4.5 Article

Integrated genetic and metabolic landscapes predict vulnerabilities of temozolomide resistant glioblastoma cells

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NATURE RESEARCH
DOI: 10.1038/s41540-020-00161-7

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  1. DST-INSPIRE
  2. DST
  3. CSIR
  4. Department of Science and Technology, India (SERB) [GAP292826, EMR/2016/000167]
  5. Council for Scientific and Industrial Research (CSIR-India)
  6. Department of Biotechnology [GAP302126]

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The study revealed differential metabolic responses to TMZ in resistant neurospheroidal and susceptible glioblastoma cell lines, with identified mutations in signaling pathway regulators of growth and energy metabolism. Using an integrated approach, rewiring in glycolysis, TCA cycle, malate aspartate shunt, and oxidative phosphorylation pathways were identified as key factors in shaping TMZ resistance. Differential killing of TMZ resistant NSP by Rotenone at low concentrations, with an IC50 value three orders of magnitude lower than in U87MG, highlights the effectiveness of the integrated systems-based approach.
Metabolic reprogramming and its molecular underpinnings are critical to unravel the duality of cancer cell function and chemo-resistance. Here, we use a constraints-based integrated approach to delineate the interplay between metabolism and epigenetics, hardwired in the genome, to shape temozolomide (TMZ) resistance. Differential metabolism was identified in response to TMZ at varying concentrations in both the resistant neurospheroidal (NSP) and the susceptible (U87MG) glioblastoma cell-lines. The genetic basis of this metabolic adaptation was characterized by whole exome sequencing that identified mutations in signaling pathway regulators of growth and energy metabolism. Remarkably, our integrated approach identified rewiring in glycolysis, TCA cycle, malate aspartate shunt, and oxidative phosphorylation pathways. The differential killing of TMZ resistant NSP by Rotenone at low concentrations with an IC50 value of 5 nM, three orders of magnitude lower than for U87MG that exhibited an IC50 value of 1.8 mM was thus identified using our integrated systems-based approach.

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