Merged Targeted Quantification and Untargeted Profiling for

Subscriber access provided by Nottingham Trent University

Article

Merged Targeted Quantification and Untargeted Profiling for Comprehensive Assessment of Acylcarnitine and Amino Acid Metabolism Tony Teav, Hector Gallart-Ayala, Vera van der Velpen, Florence Mehl, Hugues Henry, and Julijana Ivanisevic Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.9b02373 • Publication Date (Web): 13 Aug 2019 Downloaded from pubs.acs.org on August 13, 2019

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

Merged Targeted Quantification and Untargeted Profiling for Comprehensive Assessment of Acylcarnitine and Amino Acid Metabolism Tony Teav†‡, Héctor Gallart-Ayala†‡, Vera van der Velpen†, Florence Mehl†⊥, Hugues Henry°* and Julijana Ivanisevic†* † Metabolomics ⊥Vital-IT

Platform, Faculty of Biology and Medicine, University of Lausanne, 1005 Lausanne, Switzerland

– Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland

°Innovation and Development Laboratory, Clinical Chemistry Service, Lausanne University Hospital, 1011 Lausanne, Switzerland

‡Both authors contributed equally to this manuscript *Authors to whom correspondence should be addressed: Julijana Ivanisevic E-mail: [email protected] Hugues Henry E-mail: [email protected]

1 ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 43

Abstract Acylcarnitines and amino acids are key players in energy metabolism, however, analytical methods for comprehensive and straightforward quantitative profiling of these metabolites, without derivatization or use of ion-pairing agents, are lacking. We therefore developed a hydrophilic interaction chromatography ( HILIC)-based high-resolution mass spectrometry (HRMS) method for the simultaneous quantification of acylcarnitines and amino acids in a single run, while taking advantage of HRMS data acquired in full scan mode to screen for additional derivatives and other polar metabolites. A single-step metabolite extraction with internal standard mixture (in methanol) warranted high-throughput sample preparation whose applicability was demonstrated on a panel of human biofluids (i.e. blood plasma, CSF and urine) and brain tissue. Method accuracy was within 90-106 % of validated NIST reference plasma concentrations for the panel of measured amino acids. Amino acid and acylcarnitine extraction recoveries were 87-100 % on average, depending on the concentration range spiked. The coefficient of variation was 1-10 % and 1-25 % for intra- and inter-day measurements, respectively, with the highest values for the metabolites at the limit of quantification, depending on the biofluid. Acylcarnitine and amino acid signatures or chemical composition barcodes of the different biofluids and human brain tissue were acquired and biofluid- and tissue-associated differences were discussed in the context of their respective physiological roles. Significant differences were observed in the amino acid profiles whereas acylcarnitine composition did not show biofluid-characteristic or brain region-specific pattern. The retrospective exploration of full scan all-ion-fragmentation data allowed us to extract the information on unsaturated and hydroxylated acylcarnitine species, amines, and purine and pyrimidine metabolites. This merged targeted and untargeted approach provides an innovative strategy for simultaneous and comprehensive assessment of acylcarnitine and amino acid metabolism in clinical research studies using relevant biofluids and tissue extracts. 2 ACS Paragon Plus Environment

Page 3 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Introduction Acylcarnitines (ACs) and amino acids (AAs) are key players in energy metabolism and their circulating levels are indicative of inborn errors of metabolism as well as acquired metabolic disorders later in life, such as obesity, type 2 diabetes mellitus and cancer1-3. Increased levels of both branchedchain AAs (BCAA) and long-chain ACs (LCACs), for example, have been reported as associated with insulin resistance and predictive of type 2 diabetes in several human population studies4, 5. While most of the clinical studies have focused on ACs and AAs as diagnostic markers of metabolic diseases, the growing body of evidence supports the concept that these metabolites are bioactive and directly influence and modulate cell metabolism and health outcomes5-7. Acylcarnitines are transport intermediates in the oxidative catabolism of fatty acids (i.e. βoxidation) and amino acids, a process that takes place in mitochondria and generates two times more energy than can be produced from glucose8. Although they were mainly exploited as biomarkers of inherited enzymatic fatty acid oxidation disorders (FAODs), their physiological roles in the regulation of cardiac function, ion balance and membrane permeability, insulin signaling, inflammation and cellular stress have been underlined in several recent works5. These modulator effects of short- and long-chain ACs suggest that they directly contribute to different physiological and pathophysiological processes (such as cardiac ischemia, insulin sensitivity and inflammation) well beyond energy production4, 5, 9, 10. Amino acids are known to serve as precursors for protein, nucleotide, cofactor, neurotransmitter and lipid synthesis, but they also act as potent nutrient signals whose sensing is integrated via mTOR signaling pathway to regulate cell metabolism, growth, proliferation and survival11-13. This carbon and nitrogen sensing is conserved across eukaryotes and its deregulation has been reported in cancer, metabolic syndrome and diabetes14. Despite their physiological importance as indicators and modulators of cellular metabolic status, analytical methods for combined and comprehensive measurement of ACs and AAs are scarce (due to 3 ACS Paragon Plus Environment


Page 4 of 43

difference in their chemical properties) and typically involve derivatization or the use of ion pairing agents to improve the metabolite retention and mass spectrometry (MS) signal intensity10, 15-17. The chemical modification by derivatization is widely used in liquid chromatography-mass spectrometry (LC-MS) analyses of AAs and ACs to enhance the measurement sensitivity and selectivity18; however, it represents an additional step in sample preparation thus introducing an additional bias dependent on the yield of reaction. Moreover, the derivatization is usually specific to one group of metabolites (for example, AQC - 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate19 or PITC-24phenylisothiocyanate20 for amino acids; ACs are mainly derivatized to their butylesters10, 21) and thus requires separate sample preparation and/or LC-MS analysis for AAs and ACs. Several reversed-phase chromatography (RPLC)-based methods have been developed to measure the amino acids following the derivatization while the ACs are usually quantified with or without derivatization, using RPLC22, 23 or flow-injection analysis (FIA) only. Flow injection analysis is mainly applied for rapid dry-blood spot screening for newborn errors of metabolism. In general, the developed methods that combine the measurement of both classes, ACs and AAs, use ion-pairing agents and focus on very limited set of amino acids and ACs detectable in whole blood or plasma as diagnostic markers of inborn errors of metabolism16, 24, 25. The simultaneous analysis of a larger panel of short, medium and long-chain ACs and of AAs at different levels of the system (e.g. plasma, cerebrospinal fluid, urine) and at the organ level (i.e. tissue extracts) could bring the important insights for better understanding of pathological biochemistry (related to amino acid and fatty acid oxidation) in acquired, noncommunicable metabolic disorders, such as cancer, diabetes

and

neurodegenerative

diseases.

Recent

advancements

in

hydrophilic

interaction

chromatography (HILIC) robustness, and mass spectrometry (MS) sensitivity and scanning speed, allowed for the challenge of metabolite separation and MS sensitivity to be addressed without derivatization and ion-pairing26-28. The purpose of this work was to develop a straightforward, robust and sensitive analytical method to quantify the most biologically relevant panel of both, ACs and AAs, and 4 ACS Paragon Plus Environment

Page 5 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


their derivatives using stable isotope-labeled analogues or appropriate surrogates. The method was validated and applied to different clinically relevant biofluids (plasma, urine and CSF) and human brain samples (without neurological disorder) from two different brain regions. A single-step, protein precipitation – metabolite extraction followed by HILIC –HRMS in full scan mode was applied to facilitate metabolite separation and allow for the retrospective analysis of many additional precursors and derivatives together with other relevant polar metabolites.

Materials and methods Reagents and Chemicals. LC-MS grade water and organic solvents (acetonitrile, methanol), formic acid, hydrochloric acid solution and ammonium formate were purchased from Biosolve Chimie (Dieuze, France), Sigma-Aldrich (Darmstadt, Germany) and Merck (Darmstadt, Germany). The analytical standards and deuterated,

15

N- or

13

C-labeled internal standards (IS, listed in Table S1 together with

product references) were obtained from Sigma-Aldrich, Cambridge Isotopes Laboratories (Tewksbury, Massachussetts, US) and Larodan (Solna, Sweden).The list of assigned IS analogues and appropriate surrogates for each metabolite is given in Table S2. Calibration Solutions and Internal Standard Mixtures. The initial standard mixture of ACs was prepared in methanol while for AAs, three separate standard mixtures were prepared in HCl 0.1 N water. These stock AC and AA calibration solutions were further diluted to match previously reported concentration ranges for selected metabolites in human plasma and tissues29. The AA concentrations in the highest-level calibrator spanned from 100 µM to 5000 µM while the AC (including free carnitine) concentrations ranged from 6 µM to 120 µM. The subsequent 11 points of the calibration curve were prepared by serial dilutions of this highest calibrator using HCl 0.1 N water or methanol/water (1/1; v/v) for AAs amino acids and for ACs, respectively. The stock IS mixture for AAs was prepared in 0.1% formic acid in water with a final concentration of 1250 µM for each AA. The stock IS mixture for ACs 5 ACS Paragon Plus Environment


Page 6 of 43

was prepared in methanol with concentrations ranging from 7.6 µM to 152 µM depending on the molecular species. Both stock IS mixtures were combined and diluted 1/500 times prior to the sample spike. Human Biofluid Samples. Standard Reference Material for human plasma (SRM 1950) approved by National Institute of Standards and Technology (NIST) was purchased from Sigma-Aldrich. Human plasma, urine and cerebrospinal fluid samples were obtained by pooling the residual samples collected at Lausanne University Hospital (CHUV) following the anonymization. These pools were prepared from ten individuals without any manifest metabolic disorder and stored at -80°C immediately after collection and until analysis. Human Brain Tissue. Post-mortem human brain tissue samples were obtained from the biobank of brain tissue at Lausanne University Hospital (CHUV). Specific brain regions were collected at autopsy and immediately frozen at -80°C. Clinical diagnosis was given following the neuropathological examination and all controls were confirmed without neurological disorder30, 31. The collection of brain tissue samples was approved by the local Ethics Committee of the Lausanne University Hospital (#121/05)31. Clinical metadata, including the patient’s age, sex, and post mortem delay (in hours) are provided in the Table S3. Brain Tissue Homogenization. Tissue was pre-weighed directly in the lysis tubes (soft tissue homogenizing CK 14 tubes, Bertin Technologies, Rockville, MD, US) and pre-extracted by the addition of ice-cold MeOH:H2O (4:1; v:v) with 0.1 % formic acid (150 μL of solvent / 10 mg of tissue32, 33) and ceramic beads, in the Cryolys Precellys 24 sample Homogenizer (2 x 20 seconds at 10000 rpm, Bertin Technologies, Rockville, MD, US). The bead beater was air-cooled by a flow rate at 110 L/min at 6 bar. Homogenized extracts were centrifuged for 15 minutes at 20000 g at 4°C (Hermle, Gosheim, Germany). The supernatant (metabolite extract) was removed and used as described below in the sample preparation section. Total protein content of the extracted tissue samples was determined for sample amount normalization. Protein pellets were resuspended in the in-house prepared lysis buffer (containing 20 mM 6 ACS Paragon Plus Environment

Page 7 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Tris-HCl (pH 7.5), 4 M guanidine hydrochloride, 150 mM NaCl, 1 mM Na2EDTA, 1 mM EGTA, 1% Triton, 2.5 mM sodium pyrophosphate, 1 mM beta-glycerophosphate, 1 mM Na3VO4, and 1 µg/mL leupeptin) using the Cryolys Precellys 24 sample Homogenizer (2 x 20 seconds at 10000 rpm).The BCA Protein Assay Kit (Thermo Scientific, Masschusetts, US) was used to measure total protein concentration on a microplate reader at 562nm (Hidex, Turku, Finland). Sample and Calibration Curve Preparation. For absolute quantification of ACs and AAs, samples were prepared by adding 250 μL of the diluted (1/500) stock IS mixture of AAs & ACs in methanol to an aliquot of urine (5 µL), plasma (20 µL), CSF (20 µL) and tissue extract (20 µL). This solution was completed to 300 µL with 0.1% formic acid in water. Samples were then vortexed and centrifuged for 15 minutes at 4°C and 2700 g. The resulting supernatant was transferred to LC-MS vials and injected into the LC-HRMS system. Ten-point calibration curves were generated following the same procedure as for the samples; by addition of 250 µL of 1/500 diluted IS mixture to each pre-prepared calibrator (10 µL of amino acid and 10 µL of acylcarnitine calibrator, containing the increasing amount of each standard) and completed to 300 µL with 0.1 % FA in water. The concentration range of each calibration curve per compound is given in Table S4. LC-HRMS Analysis. A Vanquish Horizon (Thermo Fisher Scientific) ultra-high performance liquid chromatography (UHPLC) system coupled to Q Exactive™ Focus interfaced with the Heated Electrospray Ionisation (HESI) source was used for the combined targeted quantification and untargeted profiling of ACs and AAs in addition to other polar metabolites. The separation was carried out using a BEH Amide column (1.7 μm, 100 mm × 2.1 mm I.D) (Waters, Massachusetts, US). The mobile phase was composed of A = 20 mM ammonium formate and 0.1 % formic acid in water and B = 0.1 % formic acid in ACN. The gradient elution from 95% B (0-2 min) to 65% B (14 min) reaching 50% B at 16 min was applied and followed by 4 min post-run for column re-equilibration. The flow rate was 400 μL/min, column temperature 25°C and the sample injection volume was 2 μL. HESI source conditions operating in positive 7 ACS Paragon Plus Environment


Page 8 of 43

mode were set as follows: sheath gas flow at 60, aux gas flow rate at 20, sweep gas flow rate at 2, spray voltage at +3 kV, capillary temperature at 300°C, s-lens RF level at 60 and aux gas heater temperature at 300°C. Full scan HRMS acquisition mode (m/z 50−750) was used with the following MS acquisition parameters: mass resolving power at 70,000 FWHM, 1 µscan, 1e6 AGC and 100 ms as maximum inject time. For untargeted analysis of brain tissue extracts, a pooled sample of all extracts (representative of the entire sample set) was analyzed periodically (every 4 samples) throughout the overall analytical run and used as quality control (QC) to correct the signal intensity drift and remove the metabolite features with poor reproducibility (Coefficient of variation (CV) > 30%). In addition, to facilitate metabolite identification in untargeted profiling, pooled samples were analyzed using all-ion fragmentation (AIF) (m/z range 50 to 750) at 10, 20 and 30 eV with the following parameters: mass resolving power at 35,000 FWHM, 1 µscan, 1e6 AGC and maximum inject time at 100 ms. Quantitative Data Processing. Raw LC-HRMS data files were processed using Thermo Scientific Xcalibur 4.1 (Thermo Fisher Scientific). Peak areas of target metabolites were auto-integrated, manually curated and corrected where necessary. Concentration of each compound was calculated as the peak area ratio between analyte and IS (“stable isotope dilution”). For the analytes without matching IS, the appropriate analyte-IS pairs were selected according to the closest retention time and chemical structure. Targeted Method Performance. For all analytes and matrices, calibration curves were linear or fitted by linear regression with weighting, and correlation coefficients (r2) were greater than 0.98 (Table S4). As plasma (dialyzed or charcoal stripped) cannot be completely depleted of some amino acids (data not shown) the dynamic range (Figure S1) and LLOQ of the method was evaluated by spiking the human plasma (n=5 independently prepared replicates) with serial dilutions of stable isotope-labeled standard mixture containing as many metabolites as possible (i.e. available on the market), in the lower concentration range from 0.00064 µM to 10 µM. The LLOQ was determined as the lowest calibration point for which the CV was < 20%, according to FDA guidelines (Table S4). Method precision was 8 ACS Paragon Plus Environment

Page 9 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


evaluated by determining intra- and inter-day measurement reproducibility in independently prepared pooled samples of plasma, urine and CSF (10 replicates analyzed per matrix, per day and over three different days). Method accuracy was examined against the concentrations reported in NIST reference plasma material. Recoveries were calculated as the absolute difference between the concentrations measured in a pooled plasma sample before (at baseline) and after spiking with AA and AC calibrators at low and high concentration levels (using six replicates). Untargeted Data Processing. Raw LC-HRMS data was converted to mzXML files using ProteoWizard MS Convert. mzXML files were uploaded to XCMS plus software (standalone server hosted by Vital-IT bioinformatics facility) for data processing including peak detection, retention time correction, peak alignment, and isotope annotation34-36. Data was processed using the following settings: centWave algorithm for feature detection (Δm/z = 20 ppm, minimum peak width = 5 sec and maximum peak width = 30 sec, S/N threshold = 6, mzdiff = 0.01, integration method = 1, prefilter peaks = 3, prefilter intensity = 1000, noise filter = 0); obiwarp settings for retention time correction (profStep = 1); and parameters for chromatogram alignment, including mzwid = 0.015, minfrac = 0.5 and bw = 537. The relative quantification of metabolite features was based on Extracted Ion Chromatogram (EIC) areas. The obtained tables (containing peak areas of detected metabolite features across all samples) were exported to “R” software (http://cran.r-project.org/) and signal intensity drift correction and noise filtering using CV (feature in QC) > 30% was applied. The remaining table of metabolite features was matched against the Human Metabolome Database (HMDB)29 based on feature accurate mass errors ≤ 10 ppm. The list of hits was further manually curated taking into account the biological relevance of the hit (endogenous vs. exogenous metabolites) and the presence of the “true” peak shape (using the interactive XCMS Results Table functionality). The metabolite identifications were done by manual alignment of extracted ion chromatograms of precursor ion at 0 eV and its predicted product ions at 10 and 30 eV (acquired on the pooled samples using AIF) and validated by MS/MS38 matching of these aligned product ions against 9 ACS Paragon Plus Environment


Page 10 of 43

METLIN (https://metlin.scripps.edu/) standard spectral library39, 40. Statistical data analysis. Correlation between the measured and reported metabolite concentrations in NIST plasma reference material was evaluated using linear regression modeling. Relative proportions of different carnitines and amino acids in analyzed biofluids and brain tissue were calculated as a percentage of total carnitine (free carnitine + acylcarnitine) or total amino acid concentration measured either in the pooled sample (for biofluids) or across all the samples of frontal cortex (n=6) and of hippocampus (n=6).


Page 11 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Results and Discussion To obtain a quantitative profile of a large panel of ACs and AAs, while simultaneously expanding the view on the polar metabolome in biofluids and tissue extracts, we have designed a merged targeted quantification and untargeted profiling in a single HILIC-HRMS method (Figure 1). This merged approach, using the simultaneous exploration of high resolution data acquired AIF mode (at different collision energies) allowed for the profiling of 30 carnitine species (including short-chain species - substrates of amino acid oxidation, medium-chain and long-chain species, and additional unsaturated and hydroxy-ACs) and 40 AAs and derivatives, of which 12 and 34 were quantified in an absolute manner. Acylcarnitine and amino acid metabolism constitutes a dominant component of the metabolic network41 and thus their quantitative profiles combined with an integrated view on central carbon metabolism can provide a global insight into the metabolic deregulation in metabolic diseases. Sample Preparation. Extraction of acylcarnitine and amino acids, their derivatives and additional polar metabolites, from biofluids and tissue samples, was achieved using methanol containing stable isotopelabeled internal standards (IS) as the best compromise in maximizing metabolite extraction (whilst spiking the samples with the IS mixture) and simultaneous protein precipitation27, 42. The optimal sample amount (volume in μL) was determined to allow for quantification within designed calibration curve ranges and can be further adjusted (from 5µL up to 50μL) depending on the amino acid and acylcarnitine content in the biological matrix (i.e. biofluid and cell or tissue type) to be analyzed (Figure 1A). The IS mixture contained 35 deuterated, 15N- or 13C-labeled standards (Table S1 and S2) to cover the broad range of AC and AA molecular species. Spiking the samples with this IS mixture enabled absolute quantification by correction for variation due to sample preparation and ionization efficiency (due to matrix effects). For metabolites for which the isotopically labelled analogues were not available, we assigned the appropriate surrogates with the most similar chemical structures and closest chromatographic retention.



Page 12 of 43

Figure 1. Merged targeted quantification and untargeted profiling workflow for comprehensive assessment of acylcarnitine and amino acid metabolism in clinically relevant biofluids and tissue samples. (A) Metabolite extraction protocol for the simultaneous analysis of acylcarnitines and amino acids, their derivatives and other polar metabolites. IS mix – Internal Standard mixture (500x diluted) (B) HILIC chromatographic separation of 34 amino acids and 12 acylcarnitines that were quantified in an absolute fashion. (1) Stearoylcarnitine (C18), (2) Palmitoylcarnitine (C16), (3) Tetradecanoylcarnitine (C14), (4) Dodecanoylcarnitine (C12), (5) Decanoylcarnitine (C10), (6) Octanoylcarnitine (C8), (7) Hexanoylcarnitine (C6), (8) Isovalerylcarnitine (C5), (9) Creatinine, (10) Butyrylcarnitine (C4), (11) Propionylcarnitine (C3), (12) Acetylcarnitine (C2), (13) Phenylalanine, (14) Leucine, (15) Tryptophan, (16) Kynurenine, (17) Isoleucine, (18) Methionine, (19) Gamma-aminobutyrate, (20) Carnitine (C0), (21) Taurine, (22) Valine, (23) Proline, (24) Tyrosine, (25) Pipecolate, (26) Alpha-aminobutyrate, (27) BetaAlanine & Sarcosine, (28) Creatine, (29) Guanidinoacetate, (30) Trans-4-hydroxyproline , (31) Alanine, (32) Threonine, (33) Glycine, (34) Alpha- aminoadipate, (35) Glutamine, (36) Glutamate, (37) Serine, (38) Homocitrulline, (39) Asparagine, (40) Citrulline, (41) Arginine, (42) Histidine, (43) Lysine, (44) Anserine, (45) Carnosine, (46) Ornithine.

Chromatographic Separation. Extracted metabolites were separated by HILIC chromatography based on the chromatographic conditions previously developed in our laboratory for the analysis of polar 12 ACS Paragon Plus Environment

Page 13 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


compounds in CSF samples27. These conditions were adapted in order to improve the retention and separation of a large panel of acycarnitines and amino acids included in this study. Briefly, the mobile phases were composed of (A) 20 mM ammonium formate and 0.1% formic acid and (B) acetonitrile with 0.1% formic acid providing good peak shape and reproducibility for both ACs and AAs (Figure 1B). The gradient composed of two consecutive linear steps enhanced the chromatographic resolution of long-chain acylcarnitines and provided a good separation to allow for quantification of several amino acid isomers such as isoleucine/leucine (albeit baseline separation was not achieved), β-alanine/alanine/sarcosine and γ-aminobutyrate/α-aminobutyrate (Figure S2). This chromatographic separation was evaluated for the retention time stability demonstrating relative standard deviations (RSD) < 2% (Table S2) for all the metabolites and across all different matrices (plasma, urine, CSF and brain tissue) analyzed in this study. The advantage of this HILIC-based approach compared to previous work22, 23 lies in the simultaneous analysis of a large panel of chemically diverse, ACs and AAs in a single-run. Calibration and Limits of Quantification. For quantification of acylcarnitines and amino acids the IS mixture (250 µL) was added to the samples. Calibration curves were generated in the same way by addition of this IS mixture (250 µL) to pre-prepared calibrators of different AC and AA concentrations. To cover the wide concentration ranges of the AC and AA panel in different biological matrices, calibration curves were fitted to linear or quadratic regression models with appropriate weighting factors (equal or 1/X) leading to a coefficient of determination (r2) value for each analyte greater than 0.98 (in their respective concentrations ranges, see Table S4). Limits of detection and quantification were determined as described in Materials and Methods. The LOQs of developed method allowed for quantification of the selected panel of ACs and AAs in plasma, CSF, urine and brain tissue extract and ranged from 10 nM for several long-chain acylcarnitines to 4 µM for alanine and glycine (reported in Table S4). 13 ACS Paragon Plus Environment


Page 14 of 43

Method Accuracy, Precision and Recovery. The developed method was evaluated for accuracy, precision and recovery (regarding the absolute quantification) of 34 amino acids and 12 saturated acylcarnitines with different alkyl chain lengths (short, medium and long). The accuracy of the method was tested by analysis of 10 independently prepared (i.e. separately extracted) aliquots of the human plasma NIST reference material (Certificate of Analysis, NIST 1950). Accuracies between 84.2 and 103.5 % were observed for the panel of amino acids, demonstrating a high correlation with the reported NIST reference values (r2 = 0.982)43 (Figure 2, Table 1).

Figure 2. Measured NIST values versus validated (i.e. reported) NIST reference plasma concentrations. Error bars represent the standard deviation of each measurement (see Table 1). Intra- and inter-day method precision were determined by analysis of 10 independently prepared aliquots of each matrix (plasma, CSF and urine), over one and three different days, and thus comprise the analytical variability of sample preparation and of sample analysis. Intraday precision was lower than 10% for all biofluids, with the exception of carnitine and beta-alanine/sarcosine in urine (Table S2). The interday precision was lower than 25% for quantified metabolites across all evaluated matrices (Table S2). Method recovery was evaluated by the analysis of pooled human plasma (n=6) spiked at low and high concentration levels corresponding to calibration level 3 and 6 (out of 10), respectively. For recovery 14 ACS Paragon Plus Environment

Page 15 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


calculation, the baseline concentrations (measured in a pooled plasma prior to spike) were subtracted from the concentrations measured in spiked plasma samples. Recoveries at the low concentration level spike were between 70% - 112% with the exception of 2-aminoadipic acid, phenylalanine, tyrosine and valine that showed recoveries between 59- 65% and 141.1% for creatine. Recoveries at the high concentration level spike spanned from 83% to 118%, except for creatinine and 2-aminoadipate for which the recoveries were 60% and 70%, respectively. Recoveries for the entire panel of acylcarnitines was from 93% to 106% (Table S4). Table 1. Amino acid and acylcarnitine concentrations measured in NIST plasma reference material by our method and reported by the National Institute of Standards and Technology56, concentrations measured in pooled urine and pooled cerebrospinal fluid samples. Measured concentration for each metabolite is provided in μM as Mean  SD, for urine the concentrations are reported to creatinine µmol/mmol of creatinine. Measured concentration of creatinine in urine was 7405 ± 229 µM.

CARNITINE (C0) ACETYLCARNITINE (C2) PROPIONYLCARNITINE (C3) BUTYRYLCARNITINE (C4) ISOVALERYLCARNITINE (C5) HEXANOYLCARNITINE (C6) OCTANOYLCARNITINE (C8) DECANOYLCARNITINE (C10) DODECANOYLCARNITINE (C12) TETRADECANOYLCARNITINE (C14) PALMITOYLCARNITINE (C16) STEAROYLCARNITINE (C18) ALANINE ALPHA-AMINOADIPATE ALPHA-AMINOBUTYRATE ANSERINE ARGININE ASPARAGINE BETA-ALANINE/SARCOSINE CARNOSINE CITRULLINE

NIST plasma measured

NIST plasma reported

Urine

CSF

31.1 ± 0.5 5.9 ± 0.1 0.3 ± 0.01 0.2 ± 0.004 0.1 ± 0.002 0.0 ± 0.001 0.1 ± 0.001 0.1 ± 0.003 0.0 ± 0.001 0.0 ± 0.001 0.1 ± 0.003 0.0 ± 0.001 294.8 ± 6.9 15.7 ± 0.5 82.0 ± 1.6 37.3 ± 1.1 1.0 ± 0.1 32.3 ± 0.6

300 ± 26 81.4 ± 2.3 -

20.6 ± 2.5 18.1 ± 1.0 0.4 ± 0.02 1.1 ± 0.05 0.3 ± 0.005 0.02 ± 0.0004 0.1 ± 0.001 0.0 ± 0.0005 25.4 ± 0.9 2.6 ± 0.1 0.3 ± 0.0 1.6 ± 0.0 9.2 ± 0.2 0.2 ± 0.04 0.5 ± 0.03 0.7 ± 0.02

3.7 ± 0.1 1.5 ± 0.02 0.1 ± 0.002 0.1 ± 0.001 0.02 ± 0.001 0.003 ± 0.0003 138.5 ± 6.9 4.3 ± 0.1 23.5 ± 0.5 13.0 ± 0.2 6.2 ± 0.1



CREATINE CREATININE GAMMA-AMINOBUTYRATE GLUTAMATE GLUTAMINE GLYCINE GUANIDINOACETATE HISTIDINE HOMOCITRULLINE ISOLEUCINE KYNURENINE LEUCINE LYSINE METHIONINE ORNITHINE PHENYLALANINE PIPECOLATE PROLINE SERINE TAURINE THREONINE TRANS-4-HYDROXYPROLINE TRYPTOPHAN TYROSINE VALINE

31.5 ± 0.6 59.3 ± 1.0 64.2 ± 1.4 460.4 ± 16.7 248.4 ± 7.4 1.8 ± 0.1 67.4 ± 2.3 42.3 ± 1.8 105.9 ± 2.4 159.3 ± 3.4 20.6 ± 0.4 60.8 ± 1.6 51.9 ± 1.0 2.3 ± 0.1 176.5 ± 2.8 84.1 ± 1.5 34.4 ± 0.9 111.0 ± 2.7 16.8 ± 0.3 44.6 ± 1.0 56.2 ± 1.4 176.5 ± 5.2

60 ± 0.9 245 ± 16 72.6 ± 3.6 55.5 ± 3.4 100.4 ± 6.3 140 ± 14 22.3 ± 1.8 51 ± 7 177 ± 9 95.9 ± 4.3 119.5 ± 6.1 57.3 ± 3.0 182.2 ± 10.4

Page 16 of 43

125.9 ± 5.6 * 2.5 ± 0.1 42.7 ± 1.3 153.3 ± 3.3 19.3 ± 0.4 89.4 ± 1.2 22.1 ± 0.5 27.0 ± 0.4 0.8 ± 0.0 1.1 ± 0.1 11.7 ± 0.2 1.2 ± 0.1 28.6 ± 0.7 148.0 ± 2.3 14.8 ± 0.2 1.8 ± 0.1 5.2 ± 0.3 7.9 ± 0.1 -

48.8 ± 0.7 60.3 ± 0.9 1.6 ± 0.1 531.6 ± 12.9 25.3 ± 1.1 0.2 ± 0.02 17.9 ± 0.6 17.9 ± 0.6 32.3 ± 0.7 45.2 ± 0.9 3.9 ± 0.1 7.3 ± 0.3 41.2 ± 0.8 11.6 ± 0.2 23.5 ± 0.7 6.4 ± 0.1 34.3 ± 0.6 5.6 ± 0.2 3.9 ± 0.1 16.2 ± 0.2 44.6 ± 3.5

Acylcarnitine and Amino Acid Profiles of Human Biofluids. To demonstrate the versatility of our method and examine the differences in the acylcarnitine and amino acid content across different biofluids, quantitative profiles were acquired for pooled human plasma (i.e. NIST material), urine and CSF. The amino acid profile was specific to each matrix with heterogeneous distribution of different species while the carnitine profile was similar among analyzed biofluids with free carnitine and acetylcarnitine dominating the profile (> 50% and 15%, respectively, of the total carnitine pool including free carnitine and acylcarnitine esters, Figure 3A). Measured concentrations of carnitine and acetylcarnitine were higher in plasma (31.09  0.5 μM and 5.9  0.1 μM, respectively) compared to CSF (3.7  0.1 μM and 1.5  0.02 μM) while the concentrations in urine (scaled to creatinine) ranged from 20.58  2.5 for carnitine down to 18.1 0.9 μmol/mmol creatinine for acetylcarnitine (Table 1). Acetylcarnitine is the 16 ACS Paragon Plus Environment

Page 17 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


most widely distributed short-chain ester of carnitine in the body. Other short- and medium-chain acylcarnitines constituted less than 5% of the total profile in all three biofluids and long-chain acylcarnitines (C14:0 - C18:0 carnitine) were measured only in plasma with concentrations ranging from 0.01 to 0.05 M. The long-chain acylcarnitines in CSF and in urine were below the LOQ presumably due to low sample amounts used for the analysis. This sensitivity issue could be adjusted by the extraction of more material, specifically in the case of urine where only 5 µL were used to compensate for potential matrix effects (as a consequence of several other highly abundant metabolites). In general, the measured concentrations of acylcarnitines were in accordance with previously reported concentrations in human biofluids44-46. This was also supported by the acylcarnitine to free carnitine ratio ≤ 0.4 in plasma in healthy physiological state47. The high proportion of carnitine and acetylcarnitine (compared to other carnitine esters in the pool) reflects their physiological roles, with 75% of carnitine in humans being absorbed from diet and distributed by circulation to different organs (mainly muscle, liver, kidney, brain, etc.) for the shuttling of activated long-chain fatty acids into the mitochondria for β-oxidation. Inside the cell, carnitine can readily be converted to acetylcarnitine by the enzymatic addition of acetyl group, and back again, depending on the metabolic needs of the cell. In general, the carnitine profile (species type and concentration) in biofluids mirrors their profile in tissue. A net-efflux of acylcarnitines from the cells (i.e. mitochondria) and tissue and their accumulation in the plasma occurs as a consequence of impaired oxidation to prevent the accumulation of potentially toxic acyl-CoA intermediates in mitochondria5. Thus, the acylcarnitine efflux serves as a detoxification process and plasma, urine, and CSF as sinks for cellular/tissue sequestration. Alternatively, these biofluids may also serve as a means of acylcarnitine transportation between cells or organs. Significant increase of acylcarnitines in human blood plasma and urine has been observed in specific inborn errors of metabolism (e.g. medium-chain acylCoA dehydrogenase deficiency or MCAD, long-chain acyl-CoA dehydrogenase deficiency or LCAD, etc. see 17 ACS Paragon Plus Environment


Page 18 of 43

Table S5) and in acquired metabolic and/or mitochondria-related disorders, such as obesity, type 2 diabetes and metabolic syndrome. The amino acid profile of analyzed biofluids was highly heterogeneous compared to carnitine profile, which also reflects various physiological roles of amino acids implicated in a wide range of biochemical pathways. In addition, the distribution of amino acid species was characteristic of each biofluid. In human plasma most of the amino acids were relatively evenly distributed (Figure 3B) with glutamine present in highest concentrations (> 450 µM) followed by alanine, glycine, proline, valine, lysine, threonine and leucine, ranging from 300 down to 100 µM (Table 1). The majority of other amino acids were within the 10-100 M range with the exception of pipecolate, guanidinoacetate, β-alanine and α-aminoadipate, measured in low μM concentrations (< 2 μM). A few species were below the level of quantification (i.e. homocitrulline, anserine, carnosine, and GABA) in this pooled human plasma NIST reference sample. The amino acid concentrations measured in pooled plasma by our method were in the range of values reported in HMDB48, 49 and in accordance with the ranges reported in a few recent population studies50, 51.

The high content of specific, essential and non-essential, amino acids in blood plasma is vital for

supplying tissues with a source of nitrogen and carbon for protein, neurotransmitter, hormone, nucleotide and lipid synthesis, as well as for removing the metabolic by-products of cell metabolism, such as the excess of ammonia52. Both glutamine and alanine, the two dominant amino acids in plasma, are nonessential amino acids produced and released by muscle and implicated in several different biological processes52. For example, they regulate glucose metabolism via alanine-glucose cycle and are major sources of energy for immune cell division in the immune response53, 54. In addition, they are also involved (among other amino acids) in the shuttle of ammonia across the cell membranes in and out of the extracellular space. The urine amino acid profile was dominated by creatinine (7 mM in the analyzed pooled sample), which is produced at a relatively constant rate as a catabolite of creatine and creatine phosphate in muscle energy 18 ACS Paragon Plus Environment

Page 19 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


metabolism. Together with other water-soluble metabolism by-products from the bloodstream, creatinine is transported by blood circulation to the kidneys and thereupon filtered and excreted in the urine. Several amino acids (scaled to creatinine) including glycine, taurine and histidine were present at high concentrations, from 90 to 150 μmol / mmol creatinine in this pooled urine sample. Other amino acids were excreted in smaller quantities, from 5 μmol / mmol creatinine for tryptophan to 40 μmol / mmol creatinine for glutamine which is consistent with the existing literature44. However, it is important to specify that an average metabolite in normal urine can exhibit high variations, from  50% and up to 400%, assumingly associated with the dietary intake44. Some amino acids, such as histidine, for example, correlate with the dietary protein intake55. In general, most of amino acids are secreted in urine at levels characteristic for an individual and although, due to its accessibility, urine is the biofluid most frequently used for diagnostics in clinics, this high intra- and inter-individual variation needs to be properly taken into account when drawing conclusions about potential disease biomarkers44.



Page 20 of 43

Figure 3. Amino acid (A) and acylcarnitine (B) signatures (or chemical composition barcodes) of human blood plasma (NIST reference material), urine (pooled anonymized sample) and CSF (pooled anonymized sample). Metabolites are presented in the same order to facilitate the profile comparison. The measured absolute concentrations are given in the Table 1. The amino acid profile of CSF is comparable to the plasma profile in terms of composition (i.e. qualitatively) with the most two abundant amino acids being glutamine (> 500 M) and alanine (> 100 M) in both biofluids. In addition, both CSF and plasma have a relatively even distribution of creatinine, creatine, lysine, valine, phenylalanine, threonine, leucine, glycine, arginine and serine in the concentration 20 ACS Paragon Plus Environment

Page 21 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


range from 60 down to 20 μM (Table 1). This similarity in profile between CSF and plasma is due to the circulation of nutrients by the CSF filtered from the blood and removal of metabolic by-products from the extracellular fluid in the brain. Rich CSF (and plasma) content in glutamine reflects glutamine production in the brain and the skeletal muscle, a major mechanism by which the brain and human body in general removes and thus detoxifies ammonia46. In addition to glutamate and -aminobutyric acid (GABA) included in the targeted list, several neurotransmitters and their metabolites were detected and identified in the retrospective exploration of untargeted data from the CSF as well as brain tissue extracts (e.g. Nacetylasparate, acetylcholine, choline, see Table 2).

Acylcarnitine and Amino Acid Profile of Human Brain Tissue. To demonstrate method applicability on tissue and explore the acylcarnitine and amino acid composition in brain tissue, targeted quantification of ACs and AAs was performed in tissue extracts obtained from two different regions of human brain, frontal cortex and hippocampus. Phenotypic characteristics of the subjects from whom the brain tissue was collected are given in Table S3. No neurological disorder was reported in the selected subjects and the cause of death was not of neurological origin. Due to the lack of statistical power (as a consequence of low sample size vs. high inter-individual variability in human population) and the obvious effect of gender, age, cause of death and postmortem delay on the metabolic profile of brain tissue we have confined the data analysis to an exploratory comparison with the aim to depict the acylcarnitine and amino acid composition and diversity in brain tissue. An averaged profile for each brain region is shown in Figure 4 and the individual profiles (per subject and brain region) are provided in Figures S3 and S4 annotated with their respective phenotypic characteristics: gender, age and postmortem delay (concentration data is given in Table S6). The composition of the acylcarnitine signature was fairly constant with the most abundant species (expressed in % of total carnitine profile) as follows: free carnitine (> 60%, at 1.7 ± 0.5 µM), acetylcarnitine (> 25%, at 0.6 ± 0.18 µM), butyrylcarnitine (5%, at 0.095 ± 0.045 µM (C4)), 21 ACS Paragon Plus Environment


Page 22 of 43

propionylcarnitine (at 0.029 ± 0.01 µM (C3)) and all the other medium and long-chain acylcarnitines representing less than 1% of the total carnitine profile. This tissue profile, considering species diversity and concentration, was also reflected in biofluids, thus demonstrating the roles of carnitine and acetylcarnitine as key-mediators of fatty acid transport (in and out of mitochondria) and oxidation. The amino acid profile was specific to brain tissue, in terms of molecular species diversity and abundance, thus reflecting the brain function, particularly related to the neurotransmission process. The amino acid composition (in terms of proportions and not the absolute concentrations) across different samples was uniform despite of gender, age, region and post-mortem delay differences (Figure S3).

Figure 4. Acylcarnitine (A) and amino acid (B) signatures or chemical composition barcodes of human 22 ACS Paragon Plus Environment

Page 23 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


brain tissue. Metabolite composition was averaged per brain region and presented in the same order to facilitate the profile comparison. The absolute concentrations are given in the Table S6 and individual profiles (per subject and per region) are presented in Figure S3 and S4. The most abundant amino acids found in brain tissue (considering an averaged profile of frontal cortex and hippocampus) were GABA (at 491 ± 136 µM, mean ± SD), creatine (at 461  51 µM), glutamate (at 390 ± 76 µM), glutamine (at 300 ± 96 µM), glycine (at 71 ± 30 µM), taurine (at 63 ± 9 µM), alanine (at 40 ± 9 µM) and serine (at 36 ± 14 µM). GABA or -aminobutyric acid is the primary inhibitory neurotransmitter while the glutamate is the main excitatory amino acid in the central nervous system (CNS) that regulates the excitability of neurons in the brain. Glutamine serves as a precursor of both of these neurotransmitter amino acids, thus representing the essential currency for the neurotransmission or transfer of information between neurons57. Production and release of these neurotransmitter amino acids is regulated by the glutamine-glutamate-GABA cycle. In order to maximize the signal-to-noise ratio after the release of glutamate, its uptake is constantly maintained by astrocytes. In the astrocytes, glutamate is being converted to glutamine and further transported to neurons where it forms glutamate that is used for neurotransmission58. To support the glutamate-glutamine cycling, alanine serves as a carrier for ammonia transfer between astrocytes and neurons59. In addition to GABA, glycine also plays a role of an inhibitory neurotransmitter particularly in the spinal cord, in the brainstem, and in the retina60. Glycine is formed from serine that is also an important precursor of sphingomyelin synthesis and myelin sheaths production. Beyond neurotransmitters and their precursors, creatine was also measured in high concentration in the analyzed brain tissue, reflecting its important role as an energy reservoir metabolite (together with its phosphorylated form – phosphocreatine), essential for maintaining the ATP levels in the brain as an organ with high energy turnover rates. Many in vitro and in vivo creatine supplementation studies have shown that creatine has the anti-oxidant, anti-apoptotic and neuroprotective role and thus might be beneficial in the treatment of neurological disorders for the enhancement of cognitive function however, the treatment 23 ACS Paragon Plus Environment


Page 24 of 43

efficiency remains to be proven61-63. Taurine is also found in high concentrations in brain tissue where it plays a role of a neuromodulator, a neurotransmitter, a neuro-protective agent and a potent regulator of calcium intracellular homeostasis64. It is also known for its osmoregulatory role, taking part in cell volume regulation. Taurine is classified as an inhibitory neurotransmitter although no taurine-specific receptors have been identified so far. It was also shown to be a critical trophic factor in CNS development and his activity as a neuroprotective agent (against the glutamate-induced neurotoxicity) was studied in the context of ageing and age-related neurodegenerative diseases. Among other amino acids present in brain tissue, the aromatic amino acids, tyrosine and phenylalanine, are the biosynthetic precursors for the neurotransmitters dopamine, adrenaline, norepinephrine and serotonin. Finally, the individual signatures were analyzed with an unsupervised multivariate analysis to explore the contribution of different factors, such as sex, region, age and postmortem delay that could potentially drive the sample clustering. Although the sample size is too low to draw any conclusions, a significant amount of variance tends to be explained mainly by regional and sex differences (Figure 5).

Figure 5. Exploratory unsupervised multivariate analysis - Principal Component Analysis (PCA, unit 24 ACS Paragon Plus Environment

Page 25 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


variance scaling) of individual brain tissue acylcarnitine and amino acid profiles. A) Scores plot. B) Loadings plot. Subject’s age and postmortem delay were used to label each sample. Analysis of the two first principal components (42.4% and 18.1% of total explained variance respectively) shows that quantification of acylcarnitine and amino acid profiles allows the clustering of samples according to brain regions (Figure 5A). One frontal cortex (FC) sample (69/12) seems to be significantly different from the other FC samples. A PCA was done after removal of this sample (data not shown) and no major change was observed regarding sample and variable distribution in the space of the two first principal components. The loadings plot (Figure 5B) shows that frontal cortex samples are characterized by a higher content of acylcarnitines while hippocampus samples present a higher content of amino acids. These regional brain differences were already highlighted in several studies relating the metabolic profiles to differences in region function and cognitive tasks40, 65, 66. Expanded View on Acylcarnitine and Amino Acid Metabolome using Untargeted HRMS AIF Data. Data acquisition in a full scan mode on a HRMS mass spectrometer enabled the combination of the targeted quantitation of acylcarnitines and amino acids with the retrospective analysis of untargeted data, thus expanding the coverage of interconnected metabolites and biochemical pathways. Using dataindependent analysis of pooled quality control samples (QCs) in all-ion-fragmentation (AIF) mode, sequentially at four collision energies: 0, 10, 20 and 30 eV, the fragmentation (MS/MS) data from each precursor ion were obtained for identification purpose67. XCMS software was first used for data processing including peak detection, retention time correction, profile alignment, and isotope annotation. List of hits (against METLIN and HMDB) was manually curated taking into account the biological relevance of the putative identity hit and a presence of a “true” chromatographic peak at the MS1 level. Additional metabolites were identified using the data acquired in AIF mode by the alignment of the extracted ion chromatograms (EICs) of precursor ions (full scan at 0 eV, defined by their accurate mass 25 ACS Paragon Plus Environment


Page 26 of 43

and RT) and a characteristic product ion (extracted from the product spectra for each precursor peak acquired in full scan at 10, 20 or 30 eV) to re-establish the precursor-product link68, 69. The alignment of EICs of a precursor (full scan at 0 eV) and product ions (at 10/20/30 eV) needs to be optimal with regards to peak apex retention times and chromatographic peak shape in general (Figure 6A). Metabolite identity was confirmed by MS/MS similarity matching of the aligned (i.e. deconvoluted) product ions against METLIN spectral library (Figure 6B). With this approach we could identify and semi-quantify many additional unsaturated and hydroxy acylcarnitine species, by aligning the EICs for precursor ions (at 0 eV) and the characteristic product ion of acylcarnitines (m/z 85.0279) at 20eV (Table 2, Figure S5).

Figure 6. Metabolite identification using the untargeted HRMS data acquired in All-Ion-Fragmentation (AIF) mode. (A) Aligned extracted ion chromatograms (EICs) of precursor (m/z: 137.0459) and characteristic product ions of hypoxanthine in pooled QC brain samples. (B) Deconvoluted MS/MS similarity matching Besides acylcarnitines, other amino acids and their derivatives – amines, as well as some purines and pyrimidines were identified by AIF data exploration as demonstrated on Figure 6 for the hypoxanthine. Moreover, we could also extract the information of specific complex lipid species, such as sphingolipids (i.e. sphingomyelins) that were abundant in plasma, CSF and brain tissue. The list of identified metabolites in each biofluid and tissue using the accurate mass, aligned MS/MS spectra and retention times is 26 ACS Paragon Plus Environment

Page 27 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


presented in Table 2. It should be noted that some metabolites can be derived by in-source fragmentation of other metabolites, like hypoxanthine from inosine. Therefore, it is of outmost importance to verify their retention times with the analysis of pure standards in the same analytical conditions. The extracted information on these additional acylcarnitines and amino acids, as well as their derivatives and other closely related polar and non-polar metabolites is essential for retrieving a more complete picture of metabolic deregulations and their interpretation in a biochemically relevant context. Thus merging the targeted quantification with an untargeted profiling, while combining the high sensitivity with highresolution might represent a coherent approach in biomedical and clinical research, and beyond.


Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Page 28 of 43

Table 2. List of additional acylcarnitine and amino acid metabolites, and other polar metabolites identified using the HRMS-AIF data. Metabolite Acetylcholine Acetylserine Acetyllysine Acetylspermidine Adenine Adenosine/deoxyguanosine ADMA/SDMA Choline Cystathionine Glycerophosphocholine Guanidinobutanoate Guanosine Hypoxanthine Inosine Methyl-adenosine N-acetylaspartate (NAA) N-acetyl-Asp-Glu (NAAG) Nicotinamide SM(d18:1/16:0) SM(d18:1/18:0) SM(d18:1/18:1) Thiamine Hydroxypropionylcarnitine (C3-OH-Car) Tiglylcarnitine (C5:1-Car) Octenoylcarnitine (C8:1-Car) Nonaylcarnitine (C9-Car) 9-decenoylcarnitine (C10:1-Car) Decadienylcarnitine (C10:2-Car) Dodecenoylcarnitine (C12:1-Car) Tetradecenoylcarnitine (C14:1-Car) Tetradecadienylcarnitine (C14:2-Car) Hexadecadienylcarnitine (C16:2-Car) Hexadecenoylcarnitine (C16:1-Car) Hydroxyhexadecadienylcarnitine (C16:2-OH-Car) Octadecadienylcarnitine (C18:2-Car) Octadecenoylcarnitine (C18:1-Car) Arachidonoylcarnitine (C20:4-Car) Behenoylcarnitine (C22:0-Car) Lignoceroylcarnitine (C24:0-Car) C26:0-Carnitine

RT 5.98 10.55 9.64 11.62 3.81 4.11 11.48 4.25 14.11 10.88 4.15 7.24 3.64 5.38 8.16 4.33 7.23 1.23 5.25 5.25 5.25 7.91 4.6 4.34 3.44 2.71 2.8 3.2 2.56 2.37 2.44 2.32 2.26 2.53 2.22 2.16 2.15 2 1.92 0.6

m/z (MS level) 146.1174 148.0604 189.1233 188.1758 136.062 268.1038 203.1502 104.1071 223.0743 258.1101 146.0926 284.0991 137.0456 269.0890 282.1200 176.0552 305.0979 123.0552 703.5745 731.6049 729.5903 265.1118 234.1347 244.1542 286.2013 302.2324 314.2323 312.2167 342.2636 370.2950 368.2793 396.3105 398.3262 412.3054 424.3417 426.3575 448.3416 484.4357 512.4670 540.1596

Mass Error (ppm) -1.36 ppm 0.00 ppm -0.53 ppm 0.53 ppm 1.47 ppm -0.75 ppm -0.49 ppm 0.96 ppm -1.81 ppm 0.00 ppm 1.37 ppm 0.70 ppm -1.46 ppm 3.71 ppm 1.06 ppm -0.56 ppm 0.00 ppm -0.81 ppm -0.57 ppm -1.77 ppm -2.74 ppm 0.00 ppm 4.83 ppm -0.35 ppm 0.35 ppm -0.74 ppm -0.97 ppm -0.88 ppm -0.75 ppm 0.50 ppm -0.67 ppm -0.79 ppm -0.77 ppm -1.33 ppm -1.10 ppm -0.67 ppm -1.11 ppm -0.69 ppm -0.58 ppm -0.55 ppm

m/z (MS/MS) product 87.0441 ions 130.0498; 106.0500 88.0396 126.0913 117.1020; 72.0812 119.034 136.0618 172.1078; 116.0707 88.0869 60.814 134.0268 104.1071 128.0815; 87.0442 152.0565 119.0352; 110.0349 94.0402 119.0351 137.0458; 110.0348 150.0775 60.0814 148.0604; 130.0499 80.0499 184.0731 184.0731 184.0731 122.0710; 81.0449 85.0279 85.0279 85.0279 85.0279 85.0279 85.0279 85.0279 85.0279 85.0279 85.0279 85.0279 85.0279 85.0279 85.0279 85.0279 85.0279 85.0279 85.0279

Plasma x

CSF x x

x x

x x

Urine x x x x x

x x

x x

x x

x

x x

x x x

x x x

x x x x x

x

x x x x x x x x x x x x x x x x x x x x x x x x x

x x x x x

x

Brain Tissue x x x x x x x x x x x x x x x x x x x x x x

x

x x

ACS Paragon Plus Environment

28

x

Page 29 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Conclusions A high-throughput, merged targeted and untargeted full scan HRMS method was developed to simultaneously measure a large panel of both, amino acids and acylcarnitines (> 60 species detected and 46 quantified in an absolute fashion comprising short-chain, medium-chain and long-chain acylcarnitine species) in a single-run HILIC-based method following a single-step extraction. To demonstrate the broad applicability of our method, this set of biologically and clinically relevant acylcarnitine (AC) and amino acid (AA) species and their derivatives was measured in several biofluids (human plasma, urine and CSF) and in human brain tissue extracts. Robust hydrophilic interaction chromatography allowed for the efficient metabolite separation thus ensuring the specificity and sensitivity of metabolite measurement without derivatization or usage of MS-incompatible ion pairing agents. Method accuracy was successfully validated using the NIST plasma reference material. Method precision was evaluated as the reproducibility of intra- and inter-day measurement and method recovery was determined at low and high concentration spike. As a proof of principle, acylcarnitine and amino acid signatures or chemical composition barcodes of human plasma, urine, CSF and human brain tissue were recorded using pooled samples and a collection of postmortem brain tissues from individuals without neurological disorders. While the acylcarnitine profile was relatively constant across different sample types, each biofluid and the brain tissue were characterized by their own, specific amino acid metabolic signatures associated with physiological roles of different biofluids and the cellular metabolism related to neurotransmission in the brain tissue. Exploration of high-resolution data acquired in full scan and All-Ion-Fragmentation mode enabled the identification and relative quantification of many additional AC and AA derivatives together with other polar metabolites, from the same analysis. This breadth of coverage (including many unsaturated and hydroxylated acylcarnitines, amino acid derivatives and purines and pyrimidines) is a strong added value of this method designed specifically for clinical research studies also due to the versatility of its application to the profiling of different biofluids (e.g. plasma, urine, CSF) and tissue extracts. 29 ACS Paragon Plus Environment


Page 30 of 43

Supporting information Supplemental data on method validation and performance (including precision, accuracy, limits of quantification and chromatographic resolution), and sample analysis are presented in several additional Tables and Figures in Supporting Information.

References 1. Wang, T. J.; Ngo, D.; Psychogios, N.; Dejam, A.; Larson, M. G.; Vasan, R. S.; Ghorbani, A.; O'Sullivan, J.; Cheng, S.; Rhee, E. P.; Sinha, S.; McCabe, E.; Fox, C. S.; O'Donnell, C. J.; Ho, J. E.; Florez, J. C.; Magnusson, M.; Pierce, K. A.; Souza, A. L.; Yu, Y.; Carter, C.; Light, P. E.; Melander, O.; Clish, C. B.; Gerszten, R. E., 2-Aminoadipic acid is a biomarker for diabetes risk. J Clin Invest 2013, 123 (10), 4309-17. 2. Bi, X.; Henry, C. J., Plasma-free amino acid profiles are predictors of cancer and diabetes development. Nutrition & diabetes 2017, 7 (3), e249-e249. 3. Wang, T. J.; Larson, M. G.; Vasan, R. S.; Cheng, S.; Rhee, E. P.; McCabe, E.; Lewis, G. D.; Fox, C. S.; Jacques, P. F.; Fernandez, C.; O'Donnell, C. J.; Carr, S. A.; Mootha, V. K.; Florez, J. C.; Souza, A.; Melander, O.; Clish, C. B.; Gerszten, R. E., Metabolite profiles and the risk of developing diabetes. Nat. Med. 2011, 17 (4), 448-53. 4. Newgard, C. B.; An, J.; Bain, J. R.; Muehlbauer, M. J.; Stevens, R. D.; Lien, L. F.; Haqq, A. M.; Shah, S. H.; Arlotto, M.; Slentz, C. A.; Rochon, J.; Gallup, D.; Ilkayeva, O.; Wenner, B. R.; Yancy, W. S., Jr.; Eisenson, H.; Musante, G.; Surwit, R. S.; Millington, D. S.; Butler, M. D.; Svetkey, L. P., A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009, 9 (4), 311-26. 5. McCoin, C. S.; Knotts, T. A.; Adams, S. H., Acylcarnitines—old actors auditioning for new roles in metabolic physiology. Nature Reviews Endocrinology 2015, 11, 617. 6. Rinschen, M. M.; Ivanisevic, J.; Giera, M.; Siuzdak, G., Identification of bioactive metabolites using activity metabolomics. Nat. Rev. Mol. Cell Biol. 2019. 7. Yang, Q.; Vijayakumar, A.; Kahn, B. B., Metabolites as regulators of insulin sensitivity and metabolism. Nat. Rev. Mol. Cell Biol. 2018, 19 (10), 654-672. 8. Jones, L. L.; McDonald, D. A.; Borum, P. R., Acylcarnitines: role in brain. Prog. Lipid Res. 2010, 49 (1), 61-75. 9. Schooneman, M. G.; Vaz, F. M.; Houten, S. M.; Soeters, M. R., Acylcarnitines: Reflecting or Inflicting Insulin Resistance? Diabetes 2013, 62 (1), 1-8. 10. Giesbertz, P.; Ecker, J.; Haag, A.; Spanier, B.; Daniel, H., An LC-MS/MS method to quantify acylcarnitine species including isomeric and odd-numbered forms in plasma and tissues. J. Lipid Res. 2015, 56 (10), 2029-39. 11. Yoon, M. S.; Choi, C. S., The role of amino acid-induced mammalian target of rapamycin complex 1(mTORC1) signaling in insulin resistance. Exp Mol Med 2016, 48 (1), e201. 12. Goberdhan, D. C. I.; Wilson, C.; Harris, A. L., Amino Acid Sensing by mTORC1: Intracellular Transporters Mark the Spot. Cell Metab. 2016, 23 (4), 580-589. 13. Mulleder, M.; Calvani, E.; Alam, M. T.; Wang, R. K.; Eckerstorfer, F.; Zelezniak, A.; Ralser, M., Functional Metabolomics Describes the Yeast Biosynthetic Regulome. Cell 2016, 167 30 ACS Paragon Plus Environment

Page 31 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(2), 553-565.e12. 14. Wolfson, R. L.; Sabatini, D. M., The Dawn of the Age of Amino Acid Sensors for the mTORC1 Pathway. Cell Metab. 2017, 26 (2), 301-309. 15. Minkler, P. E.; Stoll, M. S.; Ingalls, S. T.; Kerner, J.; Hoppel, C. L., Quantitative acylcarnitine determination by UHPLC-MS/MS--Going beyond tandem MS acylcarnitine "profiles". Mol. Genet. Metab. 2015, 116 (4), 231-41. 16. Roy, C.; Tremblay, P. Y.; Bienvenu, J. F.; Ayotte, P., Quantitative analysis of amino acids and acylcarnitines combined with untargeted metabolomics using ultra-high performance liquid chromatography and quadrupole time-of-flight mass spectrometry. J. Chromatogr. B Analyt. Technol. Biomed. Life. Sci. 2016, 1027, 40-9. 17. Vreken, P.; van Lint, A. E.; Bootsma, A. H.; Overmars, H.; Wanders, R. J.; van Gennip, A. H., Quantitative plasma acylcarnitine analysis using electrospray tandem mass spectrometry for the diagnosis of organic acidaemias and fatty acid oxidation defects. J Inherit Metab Dis 1999, 22 (3), 302-6. 18. Sakaguchi, Y.; Kinumi, T.; Yamazaki, T.; Takatsu, A., A novel amino acid analysis method using derivatization of multiple functional groups followed by liquid chromatography/tandem mass spectrometry. Analyst 2015, 140 (6), 1965-1973. 19. Armenta, J. M.; Cortes, D. F.; Pisciotta, J. M.; Shuman, J. L.; Blakeslee, K.; Rasoloson, D.; Ogunbiyi, O.; Sullivan, D. J., Jr.; Shulaev, V., Sensitive and rapid method for amino acid quantitation in malaria biological samples using AccQ.Tag ultra performance liquid chromatography-electrospray ionization-MS/MS with multiple reaction monitoring. Anal. Chem. 2010, 82 (2), 548-58. 20. Wang, D.; Fang, S.; Wohlhueter, R. M., N-terminal derivatization of peptides with isothiocyanate analogues promoting Edman-type cleavage and enhancing sensitivity in electrospray ionization tandem mass spectrometry analysis. Anal. Chem. 2009, 81 (5), 1893-900. 21. Gucciardi, A.; Pirillo, P.; Di Gangi, I. M.; Naturale, M.; Giordano, G., A rapid UPLCMS/MS method for simultaneous separation of 48 acylcarnitines in dried blood spots and plasma useful as a second-tier test for expanded newborn screening. Anal. Bioanal. Chem. 2012, 404 (3), 741-51. 22. Xiang, L.; Wei, J.; Tian, X. Y.; Wang, B.; Chan, W.; Li, S.; Tang, Z.; Zhang, H.; Cheang, W. S.; Zhao, Q.; Zhao, H.; Yang, Z.; Hong, Y.; Huang, Y.; Cai, Z., Comprehensive Analysis of Acylcarnitine Species in db/db Mouse Using a Novel Method of High-Resolution Parallel Reaction Monitoring Reveals Widespread Metabolic Dysfunction Induced by Diabetes. Anal. Chem. 2017, 89 (19), 10368-10375. 23. Yu, D.; Zhou, L.; Xuan, Q.; Wang, L.; Zhao, X.; Lu, X.; Xu, G., Strategy for Comprehensive Identification of Acylcarnitines Based on Liquid Chromatography–High-Resolution Mass Spectrometry. Anal. Chem. 2018, 90 (9), 5712-5718. 24. Chace, D. H.; Kalas, T. A.; Naylor, E. W., Use of tandem mass spectrometry for multianalyte screening of dried blood specimens from newborns. Clin. Chem. 2003, 49 (11), 1797-817. 25. Roy, C.; Tremblay, P.-Y.; Anassour-Laouan-Sidi, E.; Lucas, M.; Forest, J.-C.; Giguère, Y.; Ayotte, P., Risk of gestational diabetes mellitus in relation to plasma concentrations of amino acids and acylcarnitines: A nested case-control study. Diabetes Res. Clin. Pract. 2018, 140, 183-190. 26. Spalding, J. L.; Naser, F. J.; Mahieu, N. G.; Johnson, S. L.; Patti, G. J., Trace Phosphate Improves ZIC-pHILIC Peak Shape, Sensitivity, and Coverage for Untargeted Metabolomics. J. Proteome Res. 2018, 17 (10), 3537-3546. 27. Gallart-Ayala, H.; Konz, I.; Mehl, F.; Teav, T.; Oikonomidi, A.; Peyratout, G.; van der Velpen, V.; Popp, J.; Ivanisevic, J., A global HILIC-MS approach to measure polar human 31 ACS Paragon Plus Environment


Page 32 of 43

cerebrospinal fluid metabolome: Exploring gender-associated variation in a cohort of elderly cognitively healthy subjects. Anal. Chim. Acta 2018. 28. Miller, J. H. t.; Poston, P. A.; Karnes, H. T., A quantitative method for acylcarnitines and amino acids using high resolution chromatography and tandem mass spectrometry in newborn screening dried blood spot analysis. J. Chromatogr. B Analyt. Technol. Biomed. Life. Sci. 2012, 903, 142-9. 29. Wishart, D. S.; Knox, C.; Guo, A. C.; Eisner, R.; Young, N.; Gautam, B.; Hau, D. D.; Psychogios, N.; Dong, E.; Bouatra, S.; Mandal, R.; Sinelnikov, I.; Xia, J.; Jia, L.; Cruz, J. A.; Lim, E.; Sobsey, C. A.; Shrivastava, S.; Huang, P.; Liu, P.; Fang, L.; Peng, J.; Fradette, R.; Cheng, D.; Tzur, D.; Clements, M.; Lewis, A.; De Souza, A.; Zuniga, A.; Dawe, M.; Xiong, Y.; Clive, D.; Greiner, R.; Nazyrova, A.; Shaykhutdinov, R.; Li, L.; Vogel, H. J.; Forsythe, I., HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 2009, 37 (Database issue), 25. 30. Porchet, R.; Probst, A.; Draberova, E.; Draber, P.; Riederer, I. M.; Riederer, B. M., Differential subcellular localization of phosphorylated neurofilament and tau proteins in degenerating neurons of the human entorhinal cortex. Neuroreport 2003, 14 (7), 929-33. 31. Leuba, G.; Savioz, A.; Vernay, A.; Carnal, B.; Kraftsik, R.; Tardif, E.; Riederer, I.; Riederer, B. M., Differential changes in synaptic proteins in the Alzheimer frontal cortex with marked increase in PSD-95 postsynaptic protein. J Alzheimers Dis 2008, 15 (1), 139-51. 32. Want, E. J.; Masson, P.; Michopoulos, F.; Wilson, I. D.; Theodoridis, G.; Plumb, R. S.; Shockcor, J.; Loftus, N.; Holmes, E.; Nicholson, J. K., Global metabolic profiling of animal and human tissues via UPLC-MS. Nat. Protocols 2013, 8 (1), 17-32. 33. Vorkas, P. A.; Isaac, G.; Anwar, M. A.; Davies, A. H.; Want, E. J.; Nicholson, J. K.; Holmes, E., Untargeted UPLC-MS Profiling Pipeline to Expand Tissue Metabolome Coverage: Application to Cardiovascular Disease. Anal. Chem. 2015, 87 (8), 4184-4193. 34. Gowda, H.; Ivanisevic, J.; Johnson, C. H.; Kurczy, M. E.; Benton, H. P.; Rinehart, D.; Nguyen, T.; Ray, J.; Kuehl, J.; Arevalo, B.; Westenskow, P. D.; Wang, J.; Arkin, A. P.; Deutschbauer, A. M.; Patti, G. J.; Siuzdak, G., Interactive XCMS Online: Simplifying Advanced Metabolomic Data Processing and Subsequent Statistical Analyses. Anal. Chem. 2014, 86 (14), 69316939. 35. Smith, C. A.; Want, E. J.; O'Maille, G.; Abagyan, R.; Siuzdak, G., XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 2006, 78 (3), 779-87. 36. Kuhl, C.; Tautenhahn, R.; Böttcher, C.; Larson, T. R.; Neumann, S., CAMERA: An Integrated Strategy for Compound Spectra Extraction and Annotation of Liquid Chromatography/Mass Spectrometry Data Sets. Anal. Chem. 2011, 84 (1), 283-289. 37. Libiseller, G.; Dvorzak, M.; Kleb, U.; Gander, E.; Eisenberg, T.; Madeo, F.; Neumann, S.; Trausinger, G.; Sinner, F.; Pieber, T.; Magnes, C., IPO: a tool for automated optimization of XCMS parameters. BMC Bioinformatics 2015, 16, 118. 38. Mylonas, R.; Mauron, Y.; Masselot, A.; Binz, P. A.; Budin, N.; Fathi, M.; Viette, V.; Hochstrasser, D. F.; Lisacek, F., X-Rank: a robust algorithm for small molecule identification using tandem mass spectrometry. Anal. Chem. 2009, 81 (18), 7604-10. 39. Guijas, C.; Montenegro-Burke, J. R.; Domingo-Almenara, X.; Palermo, A.; Warth, B.; Hermann, G.; Koellensperger, G.; Huan, T.; Uritboonthai, W.; Aisporna, A. E.; Wolan, D. W.; Spilker, M. E.; Benton, H. P.; Siuzdak, G., METLIN: A Technology Platform for Identifying Knowns and Unknowns. Anal. Chem. 2018, 90 (5), 3156-3164. 40. Ivanisevic, J.; Epstein, A. A.; Kurczy, M. E.; Benton, P. H.; Uritboonthai, W.; Fox, H. S.; Boska, M. D.; Gendelman, H. E.; Siuzdak, G., Brain region mapping using global metabolomics. 32 ACS Paragon Plus Environment

Page 33 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Chem. Biol. 2014, 21 (11), 1575-84. 41. Park, J. O.; Rubin, S. A.; Xu, Y.-F.; Amador-Noguez, D.; Fan, J.; Shlomi, T.; Rabinowitz, J. D., Metabolite concentrations, fluxes and free energies imply efficient enzyme usage. Nat. Chem. Biol. 2016, 12, 482. 42. Ivanisevic, J.; Zhu, Z.-J.; Plate, L.; Tautenhahn, R.; Chen, S.; O’Brien, P. J.; Johnson, C. H.; Marletta, M. A.; Patti, G. J.; Siuzdak, G., Toward ‘Omic Scale Metabolite Profiling: A Dual Separation–Mass Spectrometry Approach for Coverage of Lipid and Central Carbon Metabolism. Anal. Chem. 2013, 85 (14), 6876-6884. 43. Simon-Manso, Y.; Lowenthal, M. S.; Kilpatrick, L. E.; Sampson, M. L.; Telu, K. H.; Rudnick, P. A.; Mallard, W. G.; Bearden, D. W.; Schock, T. B.; Tchekhovskoi, D. V.; Blonder, N.; Yan, X.; Liang, Y.; Zheng, Y.; Wallace, W. E.; Neta, P.; Phinney, K. W.; Remaley, A. T.; Stein, S. E., Metabolite profiling of a NIST Standard Reference Material for human plasma (SRM 1950): GC-MS, LC-MS, NMR, and clinical laboratory analyses, libraries, and web-based resources. Anal. Chem. 2013, 85 (24), 11725-31. 44. Bouatra, S.; Aziat, F.; Mandal, R.; Guo, A. C.; Wilson, M. R.; Knox, C.; Bjorndahl, T. C.; Krishnamurthy, R.; Saleem, F.; Liu, P.; Dame, Z. T.; Poelzer, J.; Huynh, J.; Yallou, F. S.; Psychogios, N.; Dong, E.; Bogumil, R.; Roehring, C.; Wishart, D. S., The human urine metabolome. PLoS One 2013, 8 (9), e73076-e73076. 45. Psychogios, N.; Hau, D. D.; Peng, J.; Guo, A. C.; Mandal, R.; Bouatra, S.; Sinelnikov, I.; Krishnamurthy, R.; Eisner, R.; Gautam, B.; Young, N.; Xia, J.; Knox, C.; Dong, E.; Huang, P.; Hollander, Z.; Pedersen, T. L.; Smith, S. R.; Bamforth, F.; Greiner, R.; McManus, B.; Newman, J. W.; Goodfriend, T.; Wishart, D. S., The Human Serum Metabolome. PLoS One 2011, 6 (2), e16957. 46. Wishart, D. S.; Lewis, M. J.; Morrissey, J. A.; Flegel, M. D.; Jeroncic, K.; Xiong, Y.; Cheng, D.; Eisner, R.; Gautam, B.; Tzur, D.; Sawhney, S.; Bamforth, F.; Greiner, R.; Li, L., The human cerebrospinal fluid metabolome. J. Chromatogr. B Analyt. Technol. Biomed. Life. Sci. 2008, 871 (2), 164-73. 47. Bellinghieri, G.; Santoro, D.; Calvani, M.; Mallamace, A.; Savica, V., Carnitine and hemodialysis. Am. J. Kidney Dis. 2003, 41 (3 Suppl 1), S116-22. 48. Psychogios, N.; Hau, D. D.; Peng, J.; Guo, A. C.; Mandal, R.; Bouatra, S.; Sinelnikov, I.; Krishnamurthy, R.; Eisner, R.; Gautam, B.; Young, N.; Xia, J.; Knox, C.; Dong, E.; Huang, P.; Hollander, Z.; Pedersen, T. L.; Smith, S. R.; Bamforth, F.; Greiner, R.; McManus, B.; Newman, J. W.; Goodfriend, T.; Wishart, D. S., The human serum metabolome. PLoS One 2011, 6 (2), e16957e16957. 49. Wishart, D. S.; Feunang, Y. D.; Marcu, A.; Guo, A. C.; Liang, K.; Vázquez-Fresno, R.; Sajed, T.; Johnson, D.; Li, C.; Karu, N.; Sayeeda, Z.; Lo, E.; Assempour, N.; Berjanskii, M.; Singhal, S.; Arndt, D.; Liang, Y.; Badran, H.; Grant, J.; Serra-Cayuela, A.; Liu, Y.; Mandal, R.; Neveu, V.; Pon, A.; Knox, C.; Wilson, M.; Manach, C.; Scalbert, A., HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2018, 46 (D1), D608-D617. 50. Yamamoto, H.; Kondo, K.; Tanaka, T.; Muramatsu, T.; Yoshida, H.; Imaizumi, A.; Nagao, K.; Noguchi, Y.; Miyano, H., Reference intervals for plasma-free amino acid in a Japanese population. Ann. Clin. Biochem. 2016, 53 (Pt 3), 357-64. 51. Imaizumi, A.; Adachi, Y.; Kawaguchi, T.; Higasa, K.; Tabara, Y.; Sonomura, K.; Sato, T.-a.; Takahashi, M.; Mizukoshi, T.; Yoshida, H.-o.; Kageyama, N.; Okamoto, C.; Takasu, M.; Mori, M.; Noguchi, Y.; Shimba, N.; Miyano, H.; Yamada, R.; Matsuda, F., Genetic basis for plasma amino acid concentrations based on absolute quantification: a genome-wide association study in the Japanese population. Europ. J. Hum. Genet. 2019, 27 (4), 621-630. 33 ACS Paragon Plus Environment


Page 34 of 43

52. Ivanisevic, J.; Elias, D.; Deguchi, H.; Averell, P. M.; Kurczy, M.; Johnson, C. H.; Tautenhahn, R.; Zhu, Z.; Watrous, J.; Jain, M.; Griffin, J.; Patti, G. J.; Siuzdak, G., Arteriovenous Blood Metabolomics: A Readout of Intra-Tissue Metabostasis. Scientific Reports 2015, 5, 12757. 53. Smith, R. J., Glutamine metabolism and its physiologic importance. JPEN. J. Parenter. Enteral Nutr. 1990, 14 (4 Suppl), 40s-44s. 54. Newsholme, P., Why Is L-Glutamine Metabolism Important to Cells of the Immune System in Health, Postinjury, Surgery or Infection? The Journal of Nutrition 2001, 131 (9), 2515S-2522S. 55. Dunstan, R. H.; Sparkes, D. L.; Macdonald, M. M.; De Jonge, X. J.; Dascombe, B. J.; Gottfries, J.; Gottfries, C. G.; Roberts, T. K., Diverse characteristics of the urinary excretion of amino acids in humans and the use of amino acid supplementation to reduce fatigue and sub-health in adults. Nutr. J. 2017, 16 (1), 19-19. 56. Phinney, K. W.; Ballihaut, G.; Bedner, M.; Benford, B. S.; Camara, J. E.; Christopher, S. J.; Davis, W. C.; Dodder, N. G.; Eppe, G.; Lang, B. E.; Long, S. E.; Lowenthal, M. S.; McGaw, E. A.; Murphy, K. E.; Nelson, B. C.; Prendergast, J. L.; Reiner, J. L.; Rimmer, C. A.; Sander, L. C.; Schantz, M. M.; Sharpless, K. E.; Sniegoski, L. T.; Tai, S. S.; Thomas, J. B.; Vetter, T. W.; Welch, M. J.; Wise, S. A.; Wood, L. J.; Guthrie, W. F.; Hagwood, C. R.; Leigh, S. D.; Yen, J. H.; Zhang, N. F.; Chaudhary-Webb, M.; Chen, H.; Fazili, Z.; LaVoie, D. J.; McCoy, L. F.; Momin, S. S.; Paladugula, N.; Pendergrast, E. C.; Pfeiffer, C. M.; Powers, C. D.; Rabinowitz, D.; Rybak, M. E.; Schleicher, R. L.; Toombs, B. M.; Xu, M.; Zhang, M.; Castle, A. L., Development of a Standard Reference Material for metabolomics research. Anal. Chem. 2013, 85 (24), 11732-8. 57. Albrecht, J.; Sidoryk-Wegrzynowicz, M.; Zielinska, M.; Aschner, M., Roles of glutamine in neurotransmission. Neuron Glia Biol 2010, 6 (4), 263-76. 58. Schousboe, A., Metabolic signaling in the brain and the role of astrocytes in control of glutamate and GABA neurotransmission. Neurosci. Lett. 2019, 689, 11-13. 59. Broer, S.; Broer, A.; Hansen, J. T.; Bubb, W. A.; Balcar, V. J.; Nasrallah, F. A.; Garner, μμB.; Rae, C., Alanine metabolism, transport, and cycling in the brain. J. Neurochem. 2007, 102 (6), 1758-1770. 60. Lopez-Corcuera, B.; Geerlings, A.; Aragon, C., Glycine neurotransmitter transporters: an update. Mol. Membr. Biol. 2001, 18 (1), 13-20. 61. Avgerinos, K. I.; Spyrou, N.; Bougioukas, K. I.; Kapogiannis, D., Effects of creatine supplementation on cognitive function of healthy individuals: A systematic review of randomized controlled trials. Exp. Gerontol. 2018, 108, 166-173. 62. Rae, C. D.; Bröer, S., Creatine as a booster for human brain function. How might it work? Neurochem. Int. 2015, 89, 249-259. 63. Bender, A.; Klopstock, T., Creatine for neuroprotection in neurodegenerative disease: end of story? Amino Acids 2016, 48 (8), 1929-40. 64. Wu, J.-Y.; Prentice, H., Role of taurine in the central nervous system. J. Biomed. Sci. 2010, 17 Suppl 1 (Suppl 1), S1-S1. 65. Paglia, G.; Stocchero, M.; Cacciatore, S.; Lai, S.; Angel, P.; Alam, M. T.; Keller, M.; Ralser, M.; Astarita, G., Unbiased Metabolomic Investigation of Alzheimer's Disease Brain Points to Dysregulation of Mitochondrial Aspartate Metabolism. J. Proteome Res. 2016, 15 (2), 608-18. 66. Choi, W. T.; Tosun, M.; Jeong, H.-H.; Karakas, C.; Semerci, F.; Liu, Z.; Maletić-Savatić, M., Metabolomics of mammalian brain reveals regional differences. BMC Syst. Biol. 2018, 12 (Suppl 8), 127-127. 67. Cajka, T.; Fiehn, O., Toward Merging Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and Lipidomics. Anal. Chem. 2016, 88 (1), 524-45. 68. Naz, S.; Gallart-Ayala, H.; Reinke, S. N.; Mathon, C.; Blankley, R.; Chaleckis, R.; 34 ACS Paragon Plus Environment

Page 35 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Wheelock, C. E., Development of a Liquid Chromatography-High Resolution Mass Spectrometry Metabolomics Method with High Specificity for Metabolite Identification Using All Ion Fragmentation Acquisition. Anal. Chem. 2017, 89 (15), 7933-7942. 69. Tsugawa, H.; Cajka, T.; Kind, T.; Ma, Y.; Higgins, B.; Ikeda, K.; Kanazawa, M.; VanderGheynst, J.; Fiehn, O.; Arita, M., MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods 2015, 12, 523.

Acknowledgments The authors thank to the Foundation Pierre Mercier and Faculty of Biology and Medicine, University of Lausanne, for funding assistance. We thank to Dr. Beat Riederer from the Department of Psychiatry (at the University Hospital Lausanne – CHUV) and Department of Fundamental Neurosciences (at the Faculty of Biology and Medicine, University of Lausanne) for providing the human brain tissue that was collected in the context of Dr. Riederer’s previous work and was stored in CHUV biobank.

Conflict of Interest Disclosure The authors declare no competing financial interest.



Page 36 of 43

Table of Contents (TOC)


Page 37 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


TOC



Figure 1. Merged targeted quantification and untargeted profiling workflow for comprehensive assessment of acylcarnitine and amino acid metabolism in clinically relevant biofluids and tissue samples. (A) Metabolite extraction protocol for the simultaneous analysis of acylcarnitines and amino acids, their derivatives and other polar metabolites. IS mix – Internal Standard mixture (500x diluted) (B) HILIC chromatographic separation of 34 amino acids and 12 acylcarnitines that were quantified in an absolute fashion. (1) Stearoylcarnitine (C18), (2) Palmitoylcarnitine (C16), (3) Tetradecanoylcarnitine (C14), (4) Dodecanoylcarnitine (C12), (5) Decanoylcarnitine (C10), (6) Octanoylcarnitine (C8), (7) Hexanoylcarnitine (C6), (8) Isovalerylcarnitine (C5), (9) Creatinine, (10) Butyrylcarnitine (C4), (11) Propionylcarnitine (C3), (12) Acetylcarnitine (C2), (13) Phenylalanine, (14) Leucine, (15) Tryptophan, (16) Kynurenine, (17) Isoleucine, (18) Methionine, (19) Gamma-aminobutyrate, (20) Carnitine (C0), (21) Taurine, (22) Valine, (23) Proline, (24) Tyrosine, (25) Pipecolate, (26) Alpha-aminobutyrate, (27) Beta-Alanine & Sarcosine, (28) Creatine, (29) Guanidinoacetate, (30) Trans-4-hydroxyproline , (31) Alanine, (32) Threonine, (33) Glycine, (34) Alpha- aminoadipate, (35) Glutamine, (36) Glutamate, (37) Serine, (38) Homocitrulline, (39) Asparagine, (40) Citrulline, (41) Arginine, (42) Histidine, (43) Lysine, (44) Anserine, (45) Carnosine, (46) Ornithine.


Page 38 of 43

Page 39 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 2. Measured NIST values versus validated (i.e. reported) NIST reference plasma concentrations. Error bars represent the standard deviation of each measurement (see Table 1).



Figure 3. Amino acid (A) and acylcarnitine (B) signatures (or chemical composition barcodes) of human blood plasma (NIST reference material), urine (pooled anonymized sample) and CSF (pooled anonymized sample). Metabolites are presented in the same order to facilitate the profile comparison. The measured absolute concentrations are given in the Table 1.


Page 40 of 43

Page 41 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 4. Acylcarnitine (A) and amino acid (B) signatures or chemical composition barcodes of human brain tissue. Metabolite composition was averaged per brain region and presented in the same order to facilitate the profile comparison. The absolute concentrations are given in the Table S6 and individual profiles (per subject and per region) are presented in Figure S3 and S4.



Figure 5. Exploratory unsupervised multivariate analysis - Principal Component Analysis (PCA, unit variance scaling) of individual brain tissue acylcarnitine and amino acid profiles. A) Scores plot. B) Loadings plot. Subject’s age and postmortem delay were used to label each sample.


Page 42 of 43

Page 43 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 6. Metabolite identification using the untargeted HRMS data acquired in All-Ion-Fragmentation (AIF) mode. (A) Aligned extracted ion chromatograms (EICs) of precursor (m/z: 137.0459) and characteristic product ions of hypoxanthine in pooled QC brain samples. (B) Deconvoluted MS/MS similarity matching


Merged Targeted Quantification and Untargeted Profiling for

Recommend Documents