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NMR based metabolic snapshot from Minibronchoalveolar lavage fluid: an approach to unfold human respiratory metabolomics Akhila Viswan, Raj Kumar Sharma, Afzal Azim, and Neeraj Sinha J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.5b00919 • Publication Date (Web): 20 Nov 2015 Downloaded from http://pubs.acs.org on November 21, 2015

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Journal of Proteome Research

NMR based metabolic snapshot from Minibronchoalveolar lavage fluid: an approach to unfold human respiratory metabolomics Akhila Viswan 1,2, Raj Kumar Sharma1, Afzal Azim3* and Neeraj Sinha1* 1

Centre of Biomedical Research, SGPGIMS Campus, Raebarelly Road, Lucknow – 226014 INDIA 2

Faculty of Engineering and Technology, Dr. A. P. J Abdul Kalam Technical University, Lucknow – 226021, INDIA 3

Department of Critical Care Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow – 226014, INDIA

*

Author to whom correspondence should be address;

Afzal Azim ([email protected]), Phone : +91 – 9984988393 Neeraj Sinha ([email protected]), Phone : +91 – 0451094389

1 ACS Paragon Plus Environment


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Abstract Utility of Mini Bronchoalveolar lavage (mBAL) and its applicability in metabolomics has not been explored in the field of human respiratory disease. mBAL “an archetype” of the local lung environment ensures a potent technique to get the snapshot of the epithelial lining fluid afflicted to human lung disorders. Characterization of the mBAL fluid has potential to help in elucidating the composition of the alveoli and airways in the diseased state, yielding diagnostic information of clinical applicability. In this study one of the first attempts has been made to comprehensively assign and detect metabolites in mBAL fluid, extracted from human lungs, by the composite use of 800 MHz one dimensional and two dimensional NMR, J-resolved homonuclear spectroscopy, COSY, TOCSY and heteronuclear HSQC correlation methods. A foremost all-inclusive sketch of the 50 metabolites have been corroborated and assigned, which can be a resourceful archive to further lung directed metabolomics, prognosis and diagnosis. Thus NMR based mBALF studies, as proposed in this article, will leverage many more prospective respiratory researches for routine clinical application and proves to be a viable approach to mirror the key predisposing factors contributing to the onset of lung disease.

Keywords: mini Bronchoalveolar lavage fluid, biofluids, assignment, two dimensional NMR, lung disease


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INTRODUCTION Since medieval times the importance of biofluids in seeking disease pathophysiology has been well established1. Composite biofluids analysis can truly reflect the response of dietary changes, drug effects, disease course and outcome which comprise an intricate network of endogenous metabolites with polar, soluble components2. Metabolites hold importance in the realm of clinical utility as an indicator of the physiological state and possible index to the biological pathway involved3-5. The analytical platform of metabolomics has been employed to monitor and ascertain disease susceptibility correlated with the altered and aberrant metabolite concentration6. Such metabolic studies have been carried out extensively in biofluids to decipher the information of the lung microenvironment in the state of disease and debilitation7,8. High resolution Nuclear Magnetic Resonance (NMR) spectroscopy based metabolomics with its manifold application holds the key to unfold much of the biochemical information left unexplored in the complex biofluids mixture9,10. NMR spectroscopy has emerged as a powerful analytical platform with an edge over other tools by being non-destructive, non-selective, reproducible, minimal sample preparation and conducive for timely analysis11. The metabolites their composition and interaction for putative biomarkers can be ascertained from a single biological sample12. NMR provides a robust means to fingerprint diagnostic parameters encoded inside the vast repertoire of lipids, proteins and endogenous metabolic byproducts which possibly illustrates the patho-biological state of the lung compartment13. Many prior NMR spectroscopic studies have been executed in serum14, exhaled breath condensate (EBC)15, plasma16, sputum, lung tissue and cells from lung parenchyma

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, urine18, Bronchoalveolar lavage fluid (BALF)19,20 with an aim to determine the

implicated biomarkers in the lung disease. The broad protein signals and domination of 3 ACS Paragon Plus Environment


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lipoproteins in serum and plasma with a limited detection limit21, the complex and nonspecific composition of urine22, multiple cell types in lung tissue which obscures analysis and interpretation, coherent nature of EBC hampers the enumeration of meaningful biomarkers and the expertise required in BALF23 impedes the acquired diagnostic information24. Thus a more reliable yet informative bio fluid for disease specific analysis is vital and necessitated. The varied limitations imposed by these fluids are annulled by mini Bronchoalveolar lavage (mBAL) fluid for the lung directed disease assessment rendering it promising fluid for discrete analysis. mBAL during fiber-optic bronchoscopy offers a feasible and minimally invasive technique for the diagnosis of various diffuse lung diseases and respiratory tract infections as it is a routine medical procedure opted by clinicians. mBALF is conventionally a non-endogenous fluid that is introduced into the lung and harvested meticulously through aspiration25. mBALF collected from proximal alveoli and bronchi has been explored by NMR spectroscopy stating its significance as diagnostic fluid at par with BALF26. Thorough investigation of mBALF can lead to timely diagnosis of different lung ailments like emphysema, idiopathic pulmonary fibrosis, chronic obstructive pulmonary disease, asthma, pulmonary transplantation, sarcoidosis, interstitial lung disease, pneumonia, bronchiolitis and acute respiratory distress syndrome (ARDS)23,27-32. Any biochemical aberrations get reflected and can be attained for phenotyping the various lung disorders33. Paucity of studies limits the diagnostic accuracy of non bronchoscopic distal airway sampling procurement by mBALF and its application in many diseases25,26,34. Our study highlights the importance of mini bronchoalveolar lavage fluid (mBALF) analysis to reveal the cellular milieu of lung in response to external stimuli by identifying the collection of metabolites left unmapped. To get an overview of the diverse metabolites and the dynamic changes reflecting aberrant metabolism spectral features are assigned by their 4 ACS Paragon Plus Environment

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distinctive NMR chemical shifts and coupling constants. Unambiguous assignment which is severely limited by the spectral overlap of one dimensional (1D) NMR is overcome by two dimensional (2D) NMR methods35 which addresses the high chemical shift degeneracy and incomplete information of reference spectra from databases encountered in 1D36-38 NMR methods. The combined approach of higher field and more dispersive 2D spectroscopy39 enables recognition of individual metabolites even within complex and heterogeneous mBALF. Precise assignment techniques specific and peculiar to sample dynamics like heterogenousity, viscosity, motional artefacts, different nuclear spin relaxivities, diffusion and compartmentation has been taken into consideration before optimization and identification40. Substantial assignment of mBALF using multidimensional 2D approaches can measure and assess the candidate biomarkers which can be a clue to disease etiology. Detailed metabolic framework will surely help to screen the discriminating and pertinent metabolites responsible for disease pathology which are contributors to biological difference. Therefore specific, sensitive, accurate and timely assignment will not only supplement clinical and translational research but will also help to infer complex biochemical aspects41. To the best of our knowledge our study represents a first and foremost complete preliminary catalogue to metabolic atlas of mBALF which can further initiate prospective respiratory research. The new pool of metabolites detected in mBALF will provide a scaffold for further quantitation study and alleviation of respiratory disease with these representative metabolites as referral guide.



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MATERIALS AND METHODS 1.1 Sample collection Human mBALF sample was collected from the patient enrolled at Intensive Care Unit (ICU) of a tertiary care medical centre in Sanjay Gandhi Post Graduate Institute of Medical Sciences (SGPGIMS), Lucknow, INDIA and NMR experiments executed at Centre of Biomedical Research (CBMR), Lucknow, INDIA. The study protocol was approved by the institute’s ethical committee and written informed consent was duly obtained from the subject or their surrogate decision makers. The patient included in the study group was suffering from acute respiratory distress syndrome (ARDS) as specified by American European Consensus Conference (AECC) enrolled at ICU admission of SGPGIMS which categorizes ARDS on the basis of partial pressure of oxygen / fraction of inspired oxygen (P/F) ratio. 1.2 Sample processing The sample was acquired using one of the standardized nonbronchoscopic techniques in ICU to collect mBALF in mechanically ventilated patients. Patient’s mBALF was harvested using the non bronchoscopic “catheter in catheter” technique within 24 hours of diagnosis. In the present study 10 ml sterile distilled water was used for sample collection for the patient. mBALF sample collected in mucus trap was transferred to a collection vial and immediately quenched in liquid nitrogen till further processing. The sample was preserved after centrifuging at 16000 rpm for 10 min at 4 oC to remove the particulate matter. The supernatant stored at -80 oC till further NMR experiments was performed. NMR spectroscopy experiments were performed with 350 µl BALF sample for each experiment.


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1.3 NMR spectroscopy NMR spectral acquisition was performed with 550 µl sample having 350 µl BALF with 200 µl buffer to attenuate the variation in pH. Trimethylsilyl propanoic acid (TSP) (6.53 mM) was added in the buffer (0.1 M Na2HPO4/NaH2PO4, pH-7.4) for internal chemical shift reference along with 10% D2O for field frequency lock. An 800 MHz Bruker NMR spectrometer with a cryogenically cooled triple-resonance TCI (1H,

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C,

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N, and 2H lock) probe at 300 K was used

for 1D proton (1H) NMR data acquisition of BALF samples. All 1D (noesygppr1d in Bruker library) spectra with water suppression were acquired using a 90o flip angle, a 20 ppm spectral width and a relaxation delay of 5 seconds, 32 transients were collected into 64 k data points with an acquisition time (Taq) of 1.99 seconds and 16 dummy scans. 1H 1D spectra were referenced to the TSP signal (δ=0.00 ppm). The Free induction decays (FIDs) were multiplied by an exponential weighting function corresponding to a line broadening function of 0.3 Hz and zero filled before Fourier transformation. The acquired spectra were manually phased and baseline corrected. The distinctive chemical shifts in the frequency domain originating from sets of spectral measurements were assigned for respective metabolites. Low molecular weight metabolite resonances present in 1D spectrum (Figure 1) were identified using biological magnetic resonance data bank (BMRB) database42, human metabolome database (HMDB)43, MetaboMiner44, established literature values and reference spectra from standard compounds. Further confirmation of metabolites was achieved using 2D spectra including heteronuclear single quantum correlation (HSQC) (Figure 2), J-resolved (JRES) homonuclear spectroscopy (Figure S1), Correlation spectroscopy (COSY) (Figure S2) and Total correlation spectroscopy (TOCSY) (Figure S3).



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Spectral overlap in 1D is reduced by employing 1H-1H COSY. A COSY spectrum using two pulse sequences was recorded in the magnitude mode with multiple quantum filter using quadrature phase during relaxation delay of 2 seconds. A data matrix 2048x516 points was recorded with 40 transients per increment and covering spectral width of 13 ppm in both dimensions. The data was zero filled to 2048×2048 points and a sine bell shaped window function was applied before Fourier transformation TOCSY provides absorption line shapes for both cross peaks and diagonal with observation of multiple relay cross peaks. In the analysis of mixtures TOCSY holds additional benefit where the magnetization is dispersed over entire spin system with cross peak information obtained from nuclei that are not directly J-coupled. TOCSY was acquired in the phase sensitive mode using echo-antiecho time proportional phase increment and the two MLEV 17 pulse sequence for excitation and spin lock. A total of 2048x1024 data points with 40 transients per increment were recorded spanning spectral width of 15 ppm in both dimension. For TOCSY prior to Fourier transform, the FIDs were weighted in both dimensions by a sine-bell function and zero-filled in the t1 dimension to 2048 data points. 2D JRES homonuclear spectroscopy45 separates the chemical shift and the line splitting caused by J-coupling into two orthogonal frequency directions with two pulse echo sequence thereby enabling spectral assignments, metabolite specificity and increasing peak dispersion compared to 1D. The tilting of the 2D spectrum by 45o and symmetrizing around horizontal axis results in a homonuclear broadband decoupled 1D spectrum thus simplifying the spectra by removing multiplicity. The optional skyline projection through the JRES spectral map results in a more comprehensible less convoluted spectral profile of the 1H decoupled NMR metabolite spectrum. 2D JRES spectra were performed in magnitude mode to avoid phasing. Relaxation 8 ACS Paragon Plus Environment

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delay of 5 seconds with 32 K data points, 36 transients per 128 t1 increments covering spectral width of 16 ppm in t1 dimension and 0.097 ppm in t2 dimension was setup. Prior to Fourier transform, FID signal were weighted in both dimensions by a sine bell shaped window function for resolution enhancement and zero filled to 256 data points in the t1 dimension. By employing HSQC, metabolite assignments are made more feasible due to the 1H-13C chemical shift correlation, distinct chemical shift assignments, larger

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C chemical shift

evolution and simplified spectrum without splitting from J-coupling. A gradient enhanced phase sensitive version of HSQC with adiabatic pulses was collected in echo-antiecho mode. 1H-13C HSQC correlation spectra through the

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C chemical shift dispersion of double quantum

coherence were recorded with inverse detection and

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C decoupling. A total of 2048x824 data

points were collected over a bandwidth of 16 ppm in 1H and 165 ppm in 13C dimensions using 24 scans per increment and a relaxation delay of 2 second. Zero filling in t1 yielded a transformed 2D dataset of 2048×2048 data points. Spectra processed by forward linear prediction up to 1648 points in t1 dimensions and multiplication by shifted sine-bell squared apodization function was employed in both t1 and t2 dimension prior to Fourier transform. RESULTS AND DISCUSSION An extensive and comprehensive assignment of 50 metabolites in mBAL fluid based on the comparison of 1H and

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C chemical shift, spin-spin coupling constant, peak multiplicity with

their chemical group and reference is tabulated in Table 1. The spectral resolution has been increased by extending the chemical shift information in the second dimension with high sensitivity achieved by 800 MHz spectrometer. A complete glossary of the metabolic profile of mBALF has been screened using 1D with the complementary approach of JRES, COSY,



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TOCSY and HSQC to confirm assignment and

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C chemical shift resonance. Multidimensional

2D techniques allowed a complete in depth coverage of the core metabonome and lung compartment specific metabolites. Prior to processing and assignment, all the 1D and 2D spectra were referenced to the 0ppm (δ=0ppm) of methyl resonance of TSP. The 1D 1H NMR spectroscopy (Figure 1) of human BALF is mostly dominated by low molecular weight metabolites like short chain fatty acids, branched chain amino acids, monosaccharides, aliphatic and aromatic amino acids, Tricarboxylic acid cycle intermediates, purine and pyrimidine metabolites and organic acids. The aromatic portion constituting 6-9 ppm is evident from formate, histidine, phenylalanine, tyrosine, urea, uracil which make up the nucleic acid breakdown products. Citrate and histidine molecules are sensitive to pH or osmolality thereby undergo considerable shift in resonant frequency either up field or downfield. Citric acid was characterized by its doublet pattern at 2.67 ppm and 2.76 ppm with histidine multiplets at 3.14 ppm, 3.23 ppm, 3.97 ppm, singlets at 7.04 ppm and 7.76 ppm. Majority of the crowded and overlapped regions were observed in 3-4 ppm range with predominant forms of energy derivatives like glucose, creatine, lactate and amino acids, water soluble nutrients which were resolved and assigned by 2D approach. 2D techniques have been employed to remove strong coupling artifacts which result in additional peaks and to overcome the ambiguities in the complex overlapped regions of aliphatic methyl groups along with the cluster of multiplets in mid frequency range of 3 ppm-4.5 ppm. Resonances from many metabolites were overlapped creating a complex multiplet at 1.71-1.73 ppm which were identified and assigned to leucine, lysine and arginine by 2D approach. Deconvolution of complex mixture was attainable by exploiting the connectivity of TOCSY and COSY resolving the multiplets of lysine, arginine, proline and glutamate which can only be tentatively assigned by 1D. 10 ACS Paragon Plus Environment

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Cross peaks in COSY (Figure S2) give information about the directly coupled protons. The distinguished triplet at 1.18 ppm of ethanol correlated with signal at 3.64 ppm in the COSY plot and 19.48 ppm and 60.13 ppm

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C shift corresponded to CH3 triplet and CH2 quartet in

HSQC. Similarly quartet peak of lactate at 4.12 ppm correlated with 1.33 doublet which were further identified by HSQC cross peaks at 71.41 ppm and 22.90 ppm

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C chemical shift

respectively. COSY spectra are mostly governed by high molecular weight metabolites. Bulk of amino acid resonances and their spin-spin connectivity are deduced by phase sensitive COSY. The γCH2-δCH2, εCH2- δCH2, βCH2- αCH, βCH2- γCH2connectivity of lysine was inferred by COSY. The βCH2- αCH, βCH2- γCH2 and γCH2-δCH2 of arginine, αCH- βCH2, αCH2- γCH2, and βCH2- γCH2 of citrulline was also determined. Similarly αCH- βCH2 of tyrosine, histidine and glutamate were concluded by COSY. TOCSY resolves the resonances off the diagonal, therefore is useful for amino acid and carbohydrates assignment. It provides connectivity in spin system up to 5-6 bonds hence additional information can be ascertained. In the Figure S3, TOCSY map aided in the identification of the spin system of lysine and arginine multiplets. Small molecules examined by TOCSY included lactate, taurine and anomers of α-glucose and β- glucose. Using TOCSY the cross peaks coming from α, β CH2 group of choline and α and β protons of serine was well distinguished. The signals from multiplets appeared as singlets with the help of skyline projection in JRES spectra (Figure S1). JRES was mostly dominated by low molecular weight metabolites like acetate, alanine, lactate, and valine. Upfield region of NMR spectrum in JRES homonuclear spectroscopy included histidine, tyrosine and two CH2 triplets of taurine. The singlet due to acetate, betaine, choline, creatine, creatinine, fumaric acid, glycine, pyruvate and succinate which were not observed as off diagonal diagnostic cross peaks in COSY 11 ACS Paragon Plus Environment


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and TOCSY, were confirmed by HSQC (Figure 2). In the low frequency range a superimposed doublet of organic acid lactate and amino acid threonine was observed due to CH3 group at 1.33 ppm and was well distinguished by

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C chemical shift. Taking advantage of the large

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C

chemical shift dispersion of 1H-13C HSQC spectra the whole spin system of α-β glucose cross peaks were assigned. Contribution of α-β glucose identified in HSQC was based on the chemical shift resonating from the anomeric proton 5.23 ppm and 4.64 ppm respectively. Sparse metabolites in the low field spectral region (5-10 ppm) of NMR spectrum were unequivocally assigned compared to other protons originating from the crowded regions of 3 ppm-4.5 ppm. In the high frequency range number of low intensity signal from formate were difficult to detect and thus assigned with the help of HSQC cross peak correlation. The fused peaks denoting the aromatic compound rings of phenylalanine, histidine, and proline were well resolved by HSQC. The fused triplets of leucine due to δ-CH3 at 0.95 ppm, 0.97 ppm and δ-CH3 of isoleucine at 0.93 ppm were well separated by HSQC. The diagnostic singlet of adenine by 1H was also assigned to 8.19 ppm, 8.21 ppm and 13C chemical shift to 152 ppm and155 ppm respectively by HSQC. These assigned metabolites can be significantly evaluated for their explicit role in chronic and acute lung ailments by implementing pattern recognition and biomarker validation workflow. The metabolic fingerprint of specific lung compartment due to mBAL characterization will help to deduce the differentiating metabolites like choline, lactate46, ethanol, taurine, threonine, myoinositol, betaine, glycine which have earlier been ascribed to lung diseases19,47,48. Thus breakthrough in NMR based metabolomics in respiratory research calls for enhanced peak detection, unambiguous annotation and univocal assignment employing multidimensional 2D approach. The improved sensitivity, resolution, versatility and less likelihood of peak overlap in 2D NMR assures to identify unprecedented number of metabolites 12 ACS Paragon Plus Environment

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in biological mixture thus facilitating functional lung biomarkers of disease. Such methodical protocol can only direct and translate NMR lung metabolomics for conventional clinical purpose. This first attempt to illustrate mBALF as a potent bio fluid in studying the lung compartment at its closest with possible representation of extensive metabotype will be an advancement towards understanding the lung physiology. Detection of 50 metabolites in mBALF will commence the widespread use of such pool of metabolites in elucidating potential discriminating metabolites attributed to a disease. The present study of mBALF NMR study holds relevance for depicting the biological state of lung for further disease interpretation. CONCLUSION NMR spectroscopic detection of human lung metabolites will lead to a new paradigm with an insight of the complex biochemical composition inside the mBALF mixture. A total of 50 metabolites have been assigned in mBALF matrix providing a unique biological window to different aspects of the intricate metabolic network of NMR detectable metabolites. NMR provides a robust, complementary platform to interpret the biological behavior by assigning the comprehensive ensemble of metabolites. Accurate assignment and descriptive outline of all possible metabolites through NMR manifests an information rich method which can usher new possibilities and prospects for disease related analysis. It can be envisioned that such measures will truly aid in predicting the underlying lung disease susceptibility, mechanism and follow-up, bridging the gap between genotype and phenotype.



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Acknowledgement: Authors acknowledge Department of Science and Technology (DST), INDIA for research fellowship. Table 1: 1D and 2D assignment of the metabolites characterized in mBALF with their respective 1

H and 13C chemical shift, peak multiplicity (s = singlet, d = doublet, q = quartet, m = multiplet, t

= triplet, dd = doublet of doublet), chemical groups and reference spectra. Figure legends: Figure 1: Representative 800 MHz 1H NMR spectrum of mBALF obtained from ARDS patient with enhanced detection of lung directed diseased metabolites. Three different spectral region of the 1D 1H spectrum is depicted in a) 0.8-3.1ppm b) 3.1-4.5ppm c) 5.0-8.7ppm. Key:1- Acetate, 2- Adenine, 3- Alanine, 4- γ-Aminobutyric acid, 5- Aspargine, 6- Aspartic acid, 7- Arginine, 8Betaine, 9- Choline, 10- Citrate, 11- Creatine, 12- Creatinine, 13- Citrulline, 14- Cysteine, 15Ethanol, 16- Ethanolamine, 17- Formate, 18- Fumaric acid, 19- α-Glucose, 20- β-glucose, 21Glutamate, 22- Gluconic acid. 23- Glycine, 24- Glycerol, 25- Histidine, 26- Homoserine, 27Hydroxyisocaproic acid, 28- Isobutyrate, 29- Isoleucine, 30- Lactate, 31- α-Lactose, 32- βlactose, 33- Leucine, 34- Lysine, 35- Methionine, 36- Myoinositol, 37- N-acetyl L alanine, 38Ornithine, 39- Phenylalanine, 40- Proline, 41- Pyruvate, 42- Succinate, 43- Serine, 44- Taurine, 45- Threonine, 46- Tryptophan, 47- Tyrosine, 48- Uracil, 49- Urea, 50- Valine Figure 2: 1H-13C HSQC spectrum of mBALF collected from ARDS patient symbolic of diseased lung specific metabolites with enhanced characterization. Two different spectral region of the HSQC spectrum is depicted in a) 0.8-4.5ppm b) 6.6-7.8ppm. Key:1- Acetate, 2- Adenine, 3Alanine, 4- γ-Aminobutyric acid, 5- Aspargine, 6- Aspartic acid, 7- Arginine, 8- Betaine, 914 ACS Paragon Plus Environment

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Choline, 10- Citrate, 11- Creatine, 12- Creatinine, 13- Citrulline, 14- Cysteine, 15- Ethanol, 16Ethanolamine, 17- Formate, 18- Fumaric acid, 19- α-Glucose, 20- β-glucose, 21- Glutamate, 22Gluconic acid. 23- Glycine, 24- Glycerol, 25- Histidine, 26- Homoserine, 27- Hydroxyisocaproic acid, 28- Isobutyrate, 29- Isoleucine, 30- Lactate, 31- α-Lactose, 32- β-lactose, 33- Leucine, 34Lysine, 35- Methionine, 36- Myoinositol, 37- N-acetyl L alanine, 38- Ornithine, 39Phenylalanine, 40- Proline, 41- Pyruvate, 42- Succinate, 43- Serine, 44- Taurine, 45- Threonine, 46- Tryptophan, 47- Tyrosine, 48- Uracil, 49- Urea, 50- Valine Supporting Information: Additional 2D spectrum, as explained in the text, are given in supporting information. These 2D spectrum are S1 : JRES spectrum, S2: COSY, S3 : TOCSY



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(32) Blackburn, M. R.; Volmer, J. B.; Thrasher, J. L.; Zhong, H.; Crosby, J. R.; Lee, J. J.; Kellems, R. E. Metabolic Consequences of Adenosine Deaminase Deficiency in Mice Are Associated with Defects in Alveogenesis, Pulmonary Inflammation, and Airway Obstruction. J EXP MED 2000, 192, 159-170. (33) Serkova, N. J.; Van Rheen, Z.; Tobias, M.; Pitzer, J. E.; Wilkinson, J. E.; Stringer, K. A. Utility of magnetic resonance imaging and nuclear magnetic resonance-based metabolomics for quantification of inflammatory lung injury. Am J Physiol Lung Cell Mol Physiol 2008, 295, L152-L161. (34) Singh, C.; Rai, R.; Azim, A.; Sinha, N.; Baronia, A. Mini-bronchoalveolar lavage fluid can be used for biomarker identification in patients with lung injury by employing 1H NMR spectroscopy. Critical Care 2013, 17, 430. (35) Aue, W. P.; Bartholdi, E.; Ernst, R. R. Two-dimensional spectroscopy. Application to nuclear magnetic resonance. J. Chem. Phys. 1976, 64, 2229-2246. (36) Fan, T. W. M. Metabolite profiling by one- and two-dimensional NMR analysis of complex mixtures. Prog. Nucl. Magn. Reson. Spectrosc. 1996, 28, 161-219. (37) Viant, M. R. Improved methods for the acquisition and interpretation of NMR metabolomic data. Biochem. Biophys. Res. Commun. 2003, 310, 943-948. (38) Bingol, K.; Brüschweiler, R. Multidimensional Approaches to NMR-Based Metabolomics. Anal. Chem 2014, 86, 47-57. (39) Holmes, E.; Foxall, P. J. D.; Spraul, M.; Duncan Farrant, R.; Nicholson, J. K.; Lindon, J. C. 750 MHz 1H NMR spectroscopy characterisation of the complex metabolic pattern of urine from patients with inborn errors of metabolism: 2-hydroxyglutaric aciduria and maple syrup urine disease. J. Pharm. Biomed. Anal. 1997, 15, 1647-1659. (40) Nicholson, J. K.; Foxall, P. J. D.; Spraul, M.; Farrant, R. D.; Lindon, J. C. 750 MHz 1H and 1H-13C NMR Spectroscopy of Human Blood Plasma. Anal. Chem 1995, 67, 793-811. (41) Nagana Gowda, G. A.; Gowda, Y. N.; Raftery, D. Expanding the Limits of Human Blood Metabolite Quantitation Using NMR Spectroscopy. Anal. Chem 2015, 87, 706-715. (42) Ulrich, E. L.; Akutsu, H.; Doreleijers, J. F.; Harano, Y.; Ioannidis, Y. E.; Lin, J.; Livny, M.; Mading, S.; Maziuk, D.; Miller, Z.; Nakatani, E.; Schulte, C. F.; Tolmie, D. E.; Kent Wenger, R.; Yao, H.; Markley, J. L. BioMagResBank. Nucleic Acids Res 2008, 36, D402-D408. (43) Wishart, D. S.; Jewison, T.; Guo, A. C.; Wilson, M.; Knox, C.; Liu, Y.; Djoumbou, Y.; Mandal, R.; Aziat, F.; Dong, E.; Bouatra, S.; Sinelnikov, I.; Arndt, D.; Xia, J.; Liu, P.; Yallou, F.; Bjorndahl, T.; Perez-Pineiro, R.; Eisner, R.; Allen, F.; Neveu, V.; Greiner, R.; Scalbert, A. HMDB 3.0—The Human Metabolome Database in 2013. Nucleic Acids Res 2013, 41, D801-D807. (44) Xia, J.; Bjorndahl, T.; Tang, P.; Wishart, D. MetaboMiner - semi-automated identification of metabolites from 2D NMR spectra of complex biofluids. BMC Bioinformatics 2008, 9, 507. (45) Ludwig, C.; Viant, M. R. Two-dimensional J-resolved NMR spectroscopy: review of a key methodology in the metabolomics toolbox. Phytochem. Anal 2010, 21, 22-32. (46) DeBacker, D.; Creteur, J.; Zhang, H.; Norrenberg, M.; Vincent, J.-L. Lactate Production by the Lungs in Acute Lung Injury. Am J Respir Crit Care Med 1997, 156, 1099-1104. (47) Atzei, A.; Atzori, L.; Moretti, C.; Barberini, L.; Noto, A.; Ottonello, G.; Pusceddu, E.; Fanos, V. Metabolomics in paediatric respiratory diseases and bronchiolitis. J Matern Fetal Neonatal Med 2011, 24, 59-62. (48) Izquierdo-García, J.; Nin, N.; Ruíz-Cabello, J.; Rojas, Y.; de Paula, M.; López-Cuenca, S.; Morales, L.; Martínez-Caro, L.; Fernández-Segoviano, P.; Esteban, A.; Lorente, J. A metabolomic approach for diagnosis of experimental sepsis. Intensive Care Med 2011, 37, 2023-2032.


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Table 1: 1D and 2D assignment of the metabolites characterized in mBALF with their respective 1

H and 13C chemical shift, peak multiplicity (s = singlet, d = doublet, q = quartet, m = multiplet, t

= triplet, dd = doublet of doublet), chemical groups and reference spectra S. No 1

Molecule

2

Adenine

3

Alanine

4

γAminobutyric acid

5

6

7

8

9

1

13

H shift 1.92

Multiplicity

Assignment

s

CH3

C shift 26.00

8.19 8.21 1.48

s s d

CH-8 CH-2 βCH3

152 155 19.00

3.78 1.91

q m

αCH βCH2

53.41 25.88

Aspargine

2.28 3.01 2.84

t t m

αCH2 γCH2 βCH2

36.55 41.88 37.52

Aspartic acid

2.94 4.00 2.67

m dd dd

βCH2 αCH βCH2

37.52 54.00 39.36

Arginine

2.80 3.86 1.68

dd dd m

βCH2 αCH γCH2

39.36 54.60 26.96

Betaine

1.91 3.23 3.76 3.26

m t t s

βCH2 δCH2 αCH N(CH3)3

30.48 43.27 57.23 56.21

Choline

3.90 3.20

s s

CH2 N(CH3)3

68.90 56.60

3.52 4.06

m m

NCH2 OCH2

70.06 58.50

Acetate


Observed 1D,JRES, HSQC 1D, JRES 1D,JRES, HSQC,TOCSY COSY 1D,JRES, HSQC, TOCSY,COSY

1D,JRES, HSQC, TOCSY,COSY

1D,JRES, HSQC,JRES, TOCSY,COSY

1D,JRES, COSY,TOCSY ,HSQC,JRES

1D,JRES, HSQC 1D,JRES, HSQC, COSY,TOCSY


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 20 of 27

10

Citrate

2.67

d

CH2

42.81

11

Creatine

2.76 3.03

d s

CH2 CH3

42.81 39.55

s s

CH2 CH3

56.41

Creatinine

3.93 3.05

Citrulline

4.06 1.56

s m

CH2 γ-CH2

14

Cysteine

1.86 3.12 3.76 3.06

m m t m

βCH2 αCH2 αCH CH2

15

Ethanol

3.93 1.18

dd t

CH CH3

19.48

q

Ethanolamine

3.64 3.13

CH2 O-CH2

60.13 44.11

N-CH2 CH CH C4 H

60.41

Formate Fumaric acid α-Glucose

3.81 8.44 6.52 3.40

β-glucose

3.54 3.71 3.83 3.85 5.24 3.25

C2 H C3 H C6 H C5 H C1 H C2 H

74.23 75.54 74.28 63.44 94.95 77.06

C4 H C5 H C3 H C6 H C6 H C1 H

72.48 78.66 78.24 63.69 63.44 98.71

12

13

16

17 18 19

20

3.42 3.48 3.49 3.74 3.91 4.65

s s

d

d 20

ACS Paragon Plus Environment

1D,JRES, HSQC, COSY,TOCSY 1D, JRES,HSQC 1D,JRES, HSQC

27.93

1D,JRES, HSQC, COSY,TOCSY

30.65 42.34 57.74 1D, COSY,TOCSY

72.48

1D,JRES, HSQC, COSY,TOCSY 1D,JRES, HSQC, COSY,TOCSY 1D,JRES 1D.JRES 1D,JRES, HSQC, COSY,TOCSY


Page 21 of 27

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


Glutamate

2.03

m

β-CH2

Gluconic acid

2.13 2.36 3.78 3.66

m m t m

β-CH2 γ-CH2 α-CH CH2

65.23

23

Glycine

3.76 3.83 4.02 4.12 3.56

m m t d s

CH CH2 CH CH CH2

75.22 65.23 73.61 76.88 44.25

24

Glycerol

3.55

dd

CH2

65.3

Histidine

3.64 3.77 3.11

dd dd dd

CH2 CH β-CH2

65.3 74.82 31.15

dd dd s s m

β-CH2 α-CH C4 H C2 H CH2

31.15 57.58 119.6 138.8

Homoserine

3.21 3.97 7.07 7.82 2.00 2.11 3.76 3.84 0.91

m m dd d

CH2 CH2 CH CH3

m m dd d

CH2 CH

Isobutyrate

1.50 1.72 4.03 1.07

Isoleucine

2.35 0.93

m t

CH δ-CH3

13.83

1.01 1.26 1.45 1.96 3.67

d m m m d

β-CH3 γ-CH2 γ-CH2 β-CH α-CH

17.45 27.23 27.23 38.63 62.47

21

22

25

26

27

28

29

Hydroxyisoca proic acid

1D,JRES, COSY,TOCSY


1D,JRES, HSQC 1D,JRES, HSQC, COSY,TOCSY


1D,JRES

1D, JRES COSY,TOCSY

CH3

21

1D,JRES, HSQC

1D, JRES 1D,JRES, HSQC, COSY,TOCSY


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 22 of 27

30

Lactate

1.33

d

CH3

22.90

α-Lactose

4.12 3.27

q t

CH

31

71.41 76.92

m m m m m d d m

75.14 75.27 74.15

β-lactose

3.54 3.66 3.81 3.86 3.94 4.45 5.23 3.66

Leucine

4.63 0.95

d t

δ-CH3

98.82 23.74

Lysine

0.97 1.70 1.70 3.73 1.49

t m m t m

δ-CH3 β-CH2 γ-CH α-CH γ-CH2

24.85 26.83 42.55 56.26 24.29

Methionine

1.73 1.90 3.03 3.74 2.13

m m t t s

δ-CH2 β-CH2 ε-CH2 α-CH S-CH3

29.19 32.52 41.67 57.03 16.74

Myoinositol

2.16 2.63 3.85 3.27

m t t t

β-CH2 S-CH2 α-CH C5 H

32.28 31.72 56.71 77.0

3.54 3.62 4.05 1.33

dd dd t d

H1/H3 H4/H6 C2 H β-CH3

73.67 75.04 75.04 20.27

32

33

34

35

36

37

N-acetyl L alanine

72.78 106.4 94.86 80.90


1D,JRES, HSQC, COSY,TOCSY 1D,JRES, HSQC, COSY,TOCSY

1D,JRES, HSQC, COSY,TOCSY 1D,JRES, HSQC, COSY,TOCSY




1D,JRES, HSQC,COSY,

Page 23 of 27

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


TOCSY 2.01

s

Ornithine

4.12 1.72

q m

N-acetyl CH3 α-CH γ-CH2

Phenylalanine

1.81 1.93 3.04 3.79 3.12

m m t t m

γ-CH2 β-CH2 δ-CH2 α-CH β-CH2

39.25

Proline

3.28 3.98 7.32 7.37 7.42 2.01

m m m m m m

β-CH2 α-CH C2H/C6H C4 H C3H/C5H γ-CH2

39.25 58.83 132.2 130.5 131.9 26.65

41 42

Pyruvate Succinate

2.07 2.35 3.34 3.42 4.12 2.36 2.40

m m m m m s s

β-CH2 β-CH2 δ-CH2 δ-CH2 α-CH CH2 CH

31.84 31.84 48.95 48.95 64.10 29.67 36.21

43

Serine

3.83

m

α-CH

59.25

44

Taurine

3.95 3.99 3.25

m m t

β-CH2 β-CH2 S-CH2

63.15 63.15 50.41

45

Threonine

3.42 1.32

t d

N-CH2 γ-CH3

38.22 22.29

d m dd

α-CH β-CH CH2

63.35 68.85

Tryptophan

3.57 4.25 3.30

38

39

40

46


24.76 53.86 1D, JRES,COSY, TOCSY



1D, HSQC 1D, JRES,HSQC 1D, JRES,HSQC, COSY,TOCSY

1D, JRES,HSQC, COSY,TOCSY 1D,JRES, HSQC, COSY,TOCSY

1D, COSY,TOCSY


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

47

48

49 50

Page 24 of 27

Tyrosine

3.47 4.04 7.19 7.28 7.31 7.54 7.74 3.06

dd dd t t s d d dd

CH2 CH C5H/C6H C5H/C6H C2 H C7 H C4 H CH2

Uracil

3.20 3.94 6.90 7.19 5.79

dd dd d d s

CH2 CH C3H/C5H C2H/C6H C5H

Urea Valine

7.54 5.80 0.99

d s d

C6H NH2 CH3

19.5

1.04 2.28 3.60

d m d

CH3 β-CH α-CH

20.8 31.9 63.34


1D, COSY,TOCSY

118.6 133.6 1D, COSY,TOCSY 1D, JRES 1D, JRES,HSQC, COSY,TOCSY

Page 25 of 27

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 1: Representative 800MHz 1H NMR spectrum of mBALF obtained from ARDS patient with enhanced detection of lung directed diseased metabolites. Three different spectral region of the 1D 1H spectrum is depicted in a) 0.8-3.1ppm b) 3.1-4.5ppm c) 5.0-8.7ppm. Key:1- Acetate, 2- Adenine, 3- Alanine, 4- γAminobutyric acid, 5- Aspargine, 6- Aspartic acid, 7- Arginine, 8- Betaine, 9- Choline, 10- Citrate, 11Creatine, 12- Creatinine, 13- Citrulline, 14- Cysteine, 15- Ethanol, 16- Ethanolamine, 17- Formate, 18Fumaric acid, 19- α-Glucose, 20- β-glucose, 21- Glutamate, 22- Gluconic acid. 23- Glycine, 24- Glycerol, 25Histidine, 26- Homoserine, 27- Hydroxyisocaproic acid, 28- Isobutyrate, 29- Isoleucine, 30- Lactate, 31- αLactose, 32- β-lactose, 33- Leucine, 34- Lysine, 35- Methionine, 36- Myoinositol, 37- N-acetyl L alanine, 38Ornithine, 39- Phenylalanine, 40- Proline, 41- Pyruvate, 42- Succinate, 43- Serine, 44- Taurine, 45Threonine, 46- Tryptophan, 47- Tyrosine, 48- Uracil, 49- Urea, 50- Valine 201x197mm (300 x 300 DPI)



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: 1H-13C HSQC spectrum of mBALF collected from ARDS patient symbolic of diseased lung specific metabolites with enhanced characterization. Two different spectral region of the HSQC spectrum is depicted in a) 0.8-4.5ppm b) 6.6-7.8ppm. Key:1- Acetate, 2- Adenine, 3- Alanine, 4- γ-Aminobutyric acid, 5Aspargine, 6- Aspartic acid, 7- Arginine, 8- Betaine, 9- Choline, 10- Citrate, 11- Creatine, 12- Creatinine, 13- Citrulline, 14- Cysteine, 15- Ethanol, 16- Ethanolamine, 17- Formate, 18- Fumaric acid, 19- α-Glucose, 20- β-glucose, 21- Glutamate, 22- Gluconic acid. 23- Glycine, 24- Glycerol, 25- Histidine, 26- Homoserine, 27- Hydroxyisocaproic acid, 28- Isobutyrate, 29- Isoleucine, 30- Lactate, 31- α-Lactose, 32- β-lactose, 33Leucine, 34- Lysine, 35- Methionine, 36- Myoinositol, 37- N-acetyl L alanine, 38- Ornithine, 39Phenylalanine, 40- Proline, 41- Pyruvate, 42- Succinate, 43- Serine, 44- Taurine, 45- Threonine, 46Tryptophan, 47- Tyrosine, 48- Uracil, 49- Urea, 50- Valine 211x264mm (300 x 300 DPI)


Page 26 of 27

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An Approach To Unfold Human Respiratory ... - ACS Publications

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