Amino Acid Biosignature in Plasma among Ischemic Stroke...

Research ArticleAmino Acid Biosignature in Plasma among IschemicStroke Subtypes

Vacircnia A M Goulart12 Marcelo M Sena23 Thiago O Mendes4

Helveacutecio C Menezes2 Zenilda L Cardeal 2 Maria J N Paiva2 Valeacuteria C Sandrim4

Mauro C X Pinto 5 and Rodrigo R Resende1

1Department of Biochemistry and Immunology Federal University of Minas Gerais Belo Horizonte-MG Brazil2Department of Chemistry Federal University of Minas Gerais Belo Horizonte-MG Brazil3National Institute of Science and Technology in Bioanalytics Campinas-SP Brazil4Institute of Learning and Research Santa Casa de BH Belo Horizonte-MG Brazil5Department of Pharmacology Federal University of Goias Goiania-GO Brazil

Correspondence should be addressed to Mauro C X Pinto pintomcxgmailcom

Received 24 September 2018 Revised 29 November 2018 Accepted 10 December 2018 Published 20 January 2019

Academic Editor Carl Muroi

Copyright copy 2019 Vania A M Goulart et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Ischemic stroke is a neurovascular disorder caused by reduced or blockage of blood flow to the brain which may permanentlyaffect motor and cognitive abilities The diagnostic of stroke is performed using imaging technologies clinical evaluation andneuropsychological protocols but no blood test is available yet In this work we analyzed amino acid concentrations in bloodplasma from poststroke patients in order to identify differences that could characterize the stroke etiology Plasma concentrationsof sixteen amino acids from patients with chronic ischemic stroke (n = 73) and the control group (n = 16) were determined usinggas chromatography coupled to mass spectrometry (GC-MS) The concentration data was processed by Partial Least Squares-Discriminant Analysis (PLS-DA) to classify patients with stroke and control The amino acid analysis generated a first model ableto discriminate ischemic stroke patients from control group Proline was the most important amino acid for classification of thestroke samples in PLS-DA followed by lysine phenylalanine leucine and glycine andwhile higher levels ofmethionine and alaninewere mostly related to the control samples The second model was able to discriminate the stroke subtypes like atherothromboticetiology from cardioembolic and lacunar etiologies with lysine leucine and cysteine plasmatic concentrations being the mostimportant metabolites Our results suggest an amino acid biosignature for patients with chronic stroke in plasma samples whichcan be helpful in diagnosis prognosis and therapeutics of these patients

1 Introduction

Stroke is a complex neurological syndrome which involvesa sudden abnormality in brain function by interruption ofcerebral circulation or bleeding Each year approximately795000 people in the United States are affected by stroke[1] About 610000 of these cases are first attacks and 185000are recurrent attacks [1 2] Stroke caused by ischemia con-tributes to 87 of the cases and is triggered by a vascularocclusion leading to an interruption in oxygen and glucosesupply to the brain that affects metabolic processes of theinvolved region [3 4] According to Trial of Org 10172 in

Acute Stroke Treatment (TOAST) diagnostic classificationsystem ischemic stroke can be classified into five sub-types based on its etiology atherothrombotic cardioem-bolic lacunar undetermined and other specific etiologies[5 6]

Immediately after the ischemic stroke a cascade ofbiochemical events promotes the death of brain tissue andsubsequent activation of the immune response to areaaffected [7] The severity of stroke is directly related tothe volume of the lesion the brain area involved and thetime of the start of treatment[8] Recognizing the specificcause of strokes which are etiologically heterogeneous

HindawiBioMed Research InternationalVolume 2019 Article ID 8480468 11 pageshttpsdoiorg10115520198480468

2 BioMed Research International

Table 1 Demographic data of the study group

StrokeSubjects Controls Atherothrombotic Cardioembolic Lacunar UndeterminedMale 7 13 7 10 8Female 9 7 11 10 7Age (yearsmeanplusmnSD)

5475(plusmn1234)

6870(plusmn809)

6250(plusmn1215)

6420(plusmn1128)

5493(plusmn1450)

Risk factorsHypertension () 688 80 833 85 67Diabetes mellitus() 0 30 56 30 67

Alcoholism () 188 35 278 45 267Previous stroke () 0 40 333 20 267Smoking () 313 55 444 50 333Obesity () 188 15 1111 5 133119886mRS ge 2 () - 20 444 35 266119886mRS modified Rankin Scale

has important clinical implications [9] The prognosis andadministration of early and long-term strategies to preventrelapse can vary considerably for the different stroke subtypes[10] For recovery patients should be treated with specificrestorative therapies Most patients show some improvementusually during the first 3 to 6 months after the ischemia[11]

Themost understanding of howmanifestations of neuro-plasticity are related to stroke recovery is obtained throughmultimodal techniques in brain imaging [12] Currentlythe standard techniques for a diagnosis and prognosis ofstroke are based on clinical observations and evaluation ofneuroimaging [13] Just as neuroimaging cardiac evaluationand arterial imaging are used in the diagnosis of strokedetermining its causes and mechanisms of recovery in thesame way molecular features in the form of proteins RNAmetabolites lipids and other biomarkersmay also have utility[14]

The metabolomics approach focuses on measurement ofthe relative concentrations of endogenous small moleculesin biofluids cells and tissues that characterize changes inmetabolism thus helping to unravel the metabolic state ofbiological systems [15] Advances in analytical chemistrytogether with multivariate statistical methods can allow forthe investigation of metabolites as potential biomarkers ofvarious diseases [16ndash19]

In this study we compared the amino acid profilesof patients after stroke with healthy subjects and studieddifferent ischemic stroke subtypes We developed a modelto characterize and classify plasma samples of patients fromhealthy individuals and four stroke subtypes using GC-MS associated with a multivariate method of supervisedclassification PLS-DA Our model determined the aminoacid profile that differentiates between healthy individualsfrom ischemic stroke patients and the amino acids that mostcontribute to discrimination of each stroke subtypes andcontrols

2 Methods

21 Subjects Human plasma samples were provided by Insti-tute of Education and Research Santa Casa Belo HorizonteLaboratory of Biomarker Seventy-three plasma samples frompatients diagnosed with one of the ischemic stroke subtypesby magnetic resonance imaging (MRI) based on the TOASTclassification system and being in the chronic phase ofthe disease (after 7 days of the onset of ischemic strokediagnostic) were analyzed We also analyzed 16 plasmasamples from healthy subjects (individuals without historyof ischemic or hemorrhagic stroke) which were consid-ered the control group Demographic data are presented inTable 1

The modified Rankin Scale (mRS) indicates degree ofdisability of stroke patients (index ranged from 0 to 6)Score 2 designates slight disability or individuals able tolook after own affairs without assistance Score 3 designatesmoderate disability that will worsen with the increasing ofthe score value and the maximum score 6 indicates deathThis studywas approved by theEthics Committee inResearchof Universidade Federal de Minas Gerais under number312840 It was also approved by the Ethics Committee inResearch of Santa Casa Misericordia of BeloHorizonte undernumber 315034

22 Sample Preparation The amino acid extraction protocolwas adapted from Pinto et al [20] Briefly in order toprecipitate proteins 100120583L of plasma samples were dissolvedin 900120583L of methanol at -10∘C Subsequently samples werevortexed for 2 minutes followed by centrifugation at 10000gfor 10 minutes at 25∘C The supernatant (100120583L) was trans-ferred from each sample into a glass vial and evaporated atroom temperature

Sample derivatization process was performed asdescribed by de Paiva et al [21] To each vial containingdried sample was added 15120583L of methoxamine solutiondiluted with 20mgmL pyridine (Fluka St Louis MO USA)

BioMed Research International 3

followed by 35120583L NO-bis (trimethylsilyl) trifluoroacetamide(BSTFA) + Trimethylchlorosilane (TMCS Fluka St LouisMO USA) Then each vial was vortexed for 30s andsubjected to 700W of microwave irradiation (Philco SaoPaulo SP Brazil) for 3 min After derivatization the vial wasvortexed for 10s Finally 1120583L of sample was removed with aglass microsyringe (Hamilton Bonaduz GR Switzerland)and manually injected into the chromatograph

23 Gas Chromatography Coupled to Mass Spectrometry (GC-MS) The analyses were performed on a Shimadzu chro-matograph GC-2010QP-2010 model (Kyoto Japan) witha high-performance mass analyzer quadrupole The massspectrometer was operated in the electron impact mode at70eV A fused silica nonpolar capillary column (RestekBellefont PA USA) RTX-5MS model 30m x 025mm id x025120583m) was employed The temperature program used foroven column was as follows 80∘C for 2min gradient up to120∘C at a rate of 3∘Cmin gradient to 190∘C at 8∘Cmingradient up to 300∘C at 30∘Cmin and then constant 300∘Cfor 3min The injector was operated at 280∘C in the splitlessmode for 3min and then at a 120 split ratio Helium gas(99999) was used as the carrier gas at a flow rate of1mLmin The ion source temperature was 200∘C and theGC-MS interface was kept at 260∘C The analyses wereperformed in full scan mode monitoring the mass rangeof 45-300 mz and in the single ion monitoring (SIM)mode for the selection identification and quantification ofspecific ions Signal acquisition and data processing were per-formed using the LabSolutions software (Shimadzu KyotoJapan)

24 Identification and Quantification of Amino Acids Forthe method validation commercial standards of 16 aminoacids were used (see Supplementary Materials Table S1)Standard solutions of each amino acid were prepared andanalyzed for construction of calibration curves Amino acididentification was performed by analyzing the mz fragmentsand retention time of specific abundance in the derivatizedstandard solution followed by the similarity analysis of themass spectra with the spectral library The retention timeobtained for each amino acid and the fragments used forquantification are listed in Table S1 (see SupplementaryMaterials)

25 Conversion of Peak Areas to Concentration Calibrationcurves for each amino acid were built using Excel (MicrosoftUSA) For each curve construction five different concen-trations of each standard solution were used The peakarea related to the chosen ions for the measurements wasrecorded for each concentration and used to build each curveThrough a linear regression the straight-line equation y = a+ bx was estimated obtaining the slope b and the intercepta The amino acid concentrations present in the sampleswere determined in 120583molL using the following equationpeak area+balowast(1000molar mass of amino acid)lowastsampledilution The correlation coefficients r were calculated foreach analytical curve so that all curves showed values greaterthan 099

26 Statistical Analysis The Kruskal-Wallis test followedby Dunnrsquos multiple comparison test was used to compareeach IS subtype of samples group and control group Tocomplement the visualization of differences in metabolicprofiles a heatmapwas constructed based on themean aminoacid concentration in each subtype of ischemic stroke andcontrols (see Supplementary Materials)

For supervised classification of IS subtypes and identi-fication of potential biomarkers from 16 amino acids themultivariate supervised classification method PLS- DA wasemployed [22 23] Data were handled with MetaboAnalyst30 web server (httpswwwmetaboanalystca) and PLSToolbox version 65 (Eigenvector Technologies MansonWA USA) For PLS-DA analysis the data were previouslyautoscaled and leave-one-out cross-validation (LOOCV) wasused

The samples were split into the training and test sets usingtheKennard-Stone algorithm[24]The amino acidswithmostpositivenegative regression coefficients and largest variableimportance in the projection (VIP) scores were candidatebiomarkers [25] More details about PLS-DA models andtheir analytical validation are presented in SupplementaryMaterials

3 Results

31 Amino Acid Analysis Distinguishes Patients with Strokeand Control Group Plasma amino acid concentrations arekept relatively constant in postabsorptive state of a healthyhuman however circulating amino acid levels change duringdisease There is a subtle but important change in plasmaamino acid concentrations of ischemic stroke patients Theconcentrations of 16 amino acids were compared for eachsubtype of stroke with the control group using the Kruskal-Wallis test followed by Dunnrsquos multiple comparison test toidentify statistical differences (Figure 1) Atherothromboticsubtype samples had higher concentrations of Leu and lowerconcentrations of Met when compared to control Lacu-nar subtype samples had lower concentration of Ala whencompared to health control Interestingly the undeterminedsubtype samples presented higher concentrations of Leu andPro than control samples This fluctuation in amino acidconcentration between subtypes and controls can be seen inthe heatmap Figure S1 (see Supplementary Materials)

Based on the plasma concentration of amino acidswe analyzed the biomarker potential of these molecules todiscriminate between stroke patients and healthy volunteersby PLS-DAWebuilt different PLS1-DAmodels for classifyingthe plasma samples (see more in Supplementary Materials)The first model was used to classify plasma samples in onlytwo classes as healthycontrol or stroke (independent ofthe subtype) The other models were developed in order toclassify specifically each stroke subtype

The first model (model 1) was defined considering onlytwo classes stroke (class 1) and control (class 0) (Figure 2(a))To obtain a robust model the data for an independentvalidation was separated from the external test set A sys-tematic approach was adopted using the Kennard-Stone algo-rithm (see Supplementary Materials) to select representative


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olL

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olL

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olL

lowast

lowastlowastlowastlowast

lowast

lowast

Figure 1 Differences in the amino acid concentrations between stroke subtypes and control individuals Control individuals (Ctl)atherothrombotic stroke (Ath) cardioembolic stroke (Car) lacunar stroke (Lac) and undetermined stroke (Und) Kruskal-Wallis testfollowed by Dunnrsquos multiple comparison test lowast p le 005 lowastlowast p le001 and lowastlowastlowastp le 00001

training samples inside each classThus the datawere dividedinto 62 samples for the training set and 27 samples for test setThebest PLS-DAmodelwas selected by LOOCVbased on theminimum cross-validation classification error (CVCE)

The best model was built with three latent variables (LV)Model quality can be evaluated through the estimate offigures of merit such as sensitivity ie the rate of true pos-itives (correct ischemic stroke predictions) and specificityie the rate of true negatives (correct control predictions)Other parameters used for evaluating the models were cross-validation Q2 and area under the receiver operator charac-teristic (AUROC) For training set sensitivity was 0961 andthe specificity was 0909 (Table 2) Nevertheless only twocontrol samples were incorrectly predicted as ischemic strokecases 54 and 88 (circled in red in Figure 2(b)) which canbe identified as outliers in a plot of studentized residuals atthe 997 confidence level Thus a global figure of merit canbe estimated for the quality of this model the efficiency rate(EFR) (see Supplementary Materials) The EFR for trainingand test sets of the developed model was 961 and 955respectively upon elimination of the two outliers

Once the reliability of this PLS-DAmodel was confirmedthe variable statistics (informative vectors) can be used tosearch for the most discriminant amino acids The aminoacids Met Ala Asp Pro and Cys presented the most

significant VIP scores (Figure 2(c)) The most positive andmost negative regression coefficients are the most discrimi-nant ones directly related to the power of the variables forthe classification of classes 1 (ischemic stroke) and 0 (control)respectively Pro was the most important amino acid forischemic stroke sample classification while Met and Alawere the most discriminant ones to classify control samples(Figure 2(d)) Thus the joint analysis of VIP scores andregression coefficients indicated that Pro Met and Ala werethe most discriminating amino acids for this model

Based on these results a variable selection was per-formed and these three most important amino acids (MetAla and Pro) were chosen as potential biomarkers in orderto build a new PLS-DA model (model 2) This reducedmodel was somewhat worse than the full model (Figure 3Table 2) The regression equation for the reduced model was- 020Ala - 044Met + 053Pro the negative signs on Alaand Met indicate that plasma samples of healthy individualshave higher levels of these amino acids than ischemic strokesamples In contrast the positive sign of Pro indicates thatischemic stroke samples have higher levels of Pro

32 Amino Acid Analysis Distinguishes Different Typesof Chronic Stroke To understand the most discriminantamino acids between ischemic stroke subtypes we tried


ISControl

6040 80 100200Samples

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or fo

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Gly Sar Val Leu Iso Ser Thr Asp Met Pro Cys Glu Phe Asn LysAla

Amino acids(d)

Figure 2 PLS-DA predictions to classify ischemic stroke and control samples (a) studentized residuals for thismodel at the 997 confidencelevel (b) two samples (red circles) being detected as outliers Variable importance in the projection (VIP scores) (c) and regression coefficients(d)

Table 2 Figures of merit for PLS-DA models to discriminate chronic ischemic stroke from controlhealthy plasma samples

Parameters Model 1 Model 2Sensitivity (training set) 0961 0902Specificity (training set) 0909 0818EFR (training set) 0870lowast 0870Sensitivity (test set) 0955 0955Specificity (test set) 0800 0800EFR (test set) 0755lowast 0720N∘ of LV 3 2Variance in X 6602 7964Variance in Y 6363 3162Q2 0478 0176AUROC 0800 0800lowastConsidering the detection of two control samples as outliers by the studentized y residuals at 95 confidence level the EFR is 0961 and 0955 for the trainingand test sets respectively

to develop specific PLS1-DA models (one class againstall the other classes) for discriminating each of the fourclasses atherothrombotic cardioembolic lacunar and unde-termined Undetermined ischemic stroke was the poorestmodeled class with 364 of sensitivity (data not shown)This is coherent with the fact that this is a nonhomogeneousclass in terms of etiology causes

The best-modeled class was atherothrombotic ischemicstroke (Figure 4(a) and Table 3) presenting only two falsenegatives (out of 14) in the training set and no false negative inthe test set Particularly cardioembolic and lacunar ischemic

stroke samples were poorly discriminated between themsince many samples of one class were predicted as falsepositive in the otherThe regression coefficients for predictingthese two classes presented a strong correlation (r=09987)indicating their very similar amino acid profiles Thus toverify the separation of these two classes compared to othersanothermodelwas built including cardioembolic and lacunarischemic stroke samples in a combined single class Thismodel showed better results with a sensitivity of 0833and a specificity of 0909 for the test set (Figure 4(b) andTable 3)


Table 3 Figures of merit for PLS-DA models for classifying atherothrombotic and cardioembolic plus lacunar ischemic stroke subtypes

Parameters Athelowast Card+LaclowastSensitivity (training set) 0857 0769Specificity (training set) 0811 0760EFR (training set) 0668 0529Sensitivity (test set) 1000 0833Specificity (test set) 0541 0909EFR (test set) 0541 0742N∘ of LV 4 3Variance in X 7477 6382Variance in Y 2780 3362Q2 0359 0396AUROC 0882 0883lowast Athe atherothrombotic Card cardioembolic Lac lacunar

Y Pr

edic

ted

1(IS

)

minus05

00

05

10

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20

ISControl

6040 80 100200Samples

Figure 3 PLS-DA predictions for discriminating ischemic strokefrom control samples This PLS-DA model was built with reduceddataset containing only the three most discriminant amino acidsMet Ala and Pro

The joint analysis of regression coefficients andVIP scoresfor atherothrombotic ischemic stroke samples (Figures 4(a)and 4(c)) indicated that the most discriminating amino acidswere related to the contrast between Leu versus Cys and Lysie the samples of this subtype were discriminated mainlyby higher contents of Leu and lower contents of Cys andLys Similarly by observing the same parameters for theclass formed by cardioembolic and lacunar ischemic strokesamples (Figures 4(b) and 4(d)) the main discrimination wasrelated to the contrast between Lys versus Ala and Met

Using the two first developed PLS models (model 1 andmodel 2) we can suggest a biosignature for ischemic strokedependent on Pro Met and Ala concentrations Of the threeischemic stroke subtypes the best classification was observedfor the atherothrombotic subtype while cardioembolic andlacunar ischemic stroke subtypes were more difficult todistinguish and were modeled together in a single class Thisobserved similarity may be related to the cardioembolic andlacunar subtypes etiology both can be caused by thrombosisbesides other possibilities The lacunar ischemic stroke isrelated to thrombosis of small arteries while the cardioem-bolic one is related to the obstructions of large arteries

[26 27] Wewere not able tomodel undetermined IS subtypedue to its intrinsic heterogeneity Samples of this subtypewerepredominantly classified in other subtypes likely because thissubtype does not have a common etiology and its samplesmay belong to other subtypes [28]

4 Discussion

In our study we observed that amino acids from TCA cycle(Pro Lys Ala and Leu) and the Folate cycle (Met andCys) are the most important to distinguish chronic ischemicstroke and its subtypes from health volunteers These aminoacids are involved in energy metabolism in methyl groupsgeneration and DNA synthesis (Figure 5) Recently Wangand collaborators have demonstrated that tyrosine lactateand tryptophanwere jointly enabling a high precision (917)to diagnose acute ischemic stroke within seven days ofstroke symptoms [29] Szpetnar et al 2017 demonstrated adecrease of proline and a simultaneous increase of glutamateserum level as a marker of acute ischemic stroke here wedemonstrated an increase in proline as a marker of chronicischemic stroke [30] Kimberly et al 2013 demonstrated thatvaline leucine and isoleucine are reduced in acute ischemicstroke and here we observed that leucine is increased inchronic ischemic stroke [31] Amino acids fluctuations arepresent in acute and chronic ischemic stroke and it can beused as markers for this pathology

We highlight the role of Pro as an example Proline onceoxidized to pyrroline-5-carboxylate (P5C) or its tautomerglutamic-120574-semialdehyde can be interconverted to manyother substrates and it is source for carbon exchange betweenTCA cycle and urea cycle Furthermore Pro can be storedin collagen the most abundant protein by weight in body Asnearly 25of the residues in collagen are incorporated as Procollagen can be a dump as well as a reservoir for Pro In thiscontext Pro concentrations may be elevated in plasma fromstroke patients as a product of extracellular matrix degrada-tion (mostly collagen) by metalloproteinases activation dur-ing brain ischemia (Figure 5(a)) In these low energy-supplycases Pro metabolic pathway is activated as a cell survivalstrategy During nutritional stress Pro is easily released byextracellular matrix degradation and this degradation can


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Figure 4 PLS-DA models built with 16 amino acids for classifying ischemic stroke subtypes Models for discriminating atherothromboticischemic stroke from other samples (a) and for cardioembolic plus lacunar ischemic stroke (b) VIP scores (c) for atherothrombotic ischemicstroke VIP scores (d) for cardioembolic plus lacunar ischemic stroke

result in adenosine triphosphate (ATP) production Prolineoxidase (POX) an inner mitochondrial membrane enzymecatalyzes the released proline to P5C this process generateselectrons that are donated to electron transport chain inmitochondria and generate ATP [32] There are no reportsthat indicate how long Pro concentration remains highafter a stroke in humans The use of plasminogen activator(tPA) for the stroke patient treatment can also contributeto the mobilization of Pro from the extracellular matrixThe tPA is an enzyme capable of converting plasminogento plasmin Plasmin can degrade several extracellular matrixmolecules directly as well as activating and increasing themetalloproteinases production [33]

The ischemic brain damage is associated with acuteand prolonged inflammatory response characterized by acti-vation of the inflammatory resident glial cells as well asthe infiltration of leukocytes [34] In subacute and chronicphases of stroke infiltrating leukocytes release cytokines andchemokines and excessive production of reactive oxygenspecies (ROS) leads to inductionactivation of matrix met-alloproteinases (especially MMP-9) amplifying the inflam-matory response in brain This process can lead to disrup-tion of the blood brain barrier cerebral edema neuronaldeath and hemorrhagic transformation [35] However manyproinflammatory factors play a dual role in early and latestages of stroke For example it has been demonstrated thatMMP-9 at the beginning of ischemic brain injury operatesin a deleterious manner contributing to the enlargement ofthe lesion but can promote the regeneration of the brain

and neurovascular remodeling at later stages of repair [36]Activation of matrix metalloproteinases in injury process andrepair results in amino acid release including lysine andproline[32]

Lys is negatively associated with atherothrombotic is-chemic stroke and it showed a lower concentration in thesamples of this subtype On the other hand Lys is positivelyassociated with cardioembolic and lacunar strokeThe role ofLys in stroke has not yet been elucidated but some studieshave shown an important role of this amino acid in reducingischemic injury and its contribution to angiogenesis process[37] Based on our results Lys can be pointed out as an aminoacid that can specifically discriminate atherothromboticischemic stroke patients from cardioembolic and lacunarones

Our results also demonstrated a general decrease inAla instroke patient plasma Karkela and coworkers have analyzedthe alanine concentration in cerebrospinal fluid of patientsafter acute ischemic stroke and found that alanine levelsgradually increased within the first four hours and thenthere was a rapid decline [38] Waagepetersen and coworkershave demonstrated that alanine has an important role as acarrier of nitrogen glutamatergic neurons to astrocytes [39]It has been found that there is synthesis and release of alanineby glutamatergic neurons while astrocytes are responsiblefor alanine reuptake Thus it has been proposed that thistransport through alanine can operate via supplying ammo-nia to glutamine synthesis in astrocytes and then releasingammonia through glutaminase reaction inside glutamatergic


PYRUVATE

GLUTAMINE

GLUTAMIC ACID

-KETOGLUTARATE

LEUCINE

PROLINE

ALANINE

GS

ALT

P5C

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ECM

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METHIONINE SYNTHASE(VITAMIN B12)METHYLATION

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IMMUNOMODULATION

Pyridoxal-5-phosphatevitamin B6

R-CH2

C- reactive protein

StrokeINFLAMMATIONPyridoxal-5-phosphate

vitamin B6

Cystathionine beta-synthase

(b)

Figure 5 Metabolic pathways activated in chronic ischemic stroke (a) The increase of proline is promoted by activation of extracellularmatrix degradation High levels of proline can inhibit ALT enzyme and cause alanine concentration reduction The reduction in alanineconcentration can also be related to donation of this amino group for glutamine synthesis which can be converted to glutamic acid inthe neurons and then it can be used to increase the levels of leucine (b) Decrease of methionine concentration due to its conversion tohomocysteine homocysteine increases as a result of the cofactor pyridoxal 51015840-phosphate inhibition by inflammatory mediators (C-reactiveprotein) decrease of pyridoxal 51015840-phosphate due to its migration to inflammatory sites Blue arrows activated pathways ECM extracellularmatrix MMPs matrix metalloproteinases


neurons thus forming a cycle In case of ischemic strokethere is a dysregulation of brain cell processes so we caninfer that the alanine synthesis by glutamatergic neuronsis not possible since adequate concentrations of derivativelactate pyruvate or glucose in neurons are necessary for thissynthesis to occur Another possible explanation for decreasein alanine concentrations in the plasma of stroke patientsmay be related to proline increase Furthermore Shanti etal have demonstrated that chronic administration of Prosubcutaneously in rats resulted in the inhibition of the activityof enzyme alanine aminotransferase (ALT) cytosolic andmitochondrial in brain tissues (Figure 5(a)) [40]

Leu concentration also keeps high plasma after strokeespecially in the atherothrombotic subtype Branched chainamino acids particularly Leu play an important role asamino group donors in glutamic acid synthesis in the brain[41] These amino acids readily cross the blood brain barrierand Leu can penetrate more rapidly than any other aminoacid [42] In astrocytes Leu donates the amino group to 120572-ketoglutarate converting glutamic acid into glutamine in theglial cells (Figure 5(a)) The Leu carbon skeleton is convertedto 120572-ketoisocaproate which like glutamine is released intothe extracellular medium The 120572-ketoisocaproate is capturedagain by neurons through a reverse transaminase reactionregenerating Leu in a process using glutamate The newlyformed Leu is released to extracellular medium where itcan again be used by astrocytes completing the Glu-Leucycle In neurons this cycle provides a ldquobufferrdquo mechanismfor glutamate especially when there is an excess as is thecase during the ischemic cascade in ischemic stroke Thismechanism may explain the increase in Leu concentration inthe plasma of patients with ischemic stroke [43]

In our study we observed that Met concentration washigher in the control group than in all stroke subtypesThe decrease of Met concentrations in stroke patients mayreflect the conversion of Met to homocysteine Methionineis a substrate for the enzyme methionine adenosyltransferase(MAT) being converted to S-adenosylmethionine (SAMe)which is the major donor of methyl groups to coenzymesin the body The SAMe methylation is a critical step inmany proteins stabilization including myelin [44] Amongother functions methylation is also important for protectionand stabilization of DNA molecules and influences genetranscription Furthermore SAMe is involved in polyaminesformation and serotonin and niacinamide metabolism [4445] Methionine also can be used as a precursor of homocys-teine (Figure 5(b)) When SAMe transfers its methyl groupto an acceptor S-adenosylhomocysteine is formed Hydrol-ysis of S-adenosylhomocysteine by S-adenosylhomocysteinehydrolase (SAH) leads to the formation of homocysteine andadenosine [45] Although this study did not determine homo-cysteine levels an increase of plasmatic homocysteine con-centration from patients with occlusive vascular disease oratherothrombotic and lacunar stoke is well-documented[46]Lindgren and colleagues have found that patients with strokepresent increased levels of homocysteine in relation to acutephase of the disease when compared to control group [47]

We found that Cys a nonessential amino acid is nega-tively associated with atherothrombotic stroke Inflammation

process may be related to plasma level decrease of Cys inpatients after stroke Kelly et al have found high levelsof C-reactive protein which is the primary biomarker ofinflammation in the plasma of patients after ischemic stroke[48]The contents of this protein were inversely proportionalto the levels of pyridoxal 51015840-phosphate (PLP) the activeform of vitamin B6 PLP acts as a cofactor for a varietyof enzymes including cystathionine 120573-synthase which isresponsible for homocysteine conversion into Cys Low levelsof coenzyme PLP in plasma have been associated withtheir recruitment to inflammatory sites for degradation oftryptophan via the kynurenine pathway metabolism of sph-ingolipids immunomodulatory and proliferation of immunecells [49] The decrease in plasma PLP results in decreasedCys and then contributes to the increase of homocysteinelevels (Figure 5(b)) [48]

5 Conclusion

In thiswork we identified amino acid biosignatures in plasmafrom stroke patients using a GCMS analysis The mostimportant amino acids for stroke patientrsquos determinationwere Pro Met and Ala Although patients are submittedto different environmental factors (eg medication usediet and risk factors) these amino acids were detectedas comprehensive separation between stroke patients andhealthy individuals Other amino acids Lys Leu and Cysare important to discriminate atherothrombotic subtypefrom cardioembolic and lacunar stroke Our results suggesta biosignature for patients with stroke and these aminoacids may be considered as biomarkers with great predictivepotential

Abbreviations

Ala AlanineALT Alanine aminotransferaseAsp AsparagineATP Adenosine triphosphateAUROC Area under the receiver operator characteristicCys CysteineCVCE Cross-validation classification errorERF Efficiency rateGlu Glutamic acidIS Ischemic strokeLeu LeucineLys LysineLOOCV Leave-one-out cross-validationMAT Methionine adenosyltransferaseMet MethionineMMP-9 Matrix metalloproteinase 9MRI Magnetic resonance imagingmRS Modified Rankin ScaleP5C Pyrroline-5-carboxylatePLP Pyridoxal 51015840-phosphatePhe PhenylalaninePro ProlineRNA Ribonucleic acidROS Reactive oxygen species


SAMe S-adenosylmethionineSAH S-adenosylhomocysteine hydrolaseTOAST Trial of Org 10172 in Acute Stroke TreatmentThr ThreoninetPA Plasminogen activatorVIP Variable importance in the projection

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

Thisworkwas supported by Foundation for Research Supportof Minas Gerais (FAPEMIG Brazil) National Council forScientific and Technological Development (CNPq Brazil)and Coordination for the Improvement of Higher EducationPersonnel (CAPES Brazil)

Supplementary Materials

Table S1 Retention time ions (119898119911) and specific abun-dance for each amino acid Figure S1 Heatmap of theischemic stroke amino acid datasetThecolors represent theconcentration ofmetabolites The subtypes of ischemic strokeand controls are represented in horizontal axis and aminoacids in vertical axis and are separated using hierarchical clus-tering (Wardrsquos algorithm) with the dendrogram being scaledto represent the distance between each branch (distance mea-sure Euclidean) The contrast in the average concentrationsof metabolites between the group of patients with stroke andhealthy individuals is presented (Supplementary Materials)

References

[1] D Mozaffarian E J Benjamin A S Go et al ldquoHeart diseaseand stroke statisticsmdash2016 update a report from the AmericanHeart Associationrdquo Circulation vol 133 no 4 pp e38ndashe602016


[3] S E Lakhan A Kirchgessner and M Hofer ldquoInflammatorymechanisms in ischemic stroke therapeutic approachesrdquo Jour-nal of Translational Medicine vol 7 article 97 2009

[4] P Deb S Sharma and K M Hassan ldquoPathophysiologic mech-anisms of acute ischemic stroke an overview with emphasison therapeutic significance beyond thrombolysisrdquo Pathophysi-ology vol 17 no 3 pp 197ndash218 2010

[5] H P Adams Jr B H Bendixen L J Kappelle et al ldquoClassifica-tion of subtype of acute ischemic stroke definitions for use in amulticenter clinical trialrdquo Stroke vol 24 no 1 pp 35ndash41 1993

[6] P-H Chen S Gao Y-J Wang A-D Xu Y-S Li and DWang ldquoClassifying Ischemic Stroke from TOAST to CISSrdquoCNS Neuroscience amp Therapeutics vol 18 no 6 pp 452ndash4562012

[7] C Xing K Arai E H Lo and M Hommel ldquoPathophysiologiccascades in ischemic strokerdquo International Journal of Stroke vol7 no 5 pp 378ndash385 2012

[8] N Kodumuri R Sebastian C Davis et al ldquoThe association ofinsular stroke with lesion volumerdquoNeuroImage Clinical vol 11pp 41ndash45 2016

[9] R Mikulik and N Wahlgren ldquoTreatment of acute stroke anupdaterdquo Journal of Internal Medicine vol 278 no 2 pp 145ndash165 2015

[10] J Montaner M Perea-Gainza P Delgado et al ldquoEtiologicdiagnosis of ischemic stroke subtypes with plasma biomarkersrdquoStroke vol 39 no 8 pp 2280ndash2287 2008

[11] J B Green ldquoBrain Reorganization after Strokerdquo Topics in StrokeRehabilitation vol 10 no 3 pp 1ndash20 2003

[12] CGrefkes andN SWard ldquoCortical reorganization after strokehowmuch and how functionalrdquoThe Neuroscientist vol 20 no1 pp 56ndash70 2014

[13] K S Yew and E Cheng ldquoAcute stroke diagnosisrdquo AmericanFamily Physician vol 80 no 1 pp 33ndash40 2009

[14] Y Miao and J K Liao ldquoPotential serum biomarkers in thepathophysiological processes of strokerdquo Expert Review of Neu-rotherapeutics vol 14 no 2 pp 173ndash185 2014

[15] C H Johnson J Ivanisevic and G Siuzdak ldquoMetabolomicsBeyond biomarkers and towards mechanismsrdquo Nature ReviewsMolecular Cell Biology vol 17 no 7 pp 451ndash459 2016

[16] A Alonso S Marsal and A Julia ldquoAnalytical methods inuntargeted metabolomics State of the art in 2015rdquo Frontiers inBioengineering and Biotechnology vol 3 p 23 2015

[17] F Farshidfar A M Weljie K A Kopciuk et al ldquoA validatedmetabolomic signature for colorectal cancer Exploration of theclinical value of metabolomicsrdquo British Journal of Cancer vol115 no 7 pp 848ndash857 2016

[18] D S Wishart ldquoEmerging applications of metabolomics indrug discovery and precision medicinerdquo Nature Reviews DrugDiscovery vol 15 no 7 pp 473ndash484 2016

[19] S J Kim G J Moon and O Y Bang ldquoBiomarkers for StrokerdquoJournal of Stroke vol 15 no 1 pp 27ndash30 2013

[20] M C X Pinto M J N de Paiva O C Oliveira-Lima et alldquoNeurochemical study of amino acids in rodent brain structuresusing an improved gas chromatography-mass spectrometrymethodrdquo Journal of Chemical Neuroanatomy vol 55 pp 24ndash372014

[21] M J N de Paiva H C Menezes P P Christo R R Resendeand Z D L Cardeal ldquoAn alternative derivatization methodfor the analysis of amino acids in cerebrospinal fluid by gaschromatography-mass spectrometryrdquo Journal of Chromatog-raphy B Analytical Technologies in The Biomedical And LifeSciences vol 931 pp 97ndash102 2013

[22] M Barker andW Rayens ldquoPartial least squares for discrimina-tionrdquo Journal of Chemometrics vol 17 no 3 pp 166ndash173 2003

[23] R G Brereton and G R Lloyd ldquoPartial least squares discrimi-nant analysis Taking the magic awayrdquo Journal of Chemometricsvol 28 no 4 pp 213ndash225 2014

[24] R W Kennard and L A Stone ldquoComputer aided design ofexperimentsrdquo Technometrics vol 11 no 1 pp 137ndash148 1969


[25] I-G Chong and C-H Jun ldquoPerformance of some variableselection methods when multicollinearity is presentrdquo Chemo-metrics and Intelligent Laboratory Systems vol 78 no 1-2 pp103ndash112 2005

[26] A Arboix and J Alio ldquoCardioembolic stroke clinical featuresspecific cardiac disorders and prognosisrdquo Current CardiologyReviews vol 6 no 3 pp 150ndash161 2010

[27] TWessels C Rottger M JaussM Kaps H Traupe and E StolldquoIdentification of embolic stroke patterns by diffusion-weightedMRI in clinically defined lacunar stroke syndromesrdquo Stroke vol36 no 4 pp 757ndash761 2005

[28] M Altieri P Troisi I Maestrini and G L Lenzi ldquoCryptogenicstroke Cryptic definitionrdquo Stroke vol 40 no 8 p e530 2009

[29] D Wang J Kong J Wu X Wang and M Lai ldquoGCndashMS-based metabolomics identifies an amino acid signature of acuteischemic strokerdquo Neuroscience Letters vol 642 pp 7ndash13 2017

[30] M Szpetnar A Hordyjewska I Malinowska P Golab and JKurzepa ldquoThe fluctuation of free amino acids in serum duringacute ischemic strokerdquo Current Issues in Pharmacy and MedicalSciences vol 29 no 4 pp 151ndash154 2016

[31] W T Kimberly Y Wang L Pham K L Furie and R EGerszten ldquoMetabolite profiling identifies a branched chainamino acid signature in acute cardioembolic strokerdquo Stroke vol44 no 5 pp 1389ndash1395 2013

[32] J M Phang S P Donald J Pandhare and Y Liu ldquoThemetabolism of proline a stress substrate modulates carcino-genic pathwaysrdquo Amino Acids vol 35 no 4 pp 681ndash690 2008

[33] D M Hermann and C M Matter ldquoTissue plasminogenactivator-induced reperfusion injury after stroke revisitedrdquoCirculation vol 116 no 4 pp 363ndash365 2007

[34] J Kriz andM Lalancette-Hebert ldquoInflammation plasticity andreal-time imaging after cerebral ischemiardquoActa Neuropatholog-ica vol 117 no 5 pp 497ndash509 2009

[35] R Jin G Yang and G Li ldquoMolecular insights and therapeutictargets for blood-brain barrier disruption in ischemic strokecritical role of matrix metalloproteinases and tissue-type plas-minogen activatorrdquo Neurobiology of Disease vol 38 no 3 pp376ndash385 2010

[36] D Amantea G Nappi G Bernardi G Bagetta and M TCorasaniti ldquoPost-ischemic brain damage pathophysiology androle of inflammatory mediatorsrdquo FEBS Journal vol 276 no 1pp 13ndash26 2009

[37] T Kondoh M Kameishi H N Mallick T Ono and K ToriildquoLysine and arginine reduce the effects of cerebral ischemicinsults and inhibit glutamate-induced neuronal activity in ratsrdquoFrontiers in Integrative Neuroscience vol 4 no 18 pp 1ndash10 2010

[38] J Karkela K-M Marnela J Odink T Koivula and S Kauki-nen ldquoAmino acids and glucose in human cerebrospinal fluidafter acute ischaemic brain damagerdquo Resuscitation vol 23 no2 pp 145ndash156 1992

[39] H S Waagepetersen U Sonnewald O M Larsson and ASchousboe ldquoA possible role of alanine for ammonia transferbetween astrocytes and glutamatergic neuronsrdquo Journal ofNeurochemistry vol 75 no 2 pp 471ndash479 2000

[40] N Desai Shanti K C Shashikumar and P V Desai ldquoInfluenceof proline on rat brain activities of alanine aminotransferaseaspartate aminotransferase and acid phosphataserdquoNeurochem-ical Research vol 29 no 12 pp 2197ndash2206 2004

[41] M Ruiz-Canela E Toledo C B Clish et al ldquoPlasma branched-chain amino acids and incident cardiovascular disease in thePREDIMED Trialrdquo Clinical Chemistry vol 62 no 4 pp 582ndash592 2016

[42] Q R Smith S Momma M Aoyagi and S I RapoportldquoKinetics of neutral amino acid transport across the blood-brainbarrierrdquo Journal of Neurochemistry vol 49 no 5 pp 1651ndash16581987

[43] Y Daikhin and M Yudkoff ldquoCompartmentation of brainglutamatemetabolism in neurons and gliardquo Journal ofNutritionvol 130 no 4 pp 1026Sndash1031S 2000

[44] M Varela-Rey M Iruarrizaga-Lejarreta J J Lozano et alldquoS-adenosylmethionine levels regulate the schwann cell DNAmethylomerdquoNeuron vol 81 no 5 pp 1024ndash1039 2014

[45] H Tomitori T Usui N Saeki et al ldquoPolyamine oxidase andacrolein as novel biochemical markers for diagnosis of cerebralstrokerdquo Stroke vol 36 no 12 pp 2609ndash2613 2005

[46] L BrattstromA Lindgren B Israelsson et al ldquoHyperhomocys-teinaemia in stroke prevalence cause and relationships to typeof stroke and stroke risk factorsrdquo European Journal of ClinicalInvestigation vol 22 no 3 pp 214ndash221 1992

[47] A Lindgren L Brattstrom B Norrving B Hultberg AAndersson and B B Johansson ldquoPlasma homocysteine in theacute and convalescent phases after strokerdquo Stroke vol 26 no5 pp 795ndash800 1995

[48] P J Kelly J P Kistler V E Shih et al ldquoInflammation homo-cysteine and vitamin B6 status after ischemic strokerdquo Strokevol 35 no 1 pp 12ndash15 2004

[49] L Paul PMUeland and J Selhub ldquoMechanistic perspective onthe relationship between pyridoxal 5rsquo-phosphate and inflamma-tionrdquo Nutrition Reviews vol 71 no 4 pp 239ndash244 2013

Stem Cells International

Hindawiwwwhindawicom Volume 2018


MEDIATORSINFLAMMATION

of

EndocrinologyInternational Journal of



Disease Markers


BioMed Research International

OncologyJournal of



Oxidative Medicine and Cellular Longevity


PPAR Research

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Immunology ResearchHindawiwwwhindawicom Volume 2018

Journal of

ObesityJournal of



Computational and Mathematical Methods in Medicine


Behavioural Neurology

OphthalmologyJournal of


Diabetes ResearchJournal of



Research and TreatmentAIDS


Gastroenterology Research and Practice


Parkinsonrsquos Disease

Evidence-Based Complementary andAlternative Medicine

Volume 2018Hindawiwwwhindawicom

Submit your manuscripts atwwwhindawicom


Table 1 Demographic data of the study group

StrokeSubjects Controls Atherothrombotic Cardioembolic Lacunar UndeterminedMale 7 13 7 10 8Female 9 7 11 10 7Age (yearsmeanplusmnSD)

5475(plusmn1234)

6870(plusmn809)

6250(plusmn1215)

6420(plusmn1128)

5493(plusmn1450)

Risk factorsHypertension () 688 80 833 85 67Diabetes mellitus() 0 30 56 30 67

Alcoholism () 188 35 278 45 267Previous stroke () 0 40 333 20 267Smoking () 313 55 444 50 333Obesity () 188 15 1111 5 133119886mRS ge 2 () - 20 444 35 266119886mRS modified Rankin Scale

has important clinical implications [9] The prognosis andadministration of early and long-term strategies to preventrelapse can vary considerably for the different stroke subtypes[10] For recovery patients should be treated with specificrestorative therapies Most patients show some improvementusually during the first 3 to 6 months after the ischemia[11]

Themost understanding of howmanifestations of neuro-plasticity are related to stroke recovery is obtained throughmultimodal techniques in brain imaging [12] Currentlythe standard techniques for a diagnosis and prognosis ofstroke are based on clinical observations and evaluation ofneuroimaging [13] Just as neuroimaging cardiac evaluationand arterial imaging are used in the diagnosis of strokedetermining its causes and mechanisms of recovery in thesame way molecular features in the form of proteins RNAmetabolites lipids and other biomarkersmay also have utility[14]

The metabolomics approach focuses on measurement ofthe relative concentrations of endogenous small moleculesin biofluids cells and tissues that characterize changes inmetabolism thus helping to unravel the metabolic state ofbiological systems [15] Advances in analytical chemistrytogether with multivariate statistical methods can allow forthe investigation of metabolites as potential biomarkers ofvarious diseases [16ndash19]

In this study we compared the amino acid profilesof patients after stroke with healthy subjects and studieddifferent ischemic stroke subtypes We developed a modelto characterize and classify plasma samples of patients fromhealthy individuals and four stroke subtypes using GC-MS associated with a multivariate method of supervisedclassification PLS-DA Our model determined the aminoacid profile that differentiates between healthy individualsfrom ischemic stroke patients and the amino acids that mostcontribute to discrimination of each stroke subtypes andcontrols

2 Methods

21 Subjects Human plasma samples were provided by Insti-tute of Education and Research Santa Casa Belo HorizonteLaboratory of Biomarker Seventy-three plasma samples frompatients diagnosed with one of the ischemic stroke subtypesby magnetic resonance imaging (MRI) based on the TOASTclassification system and being in the chronic phase ofthe disease (after 7 days of the onset of ischemic strokediagnostic) were analyzed We also analyzed 16 plasmasamples from healthy subjects (individuals without historyof ischemic or hemorrhagic stroke) which were consid-ered the control group Demographic data are presented inTable 1

The modified Rankin Scale (mRS) indicates degree ofdisability of stroke patients (index ranged from 0 to 6)Score 2 designates slight disability or individuals able tolook after own affairs without assistance Score 3 designatesmoderate disability that will worsen with the increasing ofthe score value and the maximum score 6 indicates deathThis studywas approved by theEthics Committee inResearchof Universidade Federal de Minas Gerais under number312840 It was also approved by the Ethics Committee inResearch of Santa Casa Misericordia of BeloHorizonte undernumber 315034

22 Sample Preparation The amino acid extraction protocolwas adapted from Pinto et al [20] Briefly in order toprecipitate proteins 100120583L of plasma samples were dissolvedin 900120583L of methanol at -10∘C Subsequently samples werevortexed for 2 minutes followed by centrifugation at 10000gfor 10 minutes at 25∘C The supernatant (100120583L) was trans-ferred from each sample into a glass vial and evaporated atroom temperature

Sample derivatization process was performed asdescribed by de Paiva et al [21] To each vial containingdried sample was added 15120583L of methoxamine solutiondiluted with 20mgmL pyridine (Fluka St Louis MO USA)









3 Results





Alanine


100

200

400

300

500Glycine


50

100

200

150

250Sarcosine


200

400

600

800Valine


50

100

200

150

250

Serine

Ctl Ath Car Lac Und

20

30

40

50Methionine


195

205

200

210Leucine


20

40

60

80

100Isoleucine

Ctl Ath Car Lac Und

10

20

30

40

Proline


1000

2000

3000

4000reonine


20

40

60

80Asparagine


10

20

30

40Aspartic acid


30

35

40

45

50

Phenylalanine


10

20

30

40

50Glutamic acid


50

100

150Cysteine


10000

20000

30000

40000

50000Lysine


32

34

36

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

lowast


lowast

lowast









ISControl

6040 80 100200Samples

minus05

00

05

10

15

20Y

Pred

icte

d 1

(IS)

(a)

ISControl

6040 80 100200Samples

minus4

minus2

0

2

4

6

8

Y Pr

edic

ted

1 (I

S)

(b)

0

1

2

3

4

5

VIP

Sco

res f

or Y

1


Amino acids(c)

minus06

minus04

minus02

00

02

04

Reg

vect

or fo

r Y1


Amino acids(d)










Y Pr

edic

ted

1(IS

)

minus05

00

05

10

15

20

ISControl

6040 80 100200Samples





4 Discussion




Samples0 20 40 60

Y Pr

edic

ted

1 (A

TE)

minus05

00

05

10

15

ATEOther

(a)

0 20 40 60 80Samples

Y Pr

edic

ted

1 (C

+L)

minus05

00

05

10

15

C+LOther

(b)

0

1

2

3

4

5

VIP

Sco

res f

or Y

1


Amino acids(c)

VIP

Sco

res f

or Y

10

1

2

3

4


Amino acids(d)








PYRUVATE

GLUTAMINE

GLUTAMIC ACID

-KETOGLUTARATE

LEUCINE

PROLINE

ALANINE

GS

ALT

P5C

POX

NH2TCA CYCLE

COLLAGEN

ECM

IMIDODIPEPTIDES

MMPs

NH2

(a)

METHIONINE

SAMe

SAH

HOMOCYSTEINE

CYSTATHIONINE

CYSTEINE


RFOLATE CYCLE

IMMUNOMODULATION


R-CH2

C- reactive protein


vitamin B6


(b)








5 Conclusion


Abbreviations




Data Availability




Acknowledgments




References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of



























3 Results





Alanine


100

200

400

300

500Glycine


50

100

200

150

250Sarcosine


200

400

600

800Valine


50

100

200

150

250

Serine

Ctl Ath Car Lac Und

20

30

40

50Methionine


195

205

200

210Leucine


20

40

60

80

100Isoleucine

Ctl Ath Car Lac Und

10

20

30

40

Proline


1000

2000

3000

4000reonine


20

40

60

80Asparagine


10

20

30

40Aspartic acid


30

35

40

45

50

Phenylalanine


10

20

30

40

50Glutamic acid


50

100

150Cysteine


10000

20000

30000

40000

50000Lysine


32

34

36

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

lowast


lowast

lowast









ISControl

6040 80 100200Samples

minus05

00

05

10

15

20Y

Pred

icte

d 1

(IS)

(a)

ISControl

6040 80 100200Samples

minus4

minus2

0

2

4

6

8

Y Pr

edic

ted

1 (I

S)

(b)

0

1

2

3

4

5

VIP

Sco

res f

or Y

1


Amino acids(c)

minus06

minus04

minus02

00

02

04

Reg

vect

or fo

r Y1


Amino acids(d)










Y Pr

edic

ted

1(IS

)

minus05

00

05

10

15

20

ISControl

6040 80 100200Samples





4 Discussion




Samples0 20 40 60

Y Pr

edic

ted

1 (A

TE)

minus05

00

05

10

15

ATEOther

(a)

0 20 40 60 80Samples

Y Pr

edic

ted

1 (C

+L)

minus05

00

05

10

15

C+LOther

(b)

0

1

2

3

4

5

VIP

Sco

res f

or Y

1


Amino acids(c)

VIP

Sco

res f

or Y

10

1

2

3

4


Amino acids(d)








PYRUVATE

GLUTAMINE

GLUTAMIC ACID

-KETOGLUTARATE

LEUCINE

PROLINE

ALANINE

GS

ALT

P5C

POX

NH2TCA CYCLE

COLLAGEN

ECM

IMIDODIPEPTIDES

MMPs

NH2

(a)

METHIONINE

SAMe

SAH

HOMOCYSTEINE

CYSTATHIONINE

CYSTEINE


RFOLATE CYCLE

IMMUNOMODULATION


R-CH2

C- reactive protein


vitamin B6


(b)








5 Conclusion


Abbreviations




Data Availability




Acknowledgments




References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of




















Alanine


100

200

400

300

500Glycine


50

100

200

150

250Sarcosine


200

400

600

800Valine


50

100

200

150

250

Serine

Ctl Ath Car Lac Und

20

30

40

50Methionine


195

205

200

210Leucine


20

40

60

80

100Isoleucine

Ctl Ath Car Lac Und

10

20

30

40

Proline


1000

2000

3000

4000reonine


20

40

60

80Asparagine


10

20

30

40Aspartic acid


30

35

40

45

50

Phenylalanine


10

20

30

40

50Glutamic acid


50

100

150Cysteine


10000

20000

30000

40000

50000Lysine


32

34

36

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

m

olL

lowast


lowast

lowast









ISControl

6040 80 100200Samples

minus05

00

05

10

15

20Y

Pred

icte

d 1

(IS)

(a)

ISControl

6040 80 100200Samples

minus4

minus2

0

2

4

6

8

Y Pr

edic

ted

1 (I

S)

(b)

0

1

2

3

4

5

VIP

Sco

res f

or Y

1


Amino acids(c)

minus06

minus04

minus02

00

02

04

Reg

vect

or fo

r Y1


Amino acids(d)










Y Pr

edic

ted

1(IS

)

minus05

00

05

10

15

20

ISControl

6040 80 100200Samples





4 Discussion




Samples0 20 40 60

Y Pr

edic

ted

1 (A

TE)

minus05

00

05

10

15

ATEOther

(a)

0 20 40 60 80Samples

Y Pr

edic

ted

1 (C

+L)

minus05

00

05

10

15

C+LOther

(b)

0

1

2

3

4

5

VIP

Sco

res f

or Y

1


Amino acids(c)

VIP

Sco

res f

or Y

10

1

2

3

4


Amino acids(d)








PYRUVATE

GLUTAMINE

GLUTAMIC ACID

-KETOGLUTARATE

LEUCINE

PROLINE

ALANINE

GS

ALT

P5C

POX

NH2TCA CYCLE

COLLAGEN

ECM

IMIDODIPEPTIDES

MMPs

NH2

(a)

METHIONINE

SAMe

SAH

HOMOCYSTEINE

CYSTATHIONINE

CYSTEINE


RFOLATE CYCLE

IMMUNOMODULATION


R-CH2

C- reactive protein


vitamin B6


(b)








5 Conclusion


Abbreviations




Data Availability




Acknowledgments




References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of




















ISControl

6040 80 100200Samples

minus05

00

05

10

15

20Y

Pred

icte

d 1

(IS)

(a)

ISControl

6040 80 100200Samples

minus4

minus2

0

2

4

6

8

Y Pr

edic

ted

1 (I

S)

(b)

0

1

2

3

4

5

VIP

Sco

res f

or Y

1


Amino acids(c)

minus06

minus04

minus02

00

02

04

Reg

vect

or fo

r Y1


Amino acids(d)










Y Pr

edic

ted

1(IS

)

minus05

00

05

10

15

20

ISControl

6040 80 100200Samples





4 Discussion




Samples0 20 40 60

Y Pr

edic

ted

1 (A

TE)

minus05

00

05

10

15

ATEOther

(a)

0 20 40 60 80Samples

Y Pr

edic

ted

1 (C

+L)

minus05

00

05

10

15

C+LOther

(b)

0

1

2

3

4

5

VIP

Sco

res f

or Y

1


Amino acids(c)

VIP

Sco

res f

or Y

10

1

2

3

4


Amino acids(d)








PYRUVATE

GLUTAMINE

GLUTAMIC ACID

-KETOGLUTARATE

LEUCINE

PROLINE

ALANINE

GS

ALT

P5C

POX

NH2TCA CYCLE

COLLAGEN

ECM

IMIDODIPEPTIDES

MMPs

NH2

(a)

METHIONINE

SAMe

SAH

HOMOCYSTEINE

CYSTATHIONINE

CYSTEINE


RFOLATE CYCLE

IMMUNOMODULATION


R-CH2

C- reactive protein


vitamin B6


(b)








5 Conclusion


Abbreviations




Data Availability




Acknowledgments




References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of






















Y Pr

edic

ted

1(IS

)

minus05

00

05

10

15

20

ISControl

6040 80 100200Samples





4 Discussion




Samples0 20 40 60

Y Pr

edic

ted

1 (A

TE)

minus05

00

05

10

15

ATEOther

(a)

0 20 40 60 80Samples

Y Pr

edic

ted

1 (C

+L)

minus05

00

05

10

15

C+LOther

(b)

0

1

2

3

4

5

VIP

Sco

res f

or Y

1


Amino acids(c)

VIP

Sco

res f

or Y

10

1

2

3

4


Amino acids(d)








PYRUVATE

GLUTAMINE

GLUTAMIC ACID

-KETOGLUTARATE

LEUCINE

PROLINE

ALANINE

GS

ALT

P5C

POX

NH2TCA CYCLE

COLLAGEN

ECM

IMIDODIPEPTIDES

MMPs

NH2

(a)

METHIONINE

SAMe

SAH

HOMOCYSTEINE

CYSTATHIONINE

CYSTEINE


RFOLATE CYCLE

IMMUNOMODULATION


R-CH2

C- reactive protein


vitamin B6


(b)








5 Conclusion


Abbreviations




Data Availability




Acknowledgments




References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of




















Samples0 20 40 60

Y Pr

edic

ted

1 (A

TE)

minus05

00

05

10

15

ATEOther

(a)

0 20 40 60 80Samples

Y Pr

edic

ted

1 (C

+L)

minus05

00

05

10

15

C+LOther

(b)

0

1

2

3

4

5

VIP

Sco

res f

or Y

1


Amino acids(c)

VIP

Sco

res f

or Y

10

1

2

3

4


Amino acids(d)








PYRUVATE

GLUTAMINE

GLUTAMIC ACID

-KETOGLUTARATE

LEUCINE

PROLINE

ALANINE

GS

ALT

P5C

POX

NH2TCA CYCLE

COLLAGEN

ECM

IMIDODIPEPTIDES

MMPs

NH2

(a)

METHIONINE

SAMe

SAH

HOMOCYSTEINE

CYSTATHIONINE

CYSTEINE


RFOLATE CYCLE

IMMUNOMODULATION


R-CH2

C- reactive protein


vitamin B6


(b)








5 Conclusion


Abbreviations




Data Availability




Acknowledgments




References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of




















PYRUVATE

GLUTAMINE

GLUTAMIC ACID

-KETOGLUTARATE

LEUCINE

PROLINE

ALANINE

GS

ALT

P5C

POX

NH2TCA CYCLE

COLLAGEN

ECM

IMIDODIPEPTIDES

MMPs

NH2

(a)

METHIONINE

SAMe

SAH

HOMOCYSTEINE

CYSTATHIONINE

CYSTEINE


RFOLATE CYCLE

IMMUNOMODULATION


R-CH2

C- reactive protein


vitamin B6


(b)








5 Conclusion


Abbreviations




Data Availability




Acknowledgments




References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of

























5 Conclusion


Abbreviations




Data Availability




Acknowledgments




References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of





















Data Availability




Acknowledgments




References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of

















































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of



















Amino Acid Biosignature in Plasma among Ischemic Stroke...

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