Transcriptional and metabolic adaptation of human neurons to the mitochondrial toxicant MPP(+)

人类神经元对线粒体毒物 MPP(+) 的转录和代谢适应

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作者:A K Krug, S Gutbier, L Zhao, D Pöltl, C Kullmann, V Ivanova, S Förster, S Jagtap, J Meiser, G Leparc, S Schildknecht, M Adam, K Hiller, H Farhan, T Brunner, T Hartung, A Sachinidis, M Leist

Abstract

Assessment of the network of toxicity pathways by Omics technologies and bioinformatic data processing paves the road toward a new toxicology for the twenty-first century. Especially, the upstream network of responses, taking place in toxicant-treated cells before a point of no return is reached, is still little explored. We studied the effects of the model neurotoxicant 1-methyl-4-phenylpyridinium (MPP(+)) by a combined metabolomics (mass spectrometry) and transcriptomics (microarrays and deep sequencing) approach to provide unbiased data on earliest cellular adaptations to stress. Neural precursor cells (LUHMES) were differentiated to homogeneous cultures of fully postmitotic human dopaminergic neurons, and then exposed to the mitochondrial respiratory chain inhibitor MPP(+) (5 μM). At 18-24 h after treatment, intracellular ATP and mitochondrial integrity were still close to control levels, but pronounced transcriptome and metabolome changes were seen. Data on altered glucose flux, depletion of phosphocreatine and oxidative stress (e.g., methionine sulfoxide formation) confirmed the validity of the approach. New findings were related to nuclear paraspeckle depletion, as well as an early activation of branches of the transsulfuration pathway to increase glutathione. Bioinformatic analysis of our data identified the transcription factor ATF-4 as an upstream regulator of early responses. Findings on this signaling pathway and on adaptive increases of glutathione production were confirmed biochemically. Metabolic and transcriptional profiling contributed complementary information on multiple primary and secondary changes that contribute to the cellular response to MPP(+). Thus, combined 'Omics' analysis is a new unbiased approach to unravel earliest metabolic changes, whose balance decides on the final cell fate.

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