Quantitative Analysis of Neurochemical Panel in Rat Brain and

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Quantitative Analysis of Neurochemical Panel in Rat Brain and Plasma by Liquid Chromatography−Tandem Mass Spectrometry Xiao Zheng, An Kang, Chen Dai, Yan Liang, Tong Xie, Lin Xie, Yin Peng, Guangji Wang,* and Haiping Hao* State Key Laboratory of Natural Medicines, Key Laboratory of Drug Metabolism & Pharmacokinetics, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China ABSTRACT: The dopamine, serotonin, and kynurenine metabolic pathways play pivotal roles on brain function, and their disturbances are closely related to various neurological diseases. Comprehensive measurements of these metabolites is thus essential for monitoring the global neurochemical responses to pathological challenges or drug intervention. However, simultaneous measurement of various neurochemcial metabolites represents a great challenge. We developed herein an original and feasible method using high-performance liquid chromatography−tandem mass spectrometry (LC−MS/MS). A chemical derivatization approach using benzoyl chloride (benzoylation) was developed to achieve better chromatographic behavior and mass-detecting sensitivity. The developed method enables a rapid quantification of 11 metabolites spanning dopamine, serotonin, and kynurenine metabolic pathways within 10.5 min. With this method, we were able to simultaneously monitor inflammation induced alternations of all these metabolites in a rat brain and in particular their dynamics in plasma matrix. The balance between the serotonin and kynurenine branches of tryptophan metabolism was disrupted by lipopolysaccharide (LPS)-induced inflammation, characterized with the overproduction of neurotoxic metabolite 3-hydroxykynurenine and decreased levels of serotonin. The measured levels of this panel of neurotransimtters ranged from 4.3 ng to 10.6 μg per gram of brain tissue. All these results suggest that the presently developed method is sufficiently sensitive and robust to simultaneously monitor a large panel of metabolites with diverse properties and a large range of concentration differences. Therefore, this method will be expected to be highly useful for comprehensive studies of the pathophysiological roles and mechanisms of these critical neurotransmitters.

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serotonergic and dopaminergic systems also exists,7,8 and the pathophysiological role of the active signaling metabolites might thereby be better-defined based on the knowledge of their counterparts.4,9 For example, an increase in brain kynurenine levels could reduce extracellular dopamine release, while specific inhibition of kynurenate synthesis enhanced extracellular dopamine levels.10,11 In addition, functional crosstalks were also observed between dopamine and several other neurotransmitters,10,11 and the neuro-regulatory effects of the kynurenines were proposed to exert through the interaction with other neurotransmitter signals,12 suggesting the potentially underexplored importance of neurotransmitter interactions in brain pathophysiology. Such intriguing findings further demonstrate that, in order to accurately decipher the neurochemical imbalance of depression and many other psychiatric disorders, it is insufficient and potentially unreliable to focus solely on a small portion of the neuroactive components.

eurotransmitters are bioactive molecules playing fundamental roles in maintaining various physiological functions of the brain, and their alternations are closely related to many neurological disorders such as depression,1 schizophrenia,2 and Parkinson’s disease.3 Redressing the disturbed neurotransmitter catabolism has become a promising strategy for designing the therapy for various neurological diseases. However, clinical benefits for this strategy are difficult to achieve partially because of the complex interplaying relationship among various types of neurotransmitters. In contrast to the early observations that abnormal levels of specific monoamine neurotransmitters (e.g., dopamine and serotonin) were responsible for the psychiatric status, more recent studies have established that multiple neurotransmitter systems are actually involved, and the net neuropsychological effects are fine-tuned by the overall balance of various types of neurotransmitters and neuromodulators.3,4 In this regard, a typical example receiving much interest is the kynurenine pathway (Figure 1B), another branch of tryptophan metabolism aside from the serotonin route.5 Specifically, the ratio between the kynurenic acid (neuroprotective) and 3-hydroxykynurenine (neurotoxic) arm is the ultimate determinant of their biological effects.6 Evidence of interactions between the © XXXX American Chemical Society

Received: August 31, 2012 Accepted: October 25, 2012

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chemicals, such as dopamine,22 norepinephrine, serotonin20 and kynurenine,23 are sensitive and indeed contribute a lot to the understanding of neurochemistry. More recently, massspectrometry-based techniques are increasingly employed to profile multiple metabolites from biological samples for targeted metabolome studies due to their favorable advantages in specificity, sensitivity, and sample throughput.24,25 In this regard, methods including LC-MS and GC-MS have been reported to analyze several neurotransmitters and their metabolites from biological systems such as microdialysates, urine, and plasma.18,26−30 However, in view of the physiological and/or pathological importance of the interplaying neurotransmitter networks as a whole, current methods restricted to assessing only a subset of the compounds of interest are apparently insufficient for providing adequate information, convenience, and throughput for global monitoring of a panel of biologically important molecules. To address the aforementioned limitations, we sought to develop an LC-MS/MS-based method that can simultaneously monitor a panel of closely related and/or interplaying neurotransmitters spanning kynurenine, serotonin, and dopamine pathways. However, for the reliable analysis of these structurally distinct components as a whole from complex matrices, sample pretreatment and chromatographic conditions require elaboration for expanding analyte coverage and achieving optimal analytical performance in mass detection. We developed and validated herein a rapid and highly sensitive method by integrating chemical derivatization and liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) analysis to enable simultaneous monitoring of a total of 11 components that optimally characterize kynurenine, serotonin, and dopamine pathways from brain tissue and plasma. To the best of our knowledge, this is the first report of a comprehensive method that allows key components on dopamine and tryptophan catabolism to be measured in parallel in a single run. With this method, the global changes of these key metabolic pathways were successfully characterized in a lipopolysaccharide (LPS)induced depression rat model. In view of the biological significance of kynurenine, serotonin, and dopamine pathways in mood disorders and also many other pathological situations, the presently developed method will be expected to be widely useful for a better understanding of the nature and role of the interplaying network of such neurotransmitters, thereby offering novel opportunities for managing diseases related to their disturbances.

Figure 1. Chemical structures of the compounds used in this study. (A) Dopamine metabolic pathway. (B) Serotonin and kynurenine branches on the metabolic route of tryptophan. (C) Internal standard caffeic acid and the derivatization regent benzoyl chloride. Abbreviations for the 11 targeted analytes are provided directly below in parentheses.

The significance of the dopamine, serotonin, and kynurenine pathways in maintaining normal brain functions and driving neurological disorders has been continually unveiled.5,13 Moreover, their significances are increasingly expanded to other diseases outside of the brain such as tumors and colitis.14,15 Given the extensive interest of these interactive metabolic networks, global profiling of the precursors and multiple downstream metabolites will provide valuable information for the dissection of disease pathology and/or assessment of treatment responses. More importantly, such a targeted metabolomic approach might shed light on the unanticipated regulatory systems that could be therapeutically targeted to combat various neurological diseases and beyond. To this end, it is highly desirable to develop a robust and efficient approach capable of exploring the dynamics of the whole panel of bioactive neurotransmitters in the central and peripheral systems. However, simultaneous measurement of these endogenous metabolites is a challenging task hampered by difficulties derived largely from their low abundance, diversity in structure and physiochemical property, matrix interference, and potential instability. A typical limitation of current neurochemical analysis is its reliance on the combination of several methods, which generally requires large sample volumes and timeconsuming analytical steps to obtain a complete picture of all the targeted species.16,17 With regard to the current analytical approaches for the metabolites on the dopamine, serotonin, and kynurenine pathway, techniques available typically include high-performance liquid chromatography (HPLC) or capillary electrophoresis (CE) coupled with electrochemical18,19 and fluorescent detectors.20,21 Some of these methods focusing on the quantification of single or a small set of neuroactive

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EXPERIMENTAL SECTION Chemicals and Reagents. 3-hydroxykynurenine and 3hydro-xyanthranilic acid were purchased from Santa Cruz biotechnology. The internal standard (IS), caffeic acid, was purchased from Acros Organics. All the other chemicals, drugs, and reagents were purchased from Sigma Aldrich. HPLC-grade acetonitrile and methanol were purchased from Fisher chemical. Animal Models of Depression. Adult male Sprague− Dawley rats (Sippr-BK, Shanghai, China) weighing between 220 and 250 g were used. Rat models of depression were induced by intraperitoneal (ip) injection with subseptic doses of lipopolysaccharide (LPS, Escherichia coli 055:B5) based on previous reports.31,32 In the first set of rats, LPS was injected (ip, 0.4 mg/kg) into rats (n = 3), and the animals were sacrificed at 4 and 8 h after the injection; the brain cortex was B

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dissected on ice and stored at −80 °C before use. In the second set of rats, heparinized blood samples were collected from the rats 0.5 h before and 2, 4, 6, 8, 10, and 12 h after ip administration with LPS (0.4 mg/kg), respectively. Control animals received an isovolumetric injection of sterile saline. All the animal experiments were conducted in compliance with the guidelines for animal handling. Sample Preparation. 100 μL of water (containing 20 mM ascorbic acid and 5.0 μg/mL caffeic acid) were added to 50 mg of rat brain tissue, and the mixtures were homogenized and sonicated in an ice bath. An aliquot of 500 μL of ice-cold acetonitrile was then added to the homogenate, and the mixture was vortexed for 5 min followed by centrifugation at 18 000 rpm for 10 min. The supernatant (500 μL) was then evaporated to dryness under vacuum. For derivatization, 25 μL of borate buffer (sodium tetraborate, 100 mM in water) and 25 μL of benzoyl chloride (2.0% in acetonitrile, v/v) were added to the residue and vortexed under room temperature for 5 min. After centrifugation at 18 000 rpm for 10 min, a 30 μL aliquot was transferred to the vial and 5 μL was injected for analysis. Plasma samples (100 μL) were spiked with 10 μL of caffeic acid (5.0 μg/mL) before precipitation with acetonitrile (500 μL). The following procedures were the same as that for the cortex samples. LC-MS/MS Analysis. A Shimazu Ultra Performace LC system (Shimazu Corporation, Kyoto, Japan) was interfaced to an API 4000+ triple quadrupole mass spectrometer (Applied Biosystems, Forster City, CA, USA) outfitted with a turbo ionspray ionization source. Instrument control, data acquisition, and analysis were performed using Analyst 1.5.2 (Applied Biosystems). The sample vials were maintained at 4 °C in a thermostatic autosampler. The chromatographic separation was achieved on a Kromasil C18 2.1 mm × 150 mm column (Akzo Nobel, Bohus, Sweden) with the column temperature set at 40 °C. The mobile phase consisted of solvent A (0.1% formic acid and 2.0 mM ammonium acetate in water) and solvent B (acetonitrile). The mobile phases were eluted at 0.2 mL/min following the gradient as follows: 30% B maintained for 1.5 min, increased to 65% at 3.5 min and held for 1.5 min, increased to 75% at 8.0 min, and then decreased to 30% at 10.0 min followed by 3.0 min for equilibration. The flow was diverted to the waste in the initial 4.5 min. The mass spectrometer was operating at the following parameters: ionspray voltage, 5.0 kV; source temperature, 550 °C; curtain gas, 20; CAD gas, 9; nebulizer gas (GS1), 55; auxiliary gas (GS2), 50. The dwell time was set at 30 ms for each ion transition. The electrospray ionization source was operated in the positive mode. MRM monitoring conditions for each compound were summarized in Table 1. Preparation of Standard Curves and Quality Control (QC) Samples. Aliquots of each standard solution were mixed to generate a stock solution containing TRP, DA, and DOPAC at 80 μg/mL and the rest at 20 μg/mL in ultrapure water/ methanol (v/v = 1:1, with 1 mM ascorbic acid) and stored at −80 °C. Before use, the stock solutions were serially diluted with methanol to give a mixed working solution series. The internal standard was prepared at 1.0 mg/mL and diluted to 5.0 μg/mL with methanol during sample analysis. To prepare the standard curve, 100 μL of brain cortex homogenates were spiked with 10 μL of the internal standard solution (5.0 μg/ mL) and 10 μL of specific working solutions to generate calibration levels covering a range of 4.0−8000.0 ng/mL for

Table 1. MRM Transition Parameters for the Targeted Metabolites analyte

precursor ion (m/z)

product ion (m/z)

retention time (min)

dwell time (ms)

DP (V)

CE (eV)

DA DOPAC HVA NE TRP 5-HT 5-HIAA KYN 3-HK 3-HAA KA IS

466.0 394.0 304.0 482.0 309.4 385.0 313.0 417.3 433.0 258.0 293.9 406.0

241.2 105.0 105.0 105.0 263.1 264.2 146.0 122.1 294.0 105.0 105.1 105.0

10.24 8.35 7.02 8.61 6.05 8.10 6.65 6.91 7.20 6.52 6.86 8.86

30 30 30 30 30 30 30 30 30 30 30 30

50 45 40 60 60 70 45 90 30 30 30 35

30 20 15 35 18 25 20 25 22 22 22 25

TRP, DA, and DOPAC and 1.0−2000.0 ng/mL for the other analytes, respectively. QC samples were prepared following the same procedure giving low, medium, and high analyte concentrations. Limit of Detection and Quantification. The limit of detection (LOD), defined as the concentration level with the signal-to-noise ratio at 3, and the limit of quantification (LOQ), defined as the concentration level with the signal-to-noise ratio at 10, were determined by the analysis of 5.0 μL of a derivatized standard mixture as there was no blank matrix available. Linearity Range. To determine the dynamic range of the method, five batches of calibration brain samples along with five blank samples were prepared and analyzed following the method mentioned above. The linearity range was investigated both in brain and plasma matrices. As the inherent existence of the endogenous substances in the matrix, the contribution of an endogenous level of each analyte as determined from the blank tissue has to be subtracted.33 The peak-area ratio between the analyte and internal standard of the blank sample was subtracted from that of the corresponding spiked sample to give the increased peak-area ratio. The calibration curves were then constructed by plotting the increased peak-area ratio with the spiked concentration using linear regression for each compound, respectively. The validation of method linearity in plasma samples were conducted following the same procedure. Accuracy, Precision, and Recovery. The accuracy and precision of the method were validated by analyzing QC samples in three analytical runs (n = 5 at each concentration level) accompanied by a set of calibration samples in each run. The increased concentration was determined by subtracting the endogenous concentration from the measured concentration of each QC sample. The accuracy was determined as the percent difference between the increased concentration and the spiked concentration and expressed as the relative error (RE). The precision was calculated as the coefficient of variance (CV) of the intraday and interday analytical results. The recovery was expressed as (increased concentration/spiked concentration) × 100%. Statistical Analysis. To illustrate the change in cerebral levels of the analytes, the data acquired were statistically analyzed by one-way analysis of variance (ANOVA), where the changes were considered significant if p < 0.05. For the plasma samples, the basal levels of the analytes were taken as the control, and data at each time point were expressed as the percent of the baseline level. C

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RESULTS AND DISCUSSION Method Development and Optimization. Although much progress has been made in methodological innovation, comprehensive monitoring of the endogenously metabolic pathway remains challenging. Quantitative analysis of the endogenous biogenic substances has its inherent complexities including trace levels (femto- or picograms per milliliter), structural diversity, potential chemical instability, and complex matrix interferences. The situation can be further complicated for the simultaneous assay of multiple neurotransmitters with varied physiochemical properties and a wide range of basal concentrations (varying by 3−4 orders of magnitude). The major challenges for the simultaneous determination of the 11 neurotransmitters selected in this study mainly include the following aspects. First, most of the target neurotransmitters are highly polar and thus weakly retained on the reverse-phase column and lower the mass detection responses partially due to the lower surface activity during the electrospray droplet formation that may place constraints on the overall analytical performance.34 Second, the levels of these metabolites span a wide concentration range, posing challenges to the dynamic range of the method. Third, instability of the catecholamine and indole moiety presented in certain analytes raises concerns about their potential degradation during sample storage or the pretreatment process. A set of strategies were applied and the chromatographic and mass conditions were optimized to address all these challenges in this study. To prevent the potential instability during the stock of standards and sample processing, ascorbic acid (20 mM) was added to prevent oxidation as previously reported.35 The LC-MS/MS-based method is a popular approach for targeted metabolome research.36,37 Although a number of the neurotransmitters could be detected directly under positive or negative ionization mode, the overall performance was compromised by a poor retention and thus unsatisfactory resolution and peak shapes of these highly polar neurochemicals on the reverse-phase columns. To improve the chromatographic performance of such highly polar analytes, we employed herein a chemical derivatization strategy that can be expected to enhance the separation efficiency and detection responses.33,38 Benzoyl chloride was chosen as the derivatization reagent because it can react with primary and secondary amines, phenols under mild conditions with high yield.39 The reaction conditions including the concentration of benzoyl chloride and the reaction time were optimized to ensure sufficient yield. Benzoylation labeling could afford satisfactory signal intensity, and the fragmental pattern featuring m/z at 105 could serve as the common product ion for all the analytes (Figure 2). To further improve the sensitivity and chromatographic performances of target analytes, we optimized the compositions of the mobile phase. By the use of 0.1% formic acid with 2 mM ammonium acetate in water as solvent A and 100% acetonitrile as solvent B, satisfactory performances in terms of peak intensity, resolution, shape, and elution time were found. A gradient elution program was further established and optimized to ensure better separation and fast elution of all the target analytes. Using the optimized gradient program, the 11 targeted analytes and internal standard could be well-separated within 10.5 min, and a single analytical run took only 13 min (Figure 3). The addition of formic acid and ammonium acetate favored the production of protonated [M + H]+ and ammonium

Figure 2. ESI-MS/MS spectra of benzoylated (A) NE and (B) TRP. The insets illustrate the mass fragment patterns of the benzoylated analytes producing a common fragment (benzoyl group, m/z 105) or a unique fragment (e.g., m/z 263).

Figure 3. Extracted ion chromatograms (XIC) of the 11 targeted analytes and the internal standard in a standard mixture solution.

adducted [M + NH4]+ ions with high intensities, thereby contributing to improved sensitivity. MRM transitions were then optimized to ensure optimal detection responses and specificity. With the benefit from benzoylation, all the analytes produced a product ion with m/z at 105 (the benzoyl group) with high abundance. Thus, for most of the target analytes, the MRM transition from the protonated pseudomolecular ions to benzyl ion at m/z 105 was selected for monitoring. However, in the cases of TRP, 5-HT, and KYN, a unique fragment ion was observed and selected for the MRM transition monitoring to maximally ensure better specificity (Figure 2B). After the determination of MRM transitions, the declustering potential (DP), and collision energy (CE) were individually optimized for such MRM transitions (Table 1). Method Validation and Analytical Performance. The quantitative metabolome study requires a simple, reliable, and comprehensive method that enables an accurate investigation into the multiplexed metabolic networks. To meet these requirements, the developed method was validated with respect to the limit of quantification, linear dynamic range, recovery, precision, and accuracy. Because all of the analytes targeted herein are endogenous metabolites, the determination of LOD D

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Table 2. Sensitivity and Linear Range sensitivty

linearity (R2)

dynamic range

analyte

LOD ng/mL (nM)

LOQ ng/mL (nM)

brain (μM)

plasma (μM)

brain

plasma

DA DOPAC HVA NE TRP 5-HT 5-HIAA KYN 3-HK 3-HAA KA

0.2(1.3) 0.05(0.3) 0.02(0.1) 0.05(0.3) 0.05(0.3) 0.05(0.3) 0.05(0.3) 0.05(0.2) 1.0(4.5) 2.0(13.1) 0.2(1.1)

0.5(3.3) 0.1(0.6) 0.1(0.5) 0.2(1.2) 0.1(0.7) 0.1(0.6) 0.1(0.5) 0.2(1.0) 2.0(8.9) 5.0(32.7) 0.5(2.6)

0.03−26.1 0.02−5.9 0.01−5.5 0.01−11.8 0.05−39.2 0.03−11.4 0.01−10.5 0.005−4.8 0.01−8.9 0.07−6.5 0.01−10.6

0.01−13.1 0.003−2,9 0.003−2.7 0.003−2.9 0.049−4.9 0.028−11.4 0.005−2.6 0.01−4.8 0.01−4.5 0.065−13.1 0.011−5.3

0.9915 0.9911 0.9990 0.9991 0.9956 0.9977 0.9992 0.9968 0.9999 0.9956 0.9932

0.9991 0.9952 0.9980 0.9985 0.9909 0.9901 0.9910 0.9921 0.9954 0.9917 0.9992

Table 3. Data for Accuracy, Precision, and Recovery of the Method intraday analyte DA

DOPAC

HVA

NE

TRP

5-HT

5-HIAA

KYN

3-HK

3-HAA

KA

interday

recovery

added amount (ng/mL)

accuracy (RE%)

precision (RSD%)

accuracy (RE%)

precision (RSD%)

%

40.0 200.0 2000.0 40.0 200.0 2000.0 10.0 50.0 500.0 10.0 50.0 500.0 40.0 200.0 2000.0 10.0 50.0 500.0 10.0 50.0 500.0 10.0 50.0 500.0 10.0 50.0 500.0 10.0 50.0 500.0 10.0 50.0 500.0

16.0 7.6 5.1 2.8 6.9 10.6 2.2 −5.7 3.3 0.4 3.6 4.1 14.8 8.5 3.0 −4.7 3.9 −8.3 8.1 7.8 −1.1 8.3 7.7 3.8 −19.4 14.9 14.1 6.5 11.4 3.2 −2.4 −9.7 0.9

9.6 2.4 8.3 6.5 1.8 6.1 5.4 6.7 7.7 1.1 9.5 3.9 4.4 3.4 5.0 8.6 1.4 5.5 9.0 7.3 11.1 5.8 7.0 5.3 8.4 6.7 7.3 7.0 8.9 7.4 5.5 10.8 5.5

5.9 7.0 6.0 5.1 9.2 7.1 3.2 3.6 3.6 1.1 −1.6 3.9 17.0 2.2 3.8 2.8 −1.8 −9.5 11.8 7.3 −3.0 8.2 10.4 7.3 −17.7 11.7 7.7 14.3 13.6 6.7 4.5 −11.3 −0.1

13.2 4.4 5.6 6.6 2.0 4.3 5.5 8.6 6.7 5.3 6.4 2.6 4.8 4.4 4.8 4.9 2.7 4.7 9.2 10.6 9.0 10.0 7.5 4.8 12.2 7.0 10.7 5.3 7.1 6.7 5.1 7.2 5.1

116.0 107.6 105.1 102.8 106.9 110.6 102.2 94.3 103.3 100.4 103.6 104.1 114.8 108.5 103.0 95.3 103.9 91.7 108.1 107.8 98.9 108.3 107.7 103.8 80.6 114.9 114.1 106.5 111.4 103.2 97.6 90.3 100.9

plotted against the added concentration using no weighting factors in the regression. Regression analysis showed that the correlation coefficients (R2) were always exceeding 0.99 for all the analytes in both the brain and plasma matrices. The data for accuracy, precision, and recovery are listed in Table 3. The intraday and interday variance were below 20% at low concentration, from 1.4% to 10.8% at medium concentration, and 2.6%− 11.1% at high concentration, supporting good precision of the method. QC sample analysis also showed

and LOQ of the method was carried out using standard mixtures. The results indicated that the LOD was in the range of 0.05−5.0 ng/mL, and the LOQ level was between 0.1 and 10.0 ng/mL (Table 2), indicating that the developed method is highly sensitive for simultaneous quantification of a whole panel of neurotransmitters. The linearity of the method was further determined from the calibration curves constructed for each analyte in brain homogenates and plasma matrices. The increased area ratio of the analyte to its internal standard was E

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4B). Of interest, the downstream transformation of KYN was shunted toward the neurotoxic 3-HK arm under inflammatory stimuli as reflected by the 3-HK/KA ratio (Figure 4C), which lends support to previous findings from the enzyme study.40 Therefore, our findings provided novel insights that catabolism of serotonin along the neurotoxic kynurenine pathway was largely responsible for the disturbed neurochemical pattern in LPS-induced depression-like behavior and might play more important roles than previously suspected. On the basis of this information, future pharmacological studies are warranted to explore the therapeutic significance of key enzymes involved in kynurenine metabolism and the underlying mechanisms. Together, our quantitative metabolome study provides a comprehensive description into critical neurochemical imbalances triggered by a systemic inflammatory challenge. These results clearly strengthened previous notions that not only the individual levels of the neuroactive molecules but also, and more importantly, the relative ratio among them was closely correlated with depressive phenotypes.41 Therefore, it is highly essential and beneficial to monitor a whole panel of neurotransmitters rather than only the so-called “classic” markers to gain an in-depth appreciation of the neurotransmitter interactions and their physiological and pathological roles. Accumulated evidence indicated that circulating levels of certain neuroactive compounds can become dysregulated in pathological conditions such as major depression,42 cognitive decline,6 and schizophrenia,3,33 indicating that the peripheral neurochemical alternations may be directly or indirectly involved in the pathological processes. To this end, we also captured the temporal changes of these neurotransmitters/ neuromodulators in rat plasma. By referring to the basal levels of each analyte, our results clearly showed that LPS challenge could divert the fluctuation of the compounds from their normal tract (Figure 5). Most of the analytes showed more dramatic changes around 8 h to 12 h, a temporal pattern coinciding with the manifestation of behavioral disturbances,43 further supporting that the dynamics of peripheral neuroactive metabolites are closely linked to the CNS functions. Considering that some of the metabolites, especially the kynurenines, are important immunoregulatory mediators, the fluctuations of such circulating neuroactive metabolites may critically contribute to the secondary injury of CNS and the pathogenesis of various neurological disorders. Interestingly, a differential pattern of the fluctuations for some mediators such as NE and KA was observed between the periphery and brain. Specifically, the cerebral level of KA showed a time-dependent decrease after LPS challenge, while KA levels in the circulation exhibited a trend to increase, especially at 12 h. This disconnection suggests that it is important to delineate the differentiated metabolic profiles and crosstalk mechanisms of neuroactive metabolites between the CNS and periphery under pathophysiological conditions. Since dysfunctional regulations of neuroactive metabolites are involved in most neurological diseases and beyond, the presently developed method facilitating the monitoring of a large panel of neuroactive metabolites in both brain tissues and the periphery can be expected to find its wide application throughout the way to the in-depth understanding of this important area. Once the relationship and underlying mechanisms of the crosstalk between peripheral and central neuroactive metabolites are clarified, it will be practicable to develop noninvasive prognostic, diagnostic, and therapeutic

good accuracy at all three concentration levels (RE < 20%), and the extraction recovery was greater than 80%. All validation experiments support that the presently developed method is accurate and reliable for the simultaneous analysis of all 11 neurotransmitters spanning dopamine, serotonin, and kynurenine metabolic pathways in real biological samples. Application to the Assay of Biological Samples. Inflammation and immune dysregulations have emerged as critical contributors to neuropsychiatric disorders. Intraperitoneal administration with LPS was well-known to trigger depressive symptoms accompanied by inflammatory responses and biochemical changes in the periphery and brain. To demonstrate the applicability of the presently developed method to monitor the metabolic cascades of the neurochemical panel, a rat depression model induced by systematic injection with LPS was adopted. We first set out to monitor the neurochemical changes in the brain at 4 and 8 h after LPS challenge when the behavioral abnormality was observed. The data summarized in Figure 4 showed that the baseline levels of

Figure 4. Comparative analysis of the analytes in rat cerebral cortex samples. (A) Basal levels of the targeted neurochemicals in rat cortex and their concentrations at 4 h and 8 h after an inflammatory challenge by ip LPS injection (n = 3); (B) Ratio of 5-HIAA/5-HT and KYN/ TRP as a measure of the turnover of serotonin and tryptophan, respectively; (C) Ratio of 5-HT/DA and 3-HK/KA as the reflection of the relative balance of the metabolic pathways. Data are expressed as mean ± SEM (n = 3) where * indicates P < 0.05 vs control and ** indicates P < 0.01 vs control.

the analytes ranged from 4.3 to 10588.6 ng/g brain tissues, indicating a large range of variance up to 4 orders of magnitude of this panel of neurotransmitters. In addition, systemic inflammation-induced behavioral changes were paralleled with the fluctuation of many analytes in the cortex in a timedependent manner. Of note, KYN, 3-HK, and KA showed the most dramatic changes upon LPS challenge. LPS-induced inflammation slightly increased the levels of the metabolites on the DA pathway; in sharp contrast, brain 5-HT levels were dramatically decreased, leading to an increased DA/5-HT ratio. Moreover, the turnover of cerebral serotonin and tryptophan was dramatically altered in LPS-challenged rats, characterized by an increased ratio of 5-HIAA/5-HT and KYN/TRP (Figure F

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robust method that could comprehensively monitor the dynamic changes of 11 neuroactive components spanning the dopamine, serotonin, and kynurenine pathways from brain tissues and plasma within a single run of analysis. The developed method was fully validated in terms of the sensitivity, linear range, precision/accuracy and recovery and has been successfully applied to the real samples analysis of LPS challenged rats. With the presently developed method, we were able to depict, for the first time, the temporal changes of a whole panel of neurotransmitters in the closely related dopamine, serotonin, and kynurenine metabolic pathways. In view of the pivotal roles of these neurotransmitters in various diseases, the presently developed method will be expected to find its broad applications in both the preclinical and clinical contexts.

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AUTHOR INFORMATION

Corresponding Author

*H.H.: e-mail, [email protected]; fax, +86-2583271060. G.W.: e-mail, [email protected]; fax, +8625-83302827. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was financially supported by the Natural Science Foundation of China (Grants 81273586 and 91029746), Natural Science Foundation of Jiangsu province (Grants BK2011065 and BK2012026), and the Program for New Century Excellent Talents in University (Grant NCET-090770).

Figure 5. Temporal changes of the 11 analytes in rat plasma after intraperitoneal injection with LPS. Data are expressed as the percent of baseline levels of each analyte. The error bar represents SEM (n = 3).

monitoring methods based on the dynamic assay of certain neuroactive biomarkers in the plasma. Since the time for a single run of analysis is around 10 min, this method can be satisfactory for the high throughout demands in analysis of a large number of samples. With the presently developed method, it is practicable to delineate the metabolic links in the complex network of neurotransmitters; the results of our biological studies highlight the importance of simultaneously handling a whole panel of functionally and metabolically related neuroactive metabolites for the better understanding of their contributions and mechanisms in the development of various neurological disorders. In view that neurotransmitter monitoring may be involved in various conditions, an ideal methodology would be expected to be highly versatile to satisfy various requirements. One major concern would be the volume of samples necessitated for analysis. Although we used a relatively large volume of samples (50 mg of brain and 100 μL of plasma sample) to develop and validate the method, our method can be highly adaptive for the analysis of small volumes of samples in view of its high sensitivity. In combination with the use of some sampling techniques such as microdialysis,39 our method may also be applicable to in vivo and online analysis.

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REFERENCES

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CONCLUSIONS The neurotransmitters in the dopamine, serotonin, and kynurenine pathways are well-known to play important physiological roles and their dysregulations were found closely related to various neuropsychiatric disorders and beyond. However, limited by the lack of an appropriate quantitative metabolomic method, very few studies have addressed the dynamic changes of such important neurotransmitters in combination, and thus little is known about their interplaying relationships. The present study is the first report describing a G

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Analytical Chemistry

Article

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