The pattern of deoxycytidine- and deoxyguanosine kinase activity in relation to messenger RNA expression in blood cells from untreated patients with B-cell chronic lymphocytic leukemia

Lotfi, Kourosh; Karlsson, Karin; Fyrberg, Anna; Juliusson, Gunnar; Jønsson, Viggo; Peterson, Curt; Eriksson, Staffan; Albertioni, Freidoun

doi:10.1016/j.bcp.2005.12.007

Cited by 21 publications

(13 citation statements)

References 39 publications

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“…Fourth, IMP dehydrogenase has a central role in developing cancers from adult stem cells (Rambhatla et al 2005) and is widely used as a therapeutic target for a range of cancer types (Jackson et al 1975;Shu and Nair 2008;Hedstrom 2009). Importantly, each of these enzymes has multiple roles in cancer signaling, gene regulation, and metabolism (Chiang 1998;AvilaFlores et al 2005;Lotfi et al 2006;Hedstrom 2009), suggesting that metabolic switches play key roles in regulation of cancer metabolism at the interface with other cellular functions. Moreover, these results suggest that the 21 metabolic switches may be used as novel drug targets for the specific type of cancer in which they are found (Supplemental Table S2).…”

Section: Control Of Fluxes In Metabolic Networkmentioning

confidence: 99%

“…Its inhibition by different drugs selectively induces apoptosis in cancer, but not in healthy cells (Borchardt et al 1984;Aarbakke et al 1986;Chiang 1998;Tan et al 2007;Hayden et al 2011), confirming its predicted role as a critical driver in cancer, but not in healthy tissues. Second, a high level of dGK is associated with leukemia (Arnér 1996), and its phosphorylation of several nucleoside analogs increases sensitivity to these anticancer drugs (Zhu et al 1998;Rodriguez et al 2002;Lotfi et al 2006). Third, DAGKs are enzymes catalyzing a key step of the phosphatidylinositol cycle, acting as a molecular switch between cell signaling and lipid metabolism (Mérida et al 2008).…”

Section: Control Of Fluxes In Metabolic Networkmentioning

confidence: 99%

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Control of fluxes in metabolic networks

et al. 2016

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Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism.[Supplemental material is available for this article.]Understanding how cellular systems are controlled on a genomescale is a central issue in biology and medicine. Metabolic networks are at the center of systems biology approaches to unraveling cellular control, because metabolism carries the life-sustaining cellular functions shaping the molecular phenotype (Sweetlove and Ratcliffe 2011). The steady-state principle and physico-chemical constraints (e.g., mass balance and thermodynamics) have been employed to reduce the number of considered network states, facilitating the prediction of genotype-phenotype relationships and intervention strategies for biotechnological or medical purposes (McCloskey et al. 2013). In particular, flux balance analysis and variations thereof have been successfully applied to the metabolic networks of unicellular organisms to predict their metabolic and cellular phenotypes (Varma and Palsson 1994). Yet, those approaches are biased (Lewis et al. 2012) because they restrict the flux space to an a priori specified reference state by assuming a cellular objective to be optimized by the organism (Schuetz et al. 2007). While optimization of biomass yield has proven useful for unicellular organisms, identification of a suitable objective for multicellular organisms remains a nontrivial endeavor (Sweetlove and Ratcliffe 2011). Other approaches, e.g., elementary flux modes (Schuster and Schuster 1993) and extreme pathways analyses (Schilling et al. 2000), do not assume a cellular objective and hence are unbiased. However, despite extensive studies and recent advances (Terzer and Stelling 2008), these unbiased approaches are limited to rather small networks due to their intrinsic computational complexity. We still lack an unbiased computational approach for systematically studying the control of large-scale metabolic networks.Here, we develop such an approach by employing the flux coupling between reactio...

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Section: Control Of Fluxes In Metabolic Networkmentioning

confidence: 99%

Section: Control Of Fluxes In Metabolic Networkmentioning

confidence: 99%

Control of fluxes in metabolic networks

et al. 2016

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“…1A), which is structurally related to both dCK and TK2 (20). Despite some overlap in substrate specificity, dCK and dGK have distinct biological functions (18,21,22) and expression patterns (22,23 (Fig. 1B), were reconstituted with human dCK (CEM-R-dCK) (Fig.…”

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confidence: 99%

[ ¹⁸ F]CFA as a clinically translatable probe for PET imaging of deoxycytidine kinase activity

Kim

Liu

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Deoxycytidine kinase (dCK), a rate-limiting enzyme in the cytosolic deoxyribonucleoside (dN) salvage pathway, is an important therapeutic and positron emission tomography (PET) imaging target in cancer. PET probes for dCK have been developed and are effective in mice but have suboptimal specificity and sensitivity in humans. To identify a more suitable probe for clinical dCK PET imaging, we compared the selectivity of two candidate compounds—[18F]Clofarabine; 2-chloro-2′-deoxy-2′-[18F]fluoro-9-β-d-arabinofuranosyl-adenine ([18F]CFA) and 2′-deoxy-2′-[18F]fluoro-9-β-d-arabinofuranosyl-guanine ([18F]F-AraG)—for dCK and deoxyguanosine kinase (dGK), a dCK-related mitochondrial enzyme. We demonstrate that, in the tracer concentration range used for PET imaging, [18F]CFA is primarily a substrate for dCK, with minimal cross-reactivity. In contrast, [18F]F-AraG is a better substrate for dGK than for dCK. [18F]CFA accumulation in leukemia cells correlated with dCK expression and was abrogated by treatment with a dCK inhibitor. Although [18F]CFA uptake was reduced by deoxycytidine (dC) competition, this inhibition required high dC concentrations present in murine, but not human, plasma. Expression of cytidine deaminase, a dC-catabolizing enzyme, in leukemia cells both in cell culture and in mice reduced the competition between dC and [18F]CFA, leading to increased dCK-dependent probe accumulation. First-in-human, to our knowledge, [18F]CFA PET/CT studies showed probe accumulation in tissues with high dCK expression: e.g., hematopoietic bone marrow and secondary lymphoid organs. The selectivity of [18F]CFA for dCK and its favorable biodistribution in humans justify further studies to validate [18F]CFA PET as a new cancer biomarker for treatment stratification and monitoring.

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“…The lack of correlation between mRNA, protein, and enzyme activity was not surprising since we have shown this before for these enzymes. [29] It is possible that their activities are post-translationally regulated, such as phosphorylation of the enzymes, which has been shown for dCK. [30] The enzyme activities may also fluctuate during cell cycle, as previously described for dCK [17 , 31] and these fluctuations may not be reflected at the mRNA and protein expression levels.…”

Section: Discussionmentioning

confidence: 99%

Nucleoside Analog Activity in Malignant Melanoma Cell Lines

Fyrberg

Lotfi

2015

Nucleosides, Nucleotides and Nucleic Acids

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Mitochondrial deoxyguanosine kinase (dGK), is an enzyme responsible for activation of nucleoside analogs (NAs) to phosphorylated compounds which exert profound cytotoxicity, especially in hematological malignancies. Screening malignant melanoma cell lines against NAs revealed high sensitivity to several of them. This was believed to be due to the high levels of dGK expression in these cells. Downregulation of dGK in the melanoma cell line RaH5 using siRNA did not cause resistance to NAs as expected, but instead cells became more sensitive. This was probably partly due to the increased activity of another mitochondrial enzyme, thymidine kinase 2, seen in transfected cells.

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The pattern of deoxycytidine- and deoxyguanosine kinase activity in relation to messenger RNA expression in blood cells from untreated patients with B-cell chronic lymphocytic leukemia

Cited by 21 publications

References 39 publications

Control of fluxes in metabolic networks

Control of fluxes in metabolic networks

[ ¹⁸ F]CFA as a clinically translatable probe for PET imaging of deoxycytidine kinase activity

Nucleoside Analog Activity in Malignant Melanoma Cell Lines

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The pattern of deoxycytidine- and deoxyguanosine kinase activity in relation to messenger RNA expression in blood cells from untreated patients with B-cell chronic lymphocytic leukemia

Cited by 21 publications

References 39 publications

Control of fluxes in metabolic networks

Control of fluxes in metabolic networks

[ 18 F]CFA as a clinically translatable probe for PET imaging of deoxycytidine kinase activity

Nucleoside Analog Activity in Malignant Melanoma Cell Lines

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[ ¹⁸ F]CFA as a clinically translatable probe for PET imaging of deoxycytidine kinase activity