Daniela Gaglio

Glutamine Deprivation Induces Abortive S-Phase Rescued by Deoxyribonucleotides in K-Ras Transformed Fibroblasts

Background Oncogene activation plays a role in metabolic reprogramming of cancer cells. We have previously shown that K-ras transformed fibroblasts have a stronger dependence on glycolysis and a reduced oxidative phosphorylation ability as compared to their normal counterparts. Another metabolic adaptation of cancer cells, that has long been established, is their propensity to exhibit increased glutamine consumption, although the effects induced by glutamine deprivation on cancer cells are still controversial. Methodology and Principal Findings Here, by using nutritional perturbations and molecular physiology, we show that reduction or complete depletion of glutamine availability in K-ras transformed fibroblasts causes a strong decrease of proliferation ability and a slower re-entry of synchronized cells into the cell cycle. The reduced proliferation is accompanied by sustained expression of cyclin D and E, abortive S phase entrance and is dependent on Ras signalling deregulation, since it is rescued by expression of a dominant negative guanine nucleotide exchange factor. The growth potential of transformed cells as well as the ability to execute the G1 to S transition is restored by adding the four deoxyribonucleotides, indicating that the arrest of proliferation of K-ras transformed cells induced by glutamine depletion is largely due to a reduced supply of DNA in the presence of signalling pathways promoting G1 to S transition. Conclusions and Significance Our results suggest that the differential effects of glutamine and glucose on cell viability are not a property of the transformed phenotype per se, but rather depend on the specific pathway being activated in transformation. For instance, myc-overexpressing cells have been reported to die under glutamine depletion and not under glucose shortage, while the opposite holds for ras-transformed fibroblasts as shown in this paper. These different responses of transformed cells to nutritional stress should be taken into account when designing anti-cancer therapies that aim to exploit metabolic differences between normal and transformed cells.

From cancer metabolism to new biomarkers and drug targets

Great interest is presently given to the analysis of metabolic changes that take place specifically in cancer cells. In this review we summarize the alterations in glycolysis, glutamine utilization, fatty acid synthesis and mitochondrial function that have been reported to occur in cancer cells and in human tumors. We then propose considering cancer as a system-level disease and argue how two hallmarks of cancer, enhanced cell proliferation and evasion from apoptosis, may be evaluated as system-level properties, and how this perspective is going to modify drug discovery. Given the relevance of the analysis of metabolism both for studies on the molecular basis of cancer cell phenotype and for clinical applications, the more relevant technologies for this purpose, from metabolome and metabolic flux analysis in cells by Nuclear Magnetic Resonance and Mass Spectrometry technologies to positron emission tomography on patients, are analyzed. The perspectives offered by specific changes in metabolism for a new drug discovery strategy for cancer are discussed and a survey of the industrial activity already going on in the field is reported.

Towards a systems biology approach to mammalian cell cycle: modeling the entrance into S phase of quiescent fibroblasts after serum stimulation

BackgroundThe cell cycle is a complex process that allows eukaryotic cells to replicate chromosomal DNA and partition it into two daughter cells. A relevant regulatory step is in the G0/G1 phase, a point called the restriction (R) point where intracellular and extracellular signals are monitored and integrated.Subcellular localization of cell cycle proteins is increasingly recognized as a major factor that regulates cell cycle transitions. Nevertheless, current mathematical models of the G1/S networks of mammalian cells do not consider this aspect. Hence, there is a need for a computational model that incorporates this regulatory aspect that has a relevant role in cancer, since altered localization of key cell cycle players, notably of inhibitors of cyclin-dependent kinases, has been reported to occur in neoplastic cells and to be linked to cancer aggressiveness.ResultsThe network of the model components involved in the G1 to S transition process was identified through a literature and web-based data mining and the corresponding wiring diagram of the G1 to S transition drawn with Cell Designer notation. The model has been implemented in Mathematica using Ordinary Differential Equations. Time-courses of level and of sub-cellular localization of key cell cycle players in mouse fibroblasts re-entering the cell cycle after serum starvation/re-feeding have been used to constrain network design and parameter determination. The model allows to recapitulate events from growth factor stimulation to the onset of S phase. The R point estimated by simulation is consistent with the R point experimentally determined.ConclusionThe major element of novelty of our model of the G1 to S transition is the explicit modeling of cytoplasmic/nuclear shuttling of cyclins, cyclin-dependent kinases, their inhibitor and complexes. Sensitivity analysis of the network performance newly reveals that the biological effect brought about by Cki overexpression is strictly dependent on whether the Cki is promoting nuclear translocation of cyclin/Cdk containing complexes.

Computational Strategies for a System-Level Understanding of Metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.

RAS and PKA pathways in cancer: new insight from transcriptional analysis.

Through its ability to regulate the activity of a large number of transcription factors, the Ras pathway is able to control several transcriptional programs leading to proliferation, differentiation, metabolism, cytoskeletal reorganization and immune response. Cyclic AMP (cAMP) is a ubiquitous intracellular second messenger whose major intracellular target in eukaryotes is protein kinase A (PKA). Wide evidence for cross talk between the Ras and cAMP-PKA pathways is available. After reviewing some features of Ras and PKA signalling that are relevant for cancer biology, we re-analyze available genome-wide expression data for genes encoding proteins of the downstream branch of the PKA pathway in human tumor cell lines as a function of the mutational state of the Ras pathway. The observed Ras-dependent pattern of regulation of the analyzed genes may contribute to explain how the cAMP-PKA axis is involved in oncogenic processes induced by Ras.

Cancer cell growth and survival as a system-level property sustained by enhanced glycolysis and mitochondrial metabolic remodeling

Systems Biology holds that complex cellular functions are generated as system-level properties endowed with robustness, each involving large networks of molecular determinants, generally identified by “omics” analyses. In this paper we describe four basic cancer cell properties that can easily be investigated in vitro: enhanced proliferation, evasion from apoptosis, genomic instability, and inability to undergo oncogene-induced senescence. Focusing our analysis on a K-ras dependent transformation system, we show that enhanced proliferation and evasion from apoptosis are closely linked, and present findings that indicate how a large metabolic remodeling sustains the enhanced growth ability. Network analysis of transcriptional profiling gives the first indication on this remodeling, further supported by biochemical investigations and metabolic flux analysis (MFA). Enhanced glycolysis, down-regulation of TCA cycle, decoupling of glucose and glutamine utilization, with increased reductive carboxylation of glutamine, so to yield a sustained production of growth building blocks and glutathione, are the hallmarks of enhanced proliferation. Low glucose availability specifically induces cell death in K-ras transformed cells, while PKA activation reverts this effect, possibly through at least two mitochondrial targets. The central role of mitochondria in determining the two investigated cancer cell properties is finally discussed. Taken together the findings reported herein indicate that a system-level property is sustained by a cascade of interconnected biochemical pathways that behave differently in normal and in transformed cells.

Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models

The metabolic rearrangements occurring in cancer cells can be effectively investigated with a Systems Biology approach supported by metabolic network modeling. We here present tissue-specific constraint-based core models for three different types of tumors (liver, breast and lung) that serve this purpose. The core models were extracted and manually curated from the corresponding genome-scale metabolic models in the Human Metabolic Atlas database with a focus on the pathways that are known to play a key role in cancer growth and proliferation. Along similar lines, we also reconstructed a core model from the original general human metabolic network to be used as a reference model. A comparative Flux Balance Analysis between the reference and the cancer models highlighted both a clear distinction between the two conditions and a heterogeneity within the three different cancer types in terms of metabolic flux distribution. These results emphasize the need for modeling approaches able to keep up with this tumoral heterogeneity in order to identify more suitable drug targets and develop effective treatments. According to this perspective, we identified key points able to reverse the tumoral phenotype toward the reference one or vice-versa.

Metformin and temozolomide, a synergic option to overcome resistance in glioblastoma multiforme models

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor with poor survival. Cytoreduction in association with radiotherapy and temozolomide (TMZ) is the standard therapy, but response is heterogeneous and life expectancy is limited. The combined use of chemotherapeutic agents with drugs targeting cell metabolism is becoming an interesting therapeutic option for cancer treatment. Here, we found that metformin (MET) enhances TMZ effect on TMZ-sensitive cell line (U251) and overcomes TMZ-resistance in T98G GBM cell line. In particular, combined-treatment modulated apoptosis by increasing Bax/Bcl-2 ratio, and reduced Reactive Oxygen Species (ROS) production. We also observed that MET associated with TMZ was able to reduce the expression of glioma stem cells (GSC) marker CD90 particularly in T98G cells but not that of CD133. In vivo experiments showed that combined treatment with TMZ and MET significantly slowed down growth of TMZ-resistant tumors but did not affect overall survival of TMZ-sensitive tumor bearing mice. In conclusion, our results showed that metformin is able to enhance TMZ effect in TMZ-resistant cell line suggesting its potential use in TMZ refractory GBM patients. However, the lack of effect on a GBM malignancy marker like CD133 requires further evaluation since it might influence response duration.

A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth : The WarburQ effect

Cancer cells share several metabolic traits, including aerobic production of lactate from glucose (Warburg effect), extensive glutamine utilization and impaired mitochondrial electron flow. It is still unclear how these metabolic rearrangements, which may involve different molecular events in different cells, contribute to a selective advantage for cancer cell proliferation. To ascertain which metabolic pathways are used to convert glucose and glutamine to balanced energy and biomass production, we performed systematic constraint-based simulations of a model of human central metabolism. Sampling of the feasible flux space allowed us to obtain a large number of randomly mutated cells simulated at different glutamine and glucose uptake rates. We observed that, in the limited subset of proliferating cells, most displayed fermentation of glucose to lactate in the presence of oxygen. At high utilization rates of glutamine, oxidative utilization of glucose was decreased, while the production of lactate from glutamine was enhanced. This emergent phenotype was observed only when the available carbon exceeded the amount that could be fully oxidized by the available oxygen. Under the latter conditions, standard Flux Balance Analysis indicated that: this metabolic pattern is optimal to maximize biomass and ATP production; it requires the activity of a branched TCA cycle, in which glutamine-dependent reductive carboxylation cooperates to the production of lipids and proteins; it is sustained by a variety of redox-controlled metabolic reactions. In a K-ras transformed cell line we experimentally assessed glutamine-induced metabolic changes. We validated computational results through an extension of Flux Balance Analysis that allows prediction of metabolite variations. Taken together these findings offer new understanding of the logic of the metabolic reprogramming that underlies cancer cell growth.

Divergent in vitro/in vivo responses to drug treatments of highly aggressive NIH-Ras cancer cells: a PET imaging and metabolomics-mass-spectrometry study

Oncogenic K-ras is capable to control tumor growth and progression by rewiring cancer metabolism. In vitro NIH-Ras cells convert glucose to lactate and use glutamine to sustain anabolic processes, but their in vivo environmental adaptation and multiple metabolic pathways activation ability is poorly understood. Here, we show that NIH-Ras cancer cells and tumors are able to coordinate nutrient utilization to support aggressive cell proliferation and survival. Using PET imaging and metabolomics-mass spectrometry, we identified the activation of multiple metabolic pathways such as: glycolysis, autophagy recycling mechanism, glutamine and serine/glycine metabolism, both under physiological and under stress conditions. Finally, differential responses between in vitro and in vivo systems emphasize the advantageous and uncontrolled nature of the in vivo environment, which has a pivotal role in controlling the responses to therapy.