Literature DB >> 29719197

Metabolic cofactors NAD(P)H and FAD as potential indicators of cancer cell response to chemotherapy with paclitaxel.

Maria M Lukina1, Varvara V Dudenkova1, Nadezhda I Ignatova2, Irina N Druzhkova2, Lyubov' E Shimolina1, Elena V Zagaynova2, Marina V Shirmanova3.   

Abstract

Paclitaxel, a widely used antimicrotubular agent, predominantly eliminates rapidly proliferating cancer cells, while slowly proliferating and quiescent cells can survive the treatment, which is one of the main reasons for tumor recurrence and non-responsiveness to the drug. To improve the efficacy of chemotherapy, biomarkers need to be developed to enable monitoring of tumor responses. In this study we considered the auto-fluorescent metabolic cofactors NAD(P)H and FAD as possible indicators of cancer cell response to therapy with paclitaxel. It was found that, among the tested parameters (the fluorescence intensity-based redox ratio FAD/NAD(P)H, and the fluorescence lifetimes of NAD(P)H and FAD), the fluorescence lifetime of NAD(P)H is the most sensitive in tracking the drug response, and is capable of indicating heterogeneous cellular responses both in cell monolayers and in multicellular tumor spheroids. We observed that metabolic reorganization to a more oxidative state preceded the morphological manifestation of cell death and developed faster in cells that were more responsive to the drug. Our results suggest that noninvasive, label-free monitoring of the drug-induced metabolic changes by noting the NAD(P)H fluorescence lifetime is a valuable approach to characterize the responses of cancer cells to anti-cancer treatments and, therefore, to predict the effectiveness of chemotherapy.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Cancer cell; Chemotherapy; Early response; Metabolic cofactors; Paclitaxel

Mesh:

Substances:

Year:  2018        PMID: 29719197     DOI: 10.1016/j.bbagen.2018.04.021

Source DB:  PubMed          Journal:  Biochim Biophys Acta Gen Subj        ISSN: 0304-4165            Impact factor:   3.770


  12 in total

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Authors:  Joe T Sharick; Christine M Walsh; Carley M Sprackling; Cheri A Pasch; Dan L Pham; Karla Esbona; Alka Choudhary; Rebeca Garcia-Valera; Mark E Burkard; Stephanie M McGregor; Kristina A Matkowskyj; Alexander A Parikh; Ingrid M Meszoely; Mark C Kelley; Susan Tsai; Dustin A Deming; Melissa C Skala
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3.  The Oxidation-Induced Autofluorescence Hypothesis: Red Edge Excitation and Implications for Metabolic Imaging.

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4.  Metabolic imaging and secondary ion mass spectrometry to define the structure and function of liver with acute and chronic pathology.

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Review 6.  Modern Approaches to Testing Drug Sensitivity of Patients' Tumors (Review).

Authors:  I N Druzhkova; M V Shirmanova; D S Kuznetsova; М М Lukina; Е V Zagaynova
Journal:  Sovrem Tekhnologii Med       Date:  2020-08-27

7.  Label-free sensing of cells with fluorescence lifetime imaging: The quest for metabolic heterogeneity.

Authors:  Evgeny A Shirshin; Marina V Shirmanova; Alexey V Gayer; Maria M Lukina; Elena E Nikonova; Boris P Yakimov; Gleb S Budylin; Varvara V Dudenkova; Nadezhda I Ignatova; Dmitry V Komarov; Vladislav V Yakovlev; Wolfgang Becker; Elena V Zagaynova; Vladislav I Shcheslavskiy; Marlan O Scully
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9.  Tracing of intracellular pH in cancer cells in response to Taxol treatment.

Authors:  Irina Druzhkova; Maria Lukina; Varvara Dudenkova; Nadezhda Ignatova; Ludmila Snopova; Alena Gavrina; Lyubov Shimolina; Vsevolod Belousov; Elena Zagaynova; Marina Shirmanova
Journal:  Cell Cycle       Date:  2021-07-25       Impact factor: 5.173

10.  Distinct metabolic profiles in Drosophila sperm and somatic tissues revealed by two-photon NAD(P)H and FAD autofluorescence lifetime imaging.

Authors:  Cornelia Wetzker; Klaus Reinhardt
Journal:  Sci Rep       Date:  2019-12-20       Impact factor: 4.379

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