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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 source of anabolic precursors. On the other hand, there is evidence that oxygenated tumour cells could be fuelled by exogenous lactate produced from glycolysis. We employed a multi-scale approach that integrates multi-agent modelling, diffusion-reaction, stoichiometric equations, and Boolean networks to study metabolic cooperation between hypoxic and oxygenated cells exposed to varying oxygen, nutrient, and inhibitor concentrations. The results show that the cooperation reduces the depletion of environmental glucose, resulting in an overall advantage of using aerobic glycolysis. In addition, the oxygen level was found to be decreased by symbiosis, promoting a further shift towards anaerobic glycolysis. However, the oxygenated and hypoxic populations may gradually reach quasi-equilibrium. A sensitivity analysis using 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 to reduce tumour growth than those blocking lactate intake transporters.

Channelling of glucose via glycogen, known as the glycogen shunt, may play an important role in the metabolism of brain tumours, especially in hypoxic conditions. We aimed to dissect the role of glycogen degradation in glioblastoma (GBM) response to ionising radiation (IR). Knockdown of the glycogen phosphorylase liver isoform (PYGL), but not the brain isoform (PYGB), decreased clonogenic growth and survival of GBM cell lines and sensitised them to IR doses of 10–12 Gy. Two to five days after IR exposure of PYGL knockdown GBM cells, mitotic catastrophy and a giant multinucleated cell morphology with senescence-like phenotype developed. The basal levels of the lysosomal enzyme alpha-acid glucosidase (GAA), essential for autolysosomal glycogen degradation, and the lipidated forms of gamma-aminobutyric acid receptor-associated protein-like (GABARAPL1 and GABARAPL2) increased in shPYGL U87MG cells, suggesting a compensatory mechanism of glycogen degradation. In response to IR, dysregulation of autophagy was shown by accumulation of the p62 and the lipidated form of GABARAPL1 and GABARAPL2 in shPYGL U87MG cells. IR increased the mitochondrial mass and the colocalisation of mitochondria with lysosomes in shPYGL cells, thereby indicating reduced mitophagy. These changes coincided with increased phosphorylation of AMP-activated protein kinase and acetyl-CoA carboxylase 2, slower ATP generation in response to glucose loading and progressive loss of oxidative phosphorylation. The resulting metabolic deficiencies affected the availability of ATP required for mitosis, resulting in the mitotic catastrophy observed in shPYGL cells following IR. PYGL mRNA and protein levels were higher in human GBM than in normal human brain tissues and high PYGL mRNA expression in GBM correlated with poor patient survival. In conclusion, we show a major new role for glycogen metabolism in GBM cancer. Inhibition of glycogen degradation sensitises GBM cells to high-dose IR indicating that PYGL is a potential novel target for the treatment of GBMs.

Protection from liver-stage malaria requires high numbers of CD8+ T cells to find and kill Plasmodium -infected cells. A new malaria vaccine strategy, prime-target vaccination, involves sequential viral-vectored vaccination by intramuscular and intravenous routes to target cellular immunity to the liver. Liver tissue-resident memory (TRM) CD8+ T cells have been shown to be necessary and sufficient for protection against rodent malaria by this vaccine regimen. Ultimately, to most faithfully assess immunotherapeutic responses by these local, specialised, hepatic T cells, periodic liver sampling is necessary, however this is not feasible at large scales in human trials. Here, as part of a phase I/II P. falciparum challenge study of prime-target vaccination, we performed deep immune phenotyping, single-cell RNA-sequencing and kinetics of hepatic fine needle aspirates and peripheral blood samples to study liver CD8+ TRM cells and circulating counterparts. We found that while these peripheral ‘TRM-like’ cells differed to TRM cells in terms of previously described characteristics, they are similar phenotypically and indistinguishable in terms of key T cell residency transcriptional signatures. By exploring the heterogeneity among liver CD8+ TRM cells at single cell resolution we found two main subpopulations that each share expression profiles with blood T cells. Lastly, our work points towards the potential for using TRM−like cells as a correlate of protection by liver-stage malaria vaccines and, in particular, those adopting a prime-target approach. A simple and reproducible correlate of protection would be particularly valuable in trials of liver-stage malaria vaccines as they progress to phase III, large-scale testing in African infants. We provide a blueprint for understanding and monitoring liver TRM cells induced by a prime-target malaria vaccine approach.

Motivation: Gene networks are complex sets of regulators and interactions that govern cellular processes. Their perturbations can disrupt regular biological functions, translating into a change in cell behaviour and ability to respond to internal and external cues. Computational models of these networks can boost translation of our scientific knowledge into medical applications by predicting how cells will behave in health and disease, or respond to stimuli such as a drug treatment. The development of such models requires effective ways to read, manipulate and analyse the increasing amount of existing, and newly deposited gene network data. Results: We developed BioSWITCH, a command-line program using the BioPAX standardised language to \"switch on\" static regulatory networks so that they can be executed in GINML to predict cellular behaviour. Using a previously published haematopoiesis gene network, we show that BioSWITCH successfully and faithfully automates the network de-coding and re-coding into an executable logical network. BioSWITCH also supports the integration of a BioPAX model into an existing GINML graph. Competing Interest Statement The authors have declared no competing interest.

A cell's phenotype is the set of observable characteristics resulting from the interaction of the genotype with the surrounding environment, determining cell behaviour. Deciphering genotype-phenotype relationships has been crucial to understand normal and disease biology. Analysis of molecular pathways has provided an invaluable tool to such understanding; however, it has typically lacked a component describing the physical context, which is a key determinant of phenotype. In this study, we present a novel modelling framework that enables to study the link between genotype, signalling networks and cell behaviour in a 3D physical environment. To achieve this we bring together Agent Based Modelling, a powerful computational modelling technique, and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict the evolution of complex multi-cellular dynamics. Importantly, this enables modelling co-occurring intrinsic perturbations, such as mutations, and extrinsic perturbations, such as nutrients availability, and their interactions. Using cancer as a model system, we illustrate the how this framework delivers a unique opportunity to identify determinants of single-cell behaviour, while uncovering emerging properties of multi-cellular growth. Footnotes * More results and discussion included

A cell’s phenotype is the set of observable characteristics resulting from the interaction of the genotype with the surrounding environment, determining cell behaviour. Deciphering genotype-phenotype relationships has been crucial to understand normal and disease biology. Analysis of molecular pathways has provided an invaluable tool to such understanding; however, it does typically not consider the physical microenvironment, which is a key determinant of phenotype. In this study, we present a novel modelling framework that enables to study the link between genotype, signalling networks and cell behaviour in a 3D microenvironment. To achieve this we bring together Agent Based Modelling, a powerful computational modelling technique, and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict the evolution of complex multi-cellular dynamics. Importantly, this enables modelling co-occurring intrinsic perturbations, such as mutations, and extrinsic perturbations, such as nutrients availability, and their interactions. Using cancer as a model system, we illustrate the how this framework delivers a unique opportunity to identify determinants of single-cell behaviour, while uncovering emerging properties of multi-cellular growth. Freely available on the web at http://www.microc.org. Research Resource Identification Initiative ID (https://scicrunch.org/): SCR 016672