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The mitochondrion is the organelle responsible for generation of most usable energy in a cell. It also plays an important role in a series of physiological processes such as apoptosis and proliferation. Although previous studies have demonstrated that nicotine modulates the morphology and function of mitochondria, the mechanism(s) underlying these effects is largely unknown. In this study, using a microarray consisting of 4,793 clones derived from a mouse dopamine cDNA library, we profiled the gene expression patterns for six brain regions (amygdala, hippocampus, nucleus accumbens, prefrontal cortex, striatum, and ventral tegmental area) of female Sprague-Dawley rats subjected to nicotine treatment for 7 days through osmotic minipump infusion. We identified a number of genes and pathways, including components of the electron transport system of mitochondria, such as cytochrome c oxidase subunit I (Mt-co1), Mt-co2, Mt-co3, cytochrome b (Mt-cyb), mitochondrial NADH dehydrogenase 4 (Mt-nd4), and Mt-nd6, that were significantly modulated by nicotine in multiple brain regions. Bioinformatics analysis provided evidence that Gene Ontology categories related to the electron transport system were overrepresented in each brain region. Finally, the results from the microarray analysis were verified by quantitative RT-PCR for four representative genes. Together, our findings imply that mitochondria are involved in neuronal adaptation to chronic nicotine exposure.
Nicotine is the primary addictive component of tobacco smoke (Dani and Harris 2005; Wonnacott et al. 2005). Through interactions with nicotinic acetylcholine receptors (nAChRs) in the central nervous system (CNS), nicotine modulates various genes and cellular pathways in target neurons. For example, nicotine upregulates the expression of immediate early genes such as c-Fos, c-Jun, Nurr77, and Egr1 (Ichino et al. 1999; Ichino et al. 2002; Shim et al. 2001). Also, mitogen-activated protein kinase (MAPK) signaling, phosphatidylinositol phosphatase signaling, growth factor signaling, and ubiquitin-proteasome pathways are modulated by chronic nicotine treatment (Konu et al. 2001; Li et al. 2004; Tang et al. 1998). Further, animal studies indicate that nicotine, like other drugs of abuse, stimulates the mesocorticolimbic dopamine system in the outer shell of the nucleus accumbens (NA) (Pontieri et al. 1996; Robbins and Everitt 1999) and modulates the release of other neurotransmitters such as norepinephrine, serotonin, acetylcholine, glutamate, and GABA (Barik and Wonnacott 2006; MacDermott et al. 1999; Schilstrom et al. 2000; Vizi and Lendvai 1999; Wonnacott 1997). Through its interaction directly or indirectly with these genes, pathways, and neurotransmitter systems, nicotine is involved in the regulation of various physiological processes (Dajas-Bailador and Wonnacott 2004; Dasgupta and Chellappan 2006; Dasgupta et al. 2006; Harkness and Millar 2002; Hwang and Li 2006; Kane et al. 2004; Robinson and Kolb 2004), as well as the morphology and function of different organelles (Cucina et al. 2000; Landais et al. 2005; Roy and Sabherwal 1998; Sallette et al. 2005).
Among the organelles affected by nicotine treatment, the mitochondrion is of particular interest because it is responsible for the generation of most usable energy and is the principal source of reactive oxygen species (ROS) in a cell. In addition, mitochondria play an important role in many physiological processes such as apoptosis and proliferation. However, the effect of nicotine on mitochondria appears to be complicated. For example, rats receiving nicotine showed alterations in mitochondrial ultrastructure such as irregular cristae and an electron-dense matrix in testis (Aydos et al. 2001), and dilated mitochondria were observed in nicotine-treated rat gingival fibroblasts (Lahmouzi et al. 2000). Fetal and neonatal exposure to nicotine also leads to detrimental changes in the mitochondria of postnatal pancreatic tissues, such as volume swelling, reduced respiratory chain activity, and increased oxidative stress (Bruin et al. 2008a; Bruin et al. 2008b; Bruin et al. 2008c). When colon adenocarcinoma-derived HCT-116 cells were treated with nicotine at a concentration similar to that in a smoker, an alteration in mitochondrial membrane potential and an increase in oxidative stress and apoptosis were noted (Crowley-Weber et al. 2003). In another study, nicotine proved to be antiapoptotic, modulating mitochondrial signaling pathways and inhibiting chemotherapy-induced apoptosis in lung cancer (Zhang et al. 2008). Nicotine treatment also can regulate the activity of mitochondria in the brain. Both acute and chronic nicotine treatment lead to increases in the expression of malate dehydrogenase and succinate dehydrogenase of the mitochondrial Krebs cycle in the rat frontoparietal cortex, but not in other regions of the brain (Turegano et al. 2001). There also is evidence that chronic nicotine treatment induces oxidative stress greatly and increases the amount of mitochondrial glutathione S-transferase, the major cellular antioxidant component, in rat brain (Bhagwat et al. 1998).
These studies clearly show the importance of mitochondria in the biological response to nicotine. However, the molecular mechanism(s) underlying the involvement of nicotine or abused substances in mitochondrial activities remains largely unknown. On the basis of a cDNA library constructed from purified embryonic ventral mesencephalic dopaminergic neurons, Barrett et al. (2001) developed a microarray consisting of 960 clones that was used to study gene expression changes during methamphetamine neurotoxicity in mouse brain. Expression of genes encoded by the mitochondrial genome, especially that of the cytochrome c oxidase subunit I of complex IV (Mt-co1), proved to be regulated significantly by amphetamine.
In this study, we sequenced and annotated more than 12,000 clones from the same cDNA library and fabricated a microarray for the non-redundant clones. The primary purpose was to identify and characterize gene expression profiles in six brain regions, namely the amygdala, hippocampus, NA, prefrontal cortex (PFC), striatum, and ventral tegmental area (VTA), of Sprague-Dawley rats in response to chronic nicotine treatment.
Twenty-two-month-old female Sprague-Dawley rats (250–300 g; Harlan Industries, Inc., Indianapolis, IN) were housed in wire-bottom cages on a 12-h light/dark cycle and were allowed food and water ad libitum. Rats received continuous subcutaneous infusions of either nicotine bitartrate (3.15 mg/kg/day; expressed as the base) in saline (treatment group) or an equal volume of saline alone (control group) via osmotic minipumps for seven days (Malin 2001). Ten animals were used for the microarray experiment and ten for real-time PCR (RT-PCR) with five animals per group (i.e., control and nicotine-treated). All experimental protocols were approved by the Institutional Animal Use Committee at the University of Virginia.
At the end of nicotine treatment, animals were euthanized with an overdose of sodium pentobarbital, and the brains were dissected out immediately after decapitation. After brain slices were cut in a dish containing ice-cold saline using a Stoelting tissue slicer (Chicago, IL), bilateral punches were excised from the amygdala, anterior area of the hippocampus, NA, PFC, striatum, and VTA using a bilateral 2.0-mm-diameter brain punch tissue set (myNeuroLab.com, St. Louis, MO) according to the coordinates of Paxinos and Watson (1986). All tissues were stored at −80°C until RNA isolation.
Total RNA was isolated separately from each brain region using Trizol reagent (Invitrogen, Carlsbad, CA). Because the amount of RNA obtained from the punches of each brain region was insufficient for the cDNA probe labeling required for array hybridization, each RNA sample was amplified using the protocol we described previously (Gutala et al. 2004; Konu et al. 2004).
All cDNA clones printed on an array were sequenced. Briefly, plasmid DNA was extracted from the clones using a Templiphil™ DNA Sequencing Template Amplification kit (Amersham Biosciences, NJ) according to the manufacturer’s protocol. All clones were sequenced using standard dye primer chemistry by an ABI PRISM® 310 Genetic Analyzer (Applied Biosystems, CA). The resulting sequences from 12,000 cDNA clones (ca. 300–800 nucleotides in length) were searched against various public genomic databases to determine the function and category of each clone.
On the basis of these sequence analysis and annotation results, we developed a cDNA microarray consisting of 4,793 non-redundant clones. Prior to printing, we prepared a cDNA insert of each clone. Briefly, E. coli containing the clones was grown overnight in GS-96 medium (BIO 101 Systems, Carlsbad, CA) followed by incubation of 10 μl of cell culture plus 90 μl of water at 95°C for 10 min to release plasmid DNA. Ten microliters of supernatant liquid containing plasmid DNA was added to a PCR cocktail containing Taq DNA polymerase in a total volume of 100 μl. The mixtures were denatured at 95°C for 3 min and then subjected to 35 cycles of denaturation (95°C, 30s), annealing (55°C, 30s), and extension (72°C, 2 min). The products were ethanol-precipitated, washed, and reconstituted in 20 μl of TE buffer. Thirty microliters of 80% dimethyl sulfoxide was added, and the mixture was printed on CMT-GAPS-coated slides (Corning, NY) using an OmniGrid MicroArrayer OGR-03 (Genomic Solutions, San Carlos, CA). Each cDNA clone was printed in duplicate on each slide. After arraying, slides were UV-linked, washed in 3× SSC twice and in H2O once, then incubated with I-Block at 60°C for 30 min, followed by washing and storage in an airtight desiccator until use.
A balanced dye-swap design was adopted for our microarray experiment. For each brain region, every RNA sample from the control group was randomly paired with a sample from the treatment group. Then, each of the paired samples was split into two aliquots, which were labeled separately with either Cy3 or Cy5. Next, the Cy3-labeled control probes and Cy5-labeled treatment probes were combined and hybridized to the same slide; the Cy5-labeled control probes and Cy3-labeled treatment probes were combined and hybridized to another slide. The probe was labeled and hybridized with the cDNA microarray using a 3DNA Array 900™ Expression Array Detection kit (Genisphere, Hatfield, PA) according to the manufacturer’s protocol. Hybridized slides were first washed in 1× SSC–0.1% SDS buffer at 60°C for 10 min and then in 2× SSC–0.2× SSC buffer for 10 min at room temperature prior to being spun down briefly and scanned on a GenePix 4000B scanner (Axon Instruments Inc., Union City, CA). A total of 60 arrays were processed for this study with 10 slides per brain region.
By scanning the arrays, we obtained the raw hybridization intensities for each element and then used the background-subtracted median intensity of each spot for further statistical analysis. We initially analyzed two replicates of each clone within a chip separately and then discarded 5% of the weakest and saturated spots in each replicate. An intensity-dependent normalization method, namely, locally weighted linear regress (Lowess), was used to normalize the data for each replicate (Yang et al. 2002). Clones with ≤ 6 valid measurements were excluded from further statistical analysis, and two replicates per chip were averaged and used as the measurement of a clone for a given sample. Further, the normalized ratios of each dye-swap pair were averaged to get the final expression measurement of each gene. Significance Analysis of Microarray (SAM) (Tusher et al. 2001) was used to detect the significantly regulated genes, and a false discovery rate (FDR) value of 0.10 was used for all the brain regions.
A bioinformatics method, namely Gene Set Enrichment Analysis (GSEA) (Subramanian et al. 2005), was used to detect overrepresented biochemical processes. The genes in our dataset were categorized by their Gene Ontology (GO) annotation. Briefly, the annotation file for mouse was downloaded from the GO website (www.geneontology.org) (Ashburner et al. 2000). All the sequence-verified genes included in the chip were searched against this annotation, and the biological process, molecular function, and cellular component categories of each gene were extracted. Each gene was assigned to one or more GO categories, and several highly related categories were combined because only a few genes were represented in each process. The constructed gene sets and the gene expression data were loaded into GSEA to detect enriched GO categories.
Real-time PCR was carried out for four representative genes, namely, ATP synthase (ATP5j; ATP synthase, H+ transporting, F0 complex, subunit F6), Mt-co1, mitochondrial NADH dehydrogenase 6 (Mt-nd6), and nuclear receptor subfamily 4, group A, member 1 (Nurr1) for all the six brain regions. The primers and TaqMan probes for the four genes were purchased from Applied Biosystems (Foster City, CA), and quantitative RT-PCR analysis was performed as described previously (Konu et al. 2004). The PCR was carried out in a volume of 20 μl using the TaqMan assay on an ABI 7000 Sequence Detection System. The 18S ribosomal RNA was used as an internal control to normalize the expression patterns of genes of interest. Data analysis was performed using a comparative Ct method (Winer et al. 1999). For each verified gene, five independent replicates were analyzed for each experimental group.
The number of differentially expressed genes differed greatly among the six brain regions. At 10% FDR, we found that 16, 28, 12, 15, 8, and 27 genes were significantly regulated in the amygdala, hippocampus, NA, PFC, striatum, and VTA, respectively (Table 1). In the amygdala, several genes involved in mitochondrial function, specifically electron transport, were upregulated, including Atp5j, Mt-co1, Mt-cyb, and Mt-nd6. Multiple genes involved in transcription also were modulated, such as chromodomain helicase DNA-binding protein 6 (Chd6), zinc finger protein CCHC domain containing 12 (Zcchc12), and zinc finger protein interacting with K protein 1 (Zik1). We also observed regulation of protein modification- and degradation-related genes, which included chaperonin subunit 5 (Cct5) and proteasome (prosome, macropain) activator subunit 4 (Psme4). In the hippocampus, mitochondrial electron transport genes were upregulated; e.g., Atp5j, Mt-co1, Mt-nd2, and Mt-nd4, as were several genes involved in protein translation, such as acidic ribosomal phosphoprotein P0 (Arbp), eukaryotic translation initiation factor 3, subunit 8 (Eif3s8), and tumor protein, translationally controlled 1 (Tpt1). We also observed alterations in the expression of genes related to signal transduction, such as CD24a (Cd24a), neurotrophic tyrosine kinase receptor type 2 (Ntrk2), and polo-like kinase 2 (Plk2).
In contrast to the amygdala and hippocampus, in the NA, mitochondrial electron transport genes were downregulated by nicotine, as evidenced by the suppression of Mt-co1, Mt-cyb, and Mt-nd6. The expression of genes related to other functions also was reduced. In the PFC, we detected the induction of genes involved in electron transport (e.g., Atp5j, Mt-co1, Mt-cyb, and Mt-nd6), protein modification or transport (Canx, Nedd4, and Stx12), and signal transduction (Ywhae). In the striatum, some genes were suppressed by nicotine, such as Mt-nd6, secretogranin II (Scg2), and voltage-gated sodium channel type III beta (Scn3b), whereas others were induced, such as insulin-like growth factor binding protein 5 (Igfbp5), nuclear receptor subfamily 4 group A member 1 (Nurr1), and Stathmin 1 (Stmn1). Among the genes detected in the VTA, those related to electron transport (e.g., Atp5j, Mt-co1, Mt-cyb, and Mt-nd6), as well as mRNA processing (e.g., heterogeneous nuclear ribonucleoprotein H1 [Hnrph1], PRP18 pre-mRNA processing factor 18 homolog [Prpf18], and RNA-binding motif protein 16 [Rbm16]) were upregulated. Whereas a number of transcription regulators such as forkhead box A1 (Foxa1), mortality factor 4 like 1 (Morf4l1), and melanocyte-specific gene-related gene 1 (Mrg1) were upregulated, melanoma antigen family D member 1 (Maged1) was suppressed by nicotine. Despite the relative diversity of genes differentially regulated by nicotine in each brain region, it is clear that one or more genes related to the mitochondrial electron transport system were significantly regulated in all these regions.
Although identification of significantly modulated genes provides clues about the expression pattern of each brain region’s response to nicotine, we were more interested in identifying the biological pathways potentially regulated by nicotine in these regions. By searching the annotated database with the GSEA algorithm for all genes included in our chip on the basis of GO information, we identified the overrepresented categories (Table 2). For the amygdala, nine enriched GO categories were identified, among which seven (i.e., electron transport, microtubule-based movement, mitochondrial function, protein folding, protein polymerization, DNA-dependent regulation of transcription, and zinc ion binding) were positively associated with nicotine exposure. Conversely, the GO categories related to cell proliferation and protein biosynthesis were negatively related to the treatment. For the hippocampus, eight GO categories were overrepresented, including three related to mitochondria (i.e., mitochondrial respiratory chain complex I, mitochondrion, and electron transport) and two others (namely, regulation of transcription from RNA polymerase II promoter and signal transduction), all of which were positively related to nicotine treatment. On the other hand, the GO categories related to protein biosynthesis and modification and nervous system development were negatively related to nicotine exposure. For the NA, eight processes were overrepresented; two of them (i.e., proteolysis and protein biosynthesis) were upregulated, and six (i.e., calcium ion binding, electron transport, mitochondrion, mRNA processing, organ morphogenesis, and RNA splicing) were downregulated. For the PFC, eight GO categories (i.e., ATP synthesis-coupled proton transport, electron transport, mitochondrion, mitochondrial respiratory chain complex I, ion transport, neuron migration, protein folding, and response to stress) were positively associated with nicotine treatment, whereas only protein biosynthesis was negatively regulated by nicotine. For the striatum, five GO categories (i.e., axon guidance, cell proliferation, mitochondrial respiratory chain complex I, mitochondrion, and protein biosynthesis) were suppressed, and three (i.e., microtubule-based process, protein polymerization, and protein amino acid phosphorylation) were induced. For the VTA, eight GO categories were upregulated, including those related to mitochondrial function (i.e., electron transport, mitochondrial respiratory chain complex I, mitochondrion, and proton-transporting two-sector ATPase complex), ion transport, small GTPase-mediated signal transduction, and two others (axon and cell adhesion). We also identified a few downregulated GO categories, including protein biosynthesis, protein complex, ribonucleoprotein complex, ribosome biogenesis and assembly, RNA binding, RNA splicing, and structural constituent of ribosome.
Similar to the expression patterns of genes modulated by nicotine, the overrepresented GO categories were region specific. This indicates the differential responses of these regions to nicotine treatment. Despite these differences, the GO categories related to the mitochondrion were enriched in all six brain regions. In the amygdala, hippocampus, PFC, and VTA, the GSEA scores (Table 2) for mitochondria-related GO categories all were positive, indicating that nicotine exposure preferentially upregulated the expression of a subset of genes, the leading-edge subset (Subramanian et al. 2005), whereas in the NA and striatum, the GSEA scores for mitochondria-related GO categories were all negative, indicating the genes of leading-edge subset were suppressed by nicotine. Specifically, in each brain region, the mitochondrial electron transport system or part of it (such as complex I or ATP synthase) were among the enriched GO categories detected by GSEA.
As shown above, both SAM and GSEA analyses indicated the mitochondrial electron transport system is regulated by chronic nicotine exposure in different brain regions. Figure 1 provides a summary of the expression changes of ten genes in these six regions. In the amygdala, two subunits of NADH dehydrogenase (complex I), Mt-nd4 and Mt-nd6, were significantly regulated, whereas two other subunits (Mt-nd1 and Mt-nd2) were slightly upregulated. Similarly, the expression of these genes was elevated in other brain regions, namely the hippocampus, PFC, and VTA. However, in the NA and striatum, their expression was decreased by nicotine. Mt-cyb of complex III was significantly induced by nicotine in the amygdala, PFC, and VTA, whereas it was suppressed in the NA. We also observed a slight elevation and inhibition of its expression in the hippocampus and striatum, respectively. The expression of three mitochondrial-DNA encoded subunits of complex IV (i.e., Mt-co1, Mt-co2, and Mt-co3) was differentially elevated in the amygdala, hippocampus, PFC, and VTA, whereas their expression was somewhat suppressed in the NA and striatum. We also identified two upregulated subunits of ATP synthase, Atp5b and Atp5j, in the amygdala, hippocampus, PFC, and VTA. Atp5j was downregulated or slightly suppressed in the NA or striatum. Consistent with the pattern of individual genes, the electron transport pathway was overrepresented in the amygdala, hippocampus, PFC, and VTA and was underrepresented in the NA and striatum.
Quantitative RT-PCR was performed on four representative genes in different brain tissues from an independent animal experiment involving the same treatments as the corresponding groups in the microarray experiment. Three of these genes (Atp5j, Mt-co1, and Mt-nd6) are related to the electron transport system and were modified in multiple brain regions. Another gene, Nurr1, which is essential for differentiation of dopaminergic neurons, was significantly regulated by nicotine in the striatum and VTA. Figure 2 presents the fold change along with the standard deviation of the four genes in response to nicotine treatment relative to the control samples, as detected by microarray and quantitative RT-PCR. This analysis revealed significant regulation of Atp5j in all the brain regions except the striatum, which is consistent with the result from the microarray analysis. Furthermore, comparison of the fold changes of each gene detected by the two molecular techniques across the six brain regions revealed a correlation coefficient of 0.85 (p = 0.032), 0.92 (p = 9.7×10−3), 0.90 (p = 0.014), and 0.82 (p = 0.044) for Atp5j, Mt-co1, Mt-nd6, and Nurr1, respectively. Although only four representative genes were selected for verification by RT-PCR, a comparison of the two methods demonstrates that the results from our microarray analyses were reproducible and reliable.
Previous studies have shown that nicotine treatment not only causes swelling and structural remodeling of mitochondria in cells of various tissues, including the brain (Jin and Roomans 1997; Maritz and Thomas 1994; Onal et al. 2004; Zimmerman and McGeachie 1987), but also regulates processes such as protein turnover (Katyare and Shallom 1988), enzyme activity (Barbieri et al. 1989; Galvin et al. 1988), and generation of ROS (Bhagwat et al. 1998; Soto-Otero et al. 2002). Here, we further demonstrate that the genes of each complex in the electron transfer system are modulated by chronic nicotine treatment. The consistent changes in the genes included in complexes I, III, IV, and V of the electron transport system in multiple brain regions indicate the importance of this system in the interaction of the CNS with nicotine.
The differential expression of the electron transport system in the six brain regions is consistent with the concept that these regions have different cellular and physiological functions. Because these regions are divergent in their cellular compositions and behaviors (e.g., neurotransmitter release, receptor expression, intracellular signaling cascades) in order to manage specific physiological activities, and because they have different energy consumption and reaction mechanisms in response to external disturbances, a region-specific transcriptional response of the electron transport system to chronic nicotine treatment would be expected. The different responses of the electron transport system in these regions to nicotine may be associated with the specific role of each region in the development of nicotine dependence. For example, the upregulation of the expression of this system in the amygdala, hippocampus, PFC, and VTA may represent the neuron’s effort to increase its energy-generating capacity or an acceleration of protein turnover. On the other hand, the suppression of the electron transport system in the NA and striatum may reflect a reduction in energy requirements in these regions or other slowdown in metabolism.
Defects of any gene in the electron transport system can disturb this system and interrupt the activity of mitochondria. For example, mutations of Mt-cyb have been associated with deficiency in complex III function and impaired mitochondrial activity (Dumoulin et al. 1996; Legros et al. 2001). Moreover, dysfunction of the mitochondrial electron transport system is implicated in several pathogenic conditions, including neurodegenerative disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) (Lin and Beal 2006). A significant and selective reduction of complex I activity was found in the substantia nigra of patients with PD. It has been reported also that a null mutation of Mt-cyb results in deficiency of complex III and an increase in free radical production in brain tissues of patients with PD (Rana et al. 2000). A deficiency of cytochrome c oxidase (complex IV) also can cause defects in energy metabolism in the brain and peripheral tissue, which may contribute to the neurodegenerative condition in AD patients (Grazina et al. 2006; Qiu et al. 2001). In these diseases, the damage to mitochondria or their components may increase production of ROS, which leads to the death of the neuron.
On the other hand, epidemiological studies have revealed an inverse correlation between smoking and the incidences of PD and AD (Picciotto and Zoli 2008; Quik 2004). Nicotine is believed to be the chemical responsible for such beneficial effects because it can stimulate the dopaminergic system and be neuroprotective in such disorders. Several mechanisms have been suggested to explain the relationship, and the most common one is that nicotine acts as an antioxidant (Obata et al. 2002; Soto-Otero et al. 2002). Also, nicotine interacts directly with the electron transport system. For example, nicotine may complete with NADH and inhibit mitochondrial NADH-ubiquinone reductase activity (Cormier et al. 2001, 2003) or regulate the electron leak at the site of complex I (Xie et al. 2005), with a consequent reduction in the amounts of ROS.
The simultaneous regulation of multiple complex genes in the electron transport system in the six brain regions observed in the current work may indicate a beneficial effect of nicotine on the nervous system. However, the mechanisms underlying the modulation of nicotine-electron transport system interactions are unclear. One of the plausible possibilities is that nicotine, which can enter cells by permeating the membrane (Sallette et al. 2005), acts as an antioxidant (Obata et al. 2002; Soto-Otero et al. 2002) and eliminates the ROS generated by mitochondria. Such interaction may eventually lead to modulation of the activity of the electron transport system, thereby affecting ATP production (Halestrap 1987, 1989; Lim et al. 2002). It also is possible that nicotine interacts directly with one or more complexes of the electron transport system (Cormier et al. 2001, 2003; Xie et al. 2005). Because the electron transport complexes are physically interdependent, regulation of any of their genes may result in modulation of the expression of genes of other complexes. For example, mutation of complex I genes results not only in complex I deficiency, but also in reduction of complex III activity (Budde et al. 2000). Further, mutations of the complex III gene Mt-cyb cause combined instability and reduced activity of complexes I and complex III (Blakely et al. 2005). On the other hand, nicotine may regulate other biochemical pathways in mitochondria through intracellular signaling pathways. In the nervous system, nicotine exerts its effect mainly through its interaction with nAChRs. Activation of nAChRs causes membrane depolarization and directly and indirectly increases the intracellular calcium concentration (McKay et al. 2007). The variation of cytosolic calcium concentration may influence the calcium homeostasis of mitochondria (Rimessi et al. 2008). The Ca2+ homeostasis is fundamental to the function of mitochondria and related to the generation of ATP, ROS, and a series of signaling pathways critical to cell life (Rego and Oliveira 2003; Rimessi et al. 2008; Szabadkai and Duchen 2008). Recent work also has demonstrated that these pathways, including the protein kinase A (PKA) and C (PKC) signaling cascades, are important regulators of mitochondrial energy production. In particular, they are able to act on the electron transport system through tyrosine phosphorylation (Huttemann et al. 2007; Salvi et al. 2005). Directly or indirectly, nicotine can trigger various intracellular signaling pathways, including PKC and MAPK, in neurons of different brain regions (Konu et al. 2001; Li et al. 2004; Quik 2004). Thus, the regulation of the intracellular signaling pathways in response to nicotine may lead to regulation of the electron transport system and, eventually, modulation of mitochondrial function.
In summary, we have demonstrated regulation of electron transport gene expression in multiple rat brain regions in response to chronic nicotine treatment. This suggests that chronic nicotine exposure modulates mitochondrial function, and likely disturbs cellular energy metabolism and the intracellular concentration of oxidative free radicals. The detection of this effect should help us to understand the molecular mechanisms underlying nicotine addiction, and may also suggest molecular targets to consider in human nicotine dependence mechanisms. Future challenges include defining the precise molecular mechanisms that underlie the response of the electron transport system to chronic nicotine treatment and to discover how these mechanisms interact with other cellular events, such as signal transduction, in addicted human smokers and nicotine-treated animals.
This project was in part supported by NIH grant DA-13783 to Ming D. Li. The authors thank Dr. David L. Bronson for his excellent editing of this manuscript.