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Jan 16, 2017 - Accendo Data LLC, Coral Springs, Florida 33067, United States. •S Supporting Information. ABSTRACT: Stroke remains a major public hea...
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Discovery of Metabolite Biomarkers for Acute Ischemic Stroke Progression Peifang Liu,†,‡,⊥ Ruiting Li,§,⊥ Anton A. Antonov,∥ Lihua Wang,† Wei Li,§ Yunfei Hua,§ Huimin Guo,§ Lijuan Wang,§ Peijia Liu,† Lixia Chen,† Yuan Tian,§ Fengguo Xu,§ Zunjian Zhang,§ Yulan Zhu,*,† and Yin Huang*,‡,§ †

Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Xuefu Road No. 246, Harbin 150001, China Key Laboratory of Myocardial Ischemia, Harbin Medical University, Ministry of Education, Xuefu Road No. 246, Harbin 150001, China § Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University, Ministry of Education, Tongjia Lane No. 24, Nanjing 210009, China ∥ Accendo Data LLC, Coral Springs, Florida 33067, United States ‡

S Supporting Information *

ABSTRACT: Stroke remains a major public health problem worldwide; it causes severe disability and is associated with high mortality rates. However, early diagnosis of stroke is difficult, and no reliable biomarkers are currently established. In this study, mass-spectrometry-based metabolomics was utilized to characterize the metabolic features of the serum of patients with acute ischemic stroke (AIS) to identify novel sensitive biomarkers for diagnosis and progression. First, global metabolic profiling was performed on a training set of 80 human serum samples (40 cases and 40 controls). The metabolic profiling identified significant alterations in a series of 26 metabolites with related metabolic pathways involving amino acid, fatty acid, phospholipid, and choline metabolism. Subsequently, multiple algorithms were run on a test set consisting of 49 serum samples (26 cases and 23 controls) to develop different classifiers for verifying and evaluating potential biomarkers. Finally, a panel of five differential metabolites, including serine, isoleucine, betaine, PC(5:0/5:0), and LysoPE(18:2), exhibited potential to differentiate AIS samples from healthy control samples, with area under the receiver operating characteristic curve values of 0.988 and 0.971 in the training and test sets, respectively. These findings provided insights for the development of new diagnostic tests and therapeutic approaches for AIS. KEYWORDS: acute ischemic stroke, metabolomics, human serum biomarkers, mass spectrometry, classify, logistic regression

1. INTRODUCTION Stroke is a significant health problem that is increasingly prevalent in developing countries and is associated with ballooning medical care costs.1 In China, ∼1.3 million people experience strokes each year, and ∼80% of these strokes are related to ischemia.2 Acute ischemic stroke (AIS) occurs when the supply of blood to the brain is suddenly blocked by blood clots, leading to the death of brain cells.3 Currently, the standard of care for AIS is thrombolysis, which attempts to dissolve the clots, restore blood flow, and preserve the surrounding brain tissue.4,5 However, the recanalization strategy can change the course of ischemia only if performed within a narrow time window. For instance, tissue plasminogen activator (tPA), the only agent approved by the Food and Drug Administration (FDA) for treating AIS, must be given intravenously within 3 h (up to 4.5 h in certain patients) of the onset of stroke symptoms and only after computed tomography (CT) scanning has ruled out hemorrhage.5 © 2017 American Chemical Society

Therefore, it is critical for AIS patients to be rapidly evaluated, diagnosed, and considered for acute treatment. In the clinical setting, CT, magnetic resonance imaging (MRI), and transcranial Doppler can be used for stroke diagnosis, but these tests are complex, time-consuming, costly, and not universally available.6 Thus the development of novel biomarkers for monitoring stroke in large population would provide an additional clinical tool for diagnosis. Recently, metabolomics studies have provided novel insights into biomarker discovery.7 Metabolomics is a powerful approach that directly represents the molecular phenotype of organisms, and it has been increasingly utilized in the clinic for uncovering sensitive biomarkers for disease diagnosis and personalized treatment.8−10 However, few metabolomics analyses of stroke have been conducted. Previously reported Received: August 26, 2016 Published: January 16, 2017 773

DOI: 10.1021/acs.jproteome.6b00779 J. Proteome Res. 2017, 16, 773−779

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Journal of Proteome Research Table 1. Clinical Characteristics of the Subjects training set case number age (years) gender (M/F) weight (kg)

58.55 ± 9.60 20/20 60.63 ± 9.59

smoking drinking TIA hypertension hyperlipidemia

22(55.0) 14(35.0) 6(15.0) 18(45.0) 8(20.0)

serum glucose (mmoL/L) total cholesterol (mmoL/L) triglyceride (mmoL/L) LDL-C (mmoL/L) creatinine (μmoL/L) a

40

5.60 5.00 1.62 3.35 73.12

± ± ± ± ±

1.04 1.52 1.10 1.28 11.56

test set pa

control

40 Anthropometric Characteristics 57.60 ± 4.86 0.400 20/20 1.000 58.65 ± 9.75 0.370 History n (%) 20(50.0) 0.654 16(40.0) 0.644 0(0.0) 0.011 8(20.0) 0.017 9(22.5) 0.785 Blood Parameters 5.12 ± 0.62 0.017 5.40 ± 0.94 0.160 1.39 ± 0.77 0.282 2.99 ± 0.68 0.120 70.60 ± 11.19 0.341

pa

case

control

26

23

59.08 ± 8.51 17/9 60.96 ± 10.63

56.96 ± 2.70 16/7 62.26 ± 8.17

0.259 0.756 0.644

11(42.3) 10(38.4) 2(7.7) 17(65.4) 7(26.9)

8(34.8) 9(39.1) 0(0.0) 5(21.7) 10(43.4)

0.590 0.962 0.174 0.002 0.224

± ± ± ± ±

0.035 0.221 0.148 0.319 0.016

5.92 4.98 1.97 3.15 84.79

± ± ± ± ±

1.25 1.15 1.16 1.14 24.80

5.24 5.37 1.55 2.86 70.43

0.78 0.96 0.77 0.71 10.22

Unpaired t tests for continuous measures and χ2 tests for categorical variables.

2. MATERIALS AND METHODS

studies mainly focused on identifying significant metabolites in biofluids to reveal systemic metabolic changes caused by cerebral ischemia but have not actively promoted the simplification or verification of metabolites indicative of the disease.11,12 For instance, Jung et al. used a nuclear magnetic resonance (NMR)-based metabolomics approach in patients with cerebral infarction and characterized several related metabolic pathways, such as anaerobic glycolysis and folic acid deficiency.11 The only exception is a liquid chromatography−tandem mass spectrometry-based targeted metabolomics study that identified branched-chain amino acids as novel biomarkers of ischemic stroke.13 Nevertheless, the targeted metabolomics approach could only determine a limited set of metabolites rather than a comprehensive list of known and unknown peaks. It is therefore possible that additional sensitive biomarkers for the accurate diagnosis of stroke cannot be detected. The main goal of this study was to reveal the global metabolic changes of AIS and discover potential biomarkers for the effective diagnosis of AIS using a nontargeted metabolomics approach and a state-of-the-art methodology. In brief, an initial training set was used containing serum from AIS patients and serum from age- and gender-matched healthy individuals. Gas chromatography−mass spectrometry (GC−MS) and liquid chromatography−mass spectrometry (LC−MS) analyses of serum were performed to identify altered metabolites and pathways associated with AIS risk. The study aimed to uncover potential biomarkers for the diagnosis of AIS by examining metabolic changes. The diagnostic potential of differential metabolites screened from the training set was further evaluated in a test set with multiple algorithms, such as random forest and support vector machine. On the basis of the analysis of the test set, a panel of five metabolites with the ability to effectively discriminate between samples from AIS patients and samples from healthy individuals was selected.

2.1. Chemicals

LC−MS-grade methanol, acetonitrile, and ethyl acetate were purchased from Merck (Germany). Analytical-grade formic acid was obtained from Nanjing Chemical Reagent (China). The GC−MS derivatization reagents, pyridine, methoxyamine hydrochloride (MOX), and N-methyl-N-trifluoroacetamide (MSTFA) were purchased from Sigma-Aldrich (USA). The internal standards (heptadecanoic acid and glyburide) and all authentic standards were also purchased from Sigma-Aldrich. 2.2. Study Subjects

AIS patients and healthy individuals were recruited from a single center, the Second Affiliated Hospital of Harbin Medical University, between October 2013 and August 2014. Inclusion criteria for the study were as follows: an initial National Institutes of Health Stroke Scale (NIHSS) score from 6 to 22, aged of 49 to 73 years, presentation ≤9 h after stroke onset, and stroke localization in the area of the middle cerebral artery. Patients with diabetes, cardiovascular diseases, or other diseases that would affect the biological indicators and metabolic profiles were excluded from the study. Healthy individuals with a stroke history or showing any sign of stroke based on CT or MRI evaluation, such as silent infarction, were excluded from the healthy control group to reduce the confounding effects of additional risk factors. Discovery metabolomics analyses began with an unbiased search for serum metabolites linked to AIS using a case-control design (training set, N = 40 cases and 40 controls). Cases were randomly selected from AIS patients, while an age- and gender-matched control group was randomly selected from healthy individuals. To evaluate the capability of metabolites to predict risk of AIS, a nonoverlapping test set of 26 cases and 23 controls was constructed using the previously described strategy. Detailed information on these subjects is summarized in Table 1. All subjects or their healthcare proxy provided written informed consent, and the study was approved by the Institutional Review Boards at the Second Affiliated Hospital. 774

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Journal of Proteome Research 2.3. Sample Preparation

analysis (PLS-DA), and univariate analysis. The details of the optimized workflow can be found in the Supporting Information. A volcano plot and Venn diagram were used to filter important features that displayed large variable importance in projection (VIP) values (VIP > 1), significant fold changes (FC > 1.2 or < −1.2), and statistical significance (adjusted p < 0.05) between the two groups. Subsequently, the preliminary identification of these differential features was conducted by searching the metabolite databases, including the NIST 11 library, Human Metabolome Database, mzCloud, METLIN, and LIPID MAPS. In addition, confirmation of potential metabolites was determined by comparison of their retention times and fragments with those of commercial standards. Finally, the functions and related metabolic pathways of these metabolites were identified using the MetaboAnalyst, LIPID MAPS, and KEGG.

Serum samples for GC−MS and LC−MS analyses were prepared as described previously.14,15 For GC−MS metabolite profiling, serum samples were diluted with ten times volume of ice-cold methanol containing heptadecanoic acid (5 μg/mL). A two-step derivatization scheme using MOX and MSTFA was employed. After derivatization, the mixture was analyzed by GC−MS. For LC−MS metabolite profiling, serum samples were diluted with seven times the volume of ice-cold acetonitrile containing glyburide (10 μg/mL). The mixture was vortexed thoroughly for 3 min and then centrifuged twice at 25 186g for 10 min at 4 °C. The supernatant was transferred for LC−MS analysis. To monitor the data quality and process variation, quality control (QC) samples containing aliquots from serum samples of all participating subjects were parallel-processed and intermittently injected throughout the run. In addition, the orders of sample preparation and injection were both randomized to avoid systematic biases.

2.6. Biomarkers Evaluation with Multiple Algorithms

We utilized a procedure for determining the importance of metabolic biomarker candidates for AIS diagnosis. The procedure built a classifier with the training data set, damaged the test data set in a systematic way for each candidate, and compared the “damage effects”, that is, how much worse the classifier performed over the damaged test data set. The outline of procedure is summarized and shown in the Supporting Information. The relative damage score (RDS) of each candidate was calculated as follows

2.4. Chromatographic and Mass Spectrometric Conditions

GC−MS analysis was performed by the GCMS-QP2010 Ultra system (Shimadzu, Japan) equipped with an electron ionization (EI) source. A 1 μL aliquot of derivatized sample was injected into an Rtx-5MS column (30.0 m × 0.25 mm i.d., 0.25 μm; Restek, USA) using split mode at a ratio of 50:1. The oven temperature was initially held at 70 °C for 2 min, then increased to 320 °C at a rate of 10 °C/min and maintained for 2 min before cooling. The flow rate of carrier gas (helium) and the temperature of injector were maintained at 1.0 mL/min and 250 °C, respectively. MS detection was achieved using 70 eV electron and full-scan mode (m/z 45−600). LC−MS analysis was performed on an UFLC-IT-TOF/MS system (Shimadzu, Japan). A 5 μL aliquot of supernatant was chromatographed on a 100 × 2.1 mm Kinetex 2.6 μm C18 column (Phenomenex, USA) using constant flow rate of 0.4 mL/min. The gradient mobile phase consisted of 0.1% formic acid (A) and acetonitrile (B), running from 95% A to 5% A within 20 min and then maintaining with 5% A for 3 min. The eluent was introduced into the ion trap/time-of-flight mass spectrometry (IT-TOF/MS) by electrospray ionization (ESI) operated in both positive and negative modes. The interface voltages of positive and negative ion mode were set to 4.5 and −3.5 kV, respectively. The other MS parameters were as follows: data acquisition range, m/z 100−1000; TOF detector voltage, 1.6 kV; drying gas pressure, 100 kPa; heat block temperature, 200 °C; and curved desorption line temperature, 200 °C. In addition, reference ions, 494.1512 (ESI+) and 492.1119 (ESI−), were employed to determine the instrument variability during the spectral acquisition.

⎛ accuracy[damaged] ⎞ RDS = ⎜1 − ⎟ × 100% accuracy[base] ⎠ ⎝

The candidates that yielded the highest RDSs were considered most important. To validate the results, different classifiers were developed using Mathematica with a variety of algorithms, including: logistic regression, naive bayes, nearest neighbors, neural network, random forest, and support vector machine. The candidates that consistently yielded notable results across classifiers were identified as having the most potential to serve as potential biomarkers in AIS. Furthermore, the diagnostic potential of the candidates was assessed using the receiver operating characteristic (ROC) curve. In general, if the value of area under ROC curve (AUC) is greater than 0.9, an outstanding classification performance is attained.

3. RESULTS 3.1. Reliability of the Analytical Methods

The analytical characteristics of metabolic profiling were studied to test the reproducibility of the sample preparation procedures and to assess the stability of GC−MS and LC−MS systems. The QC samples were prepared and analyzed with serum samples collected from the training set (nine samples) and the test set (six samples). PCA shows that all QC samples were within two times of the standard deviation (SD) in the score plots (Figure S1). The distributions of the relative standard deviation (RSD) for QC samples indicated that >96% of the sum of responses had an RSD of 0.9). Two of the metabolites were removed, and a total of 20 metabolites were retained in the biomarker candidate pool. Six classifiers were built using the training data set and different algorithms. The algorithms were applied to the test data set to verify the accuracy of the classification results. In the test set, five algorithms performed with a classification accuracy >83% (Figure S4). The nearest-neighbors algorithm performed poorly (classification accuracy 71.4%) and was discarded. Then, the test data set metabolite values were randomly shuffled, and the RDS of each metabolite was calculated (Figure 2A, Table

Figure 1. Statistical analysis for the data obtained from training set. (A) PLS-DA score plot of GC−MS data. (B) PLS-DA score plot of LC−MS data. (C) Volcano plot of VIP values. (D) Volcano plot of adjusted p values. (E) Venn diagram of VIP, adjusted p, and foldchange results. (F) z-Score plot for the comparison between AIS patients and healthy individuals.

51 features with a VIP > 1.0 were retained as the GC−MS and LC−MS data, respectively. For univariate screening, the Mann−Whitney U test was utilized to evaluate the statistical significance of each feature. The false discovery rate (FDR) was adjusted using the Benjamini−Hochberg procedure. At FDR < 5%, 530 features with an adjusted p value 1.2 or < −1.2 were included. Together, a total of 158 features were cross-selected (i.e., the intersection) based on the preliminary screening results of volcano plots (Figure 1C,D) and the Venn diagram (Figure 1E). Of these 158 features, 26 metabolites with related metabolic pathways involving amino acid metabolism, phospholipid metabolism, fatty acid metabolism, and choline metabolism were identified and summarized (Table S1). To further define effective markers, z-score plots of the metabolites were analyzed (Figure 1F). The levels of 16 metabolites were found to be significantly decreased in the cases compared with the metabolite levels in the controls. Conversely, the remaining 10 metabolites exhibited increases in the case samples.

Figure 2. Evaluation of the diagnostic potential for differential metabolites. (A) Relative damage score of each metabolite in multiple algorithms. Larger values correspond to greater importance. (B) ROC curves of the combination of five potential biomarkers. (C) Discrimination of AIS patients and healthy individuals of the combination of five potential biomarkers.

S2). Five metabolites, including serine, isoleucine, betaine, PC(5:0/5:0), and LysoPE(18:2), scored highly in at least four classifiers, suggesting their importance for accurate classification. The importance of these five metabolites for classification accuracy suggests their potential as biomarkers for AIS risk (Table 2). Furthermore, the diagnostic potential of these five metabolites was evaluated. As shown in Figure 2B, the AUC value of the ROC curve reached 0.988 in the training set and 0.971 in the test set for distinguishing AIS patients from healthy 776

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Journal of Proteome Research Table 2. Summary of Five Potential Biomarkers AUC metabolites serine isoleucine betaine PC(5:0/5:0) LysoPE(18:2)

VIP 1.47 1.48 1.25 1.77 1.44

adjust p 9.93 9.00 1.37 3.28 1.20

× × × × ×

fold change (case/control)

training set

test set

biological pathway

−1.64 −1.63 1.66 −2.37 1.74

0.823 0.832 0.723 0.927 0.785

0.894 0.829 0.625 0.911 0.676

amino acid metabolism amino acid metabolism choline metabolism phospholipid metabolism phospholipid metabolism

−6

10 10−6 10−4 10−9 10−7

Figure 3. Schematic diagram about biological significance of potential biomarkers for AIS. The bar plots represent means and standard errors.

isoleucine, aspartate, and others), fatty acid metabolism (carnitine and acetylcarnitine), carbohydrate metabolism (mannose and galactose), choline metabolism (betaine), and various membrane lipids (Figure 3). Amino acids, substrates for protein synthesis, are essential for human function. Two previous cerebral infarction metabolomics investigations have linked an abnormality in amino acid metabolism to stroke development.11,13 Consistent with previous reports, the results of this study showed that alanine, glycine, serine, isoleucine, threonine, aspartate, proline, ornithine, and lysine were markedly reduced in the serum of AIS patients. Some of these amino acids, such as isoleucine, serine, and aspartate, have been reported to act as signaling molecules that regulate a variety of cellular processes necessary for the maintenance, growth, and repair of brain function.17,18 Additionally, some amino acids serve as fuel substrates in energy metabolism for brain cells to satisfy their high energy demands.19,20 For example, threonine, serine, and alanine can be converted to pyruvate through glycolysis for energy supply. Isoleucine and aspartate are important energy sources via the TCA cycle after transformation into acetyl-CoA and oxaloacetate, respectively. Therefore, the reduction in amino acids might be attributed to their utilization as cell signaling

individuals, which was interpreted as excellent performance of classification. Figure 2C shows the prediction probability values of these five candidates. At the traditional cutoff value of 0.5, 89.7, and 92.3%, AIS patients were correctly diagnosed in the training and test sets, respectively. Thus a novel serum biomarker model including five metabolites (serine, isoleucine, betaine, PC(5:0/5:0) and LysoPE(18:2)) was ultimately defined on the basis of the comprehensive screening and verification procedure.

4. DISCUSSION 4.1. Metabolic Changes in Human Serum during AIS

AIS is caused by a sudden blockage in an artery that provides blood to the brain. When AIS occurs, the brain is unable to obtain sufficient oxygen or nutrients, causing the brain cells at the blockage site to die rapidly and release toxic chemicals that threaten the surrounding brain tissues.3 The changes in metabolome levels in the biofluids are considered to be associated with the metabolic alterations in the brain.16 By using a nontargeted metabolomics approach, this study found that AIS significantly affected a number of serum metabolites related to amino acid metabolism (alanine, glycine, serine, 777

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study, Kimberly et al. demonstrated that a reduction in the concentration of BCAA is associated with stroke severity and worse neurological outcome.13 Most interestingly, a recent study has shown that a BCAA metabolism defect resulting from Krüppel-like factor 15 (KLF15)-mediated transcriptional reprogramming is a significant signature of heart failure.27 Because myocardial ischemia and cerebral ischemia generally occur when blood vessels become narrowed or clogged with fatty deposits, BCAA metabolism may make a previously unappreciated significant contribution to AIS.28 However, this hypothesis requires further investigation.

molecules or TCA cycle intermediates for reactivation of brain function. In addition to amino acids, a set of metabolites found in phospholipids have a high association with AIS risk. Differences in levels of these metabolites were seen in the case and control groups of this study. Levels of lyso-phosphatidylethanolamine (LysoPE) were increased in the case group, whereas phosphatidic acid (PA), phosphatidylinositol (PI), phosphatidylcholine (PC), and lyso-phosphatidylcholine (LysoPC) were decreased. Phospholipids are an important class of lipids for the construction of all cell membranes. Abnormality in serum phospholipid levels in stroke patients has been reported in previous associated studies.21,22 In addition, several proinflammatory mediators, such as phospholipases and tumor necrosis factor-alpha, are reported to be strongly upregulated in the ischemic brain.23 Phospholipase A2 (PLA2) is an enzyme that hydrolyzes the membrane phospholipids, and its upregulation increases the levels of free fatty acids and lysophospholipids, for example, LysoPC and LysoPE.24 The injury of brain cells and the changes in phospholipid levels have potential association with PLA2. Another important feature found in the patient serum was the significant changes in carnitine and the related metabolite acetylcarnitine. Carnitine is well known to help the body convert fat into energy by transporting fatty acids into the mitochondria during the catabolism of lipids, while acetylcarnitine is a preferred form of carnitine for brain support because the acetyl group enables it to cross the blood−brain barrier more easily. In addition to the metabolic roles, carnitine and acetylcarnitine possess unique neurotrophic, neuroprotective, and neuromodulatory properties that may produce an essential effect in fighting various diseases.25 Research has indicated that treatment with acetylcarnitine reduces the neuropathologic injury resulting from cerebral ischemia, potentially explaining why serum levels of carnitine and acylcarnitines were increased in AIS patients in this study.26

4.3. Study Limitations

This study is not without limitations. First, the novel findings in this study are based on the profiling analyses conducted in the training and test sets, and the generated hypotheses therefore require investigation in a more targeted manner (quantitative). Additionally, this study focused on AIS because ischemia accounts for ∼80% of all strokes. Further testing of the ability of the five metabolites to differentiate AIS from hemorrhagic stroke (beyond differentiating AIS samples from those of healthy controls), would be more meaningful because the treatment of AIS with tPA can only be given after ruling out hemorrhagic stroke. Finally, this study investigated a relatively small patient cohort in China. Larger studies in broader populations will be necessary to confirm the findings of this study and to determine the value of these five metabolites as clinically useful biomarkers or therapeutic targets.

5. CONCLUSIONS This study provides novel insight into the discovery of clinically effective biomarkers for the diagnosis and progression of AIS by exploring the metabolic alterations of human serum in a training set, followed by the comprehensive verification and optimization of potential biomarkers using a test set. With an advanced MS-based metabolomics approach, metabolic features of AIS were profiled, indicating abnormality of global metabolism. Fatty acid and amino acid pathways, as well as membrane lipid metabolism, were found to be significantly affected by AIS. Compared with limited reports of metabolomics analysis of AIS, a novel serum biomarker model consisting of serine, isoleucine, betaine, PC(5:0/5:0), and LysoPE(18:2) was created based on a comprehensive screening and verification procedure using multiple algorithms. In both the training and test sets, this biomarker model was evaluated as a tool for differentiating AIS samples from healthy control samples. The AIS biomarker model in this study provides a basis for future diagnostic or therapeutic development.

4.2. Potential Implications of Five Biomarkers

The goal in metabolomics biomarker discovery is to find the simplest combination of metabolites that can effectively predict outcomes.7 Previous studies on stroke have mainly focused on screening differential metabolites.11−13 In this study, the 26 differential metabolites discovered from the training set were further evaluated with multiple algorithms to investigate their diagnostic potential in a test set. Among these differential metabolites, we found a panel of five metabolites that showed good potential to distinguish AIS patients from healthy individuals: serine, isoleucine, betaine, PC(5:0/5:0), and LysoPE(18:2) (Figure 2). ROC curves were calculated for each of the metabolites, with AUC values ranging from 0.62 to 0.93 (Table 2, Figure S5). The rational combination of the five potential biomarkers achieved better AUC values (>0.97), sensitivity, and specificity in both the training and test data sets. Compared with conventional imaging exams and biochemical analyses, these potential biomarkers are faster, have higher accuracy and stronger specificity, and are less invasive, potentially allowing for broader application. Moreover, our findings provide a focus for future experiments on investigating candidate pathways that are related to the five potential biomarkers (Figure 3). For instance, isoleucine is one of the branched-chain amino acids (BCAAs) that influence brain function by the production of energy and the synthesis of different neurotransmitters.17 As noted in this

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.6b00779. Optimized workflow. Procedure outline of biomarkers evaluation. Figure S1. Evaluation of analytical characteristics. Figure S2. PCA score plots. Figure S3. Distribution chart and heat map of differential metabolites. Figure S4. Confusion matrix of six classifiers. Figure S5. ROC curves of the five potential biomarkers. Table S1. Twenty-six identified differential metabolites. Table S2. Relative damage score of each metabolite. (PDF) 778

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(12) Jiang, Z.; Sun, J.; Liang, Q.; Cai, Y.; Li, S.; Huang, Y.; Wang, Y.; Luo, G. A metabonomic approach applied to predict patients with cerebral infarction. Talanta 2011, 84 (2), 298−304. (13) Kimberly, W. T.; Wang, Y.; Pham, L.; Furie, K. L.; Gerszten, R. E. Metabolite profiling identifies a branched chain amino acid signature in acute cardioembolic stroke. Stroke 2013, 44 (5), 1389− 1395. (14) Zhang, P.; Chen, J.; Wang, Y.; Huang, Y.; Tian, Y.; Zhang, Z.; Xu, F. Discovery of Potential Biomarkers with Dose- and TimeDependence in Cisplatin-Induced Nephrotoxicity Using Metabolomics Integrated with a Principal Component-Based Area Calculation Strategy. Chem. Res. Toxicol. 2016, 29 (5), 776−783. (15) Dai, D.; Tian, Y.; Guo, H.; Zhang, P.; Huang, Y.; Zhang, W.; Xu, F.; Zhang, Z. A pharmacometabonomic approach using predose serum metabolite profiles reveals differences in lipid metabolism in survival and non-survival rats treated with lipopolysaccharide. Metabolomics 2016, 12 (1), 1−13. (16) Ivanisevic, J.; Siuzdak, G. The Role of Metabolomics in Brain Metabolism Research. J. Neuroimmune Pharmacol 2015, 10 (3), 391− 395. (17) Fernstrom, J. D. Branched-chain amino acids and brain function. J. Nutr. 2005, 135 (6 Suppl), 1539S−46S. (18) Fernstrom, J. D. Large neutral amino acids: dietary effects on brain neurochemistry and function. Amino Acids 2013, 45 (3), 419− 430. (19) Wu, G. Functional amino acids in nutrition and health. Amino Acids 2013, 45 (3), 407−411. (20) Ribeiro, C. A.; Sgaravatti, A. M.; Rosa, R. B.; Schuck, P. F.; Grando, V.; Schmidt, A. L.; Ferreira, G. C.; Perry, M. L. S.; DutraFilho, C. S.; Wajner, M. Inhibition of brain energy metabolism by the branched-chain amino acids accumulating in maple syrup urine disease. Neurochem. Res. 2008, 33 (1), 114−124. (21) Kim, Y.-J.; Kim, O. Y.; Cho, Y.; Chung, J. H.; Jung, Y.-S.; Hwang, G.-S.; Shin, M.-J. Plasma phospholipid fatty acid composition in ischemic stroke: importance of docosahexaenoic acid in the risk for intracranial atherosclerotic stenosis. Atherosclerosis 2012, 225 (2), 418−424. (22) Fretts, A. M.; Mozaffarian, D.; Siscovick, D. S.; Sitlani, C.; Psaty, B. M.; Rimm, E. B.; Song, X.; McKnight, B.; Spiegelman, D.; King, I. B.; et al. Plasma phospholipid and dietary α-linolenic acid, mortality, CHD and stroke: the Cardiovascular Health Study. Br. J. Nutr. 2014, 112 (7), 1206−1213. (23) McCombe, P. A.; Read, S. J. Immune and inflammatory responses to stroke: good or bad? Int. J. Stroke 2008, 3 (4), 254−265. (24) Wassertheil-Smoller, S.; McGinn, A.; Allison, M.; Ca, T.; Curb, D.; Eaton, C.; Hendrix, S.; Kaplan, R.; Ko, M.; Martin, L. W.; et al. Improvement in stroke risk prediction: role of C-reactive protein and lipoprotein-associated phospholipase A2 in the women’s health initiative. Int. J. Stroke 2014, 9 (7), 902−909. (25) Virmani, A.; Binienda, Z. Role of carnitine esters in brain neuropathology. Mol. Aspects Med. 2004, 25 (5−6), 533−549. (26) Scafidi, S.; Racz, J.; Hazelton, J.; McKenna, M. C.; Fiskum, G. Neuroprotection by acetyl-L-carnitine after traumatic injury to the immature rat brain. Dev. Neurosci. 2011, 32 (5−6), 480−487. (27) Sun, H.; Olson, K. C.; Gao, C.; Prosdocimo, D. A.; Zhou, M.; Wang, Z.; Jeyaraj, D.; Youn, J.-Y.; Ren, S.; Liu, Y.; et al. Catabolic Defect of Branched-Chain Amino Acids Promotes Heart Failure. Circulation 2016, 133 (21), 2038−2049. (28) Stergiopoulos, K.; Boden, W. E.; Hartigan, P.; Möbius-Winkler, S.; Hambrecht, R.; Hueb, W.; Hardison, R. M.; Abbott, J. D.; Brown, D. L. Percutaneous coronary intervention outcomes in patients with stable obstructive coronary artery disease and myocardial ischemia: a collaborative meta-analysis of contemporary randomized clinical trials. JAMA Intern Med. 2014, 174 (2), 232−240.

AUTHOR INFORMATION

Corresponding Authors

*Y.Z.: E-mail: [email protected]. *Y.H.: E-mail: [email protected]. ORCID

Yin Huang: 0000-0001-9678-2630 Author Contributions ⊥

P.L. and R.L. contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was financially supported by the National Natural Science Foundation of China (No. 81403181), the Natural Science Foundation of Heilongjiang Province of China (No. QC2016109), and the Basic Research Program of Jiangsu Province of China (No. BK20140664). We thank the subjects for their participation in our project.

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DOI: 10.1021/acs.jproteome.6b00779 J. Proteome Res. 2017, 16, 773−779