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Victori, P.

Publications and source records attributed to Victori, P..

3 recordsLinked to original sources

Metabolic Symbiosis between Oxygenated and Hypoxic Tumour Cells: An Agent-based Modelling Study

Deregulated metabolism is one of the hallmarks of cancer. It is well-known that tumour cells tend to metabolize glucose via glycolysis even when oxygen is available and mitochondrial respiration is functional. However, the lower energy efficiency of aerobic glycolysis with respect to mitochondrial respiration makes this behaviour, namely the Warburg effect, counter-intuitive, although it has now been recognized as a source of anabolic precursors. On the other hand, there is evidence that oxygenated tumour cells could be fuelled by exogenous lactate produced from glycolysis, and that glycolytic tumour cells export lactate to the environment. Using a multi-scale approach combining multi-agent modelling, diffusion-reaction and stoichiometric equations, we simulated these cell populations and their environment, and studied the metabolic co-operation between cells exposed to different oxygen concentration and nutrient abundance. The results show that the metabolic cooperation between cells reduces the depletion of environmental glucose, resulting in an overall advantage of using aerobic glycolysis. In addition, the environmental oxygen level was found to be decreased by the symbiosis, promoting a further shift towards anaerobic glycolysis. However, the oxygenated and hypoxic cell populations may gradually reach quasi-equilibrium. A sensitivity analysis based on Latin hypercube sampling and partial rank correlation shows that the symbiotic dynamics depends on properties of the specific cell such as the minimum glucose level needed for glycolysis. Our results suggest that strategies that block glucose transporters may be more effective than those blocking lactate intake transporters to reduce tumour growth by blocking lactate production. AUTHOR SUMMARYMetabolic alteration is one of the hallmarks of cancer and the well-known metabolic alteration of tumour cells is that cells prefer to do glycolysis over mitochondrial respiration even under well-oxygenated and functional mitochondrial conditions. On the other hand, there is evidence that oxygenated tumour cells could be fuelled by exogenous lactate produced from hypoxic glycolytic cells in which it can create a metabolic co-operation between oxygenated and hypoxic cell populations. This metabolic co-operation could allow tumour cells to economically share oxygen and glucose and promote tumour survival. Using a multi-scale approach combining multi-agent modelling, diffusion-reaction and stoichiometric equations, we studied this metabolic co-operation between different populations of cells, exposed to a changing microenvironment. We predict that the tumour environmental glucose depletion is decreased while the oxygen depletion is increased by this metabolic symbiosis, promoting a further shift towards glycolysis. Our results also show that blocking glucose transporters could be more effective than blocking lactate intake transporters, because the former would disrupt both glycolysis and lactate production, drastically reducing tumour growth.

systems biology↗

Ingres: from single-cell RNA-seq data to single-cell probabilistic Boolean networks

MotivationThe current explosion of omics data has provided scientists with an unique opportunity to elucidate the inner workings of biological processes that remained opaque. For this, computational models are essential. Gene regulatory networks (GRN) have long been used as a way to integrate heterogeneous data into a discrete model, and are very useful to generate actionable hypotheses on the mechanisms governing these biological processes. Boolean networks are particularly popular for this kind of discrete models. When working with single-cell RNA-seq datasets a main focus of analysis is the differential expression between subpopulations of cells. Boolean networks are limited in this task, since they cannot easily represent different levels of expression, confined as they are to binary states. We set out to develop an algorithm that can fit Boolean networks with this kind of data, maintaining both the heterogeneity of the data and the simplicity and computational efficiency of these type of networks. ResultsHere we present Ingres (Inferring Probabilistic Boolean Networks of Gene Regulation Using Protein Activity Enrichment Scores) an open-source tool that uses single-cell sequencing data and prior knowledge GRNs to produce a probabilistic Boolean network (PBN) per each cell and/or cluster of cells in the dataset. Ingres allows to better capture the differences between cell phenotypes, using a continuous measure of protein activity while still confined to the simplicity of a GRN. We believe Ingres will be useful to better understand the heterogeneous makeup of cell populations, to gain insight into the specific circuits that drive certain phenotypes, and to use expression and other omics to infer computational cellular models in bulk or single-cell data. Availability and implementationIngres has been implemented as an R package, and it is publicly available at https://github.com/CBigOxf/ingres. It is currently being submitted to the public repository CRAN too. works seamlessly with existing software for single-cell RNA-seq analysis, and for network analysis, modelling and visualization. Contactpedro.victori@oncology.ox.ac.uk; francesca.buffa@unibocconi.it; francesca.buffa@imm.ox.ac.uk Supplementary informationSupplementary data are available online. Software documentation and an explanatory vignette are available on GitHub and as part of the R package.

bioinformatics↗

Hypoxia-induced SETX links replication stress with the unfolded protein response

The levels of hypoxia associated with resistance to radiotherapy significantly impact cancer patient prognosis. These levels of hypoxia initiate a unique transcriptional response with the rapid activation of numerous transcription factors in a background of global repression of transcription. Here, we show that the biological response to radiobiological hypoxia includes the induction of the DNA/RNA helicase SETX. In the absence of hypoxia-induced SETX, R-loop levels increase, DNA damage accumulates, and DNA replication rates decrease. SETX plays a key role in protecting cells from DNA damage induced during transcription in hypoxia. Importantly, we show that the mechanism of SETX induction is reliant on the PERK/ATF4 arm of the unfolded protein response. These data not only highlight the unique cellular response to radiobiological hypoxia, which includes both a replication stress dependent DNA damage response and an unfolded protein response but uncover a novel link between these two distinct pathways.

cancer biology↗