Transcriptional and metabolic adaptation of human neurons to the mitochondrial toxicant MPP(+) - PubMed (original) (raw)
doi: 10.1038/cddis.2014.166.
S Gutbier 1, L Zhao 2, D Pöltl 3, C Kullmann 1, V Ivanova 4, S Förster 1, S Jagtap 5, J Meiser 6, G Leparc 7, S Schildknecht 1, M Adam 1, K Hiller 6, H Farhan 8, T Brunner 9, T Hartung 2, A Sachinidis 5, M Leist 1
Affiliations
- PMID: 24810058
- PMCID: PMC4047858
- DOI: 10.1038/cddis.2014.166
Transcriptional and metabolic adaptation of human neurons to the mitochondrial toxicant MPP(+)
A K Krug et al. Cell Death Dis. 2014.
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.
Figures
Figure 1
Time course of MPP+-induced cell death events and metabolome changes. (a) Experimental scheme for cell differentiation, MPP+ exposure and sampling. In all experiments of this study, an MPP+ concentration of 5 _μ_M was used, and cells were analyzed on day 8 (d8) of differentiation (green arrow). Red arrows mark time points used for Omics analysis. Blue arrows mark time points that were analyzed in follow-up experiments. (b and c) Cell viability data: resazurin reduction and lactate dehydrogenase (LDH) release were measured and calcein-positive/-negative cells were counted. Changes of ATP and total cellular glutathione (GSH) were measured in parallel cultures and all data were normalized to untreated controls. (d) Samples obtained after 24 or 36 h of treatment with MPP+ or solvent control were analyzed by quadrupole time-of-flight liquid chromatography-mass spectroscopy (Q-TOF LC-MS). A principal component analysis (PCA) of all metabolite data (labeled by length of exposure) was performed and the first two dimensions are displayed. (e) Cells were stained with tetramethylrhodamine ethyl esther (TMRE, green) and calcein-AM (red) to identify energized mitochondria. Representative micrographs display cells treated with solvent (control) or MPP+ (24 and 48 h). (f) The number of TMRE-positive pixels in all neurites of the field was determined by an unbiased image processing algorithm. Data are means±S.D. from 3 independent experiments, and 30 fields per experiment (*_P_≤0.05)
Figure 2
Metabolic adaptations in MPP+-treated neurons. LUHMES cells were treated with 5 _μ_M MPP+ for different times, and samples were taken at day 8. Metabolite concentrations were determined by Q-TOF LC-MS in four independent experiments. Data were normalized to untreated controls and are displayed as means±S.D. Metabolites that changed significantly (_P_≤0.05, FDR adjusted) are displayed. D-Gluc, D-glucose; UDP-gal, uridinediphosphate galactose; UDP-gluc, uridinediphosphate glucose; P-creatine, phosphocreatine; Met-SO, methionine sulfoxide; SAM, _S_-adenosylmethionine; SAH, _S_-adenosylhomocysteine
Figure 3
Multiple secondary metabolic changes triggered early after exposure to MPP+. Cells were exposed to MPP+ (5 _μ_M) for different times. (a and b) Using a targeted analysis approach, the absolute levels of (a) cysteine (2.98 pmol/106 control cells) and cystine (3.6 pmol/106 control cells) as well as of the (b) polyamines putrescine (1.06 nmol/106 control cells), spermidine (0.17 nmol/106control cells) and spermine (0.28 nmol/106 control cells) were measured in three independent experiments. Data are displayed after normalization to controls (*_P_≤0.05). For background information, a scheme of ornithine–polyamine metabolism is displayed (SAM, _S_-adenosylmethionine; Arg, arginine; arrow indicates direction of regulation by MPP+). (c) Relative glucose oxidation in the TCA cycle was determined by using uniformly labeled [U-13C6]glucose and by determination of mass isotopomer distribution of citrate. M2 mass isotopomers of citrate indicate relative carbon influx from glycolysis via the pyruvate dehydrogenase complex in the TCA cycle. The dopamine transport inhibitor GBR-12935 (1 _μ_M) was used in some experiments as specificity control together with MPP+ to prevent all intracellular effects of MPP+ (*_P_≤0.05). (d) The absolute cellular concentrations of phosphatidylcholines, plasmalogens and lysophosphatidylcholines were determined in the same experiments as in (a and b). Data were normalized to those of control cells. Numbers below the bars indicate number of total acyl/carbon atoms and double bonds
Figure 4
MPP+-induced transcriptome changes and their functional annotation. (a) Cells were treated with MPP+ (5 _μ_M) for different times before samples were taken for DNA microarray-based transcriptome analysis. Probe sets significantly altered at least at one time point are displayed (FDR adjusted _P_-value of ≤0.05; fold change values ≥2). Colors represent _Z_-scores of the row-wise normalized expression values for each probe set. The Spearman's correlations of the samples are indicated above the heatmap. Gene clusters (1–4) consist of probe sets with similar expression profiles. (b) Graphs display fold changes of the top 80 regulated genes for clusters 1 and 2 and of all genes of clusters 3 and 4. The black solid line represents the mean tendency of all genes of the cluster. (c) Overrepresented gene ontology (GO) terms are displayed as wordclouds for every cluster separately. For cluster 1 (downregulated) and cluster 2 (upregulated), only the top 30 GOs with a _P_-value of ≤0.001 are displayed (remaining GOs can be found in Supplementary Table S4). For cluster 5 (all genes downregulated significantly after the 24 h time point) and cluster 6 (upregulated at 24 h), all overrepresented GOs are displayed. (d) Representative images of cells with labeled paraspeckles component 1 protein (PSPC1, red) are displayed. Cells were treated with 5 _μ_M MPP+ for 24 or 48 h and fixed for immunostaining. Compared with the nuclear counterstain (green), PSPC1 strongly decreased over time. (e) Cells were treated as in (a), and samples were analyzed by RNA sequencing (RNAseq). Overrepresented GOs were identified, and the ones that were not contained in the microarray data are displayed. A complete list is supplied in Supplementary Table S5. Calibration of wordcloud displays (indicated in dark blue): the height of the letters reflects the _P_-value of the GO
Figure 5
Time course of transcriptome changes identified by RNA sequencing and RT-qPCR. (a) Cells were treated with MPP+ (5 _μ_M) for different times before samples were taken for RNA sequencing (RNAseq) analysis. Differentially expressed transcripts were identified (FDR adjusted _P_-value of ≤0.05; fold change values ≥2). The numbers of upregulated genes are highlighted in red and the downregulated genes in blue. (b) Scatter plot of fold changes as determined by microarray or RNAseq. Each data point corresponds to one MPP+-regulated transcript. (c) Expression values for transcripts coded by mitochondrial genes were selected from RNAseq data set. Regulated complex I subunits are highlighted in orange. The scheme of complex I illustrates the location of these subunits (orange) in the protein complex. (d) Cells were treated as in (a) and mRNA was prepared after 2–48 h. The samples were analyzed by RT-qPCR for selected marker genes. Data are means±S.E.M. of three independent differentiations. Color coding indicates biological processes that the genes are involved with. ASNS, asparagine synthase; ASS1, argininosuccinate synthase 1; CBS, cystathionine-_β_-synthase; CCNB1, cyclin B1; CTH, cystathionase (cystathionine _γ_-lyase); DDIT3, DNA damage-inducible transcript 3 (CHOP, GADD153); DDIT4, DNA damage-inducible transcript 4; GADD34, growth arrest and DNA damage-inducible protein (PPP1R15A); HNRNPM, heterogeneous nuclear ribonucleoprotein M; MLF1IP, centromere protein U (MLF1 interacting protein); NOXA, phorbol-12-myristate-13-acetate-induced protein 1 (PMAIP1); NQO1, NAD(P)H dehydrogenase, quinone 1; PHGDH, phosphoglycerate dehydrogenase; PPA2, pyrophosphatase (inorganic) 2; PSAT1, phosphoserine aminotransferase 1; PSPC1, paraspeckles component 1; PSPH, phosphoserine phosphatase; SFPQ, Splicing factor proline/glutamine-rich; SHMT2, Serine hydroxymethyl-transferase; SLC3A2, solute carrier family 3 (amino acid transporter heavy chain), member 2; SLC7A11, solute carrier family 7 (anionic amino acid transporter light chain, xc- system), member 11; TXNIP1, thioredoxin-interacting protein; TYMS, thymidylate synthetase
Figure 6
Bioinformatic identification of ATF4 as upstream regulator of transcriptional upregulation. (a) Bioinformatic analysis with IPA software identified ATF4 as regulator of genes that were upregulated (cluster 2). The genes in cluster 2 that are known to be ATF4 targets are indicated, together with their extent of regulation (relative fold change of 24 h versus 0 h) according to microarray analysis. Pathways, in which the ATF4 target genes are involved, are indicated in dark blue (pw, pathway; AA, amino acid). (b) Cells were treated with MPP+ (5 _μ_M), and ATF4 mRNA levels were analyzed by RT-qPCR (relative to GAPDH expression) after different times. (c) Cell lysates were prepared after different times following MPP+ treatment. They were analyzed by western blot for key elements of the ATF4 pathway. Data are representative for 3–4 experiments. eIF2a[pS52], eukaryotic initiation factor 2 _α_-phosphorylated at serine 52
Figure 7
Combined metabolomics–transcriptomics identification of pathways affected by MPP+. Transcriptomics and metabolomics data of the 24 h treatment sample with 5 _μ_M MPP+ were used for pathway mapping. Enzymes (corresponding mRNA) and metabolites that were upregulated are displayed in red. Blue font indicates decreased levels. ATF4 targets are encircled in orange (SLC7A11 has been identified by RNAseq only, the other target genes were identified on both transcriptomics platforms). Underlying biological processes affected by the indicated changes are displayed in green. CBS, cystathionine-_β_-synthase; CTH, cystathionase; DHFR, dihydrofolatereductase; dTMP, deoxythymidine monophosphate; dUMP, deoxyuridine monophosphate; GSH, glutathione; MTHFD2, methylenetetrahydrofolate dehydrogenase; PHGDH, phosphoglycerate dehydrogenase; PSAT1, phosphoserine aminotransferase 1; PSPH, phosphoserine phosphatase; ROS, reactive oxygen species; SHMT2, serine hydroxymethyl-transferase; TYMS, thymidylate synthetase. M2 citric acid and M2 fumaric acid are downregulated mass isotopomers representing the relative downregulation of glucose oxidation in the tricarboxylic acid (TCA) cycle
Figure 8
Separation of MPP+ toxicity and GSH counterregulation in immature cells. Mature (d8) or immature cells (d5) were analyzed after treatment with MPP+ for the times indicated. (a) Intracellular GSH concentrations were determined for mature LUHMES cells (d8) treated with various concentrations (0.01, 0.5, 1, 5 and 25 _μ_M) of MPP+ for 24 h (*_P_-value ≤0.05). (b) Intracellular ATP levels (as measure of overall viability) were determined in d5 LUHMES cells treated with 1, 5 or 25 _μ_M MPP+ for the indicated times. No significant changes were observed. (c) LUHMES (d5) were treated as in (a), (MPP+ for 24 h) and intracellular GSH was determined (*_P_-value ≤0.05). (d) Intracellular GSH concentrations were determined for d5 cells treated for various durations with 1 _μ_M MPP+ (*_P_-value ≤0.05). (e) Lysates of d5 cells were prepared after exposure for indicated time periods to 1 or 5 _μ_M MPP+. Proteins were analyzed by western blot. ATF4 was visualized by immunoblotting and GAPDH was used as loading control. (f) The transsulfuration pathway enzyme cystathionase (CTH) and cystathionine-_β_-synthase (CBS) mRNA levels were evaluated by RT-qPCR in d5 cells treated with 1 _μ_M (blue) and 5 _μ_M (red) MPP+ for different times. (g) Intracellular GSH concentrations were determined for d5 cells treated with siRNA against ATF4 or CTH for 24 h (d2–d3) and subsequently with MPP+ (d3–d5). Scrambled siRNA (scr) was used as experimental negative control. Data are means±S.E.M. (_n_=4), and they are presented relative to untreated cells in each experiment. NS, nonsignificant difference between solvent and MPP+-treated cells in the presence of CTH siRNA. **_P_-value ≤0.01 (increase of GSH by MPP+ in scr-treated cells; reduction of GSH by specific siRNAs)
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