Marco Vanoni

Cell growth and cell cycle In Saccharomyces cerevisiae: basic regulatory design and protein-protein interaction network

In this review we summarize the major connections between cell growth and cell cycle in the model eukaryote Saccharomyces cerevisiae. In S. cerevisiae regulation of cell cycle progression is achieved predominantly during a narrow interval in the late G1 phase known as START (Pringle and Hartwell, 1981). At START a yeast cell integrates environmental and internal signals (such as nutrient availability, presence of pheromone, attainment of a critical size, status of the metabolic machinery) and decides whether to enter a new cell cycle or to undertake an alternative developmental program. Several signaling pathways, that act to connect the nutritional status to cellular actions, are briefly outlined. A Growth & Cycle interaction network has been manually curated. More than one fifth of the edges within the Growth & Cycle network connect Growth and Cycle proteins, indicating a strong interconnection between the processes of cell growth and cell cycle. The backbone of the Growth & Cycle network is composed of middle-degree nodes suggesting that it shares some properties with HOT networks. The development of multi-scale modeling and simulation analysis will help to elucidate relevant central features of growth and cycle as well as to identify their system-level properties. Confident collaborative efforts involving different expertises will allow to construct consensus, integrated models effectively linking the processes of cell growth and cell cycle, ultimately contributing to shed more light also on diseases in which an altered proliferation ability is observed, such as cancer.

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.

5-Fluorouracil resistant colon cancer cells are addicted to OXPHOS to survive and enhance stem-like traits

Despite marked tumor shrinkage after 5-FU treatment, the frequency of colon cancer relapse indicates that a fraction of tumor cells survives treatment causing tumor recurrence. The majority of cancer cells divert metabolites into anabolic pathways through Warburg behavior giving an advantage in terms of tumor growth. Here, we report that treatment of colon cancer cell with 5-FU selects for cells with mesenchymal stem-like properties that undergo a metabolic reprogramming resulting in addiction to OXPHOS to meet energy demands. 5-FU treatment-resistant cells show a de novo expression of pyruvate kinase M1 (PKM1) and repression of PKM2, correlating with repression of the pentose phosphate pathway, decrease in NADPH level and in antioxidant defenses, promoting PKM2 oxidation and acquisition of stem-like phenotype. Response to 5-FU in a xenotransplantation model of human colon cancer confirms activation of mitochondrial function. Combined treatment with 5-FU and a pharmacological inhibitor of OXPHOS abolished the spherogenic potential of colon cancer cells and diminished the expression of stem-like markers. These findings suggest that inhibition of OXPHOS in combination with 5-FU is a rational combination strategy to achieve durable treatment response in colon cancer.

Novel RasGRF1-derived Tat-fused peptides inhibiting Ras-dependent proliferation and migration in mouse and human cancer cells

Mutations of RAS genes are critical events in the pathogenesis of different human tumors and Ras proteins represent a major clinical target for the development of specific inhibitors to use as anticancer agents. Here we present RasGRF1-derived peptides displaying both in vitro and in vivo Ras inhibitory properties. These peptides were designed on the basis of the down-sizing of dominant negative full-length RasGRF1 mutants. The over-expression of these peptides can revert the phenotype of K-RAS transformed mouse fibroblasts to wild type, as monitored by several independent biological readouts, including Ras-GTP intracellular levels, ERK activity, morphology, proliferative potential and anchorage independent growth. Fusion of the RasGRF1-derived peptides with the Tat protein transduction domain allows their uptake into mammalian cells. Chemically synthesized Tat-fused peptides, reduced to as small as 30 residues on the basis of structural constraints, retain Ras inhibitory activity. These small peptides interfere in vitro with the GEF catalyzed nucleotide dissociation and exchange on Ras, reduce cell proliferation of K-RAS transformed mouse fibroblasts, and strongly reduce Ras-dependent IGF-I-induced migration and invasion of human bladder cancer cells. These results support the use of RasGRF1-derived peptides as model compounds for the development of Ras inhibitory anticancer agents.

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.

How Epigallocatechin-3-gallate and Tetracycline Interact with the Josephin Domain of Ataxin-3 and Alter Its Aggregation Mode.

Epigallocatechin-3-gallate (EGCG) and tetracycline are two known inhibitors of amyloid aggregation able to counteract the fibrillation of most of the proteins involved in neurodegenerative diseases. We have recently investigated their effect on ataxin-3 (AT3), the polyglutamine-containing protein responsible for spinocerebellar ataxia type 3. We previously showed that EGCG and tetracycline can contrast the aggregation process and toxicity of expanded AT3, although by different mechanisms. Here, we have performed further experiments by using the sole Josephin domain (JD) to further elucidate the mechanism of action of the two compounds. By protein solubility assays and FTIR spectroscopy we have first observed that EGCG and tetracycline affect the JD aggregation essentially in the same way displayed when acting on the full-length expanded AT3. Then, by saturation transfer difference (STD) NMR experiments, we have shown that EGCG binds both the monomeric and the oligomeric JD form, whereas tetracycline can only interact with the oligomeric one. Surface plasmon resonance (SPR) analysis has confirmed the capability of the sole EGCG to bind monomeric JD, although with a KD value suggestive for a non-specific interaction. Our investigations provide new details on the JD interaction with EGCG and tetracycline, which could explain the different mechanisms by which the two compounds reduce the toxicity of AT3.

A comparative study of Whi5 and retinoblastoma proteins: from sequence and structure analysis to intracellular networks

Cell growth and proliferation require a complex series of tight-regulated and well-orchestrated events. Accordingly, proteins governing such events are evolutionary conserved, even among distant organisms. By contrast, it is more singular the case of “core functions” exerted by functional analogous proteins that are not homologous and do not share any kind of structural similarity. This is the case of proteins regulating the G1/S transition in higher eukaryotes–i.e., the retinoblastoma (Rb) tumor suppressor Rb—and budding yeast, i.e., Whi5. The interaction landscape of Rb and Whi5 is quite large, with more than one hundred proteins interacting either genetically or physically with each protein. The Whi5 interactome has been used to construct a concept map of Whi5 function and regulation. Comparison of physical and genetic interactors of Rb and Whi5 allows highlighting a significant core of conserved, common functionalities associated with the interactors indicating that structure and function of the network—rather than individual proteins—are conserved during evolution. A combined bioinformatics and biochemical approach has shown that the whole Whi5 protein is highly disordered, except for a small region containing the protein family signature. The comparison with Whi5 homologs from Saccharomycetales has prompted the hypothesis of a modular organization of structural disorder, with most evolutionary conserved regions alternating with highly variable ones. The finding of a consensus sequence points to the conservation of a specific phosphorylation rhythm along with two disordered sequence motifs, probably acting as phosphorylation-dependent seeds in Whi5 folding/unfolding. Thus, the widely disordered Whi5 appears to act as a hierarchical, “date hub” that has evolutionary assayed an original way of modular organization before being supplanted by the globular, multi-domain structured Rb, more suitable to cover the role of a “party hub”.

An ensemble evolutionary constraint-based approach to understand the emergence of metabolic phenotypes

Constraint-based modeling is largely used in computational studies of metabolism. We propose here a novel approach that aims to identify ensembles of flux distributions that comply with one or more target phenotype(s). The methodology has been tested on a small-scale model of yeast energy metabolism. The target phenotypes describe the differential pattern of ethanol production and O2 consumption observed in “Crabtree-positive” and “Crabtree-negative” yeasts in changing environment (i.e., when the upper limit of glucose uptake is varied). The ensembles were obtained either by selection among sampled flux distributions or by means of a search heuristic (genetic algorithm). The former approach provided indication about the probability to observe a given phenotype, but the resulting ensembles could not be unambiguously partitioned into “Crabtree-positive” and “Crabtree-negative” clusters. On the contrary well-separated clusters were obtained with the latter method. The cluster analysis further allowed identification of distinct groups within each target phenotype. The method may thus prove useful in characterizing the design principles underlying metabolic plasticity arising from evolving physio-pathological or developmental constraints.

Integrative transcriptional analysis between human and mouse cancer cells provides a common set of transformation associated genes

Mouse functional genomics is largely used to investigate relevant aspects of mammalian physiology and pathology. To which degree mouse models may offer accurate representations of molecular events underlining human diseases such as cancer is not yet fully established. Herein we compare gene expression signatures between a set of human cancer cell lines (NCI-60 cell collection) and a mouse cellular model of oncogenic K-ras dependent transformation in order to identify their closeness at the transcriptional level. The results of our integrative and comparative analysis show that in both species as compared to normal cells or tissues the transformation process involves the activation of a transcriptional response. Furthermore, the cellular mouse model of K-ras dependent transformation has a good degree of similarity with several human cancer cell lines and in particular with cell lines containing oncogenic Ras mutations. Moreover both species have similar genetic signatures that are associated to the same altered cellular pathways (e.g. Spliceosome and Proteasome) or to deregulation of the same genes (e.g. cyclin D1, AHSA1 and HNRNPD) detected in the comparison between cancer cells versus normal cells or tissues. In summary, we report one of the first in-depth analysis of global gene expression profiles of a K-ras dependent mouse cell model of transformation and a large collection of human cancer cells as compared to their normal counterparts. Taken together our findings show a strong correlation in the transcriptional and pathway alteration responses between the two species, therefore validating the use of the mouse model as an appropriate tool to investigate human cancer, and indicating that the comparative analysis, as described here, offers a useful approach to identify cancer-specific gene signatures.

Systems biology for biomedical innovation

The 14th International Biotechnology Symposium and Exhibition (IBS2010) was held in Rimini, Italy on September 2010, with the attendance of more than 1600 participants from 70 different nations around the world. The meeting was devoted to “Biotechnology for the Sustainability of Human Society”, and aimed to explore the advances, frontiers and applications of biotechnology for more sustainable future and a knowledge-based bio-economy. Its program presented many new discoveries. Two sessions, “Systems Biology for Biotechnological Innovation” and “Medical and Pharmaceutical Biotechnology” were found to have several aspects in common and shed light on the efforts of biotechnology to move from the picking of low-hanging fruits to more changing endeavors: to develop a drug discovery strategy for effective personalized treatments of multifactorial diseases still in strong medical need and to design bioprocesses that could realize, on a large scale, a shift of world economy to a sustainable future. Recently, it has been observed that molecular approaches to the study of fundamental biological processes have been unable to explain complex cellular functions (such as growth, cycle, differentiation, apoptosis, senescence, transformation). Molecular Systems Biology (a discipline that integrates molecular investigations – either genome-wide or focused on specific pathways – with computational modeling and control theory) recognizes that such functions emerge as the result of the coordinated, dynamical, non-linear interactions of a large number of components and hence cannot be understood simply in terms of properties of individual molecular components. It follows, for instance, that several major diseases cannot be understood and effectively treated considering them as dependent on single gene alterations, but need to be investigated as network diseases. Biological networks – and diseased networks make no exception – are able to buffer external perturbations, while maintaining their function. Failing to consider the network in which a drug target is integrated may lead to failure in therapy, not because the drug is uneffective or aspecific, but because the network to which the target belongs is able to effectively contrast the action of the drug. By allowing the identification of fragile connections within diseased networks, network analysis and model simulation are expected to provide an essential tool for the development of effective and personalized drugs. Hence, Medical and Pharmaceutical Biotechnology has now to consider Systems Biology its new strategic approach. On the other hand Systems Biology is in continuous evolution, with experimental and computational methods being developed, tested and refined first in model systems, including unicellular organisms and genetically-defined cell lines of higher organisms, with the final goal to identify the logic of networks and circuits, i.e., how