquisition of therapy-induced GPX4 dependence and ferroptosis hypersensitivity to standard of care therapies in PCa. It demonstrated novel strategies to suppress the therapy-tolerant state that may have potential to delay and combat resistance to androgen receptor-targeted therapies, a currently unmet clinical challenge of advanced PCa. Since enhanced GPX4 dependence is an adaptive phenotype shared by several types of cancer in response to different therapies, our work might have universal implications for our understanding of metabolic events that underpin resistance to cancer therapies.

cancer biology↗

bioRxiv2019

The long non-coding RNA GHSROS reprograms prostate cancer cell lines toward a more aggressive phenotype

It is now appreciated that long non-coding RNAs (lncRNAs) are important players in the orchestration of cancer progression. In this study we characterized GHSROS, a human lncRNA gene on the opposite DNA strand (antisense) to the ghrelin receptor gene, in prostate cancer. The lncRNA was upregulated by prostate tumors from different clinical datasets. Consistently, transcriptome data revealed that GHSROS alters the expression of cancer-associated genes. Functional analyses in vitro showed that GHSROS mediates tumor growth, migration, and survival and resistance to the cytotoxic drug docetaxel. Increased cellular proliferation of GHSROS-overexpressing PC3, DU145, and LNCaP prostate cancer cell lines in vitro was recapitulated in a subcutaneous xenograft model. Conversely, in vitro antisense oligonucleotide inhibition of the lncRNA reciprocally regulated cell growth and migration, and gene expression. Notably, GHSROS modulates the expression of PPP2R2C, the loss of which may drive androgen receptor pathway-independent prostate tumor progression in a subset of prostate cancers. Collectively, our findings suggest that GHSROS can reprogram prostate cancer cells toward a more aggressive phenotype and that this lncRNA may represent a potential therapeutic target.

cancer biology↗

bioRxiv2018

No effect of administration of unacylated ghrelin on subcutaneous PC3 xenograft growth in a Rag1-/- mouse model of metabolic dysfunction

Ghrelin is a peptide hormone which, when acylated, regulates appetite, energy balance and a range of other biological processes. Ghrelin predominately circulates in its unacylated form (unacylated ghrelin; UAG). UAG has a number of functions independent of acylated ghrelin, including modulation of metabolic parameters and cancer progression. UAG has also been postulated to antagonise some of the metabolic effects of acyl-ghrelin, including its effects on glucose and insulin regulation. In this study, Rag1-/- mice with high-fat diet-induced obesity and hyperinsulinaemia were subcutaneously implanted with PC3 prostate cancer xenografts to investigate the effect of UAG treatment on metabolic parameters and xenograft growth. Daily intraperitoneal injection of 100 g/kg UAG had no effect on xenograft tumour growth in mice fed normal rodent chow or 23% high-fat diet. UAG significantly improved glucose tolerance in host Rag1-/- mice on a high-fat diet, but did not significantly improve other metabolic parameters. We hypothesise that UAG is not likely to be an effective treatment for prostate cancer, with or without associated metabolic syndrome.\n\nConflict of interestThe authors declare no conflict of interest.

cancer biology↗