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    • Because the process of dedifferentiation through the

    2018-10-20

    Because the process of dedifferentiation through the addition of Yamanaka factors is extremely inefficient, the actual contribution of de novo generated “CSC states” via oncometabolic reprogramming to cancer evolution might be a matter of conjecture. To evaluate such a situation, we first confirmed that, upon the introduction of defined reprogramming factors and subsequent partial differentiation, proliferating CSC-like calcitonin gene related peptide stably overexpressing OCT4 and SOX2 with tumor-initiating capacity (Nishi et al., 2014b) likewise arise from non-CSC, OCT4/SOX2-negative MCF10A IDH1 cells (Figure 3B). We then designed a mathematical model to investigate the expected dynamics of tumor progression when the presence of oncometabolic signals can favor differentiated cells to revert to a multipotent CSC-like state. When a native, “resident” population sustained by normal stem cells competes with “invader” clones of CSC-like cells generated de novo as the result of nuclear reprogramming, our mathematical model predicts that the chances of prolonged survival increase exponentially with the size of the reprogrammed clones (Supplemental Appendix F). By solely affecting epigenetic events involving histone methylation, oncometabolites such as 2HG can functionally replace stemness transcription factors (e.g., KLF4 and c-MYC) and accelerate the dedifferentiation rates to efficiently drive the de novo generation of reprogrammed CSC-like states, thus confirming that the possibility that metabolically driven nuclear reprogramming-like phenomena contribute to cancer initiation, and that progression cannot be neglected in terms of cancer prognosis and therapeutic planning (Brooks et al., 2015; Leder et al., 2010; Martin-Castillo et al., 2015; Menendez et al., 2014a). The experimental approach using no-2HG versus 2HG-overproducing cellular models in an identical non-transformed genomic background, functionally confirms the predictions of our stochastic model, demonstrating that an oncometabolite markedly lowers the “energy barriers” separating non-stem and stem cell attractors, diminishes the average time of reprogramming, and increases the size of the basin of attraction of the macrostate occupied by stem cells (Figure 4). For 2HG to improve nuclear reprogramming performance, it is sufficient to be present only during the first few days of reprogramming, when it appears to exert partial functional redundancy with other reprogramming factors that ensure the supply of chromatin-modifying enzymes with metabolic intermediates for the epigenetic activation of stemness-related gene networks (Goding et al., 2014; Gut and Verdin, 2013; Menendez and Alarcón, 2014).
    Discussion One of the most challenging issues in the field of cancer research is understanding how cellular metabolism influences chromatin structure and the epigenome to drive tumor formation (Johnson et al., 2015; Menendez and Alarcón, 2014; Lu and Thompson, 2012; Yun et al., 2012). To date, however, there have been no attempts to delineate predictive mathematical platforms that operatively integrate the required contribution of certain metabolites for the extensive remodeling of the epigenetic landscape that drives nuclear reprogramming (Morris et al., 2014). From a mathematical standpoint, here we introduce nucleosome modification and epigenetic regulation of lineage-specific genes as an essential element of stochastic modeling that successfully integrate the recognized ability of oncometabolites to competitively inhibit epigenetic regulation of cell differentiation with the process whereby the stemness regulatory circuitry is established during nuclear reprogramming (Ben-David et al., 2013; Shu et al., 2013). By combining mathematical modeling and computation simulation with wet-lab in vitro experiments in an isogenic model, we demonstrate the existence of bona fide oncometabolic nuclear reprogramming phenomena able to efficiently generate CSC-like states (Figure 4). Our model provides a stochastic tool as well as a conceptual framework that should be extremely useful in helping to understand and investigate the underexplored link between cellular metabolism and cancer-driving alterations in the epigenome. Beyond the numerous “common” metabolites that are used as substrates and cofactors for reactions that coordinate epigenetic status (Locasale, 2013; Johnson et al., 2015; Yun et al., 2012), a recent systems approach predicted more than 40 compounds and substructures of potential “oncometabolites” that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes (Nam et al., 2014). In this context, our model can be a starting point for future studies on the processes by which cellular metabolism influences chromatin structure and epi-transcriptional circuits to causally drive stemness in cancer tissues.