Metabolites such as acetyl-CoA and citrate play an important moonlighting role by influencing the levels of histone post-translational modifications (PTMs) and regulating gene expression. This cross talk between metabolism and epigenome impacts numerous biological processes including development and tumorigenesis. However, the extent of moonlighting activities of cellular metabolites in modulating the epigenome is unknown. We developed a data-driven screen to discover moonlighting metabolites by constructing a histone PTM-metabolite interaction network using global chromatin profiles, metabolomics, and epigenetic drug sensitivity data from over 600 cancer cell lines. Our ensemble statistical learning approach uncovered metabolites that are predictive of histone PTM levels and epigenetic drug sensitivity. We experimentally validated synergistic and antagonistic interactions between histone deacetylase and demethylase inhibitors with the metabolites kynurenic acid, pantothenate, and 1-methylnicotinamide. We apply our approach to track metabolic-epigenetic interactions during the epithelial-mesenchymal transition. Overall, our data-driven approach reveals extensive metabolic-epigenetic interactions than previously thought, with implications for reversing aberrant epigenetic alterations and enhancing epigenetic therapies.
Third-party residual cyber-risk management (RCRM) services (e.g., insurance, re-insurance) are getting increasingly popular (currently, a multi-billion-dollar annual market) with C-suites managing industrial control systems (ICSs) based upon IoT-driven cyber-physical IT and OT technology. Apart from mitigating and diversifying losses from (major) cyber-threats RCRM services positively contribute to improved cyber-security as an added societal benefit. However, it is also well known that RCRM markets (RCRM for ICSs being a mere subset) are relatively nascent and sparse. There is a huge (approximately 10-fold) supply-demand gap in an environment where (a) annual cyber-losses range in trillions of USD, and (b) CRM markets (residual or otherwise) annually worth only upto 0.25 trillion USD. The main reason for this wide gap is the age-old information asymmetry (IA) bottleneck between the demand and supply sides of the third-party RCRM market, that is significantly amplified in modern cyber-space settings. This setting primarily comprises of interdependent and intra-networked ICSs (and/or traditional IT systems) from diverse application sectors inter-networked with each other in a service supply chain environment.
In this paper, we are the first to prove that optimal cyber-risk diversification (integral to RCRM) under IA is computationally intractable, i.e., NP-Hard, for such (systemic) inter-networked societies.
Here, the term ‘optimal diversification’ implies the best way a residual and profit-minded cyber-risk manager can form a portfolio of client coverage contracts. We follow this up with the design and analysis of a computational policy that alleviates this intractability challenge for the social good. Here, the social good can be ensured through denser RCRM markets that in principle improve cyber-security. Our work formally establishes (a) the reason why it has been very difficult in practice (without suitable policy intervention) to densify IA-affected RCRM markets despite their high demand in modern CPS/ICS/IoT societies, and (b) the efficacy of our computational policy to mitigate IA issues between the supply and demand sides of an RCRM market in such societies.
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