- Open Access
A microarray study of MPP+-treated PC12 Cells: Mechanisms of toxicity (MOT) analysis using bioinformatics tools
© Xu et al; licensee BioMed Central Ltd. 2006
- Published: 15 July 2005
This paper describes a microarray study including data quality control, data analysis and the analysis of the mechanism of toxicity (MOT) induced by 1-methyl-4-phenylpyridinium (MPP+) in a rat adrenal pheochromocytoma cell line (PC12 cells) using bioinformatics tools. MPP+ depletes dopamine content and elicits cell death in PC12 cells. However, the mechanism of MPP+-induced neurotoxicity is still unclear.
In this study, Agilent rat oligo 22K microarrays were used to examine alterations in gene expression of PC12 cells after 500 μM MPP+ treatment. Relative gene expression of control and treated cells represented by spot intensities on the array chips was analyzed using bioinformatics tools. Raw data from each array were input into the NCTR ArrayTrack database, and normalized using a Lowess normalization method. Data quality was monitored in ArrayTrack. The means of the averaged log ratio of the paired samples were used to identify the fold changes of gene expression in PC12 cells after MPP+ treatment. Our data showed that 106 genes and ESTs (Expressed Sequence Tags) were changed 2-fold and above with MPP+ treatment; among these, 75 genes had gene symbols and 59 genes had known functions according to the Agilent gene Refguide and ArrayTrack-linked gene library. The mechanism of MPP+-induced toxicity in PC12 cells was analyzed based on their genes functions, biological process, pathways and previous published literatures.
Multiple pathways were suggested to be involved in the mechanism of MPP+-induced toxicity, including oxidative stress, DNA and protein damage, cell cycling arrest, and apoptosis.
- PC12 Cell
- Protein Damage
- Cell Growth Arrest
- Elicit Cell Death
DNA microarrays have been increasingly applied as a tool for the simultaneous monitoring of relative expression levels of thousands of genes for samples under various conditions, e.g., normal versus disease, and control versus drug or toxicant treatment [1–3], and offer a promising means to better understand how cells react to environmental perturbations. Their popularity, in part, is reflected by the number of microarray-related publications indexed in PubMed, which have been increasing exponentially. With the improvement of microarray chip quality and tools for data quality assurance, the focus in this field has gradually switched from determination of the gene numbers with altered expression levels to the analysis of the biological mechanism by categorizing significantly changed genes into functional groups and pathways [5–7]. Toxicogenomics, a emerging field combining genomics and bioinformatics to identify and characterize mechanisms of toxicity (MOT) for drugs and various compounds, has been developed quickly during the last several years [8–13]. Microarrays, with their power to examine genome-wide transcriptional responses, have become a key technology in toxicogenomics.
During the last two decades, MPTP (1-methyl-4-phenyl-1,2,5,6-tetrahydropyridine) – induced neurotoxicity has attracted a great deal of attention because of the similarity of its toxic effects to the biochemical changes in the brains of patients with Parkinson's disease (PD) [14, 15]. PD is a progressive neurodegenerative disorder that results in degeneration of dopaminergic neurons in the substantia nigra (SN) and dopamine depletion in the striatum . Although MPTP does not exactly reproduce PD, it has been an extremely valuable tool to model many features of PD in animals, and has led to a better understanding of crucial aspects of the sub-cellular events participating in the evolution of the PD clinical syndrome and neurotoxicity [17–20]. In order to be active, MPTP requires monoamine oxidase B to be converted into MPP+ (1-methyl-4-phenylpyridinium) . MPP+ is selectively taken up by dopaminergic neurons via the dopamine transporter of the plasma membrane [22, 23] and produces neuronal loss in substantia nigra (SN), striatal dopamine (DA) depletion and behavioral impairments in humans , primates , and mice [15, 25–28].
PC12 cells, a rat clonal pheochromocytoma cell line , possess dopamine synthesis, metabolism and transporter systems [30–32], and therefore have been used as a model for studies of MPP+ neurotoxicity and PD. A plethora of evidence has demonstrated that MPP+ depletes dopamine and elicits cell death in PC12 cells [33–36]. Previously, we demonstrated that MPP+-induced DA depletion and cell loss in PC12 cells is dose and time-course responsive . However, the mechanism of MPP+ neurotoxicity in PC12 cells is still unclear. Generally, it is believed that MPP+ directly and/or indirectly inhibits mitochondrial complex I, causing abnormal energy metabolism and increased production of reactive oxygen species (ROS), resulting in cell death [34, 36, 38]. Our study indicated that MPP+ may compromise heat shock protein (HSP) cell defense systems and cause apoptosis in NGF-differentiated PC12 cells and C56, but not CD1, mice [37, 39]. However, some studies have suggested that the effect of MPP+ on PC12 cells might be independent of ROS [35, 40]. Recent studies have also demonstrated that endoplasmic reticulum , PI-3  and cAMP pathways  may be possible targets for MPP+-induced neurotoxicity. Thus, MPP+ may cause PC12 cell injury and death via multiple complex mechanisms. Therefore, DNA microarray analysis, by measuring the expression of large numbers of genes, is a promising tool to help elucidate the mechanism of MPP+-induced toxicity in PC12 cells.
With the development of bioinformatics, various toxicogenomic databases and bioinformatics tools are available for data manipulation and mining [9, 44, 45]. The Center for Toxicoinformatics in the FDA's National Center for Toxicological Research (NCTR/FDA) has developed a public microarray data management and analysis software, called ArrayTrack, for FDA-wide microarray data storage and preliminary data analysis [10, 46]. In this study, alterations of gene expression were examined using the Agilent microarray platform and the data were analyzed using ArrayTrack and a software package called IRIDESCENT.
Cell culture and MPP+ treatment
PC12 cells at passage 15–20 (ATCC, Manassas, VA) were grown in 75 cm2tissue culture flasks at 37°C under an atmosphere of 5% CO2/95% air in RPMI 1640 medium (Sigma, St. Louis, MO) containing 10% horse serum and 5% fetal bovine serum (complete media). At about 80% confluence, cells were placed into 24-well plates and cultured for another 2 days, and then were treated with 500 μM MPP+ iodide (Sigma, 100% purity by HPLC), a dose that induces significant decrease in dopamine content and cell viability .
Twenty-four hours after MPP+ treatment, 12 wells of PC12 cells from same treatment group in a 24-well plate were pooled into one single sample. Two control and two MPP+-treated samples were prepared for microarray hybridization. Total RNA was extracted using Qiagen™ RNeasy Mini Kits (Qiagen, Valencia, CA) following the RNeasy mini protocols for isolation from animal cells (I. spin protocol). RNA quantity was measured using the NanoDrop® ND-1000 UV-Vis Spectrophotometer (Wilmington, DE), while RNA quality was monitored using Agilent 2100 bioanalyzer and expressed as RNA integrity number (RIN) value using Agilent 2100 RIN Beta Version Software (Palo Alto, CA). For all 4 RNA samples, the ratios of 260/280 (indication of protein contamination) and 260/230 (indication of reagent contamination) determined by the NanoDrop spectrophotometer were above 2, and the RIN values determined by an Agilent bioanalyzer were 10 (Best RNA quality according to the grading of Agilent 2100 RIN Software).
Agilent 22K 60-mer oligonucleotide microarray slides (Palo Alto, CA) were used and a dye-swap experimental design was applied. High-quality RNA samples (200 ng each) acquired from PC12 cells were amplified and labeled with Cy5-and Cy3-CTP (Amersham Biosciences) to produce labeled cRNA using Agilent low RNA input fluorescent linear amplification kits following the manufacturers protocol. After the amplification and labeling, the dye-incorporation ratio was determined using a Nanodrop spectrophotometer and the ratios were within 10 to 20 pmol per μg cRNA, the range the manufacturer suggests prior to hybridization. For hybridization, the Agilent 60-mer oligo microarray processing protocol (Rev. 7, SSC Wash/6-screw hybridization chamber) was strictly followed. Briefly, 750 ng Cy3-labeled control and 750 ng Cy5-labeled MPP+-treated sample were mixed and incubated with an Agilent microarray slide for 17 hours using an Agilent in situ hybridization kit following SSC buffer washing. Sample pairs were dye-swapped and processed at the same time. The washed slides were immediately dried using an ultra pure filtered N2 stream in an ozone-free Biobubble. After drying, the slides were scanned using an Axon GenePix 4000B scanner with the PMT settings at 770 for Cy5 and 670 for Cy3, and the raw data were generated using GenePix Pro 6.0 software (Axon Instruments, Union City, CA).
Data analysis and MOT interpretation
A software package called IRIDESCENT [48–50] was also used to tie responding genes back to the published literature and identify commonalities among and between genes, diseases, chemical compounds, ontological categories and FDA-approved drugs. Commonalities are scored based upon how many genes within the entire set of microarray responders are observed to be related to each ''object'' via their co-occurrence within MEDLINE abstracts versus how many relationships would be expected statistically, by chance. This enables validation of experimental data by comparing the microarray response to the published literature to verify that at least some previously documented relationships are being reproduced by the experiment. It also enables the identification of common themes and potential new leads. Graphs were drawn using GraphViz, a software package written by AT&T Labs.
Microarray data consistency
Identification of genes altered by MPP+ treatment
Genes associated with oxidative stress
Genbank Acc ID
vacuole Membrane Protein 1
leukotriene B4 12-hydroxydehydrogenase
putative ISG12(b) protein
heat shock 70 kD protein 5
homocysteine-inducible, endoplasmic reticulum stress-inducible, ubiquitin-like domain member 1
glutathione S-transferase, mu 1
ferritin light chain 1
Genes associated with DNA and protein damage
myeloid differentiation primary response gene 116
DNA-damage inducible transcript 3
t-complex protein 1
similar to ubiquitin-conjugating enzyme E2C; DNA segment, Chr 2, ERATO Doi 695, expressed
heat shock 70 kD protein 5
Genes associated with metabolic process
stearoyl-Coenzyme A desaturase 2
ornithine decarboxylase 1
similar to glucan (1,4-alpha-), branching enzyme 1; DNA segment, Chr 16, ERATO Doi 536, expressed
similar to triosephosphate isomerase 1
lactate dehydrogenase A
glyceraldehyde-3-phosphate dehydrogenase, spermatogenic
fatty acid desaturase 1
acyl-CoA synthetase long-chain family member 4
acyl-CoA synthetase long-chain family member 4
Genes associated with neurotransmission and neuronal growth
Genbank Acc ID
VGF nerve growth factor inducible
polo-like kinase 2 (Drosophila)
similar to Cytochrome b561 (Cytochrome b-561)
similar to Cytochrome b561 (Cytochrome b-561)
high mobility group box 2
basic helix-loop-helix domain containing, class B3
Genes associated with cell growth arrest and apoptosis
Genbank Acc ID
tribbles homolog 3 (Drosophila)
growth arrest specific 5
growth arrest and DNA-damage-inducible 45 alpha
vacuole Membrane Protein 1
adenosine A2a receptor
Genes associated with cell-cycle
Genbank Acc ID
transcription factor 19
S100 calcium binding protein A6 (calcyclin)
replication factor C (activator 1) 2 (40 kD)
replication factor C (activator 1) 2 (40 kD)
proliferating cell nuclear antigen
v-myc avian myelocytomatosis viral oncogene homolog
mini chromosome maintenance deficient 6 (S. cerevisiae)
similar to NUF2R protein
similar to CDT1 protein
similar to S-phase kinase-associated protein 1A isoform b; organ of Corti protein 2; transcription elongation factor B (SIII), polypeptide 1-like; RNA polymerase II elongation factor-like protein OCP2; cyclin A/CDK2-associated p19
lectin, galactose binding, soluble 1
H2A histone family, member Z
core promoter element binding protein
core promoter element binding protein
core promoter element binding protein
cell division cycle 2 homolog A (S. pombe)
B-cell translocation gene 1, anti-proliferative
B-cell translocation gene 1, anti-proliferative
Genes with unknown functions
similar to transcription elongation factor IIS – mouse
acid phosphatase 5, tartrate resistant
glycoprotein (transmembrane) nmb
similar to RIKEN cDNA 2400009B11 gene
similar to genethonin 1
similar to RIKEN cDNA 2700084L22
similar to HTPAP protein
similar to RIKEN cDNA 2410030K01
similar to endoplasmic reticulum membrane protein with at least 3 transmembrane domains of bilaterial origin like (XB300)
similar to RS21-C6 protein
Genes associated with oxidative stress (Table 1)
Nine genes were functionally categorized in this group. Vmp1 is a stress-induced gene which may promote formation of intracellular vacuoles followed by cell death . Although it is still not clear the exact function of isg12(b), it is believed to play a role in resisting cellular or environmental stress. Herpud1 encodes an endoplasmic reticulum stress-inducible protein . Heat shock protein is generally induced by oxidative stress. Ephrin A1 acts as an early response protein in response to oxidative stress. This gene along with its receptor increased in response to DNA damage and is regulated by p53 in apoptosis . Ftl1 is a gene that is often induced due to iron-related oxidative stress, which is important for Parkinson's disease . The induction of these genes indicates that MPP+ may induce oxidative stress in PC12 cells. However, leukotriene B4 12-hydroxydehydrogenase which also plays a role in antioxidative function , CD24 involved in defense response  and Gstm1, a general stress-response gene, were down-regulated in response to MPP+ treatment, an outcome in opposition to the enhanced oxidative stress hypothesis. However, it should be noted that the above 3 genes are also involved in other functions. Alterations of these 3 genes may provide other mechanistic information for MPP+-induced toxicity.
Genes associated with DNA and protein damage (Table 2)
Both Myd116  and Ddit3 (OMIM: +126337) are DNA-damage inducible genes, and their induction provides direct evidence for DNA damage with MPP+ treatment. Dut is involved in purine metabolism, thus, the decrease of this enzyme may compromise the DNA-repair system. In addition, as mentioned above, increased expression of ephrin A1 is also an indication of DNA damage. Limited evidence was found to indicate protein damage. Tryptophanyl-tRNA synthetase catalyzes the first step of protein synthesis and is an essential function in the cell's protein synthesis machinery (OMIM: *191050). Cathepsin L is a protein associated with proteolysis and peptidolysis and induced by oxidative stress [58, 59]. These genes, along with heat shock protein 70, indicate cellular response to protein damage. Tcp1 functions as a cytosolic chaperone in the biosynthesis of tubulin [60, 61]. Although there is no evidence to indicate that this protein is involved in protein damage and repair, it has been reported that tubulin is involved in MPP+ elicited toxicity in PC12 cells . The decreased expression of this gene may affect the synthesis of tubulin, whose assembly into microtubules is critical to many cellular processes. It should be noted that LOC296368 has a sequence similar to ubiquitin-conjugation enzyme E2C, and is down-regulated with MPP+ treatment. However, because the exact function of this sequence is still unknown, the effect of these data on our hypothesis is unclear.
Genes associated with metabolic processes (Table 3)
If cellular functions are severely damaged, it is expected that most metabolic activities, especially fuel-utilization and energy metabolism, will be compromised. As Table 3 shows, MPP+ induces suppression of chromogranin A, a gene that modulates glucose, lipid and protein metabolism, and other genes associated with energy production, except acsl4. Therefore, we can primarily conclude that metabolic processes were down-regulated by MPP+ in PC12 cells. In addition, chromogranin A is also co-stored and co-released with catecholamines from storage granules in the adrenal medulla (OMIM: *118910). Although the function is still not known, the decrease of chromogranin A may indicate decreased dopamine content with MPP+ treatment as previously reported [33, 37].
Genes associated with neurotransmission and neuronal growth (Table 4)
MPP+-induces dopamine depletion, however, our preliminary study using real-time PCR has demonstrated that there are only minor gene expression changes with MPP+ treatment in PC12 cells (data not shown), unlike that in vivo . In this study, instead of tyrosine hydroxylase, dopa decarboxylase (Ddc) was down-regulated following MPP+ treatment. In addition, cytochrome b561, a major transmembrane protein that is specific to catecholamine and neuropeptide secretory vesicles of the adrenal medulla (OMIM: *600019), was down-regulated. As mentioned above, chromogranin A may be involved in dopamine metabolism in PC12 cells.
In PC12 cells, genes involved in neurogenesis and neuronal development were down-regulated following MPP+ treatment. These include Vgf (OMIM: *602186), Nnat , Plk2 , Hmgb2 (GO terms, biological process) and Bhlhb3 (GO terms, biological process). The decrease of these growth-related genes may suggest cell damage, cell growth arrest and a tendency toward apoptosis.
Genes associated with general cell growth arrest and apoptosis (Table 5)
With MPP+ treatment,, the expression of Gadd45a and Gas5 , genes associated with cell growth and responsive to DNA-damage stimulus , were induced. As shown above, induction of ephrin A1 also indicates p53-dependent activation in response to DNA damage . Furthermore, Trib3 (Kegg pathway) and Vmp1, which are involved in apoptosis, are both induced. Adenosine A2a receptor is also included in this group because it has been reported that Activation of A2A adenosine receptors (Adora2a) was found to prevent reactive oxygen species (ROS) formation and apoptosis in pheochromocytoma PC12 cells . The repression of this gene may facilitate apoptosis and ROS damage. In addition, this receptor is cAMP mediated, thus the repression of this receptor may be involved in subsequent cAMP regulated functions, such as CREB. ADM is one of the proteins that are cAMP pathway regulated. Although the function is still not clear, it has been reported to be highly expressed in pheochromocytoma and adrenal medulla (OMIM: *103275).
Genes associated with cell-cycle (Table 6)
It is evident that if cell growth is arrested, cell cycle and proliferation should be ceased. Thus, it is not surprising to see that genes for anti-proliferation (Btg1, Copeb) were induced and genes for cell cycle and replications were reduced. As shown in Table 6, Mcmd6 (mini chromosome maintenance deficient 6), which belongs to a family of early S-phase proteins required for DNA replication, may play a role in cell-cycle progression and DNA replication . Calcyclin (S100A6) is a calcium-binding protein that belongs to the family of S100 proteins. Its gene was discovered on the basis of its cell cycle-dependent expression. This gene is expressed at its maximal level during the transition between G0 to S phase of the cell cycle [69, 70]. Snf1lk encodes a protein kinase that may function in cell cycle regulation . Tcf19 is a transcription factor that may be involved in cell growth . H2Afz is a protein synthesized throughout the cell cycle (OMIM: *142763). These genes, along with some typical genes of the cell cycle (cdc2a, S-phase kinase-associated protein, Rfc2, Ret, Pcna), were all down-regulated by MPP+ treatment. Although the oncogene c-Myc has been considered a gene for proliferation, it is usually up-regulated during apoptosis [73, 74]. Thus, the induction of this gene is consistent with apoptotic processes induced by MPP+.
Genes with unknown functions (Table 7)
There are also some genes that cannot be categorized into gene groups. Generally this is due to their unknown functions. However, GPNMB, expressed in melanoma cell lines, was preferentially expressed in low-metastatic cell lines (OMIM: *604368). There was an inverse relationship between the expression of GPNMB compared to calcyclin (OMIM: *604368). In PC12 cells, this pattern was also observed. Acp5 is a gene related to bone growth (OMIM: *171640), which made it difficult to correlate with its function in PC12 cells.
Thus far, genes have been discussed individually and in terms of their known ontological processes. Gene categories shown above indicated that MPP+-induced toxicity might be related to the production of oxidative stress, DNA and protein damage, cell growth arrest, cell cycle/proliferation repression and apoptosis. IRIDESCENT was used to gain a broader perspective on how the 59 responding genes with known functions were related and whether these gene expression changes resonated with MPTP-induced neurotoxicity and Parkinson's disease. Only 47 of these 59 gene names, however, appeared in the literature at least once and could be used in the analysis based on the IRIDESCENT search. Genes with sequence similarity to other genes were not used (e.g. the LOC* entries). When examining how each of these genes was related to one another within the scientific literature, 34 genes had no detected relationships, suggesting that at least a few of these genes may be the result of noise or a non-specific response. It is also possible, of course, that their relationship to MPP+ and PD has not yet been documented and are thus valuable leads.
During the last decade, DNA microarrays have been one of the hottest topics in biological research and biotechnology industry. The first paper about a DNA microarray was published from Pat Brown's laboratory in Stanford University in 1995 . By the end of 2004, the number of microarray-related publications was 11,625 (http://www.pubmed.com, keyword searching: DNA microarray). However, data assurance and data quality are still great challenges in microarray studies [4, 79]. In this study, the experiment was performed under stringent quality control, with minimum standards sets for RNA purity, RNA degradation, and dye-incorporation ratios. The correlation ratio between the two sample-sets was up to 0.9, thus, we assume that variance introduced by the experimental procedure was small. The anti-correlation effect exists more or less in all dye-swapped microarray experiments, which is due to natural differences between the Cy5 and Cy3 dyes. As our data indicated, the anti-correlation was intensity-dependent, and occurred primarily with low-intensity spots. The cut-off threshold applied in this study (log intensity ≥ 8) can, therefore, largely exclude these low intensity spots. This cutoff may cause false-negative discovery because the low-expressed genes, which may be altered by treatment, are also eliminated. However, it was interesting to note that none of the genes with expression levels lower than the cutoff level were changed up to 2-fold with MPP+ treatment. This is mainly due to the dye anti-correlation effect as shown in Figure 2.
In this study, the good correlation of sample set 1 and sample set 2 (Fig. 4) indicated low variance, which made it justifiable to use fold-change criterion to determine genes that were altered following MPP+ treatment. Actually, the correlation ratio of the two sample sets was up to 0.98 if only those genes with 2-fold change were counted (data not shown). Two-fold is a somewhat arbitrary fold-change criterion that has been applied in many microarray studies [80–83], but nonetheless necessary to delineate a tentative set of responders. We have to admit that it is an arbitrary criterion, and it does not mean that the genes altered lower than 2-fold change are not meaningful. Certainly, more genes will be determined to be significant if the threshold is decreased to 1.5 or lower, but this will produce more false discovery. Currently, this criterion is still the most acceptable criterion regarding the magnitude of fold change and biological significance.
Data interpretation (mechanism of toxicity) is the ultimate and most complicated aspect of a microarray study, in part because there is no standard approach. Although there are more and more tools, websites and biological libraries to assist MOT analysis, biological knowledge and understanding the pertinent literature are generally more important for MOT analysis. Our study indicated that MPP+ may produce its toxicity via the production of oxidative stress as indicated by induction of associated genes (Vmp1, Hspa5, Herpud1, Ftl1, Efna1, Ctsl, etc.), and at the same time suppression of general defense systems (Gstm1, Cd24, Adora2a, etc.). Previous studies have demonstrated that MPP+ elicits its toxicity in PC12 cells via the production of oxidative stress [84, 85], and this study provides evidence of oxidative stress production at the gene level. Oxidative stress may be responsible for the DNA and protein damage as represented by the induction of DNA and protein damage-inducible genes. It has been reported that MPP+ elicits DNA damage and subsequent apoptosis [86, 87] and induced heat shock proteins such as HSP70, which is an indicator of protein damage and repair [88, 89]. DNA and protein damage are involved in cell growth arrest and cell cycle repression and eventually apoptosis [90, 91]. A plethora of evidence has demonstrated that MPP+ can elicit cell death in PC12 cells [34, 36, 88]. Therefore, this study confirmed the previous findings at the gene expression level, and more importantly the genes identified in this study suggested specific mechanistic pathways of MPP+-induced toxicity.
In summary, this study provides new insight on the mechanism of toxicity induced by MPP+. Our data indicate that MPP+ induces oxidative stress, elicits damage in DNA and proteins, and causes cell growth arrest, cell cycle suppression and apoptosis. Literature analysis by IRIDESCENT has revealed some genes such as PCNA and GADD45 that are likely central players in this toxic response. In addition, IRIDESCENT analysis demonstrated that some of the genes altered by MPP+ in PC12 cells are involved in MPTP induced neurotoxicity in mice and Parkinson's disease. We have also identified MMS as a compound related to the genes in our experimental transcriptional response, and since no studies on the effects of MMS on PC12 cells have been published thus far, this is a potentially promising avenue for future experimentation the mechanism of toxicity for MPTP.
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