42 research outputs found

    Explaining the dynamics of tumor aggressiveness : at the crossroads between biology, artificial intelligence and complex systems

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    Facing metastasis is the most pressing challenge of cancer research. In this review, we discuss recent advances in understanding phenotypic plasticity of cancer cells, highlighting the kinetics of cancer stem cell and the role of the epithelial mesenchymal transition for metastasis. It appears that the tumor micro-environment plays a crucial role in triggering phenotypic transitions, as we illustrate discussing the challenges posed by macrophages and cancer associated fibroblasts. To disentangle the complexity of environmentally induced phenotypic transitions, there is a growing need for novel advanced algorithms as those proposed in our recent work combining single cell data analysis and numerical simulations of gene regulatory networks. We conclude discussing recent developments in artificial intelligence and its applications to personalized cancer treatment

    Protein-driven lipid domain nucleation in biological membranes

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    Lipid rafts are heterogeneous dynamic lipid domains of the cell membranes that are involved in several biological processes, such as protein and lipid specific transport and signaling. Our understanding of lipid raft formation is still limited due to the transient and elusive nature of these domains in vivo, in contrast with the stable phase-separated domains observed in artificial membranes. Inspired by experimental findings highlighting the relevance of transmembrane proteins for lipid rafts, we investigate lipid domain nucleation by coarse-grained molecular dynamics and Ising-model simulations. We find that the presence of a transmembrane protein can trigger lipid domain nucleation in a flat membrane from an otherwise mixed lipid phase. Furthermore, we study the role of the lipid domain in the diffusion of the protein showing that its mobility is hindered by the presence of the raft. The results of our coarse-grained molecular-dynamics and Ising-model simulations thus coherently support the important role played by transmembrane proteins in lipid domain formation and stability

    Comparative analysis of metabolic and transcriptomic features of Nothobranchius furzeri

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    Some species have a longer lifespan than others, but usually lifespan is correlated with typical body weight. Here, we study the lifetime evolution of the metabolic behaviour of Nothobranchius furzeri, a killifish with an extremely short lifespan with respect to other fishes, even when taking into account rescaling by body weight. Comparison of the gene expression patterns of N. furzeri with those of zebrafish Danio rerio and mouse (Mus musculus) shows that a broad set of metabolic genes and pathways are affected in N. furzeri during ageing in a way that is consistent with a global deregulation of chromatin. Computational analysis of the glycolysis pathway for the three species highlights a rapid increase in the metabolic activity during the lifetime of N. furzeri with respect to the other species. Our results highlight that the unusually short lifespan of N. furzeri is associated with peculiar patterns in the metabolic activities and in chromatin dynamics

    Integrative analysis of pathway deregulation in obesity

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    Obesity is a pandemic disease, linked to the onset of type 2 diabetes and cancer. Transcriptomic data provides a picture of the alterations in regulatory and metabolic activities associated with obesity, but its interpretation is typically blurred by noise. Here, we solve this problem by collecting publicly available transcriptomic data from adipocytes and removing batch effects using singular value decomposition. In this way we obtain a gene expression signature of 38 genes associated to obesity and identify the main pathways involved. We then show that similar deregulation patterns can be detected in peripheral markers, in type 2 diabetes and in breast cancer. The integration of different data sets combined with the study of pathway deregulation allows us to obtain a more complete picture of gene-expression patterns associated with obesity, breast cancer, and diabetes

    Identifying inhibitors of epithelial–mesenchymal plasticity using a network topology-based approach

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    Metastasis is the cause of over 90% of cancer-related deaths. Cancer cells undergoing metastasis can switch dynamically between different phenotypes, enabling them to adapt to harsh challenges, such as overcoming anoikis and evading immune response. This ability, known as phenotypic plasticity, is crucial for the survival of cancer cells during metastasis, as well as acquiring therapy resistance. Various biochemical networks have been identified to contribute to phenotypic plasticity, but how plasticity emerges from the dynamics of these networks remains elusive. Here, we investigated the dynamics of various regulatory networks implicated in Epithelial-mesenchymal plasticity (EMP)-an important arm of phenotypic plasticity-through two different mathematical modelling frameworks: a discrete, parameter-independent framework (Boolean) and a continuous, parameter-agnostic modelling framework (RACIPE). Results from either framework in terms of phenotypic distributions obtained from a given EMP network are qualitatively similar and suggest that these networks are multi-stable and can give rise to phenotypic plasticity. Neither method requires specific kinetic parameters, thus our results emphasize that EMP can emerge through these networks over a wide range of parameter sets, elucidating the importance of network topology in enabling phenotypic plasticity. Furthermore, we show that the ability to exhibit phenotypic plasticity correlates positively with the number of positive feedback loops in a given network. These results pave a way toward an unorthodox network topology-based approach to identify crucial links in a given EMP network that can reduce phenotypic plasticity and possibly inhibit metastasis-by reducing the number of positive feedback loops

    Unjamming of active rotators

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    Active particle assemblies can exhibit a wide range of interesting dynamical phases depending on internal parameters such as density, adhesion strength or self-propulsion. Active self-rotations are rarely studied in this context, although they can be relevant for active matter systems, as we illustrate by analyzing the motion of Chlamydomonas reinhardtii algae under different experimental conditions. Inspired by this example, we simulate the dynamics of a system of interacting active disks endowed with active torques and self-propulsive forces. At low packing fractions, adhesion causes the formation of small rotating clusters, resembling those observed when algae are stressed. At higher densities, the model shows a jamming to unjamming transition promoted by active torques and hindered by adhesion. We also study the interplay between self-propulsion and self-rotation and derive a phase diagram. Our results yield a comprehensive picture of the dynamics of active rotators, providing useful guidance to interpret experimental results in cellular systems where rotations might play a role

    Molecular mechanisms of heterogeneous oligomerization of huntingtin proteins

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    There is still no successful strategy to treat Huntington's disease, an inherited autosomal disorder associated with the aggregation of mutated forms of the huntingtin protein containing polyglutamine tracts with more than 36 repeats. Recent experimental evidence is challenging the conventional view of the disease by revealing transcellular transfer of mutated huntingtin proteins which are able to seed oligomers involving wild type forms of the protein. Here we decipher the molecular mechanism of this unconventional heterogeneous oligomerization by performing discrete molecular dynamics simulations. We identify the most probable oligomer conformations and the molecular regions that can be targeted to destabilize them. Our computational findings are complemented experimentally by fluorescence-lifetime imaging microscopy/fluorescence resonance energy transfer (FLIM-FRET) of cells co-transfected with huntingtin proteins containing short and large polyglutamine tracts. Our work clarifies the structural features responsible for heterogeneous huntingtin aggregation with possible implications to contrast the prion-like spreading of Huntington's disease

    MicroRNA-222 Regulates Melanoma Plasticity

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    Melanoma is one of the most aggressive and highly resistant tumors. Cell plasticity in melanoma is one of the main culprits behind its metastatic capabilities. The detailed molecular mechanisms controlling melanoma plasticity are still not completely understood. Here we combine mathematical models of phenotypic switching with experiments on IgR39 human melanoma cells to identify possible key targets to impair phenotypic switching. Our mathematical model shows that a cancer stem cell subpopulation within the tumor prevents phenotypic switching of the other cancer cells. Experiments reveal that hsa-mir-222 is a key factor enabling this process. Our results shed new light on melanoma plasticity, providing a potential target and guidance for therapeutic studies

    Metamaterial architecture from a self-shaping carnivorous plant

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    As meticulously observed and recorded by Darwin, the leaves of the carnivorous plant Drosera capensis L. slowly fold around insects trapped on their sticky surface in order to ensure their digestion. While the biochemical signaling driving leaf closure has been associated with plant growth hormones, how mechanical forces actuate the process is still unknown. Here, we combine experimental tests of leaf mechanics with quantitative measurements of the leaf microstructure and biochemistry to demonstrate that the closure mechanism is programmed into the cellular architecture of D. capensis leaves, which converts a homogeneous biochemical signal into an asymmetric response. Inspired by the leaf closure mechanism, we devise and test a mechanical metamaterial, which curls under homogeneous mechanical stimuli. This kind of metamaterial could find possible applications as a component in soft robotics and provides an example of bio-inspired design

    Cellular targets for anticancer strategies

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    Since late 1950s the main strategies to treat cancer, besides surgery, have been radiotherapy or chemotherapy. These approaches work primarily by damaging proliferating cells at the level of DNA replication or cell division, and inducing apoptotic cell suicide as a secondary response to the damage. In recent years, efforts to improve cancer therapy have focused on the development of more selective, biological mechanism based approaches that can help to overcome tumor resistance as well as minimize toxic side effects. In the present review new strategies and new targets for biological cancer therapy will be discussed. In particular, new angiogenic pathways discovered in melanoma will be discussed in relationship to a more efficient anticancer strategy. In summary, this review tries to identify the most logical targets and the most useful mechanisms of tumor inhibition in light of new knowledge from the basic research including human genome project
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