Biology subjects

Tourigny, D. S.

Publications and source records attributed to Tourigny, D. S..

5 recordsLinked to original sources

bioRxiv2024

PTEN and ARID1A haploinsufficiency equip colonic epithelium for oncogenic transformation

Normal aged tissues are thought to exist as a patchwork of mutant clones. However, the relevance of driver mutations in normal tissue in terms of cancer initiation has not been well described. Here, we sought a quantitative understanding of how different cancer drivers achieve an age-related mutational footprint in the human colonic epithelium and to relate the clonal behaviours they generate to cancer risk. Metanalysis of contemporary multiregional sampling studies of colorectal tumours revealed many of the weak or moderate cancer drivers are trunk mutations present in the last common ancestor from which cancers arise. To study the processes by which such driver mutations could contribute to cancer predisposition, immunohistochemistry was used to detect PTEN, SMAD4 and ARID1A deficient clones in normal colon FFPE surgical resection samples (N=182 patients). Age-related changes in clone size and frequency identified positive biases in clone dynamics that acted to increase the mutational footprint for ARID1A and PTEN but not SMAD4. In vitro engineered monoallelic loss of PTEN and ARID1A implicated specific altered downstream pathways and acquired pro-oncogenic cellular fates corresponding to haploinsufficiency for these genes. In situ analysis confirmed enhanced proliferation in both PTEN and ARID1A deficient clones and creation of an immune exclusive microenvironment associated with ARID1A deficiency. The behaviours resulting from haploinsufficiency of PTEN and ARID1A exemplify how priming of the tissue through somatic mosaicism could contribute alternative combinations of genetic events leading to transformation.

cancer biology↗

bioRxiv2023

Cellular landscape of adrenocortical carcinoma at single-nuclei resolution

Adrenocortical carcinoma (ACC) is a rare yet devastating tumour of the adrenal gland with a molecular pathology that remain incompletely understood. To gain novel insights into the cellular landscape of ACC, we generated single-nuclei RNA sequencing (snRNA-seq) data sets from twelve ACC tumour samples and analysed these alongside a previously published snRNA-seq data set from normal adrenal glands (NAGs). We find the ACC tumour microenvironment to be relatively devoid of immune cells compared to NAG tissues, consistent with known high tumour purity values for ACC as an immunologically "cold" tumour. Our analysis identifies three separate groups of ACC samples that are characterised by different relative compositions of adrenocortical cell types, including two populations (ACC 1 and ACC 2) that are specifically enriched in the most aggressive tumours and display hallmarks of the epithelial to mesenchymal transition (EMT) and dysregulated steroidogenesis, respectively. In addition to cell types associated with hypoxic and metabolic signatures (ACC 3 and ACC 4) prevalent among less-aggressive tumours, we also identified and validated a population of mitotically active adrenocortical cells (ACC M) strongly overexpressing genes POLQ and DIAPH3 that possibly supports the expansion of malignant cell lineages. The smallest identified ACC specific cell type, ACC 5, displays characteristics of increased proliferation and growth factor signalling, and is therefore a potential progenitor-like or cell-of-origin candidate for the different lineages involved in adrenocortical carcinogenesis. Intriguingly, linage tracing suggests the fate adopted by malignant adrenocortical cells upon differentiation appears to be at least partly associated with the copy number or allelic balance state of the imprinted DLK1/MEG3 genomic locus, which we verified by assessing DNA methylation status among samples from the three groups of tumours defined by their different cell type compositions. Our results therefore provide new insights into the cellular heterogeneity of ACC, indicating that genetic perturbations to a hierarchical cellular differentiation mechanism underlying healthy adrenocortical renewal and zonation may explain the molecular basis for disease pathogenesis.

cancer biology↗

bioRxiv2022

Molecular Characterization of the Tumor Microenvironment in Renal Medullary Carcinoma

Renal medullary carcinoma (RMC) is a highly aggressive disease associated with sickle hemoglobinopathies and universal loss of the tumor suppressor gene SMARCB1. RMC has a relatively low rate of incidence compared with other renal cell carcinomas (RCCs) that has hitherto made molecular profiling difficult. To probe this rare disease in detail we performed an in-depth characterization of the RMC tumor microenvironment using a combination of genomic, metabolic and single-cell RNA-sequencing experiments on tissue from a representative untreated RMC patient, complemented by retrospective analyses of archival tissue and existing published data. Our study of the tumor identifies a heterogenous population of malignant cell states originating from the thick ascending limb of the Loop of Henle within the renal medulla, displaying the hallmarks of increased resistance to cell death by ferroptosis and proteotoxic stress driven by MYC-induced proliferative signals. Specifically, genomic characterization of RMC tumors provides substantiating evidence for the recently proposed dependence of SMARCB1-difficient cancers on an intact CDKN2A-p53 pathway and we suggest increased cystine-mTORC-GPX4 signaling also plays a role within transformed RMC cells. We further propose that RMC has an immune landscape comparable to that of untreated RCCs, including heterogenous expression of the immune ligand CD70 within a sub-population of tumor cells, which could provide an immune-modulatory role that serves as a viable candidate for therapeutic targeting.

cancer biology↗

bioRxiv2020

Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm

Stochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the level of cellular populations, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Despite considerable advancements in profiling the genomes, transcriptomes, and proteomes of single cells, it remains difficult to experimentally characterise their metabolism at genome-scale. Computational methods could bridge this gap toward a systems understanding of single-cell biology. To address this challenge, we developed stochastic simulation algorithm with flux-balance analysis embedded (SSA-FBA), a computational framework for simulating the stochastic dynamics of the metabolism of individual cells using genome-scale metabolic models with experimental estimates of gene expression and enzymatic reaction rate parameters. SSA-FBA extends the constraint-based modelling formalism of metabolic network modelling to the single-cell regime, enabling simulation when experimentation is intractable. We also developed an efficient implementation of SSA-FBA that leverages the topology of embedded FBA models to significantly reduce the computational cost of simulation. As a preliminary case study, we built a reduced single-cell model of Mycoplasma pneumoniae, and used SSA-FBA to illustrate the role of stochasticity on the dynamics of metabolism at the single-cell level. SIGNIFICANCEDue to fundamental challenges limiting the experimental characterisation of metabolism within individual cells, computational methods are needed to help infer the metabolic behaviour of single cells from information about their transcriptomes and proteomes. In this paper, we present SSA-FBA, the first systematic framework for modelling the stochastic dynamics of single cells at the level of genome-scale metabolic reaction networks. We provide a robust and efficient algorithm for simulating SSA-FBA models, and apply it to a case study involving the metabolism, RNA and protein synthesis and turnover of a single Mycoplasma pneumoniae cell.

systems biology↗

bioRxiv2018

Energetic substrate availability regulates synchronous activity in an excitatory neural network

Neural networks are required to meet significant metabolic demands associated with performing sophisticated computational tasks in the brain. The necessity for efficient transmission of information imposes stringent constraints on the metabolic pathways that can be used for energy generation at the synapse, and thus low availability of energetic substrates can reduce the efficacy of synaptic function. Here we study the effects of energetic substrate availability on global neural network behavior and find that glucose alone can sustain excitatory neurotransmission required to generate high-frequency synchronous bursting that emerges in culture. In contrast, obligatory oxidative energetic substrates such as lactate and pyruvate are unable to substitute for glucose, indicating that processes involving glucose metabolism form the primary energy-generating pathways supporting coordinated network activity. Our experimental results are discussed in the context of the role that metabolism plays in supporting the performance of individual synapses, including the relative contributions from postsynaptic responses, astrocytes, and presynaptic vesicle cycling. We propose a simple computational model for our excitatory cultures that accurately captures the inability of metabolically compromised synapses to sustain synchronous bursting when extracellular glucose is depleted.

neuroscience↗