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International Journal of Chronic Obstructive Pulmonary Disease logoLink to International Journal of Chronic Obstructive Pulmonary Disease
. 2016 Sep 29;11:2435–2446. doi: 10.2147/COPD.S107844

Tryptophan catabolism in acute exacerbations of chronic obstructive pulmonary disease

Makedonka Gulcev 1, Cavan Reilly 2, Timothy J Griffin 3, Corey D Broeckling 4, Brian J Sandri 1, Bruce A Witthuhn 3, Shane W Hodgson 1, Prescott G Woodruff 5, Chris H Wendt 1,6,✉,*
PMCID: PMC5047709  PMID: 27729784

Abstract

Introduction

Exacerbations are a leading cause of morbidity in COPD. The objective of this study was to identify metabolomic biomarkers of acute exacerbations of COPD (AECOPD).

Methods

We measured metabolites via mass spectrometry (MS) in plasma drawn within 24 hours of admission to the hospital for 33 patients with an AECOPD (day 0) and 30 days later and for 65 matched controls. Individual metabolites were measured via selective reaction monitoring with mass spectrometry. We used a mixed-effect model to compare metabolite levels in cases compared to controls and a paired t-test to test for differences between days 0 and 30 in the AECOPD group.

Results

We identified 377 analytes at a false discovery rate of 5% that differed between cases (day 0) and controls, and 31 analytes that differed in the AECOPD cases between day 0 and day 30 (false discovery rate: 5%). Tryptophan was decreased at day 0 of AECOPD compared to controls corresponding to an increase in indoleamine 2,3-dioxygenase activity.

Conclusion

Patients with AECOPD have a unique metabolomic signature that includes a decrease in tryptophan levels consistent with an increase in indoleamine 2,3-dioxygenase activity.

Keywords: Chronic Obstructive Pulmonary Disease, metabolomics, tryptophan

Introduction

Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death in the USA, and worldwide it is one of the most prevalent lung diseases causing significant morbidity and mortality.1,2 Some patients are prone to episodes of acute exacerbations of COPD (AECOPD) that are a frequent cause of medical visits and hospitalizations. AECOPD are a leading cause of morbidity and mortality in COPD, and there is evidence that frequent exacerbations might accelerate the natural course of COPD.36 Currently, there is no validated diagnostic test or biomarker to identify patients at risk of or with an exacerbation. Therefore, a diagnosis of an AECOPD is based on clinical symptoms for which there are no single standardized definition.

Recent technological advances in mass spectrometry have led to the emerging field of metabolomics, the study of small molecules. These small molecules consist of metabolic substrates and products, such as lipids, sugars, peptides, and foreign compounds such as drugs and their metabolites. Metabolomic profiling complements genomics and proteomics offering a snapshot into the physiology of human disease. In this respect, metabolomics has the opportunity to give us insight into mechanisms of disease and has the potential to identify biomarkers of disease.

In this study, we profiled the plasma metabolome in participants from the NIH-sponsored COPD Clinical Research Network. The cases were participants hospitalized for an AECOPD who had plasma collected within 24 hours of hospitalization and 30 days into recovery. Controls consisted of COPD participants matched for age and lung function in the absence of an AECOPD. We were able to characterize metabolomic profiles that distinguish an AECOPD and the presence of tryptophan catabolism via indoleamine 2,3-dioxygenase (IDO) activation.

Methods

Study population

Subjects (Table 1) were 33 individuals with COPD (defined as having forced expiratory volume in 1 second [FEV1] <60% predicted, FEV1 to forced vital capacity ratio <70%, and minimum 10 pack-years of smoking) hospitalized for an AECOPD in the NIH-sponsored LEUKO study.7 Plasma was collected within 24 hours of hospitalization (day 0) and then again in 30 days post-AECOPD (day 30). Controls (n=65) consisted of individuals with stable COPD from the NIH-sponsored MACRO study8 matched for age, sex, lung function, and pack-years of smoking. Participants in the MACRO study were considered high risk for exacerbation as they were either using continuous oxygen therapy or had received systemic glucocorticoid steroids, had gone to the emergency room or been hospitalized for an AECOPD within the previous year. Samples from controls were obtained at baseline, prior to randomization, and subjects had not been treated for AECOPD for at least 4 weeks at the time of the plasma collection. Two controls were matched to each case for age and lung function (FEV1) with the exception of one case with only one matched control identified. This study was approved by the University of Minnesota Institutional Review Board and met exempt status for patient consent.

Table 1.

Characteristics of case and control subjects

Characteristics Cases day 0 (N=33) Cases day 30 (N=33) Controls (N=65) P-value
Age, years 62.03 (51–78) 62.57 (50–81) 0.1
Sex: male 18 (57%) 38 (58%) 1
FEV1 percent predicted 31.18 (14.9–75.2) 34.49 (14.3–72.5) 0.07
Pack years 47.92 (11–150) 44.90 (14–144) 0.23
Beta agonist 22 (0.67) 23 (0.7) 51 (0.78) 0.42
Methacholine antagonist 15 (0.45) 7 (0.21) 21 (0.32) 0.11
LABA 22 (0.67) 24 (0.73) 48 (0.74) 0.77
LAMA 21 (0.64) 18 (0.55) 46 (0.71) 0.28
ICS 23 (0.7) 24 (0.73) 48 (0.74) 0.94
Steroids 13 (0.39) 3 (0.09) 0 (0) <0.01
Antibiotic 17 (0.52) 6 (0.18) 0 (0) <0.01

Notes: For continuous variables, the mean and the range are presented (the latter in parentheses) and for binary variables the count and the percent are provided (the latter in parentheses). The P-value tests the null hypothesis of no difference among the three groups and is obtained from a linear mixed-effects model for continuous variables and from a generalized linear mixed-effects model with logistic link for binary variables.

Abbreviations: LABA, long-acting beta-agonist; LAMA, long-acting methacholine antagonist; ICS, inhaled corticosteroid; FEV1, forced expiratory volume in 1 second.

Sample preparation

Both cases and controls had identical protocols for obtaining plasma. These were nonfasting samples drawn at the time of enrollment into the study. Briefly, blood was drawn into an ethylenediaminetetraacetic acid-containing tube, inverted 8–10 times, and then centrifuged. Following centrifugation, 1.0 mL of plasma was transferred to a microtube (Sarstedt [Nümbrecht, Germany], RNAse, and DNAse free) and immediately frozen to −70°C. The samples remained at −70°C until use and were not freeze–thawed. Plasma samples were processed using a high-performance liquid chromatography-grade cold methanol (Sigma-Aldrich Co, St Louis, MO, USA) extraction method. The extraction methanol solution was cooled to −80°C. A volume of 400 μL of cold solvent was added to 100 μL of plasma. The mixture was gently shaken for 30 seconds and incubated for 6–8 hours at −20°C, then centrifuged for 15 minutes at 13,000 rpm at 4°C (in a cold room) and the supernatant transferred to a new tube. The pellet was rinsed twice with the cold solvent and the aforementioned procedure was repeated. The resulting supernatants were pooled and dried with a SpeedVac and stored at −80°C until further processing. A volume of the starting ultra-performance liquid chromatography (UPLC) buffer was added to the dried samples after they were acidified with formic acid (5 μL of formic acid [50% v/v]), to which 95 μL of the UPLC starting buffer was added (5% acetonitrile, 94.9% water, and 0.1% formic acid). After the samples were reconstituted, the solutions were centrifuged, to pellet out insoluble material, for 5 minutes at 13,000 rpm (4°C), and the supernatants were transferred to a Waters (Waters, Milford, MA, USA) 300 μL polypropylene plastic vial. For selective reaction monitoring (SRM) analysis, 100 μL of sample was added to 3 μL 100 μm kynurenine D6 and 3 μL 1 mm tryphtophan 13C11 (Cambridge Isotope Laboratories, Inc., Tewksbury, MA, USA) prior to protein precipitation. Samples were vacuum-dried and diluted to 10−3 for tryptophan and 10−2 for kynurenine with load buffer.

UPLC-MS analysis

For UPLC-MSe analysis, a Waters Acquity UPLC coupled to a Waters Synapt G2 HDMS quadrupole orthogonal acceleration time of flight mass spectrometer was used. A Waters Acquity BEH C18 2.1×100 mm column (1.7 μm diameter particles) at 35°C was used during the following 26 minutes gradient separation with A: Water containing 0.1% formic acid; B: high-performance liquid chromatography grade acetonitrile (Fisher Scientific, Pittsburg, PA, USA) containing 0.1% formic acid, at a flow rate of 0.4 mL/minute: 3% B, 0–3 minutes; 3% B–97% B, 3–18 minutes; 97% B, 18–21 minutes; 97% B–3% B, 21–23 minutes; 3% B 23–26 minutes. Simultaneous low- and high-collision energy (CE) mass spectra were collected in centroid mode over the range mass/charge (m/z) 100–1,200 every 0.1 second during the chromatographic separation. MSe parameters in positive electrospray ionization mode were as follows: capillary, 0.30 kV; sampling cone, 35.0 V; extraction cone, 4.0 V; desolvation gas flow, 800 L/hour; source temperature, 100°C; desolvation temperature, 350°C; cone gas flow, 20 L/hour; trap CE, off (low CE collection), ramp 15–65 V (high CE collection); Lockspray configuration consisted of infusion of a 5 μg/mL solution of leucine-enkephalin (Waters); and acquisition of one mass spectrum (0.2 second scan, m/z 100–1,200) every 10 seconds. Three lockspray m/z measurements of protonated leucine-enkephalin were averaged and used to apply corrections to measured m/z values during the course of the analysis. The R software package RAMClustR was used for analyte alignment and feature detection.9

SRM analysis of tryptophan and kynurenine

Samples (10 μL) for SRM analysis were subjected to injection using an Agilent autosampler with an analytical Acquity UPLC BEH C18, 1.7 μm, 2.1×50 mm column fit with an Acquity UPLC BEH shield RP18 precolumn connected to the Applied Biosystem 5500 iontrap fit with a turbo V electrospray source. The samples were subjected to a linear gradient of 2% acetonitrile, 0.1% formic acid to 98% acetonitrile 0.1% formic acid for 10 minutes at a column flow rate of 250 μL/minute. Transitions monitored are listed in Table S1, and these were established using the instrument optimization mode with direct injection of native and heavy tryptophan and kynurenin. The data were analyzed using MultiQuant™ (ABI Sciex, Framingham, MA, USA), which provided the peak area ratio of tryptophan/tryptophan 13C11 and kynurenine/kynurenine D6 for the transitions. A standard curve was constructed using concentration ratios of tryptophan/tryptophan 13C11 and kynurenine/kynurenine D6 (Cambridge Isotope Laboritories, Inc., Tewksbury, MA, USA) from picomole to nanomole in 10 μL. Samples were run in duplicate and concentrations were determined from the standard curve. The correlation across duplicates for tryptophan was 0.9839 and for kynurenine was 0.9589.

Statistics

The processed data from the MS experiments were transformed by adding 1 to all data points and taking the logarithm as the marginal distributions of the feature data were positively skewed (1 was added as many zeroes were observed in the data). To test for differences between cases and controls, a mixed-effects model was used with random effects for cluster membership (a case plus its two matched controls formed a cluster) and fixed effects for case–control status. The p-values from the test of no group effect were then adjusted for multiple comparisons using the method of Storey, and a false discovery rate (FDR) of 0.05 was used to select features for further investigation.10 To test for differences between day 0 and day 30 among the cases, a paired t-test was used and adjustments for multiple hypothesis testing were conducted in the same manner as the test for differences between cases and controls. For the analysis of the data arising from the SRM experiments, a single mixed-effects model was fit that allowed testing for differences between cases and controls and for changes from day 0 to day 30 for tryptophan and kynurenine and their ratio. No adjustment was made for multiple hypotheses testing after fitting these models. These models also included the effects of sex, age, pack-years of smoking, lung function, and medications (steroids and antibiotics) as fixed effects and case–control group and subject as random effects (with subject effects nested within the case–control group effects) for the SRM experiments.

Results

Characteristics of study participants

We analyzed two longitudinal plasma samples from 33 individuals with a COPD exacerbation who were recruited as part of the LEUKO study. Each subject had a plasma sample obtained within the first 24 hours of being hospitalized for a COPD exacerbation and a follow-up plasma sample obtained 30 days later. Controls consisted of individuals at high risk of developing AECOPD, but were currently free from an exacerbation (Table 1). All subjects had at least a ten pack-year history of smoking, with 27% of cases and 24% of controls reporting active smoking at the time of enrollment. The FEV1 ranged in severity from moderate to very severe according to the GOLD classification (GOLD II–IV), with the average FEV1 in the GOLD class III. The majority of subjects were on long- and short-acting β-agonists. The main difference in medications was more steroid and antibiotic use in the day 0 group compared to both day 30 and controls.

Analyte profiles

We detected over 3,000 analyte signals in the plasma. An analyte refers to a discreet m/z and retention time on the mass spectrometer that correlates with a yet unknown metabolite. Currently, there is no accepted methodology to quantify analytes detected by mass spectrometry. For our study, relative abundance was measured as the sum of all peak intensities detected by the mass spectrometer that associated with the given analyte. Using a mixed-effect model to account for the pairing of multiple controls to cases, we identified 583 analytes at 10% FDR and 386 analytes at a 5% FDR that were significantly different between samples at day 0 (cases) and controls. Using a paired t-test, we detected 54 analytes at 10% FDR and 34 analytes at a 5% FDR that were significantly different between samples at day 0 and day 30. A search within the Metlin library identified that several of the analytes found were consistent with the medications zileuton and prednisolone. These medications were anticipated since zileuton was the interventional drug administered in the LEUKO trial and since treatment with steroids is a common practice in an AECOPD. No other medications were identified. These analytes were eliminated, leaving 31 and 379 analytes at 5% FDR in the two groups (day 0 vs day 30 and controls vs day 0), respectively (Tables S2 and S3). We found considerable overlap in the analytes between the two groups as depicted in the Venn diagram (Figure 1). Of the 23 analytes in common between the two groups, nine are consistent with small peptides consisting of 3–4 amino acids and three are consistent with lipids (Table S2). Figure 2 demonstrates 25 representative analytes that are differentially expressed comparing day 0 to day 30, plus values for their respective controls. This figure demonstrates that the pattern of analytes show a similar value comparing day 0 and controls.

Figure 1.

Figure 1

Venn diagram that depicts the number of analytes overlapping between the two comparison groups.

Figure 2.

Figure 2

Analyte expression.

Notes: 25/34 of the analytes identified at 5% FDR that were differentially expressed comparing day 0 and day 30. Top number is retention time and bottom number is m/z. X-axis: C = control, d0 = day 0, d30 = day 30. Y-axis: peak intensity.

Abbreviations: FDR, false discovery rate; m/z, mass/charge.

Tryptophan catabolism

One of the analytes differentially expressed in day 0 subjects compared to controls was consistent with the essential amino acid tryptophan (Trp, m/z 204.23 and 257.09 methoxytryptophan). Since tryptophan catabolism has been associated with both immune modulation and infection, we sought to quantify tryptophan and its major metabolite, kynurenine. To identify tryptophan and measure its concentration, we performed SRM. IDO is the main inducible and rate-limiting enzyme involved in tryptophan catabolism, with kynurenine as the main metabolite of the IDO pathway. IDO activity is expressed as a ratio of kynurenine to tryptophan (Kyn/Trp). Statistical models included controlling for the effects of sex, age, pack years, lung function, and medications (steroids and antibiotics). We found that tryptophan was lower at day 30 compared to day 0 and higher in controls than day 0, but this was not statistically significant after controlling for the potential confounders (Figure 3). We did find that kynurenine levels were significantly lower at day 30 compared to day 0 (P=0.00292, Figure 3). With respect to IDO activity as measured by the Kyn/Trp ratio, Kyn/Trp was higher at day 0 compared to day 30 (P=0.0352) and higher at day 0 than in controls (P=0.0338, Figure 4).

Figure 3.

Figure 3

Tryptophan and kynurenine expression.

Notes: No statistically significant differences in tryptophan levels. Kynurenine levels were significantly lower at day 0 compared to day 30 (P=0.00292).

Figure 4.

Figure 4

IDO activity as depicted by Kyn/Trp ratio.

Notes: Tryptophan and kynurenine levels were measured in plasma by SRM. Kyn/Trp values were significantly higher at day 0 compared to day 30 (P=0.0352) and higher at day 0 than in controls (P=0.0338).

Abbreviations: IDO, indoleamine 2,3-dioxygenase; SRM, selective reaction monitoring; Kyn/Trp, kynurenine/tryptophan.

Discussion

Patients with COPD often experience exacerbations, and, currently, there is no biomarker that either predicts or identifies those with an exacerbation. In this study, we identified a plasma metabolomic biosignature in COPD patients with an acute exacerbation. The largest profile was seen in COPD patients with an AECOPD (day 0) compared to matched controls. A smaller biosignature was identified in day 0 compared to day 30, and many of these analytes overlapped with the larger profile. This smaller biosignature suggests that full recovery from the exacerbation may not yet exist by day 30. This is not a surprise since one in eleven COPD patients are readmitted within 30 days following hospitalization.11 Therefore, full recovery following a severe exacerbation may take longer than 30 days. As expected among these analytes, zileuton and prednisolone were identified. Zileuton was the parent trial study drug, and patients are often immediately placed on prednisone upon admission for an AECOPD.

One of the analytes in the profile comparing day 0 to controls was consistent with tryptophan. We used SRM to accurately measure tryptophan and its main metabolite kynurenine. We found that tryptophan levels are reduced early in the course of an AECOPD (day 0) compared to “healthy” COPD patients. This decrease in tryptophan is consistent with an increased catabolism through the IDO pathway as demonstrated by an increase in Kyn/Trp. After 30 days of recovery from an AECOPD, tryptophan levels remained significantly lower compared to control subjects; however, IDO activity was no longer increased at that time. This suggests that tryptophan catabolism was decreasing by day 30, but was incomplete. In this study, longitudinal samples were limited to 30 days; therefore, we do not know whether tryptophan levels eventually normalized, similar to controls.

Tryptophan is an essential amino acid and its deficiency limits protein synthesis, resulting in cellular dysfunction and decreased proliferation. Teleologically, it is felt that tryptophan catabolism is beneficial during infection, where a decline in tryptophan levels inhibits bacterial proliferation. Recent studies have also implicated tryptophan catabolism through the IDO pathway as having antimicrobial effects. The list of pathogens sensitive to tryptophan catabolism via IDO includes respiratory pathogens common in AECOPD such as Streptococci.12 A decrease in serum tryptophan levels has been reported in pulmonary infections and predicts prognosis in both tuberculosis and community-acquired pneumonia.13,14 Since AECOPD is often due to respiratory tract infections, it is a possibility that tryptophan catabolism in that setting is actually a biomarker for infection.

Tryptophan catabolism is also an important factor in the lung microenvironment that influences immune responses. Tryptophan catabolism occurs predominantly through the activation of the enzyme IDO,15,16 producing metabolites of the kynurenine pathway. Most of the effects of tryptophan catabolism come from accumulation of its active metabolites, such as kynurenine, rather than tryptophan depletion.15,16 The generation of kynurenine through IDO activation leads to immune tolerance and an anti-inflammatory effect through the proliferation of Treg FoxP3 cells and suppression of Th17 cells.16,17 The immune tolerance effect of IDO activation has been implicated in lung cancer and HIV infection.1822 Thus, tryptophan depletion and IDO activation have both antimicrobial and anti-inflammatory effects.12

Although decreases in tryptophan and IDO activation have been reported in lung cancer and certain lung infections, little is known of its role in COPD. Perturbations in amino acids in both serum and exhaled breath have been described in COPD using mass spectrometry and NMR.23,24 Ubhi et al24 measured amino acid metabolism using mass spectrometry in COPD patients from the ECLIPSE cohort and found tryptophan levels were decreased in the serum of COPD patients with emphysema.24 However, they did not assess IDO activity. However, Maneechotesuwan et al25 found IDO activity decreased in the sputum of COPD patients that correlated with severity of disease and a reversal between the IL-10 and IL-17A balance. This suggests that a decrease in IDO activity within sputum creates an environment supporting neutrophilic inflammation.25 In our study, we found tryptophan levels to be decreased in the plasma of patients with an AECOPD consistent with an activation of IDO, as measured by kynurenine and tryptophan ratios. This increase in IDO activity was still present at day 30, but to a lesser extent. A decrease in tryptophan would have an antimicrobial effect that would be beneficial in AECOPD, along with an anti-inflammatory effect to mitigate airway injury. The role of tryptophan catabolism in COPD and possible link to lung cancer remains unknown.

Many of the analytes that were common between the two biosignatures were multiply charged and had a retention time consistent with peptides consisting of 2–4 amino acids. Peptides as biomarkers for lung disease is not a new concept – over 30 years ago, Kucich et al26 detected elevated levels of unspecified serum peptides in COPD patients as measured by immunoassays. Using metabolomic profiling, protein degradation products have been detected in the serum of COPD patients, particularly those with emphysema and cachexia.27 We have reported peptides in bronchoalveolar lavage fluid in COPD, many consistent with elastase activity.28 Further studies are necessary to determine if these would serve as a biomarker for AECOPD.

There are several limitations of this study. First, our longitudinal samples were limited to day 30 post-AECOPD. Therefore, we do not know whether tryptophan levels remained low or continued to increase relative to controls. To identify biomarkers of AECOPD, we matched controls for lung function who were also frequent exacerbators, but who had not experienced an exacerbation for at least 1 month. Therefore, we do not know whether frequent exacerbators had different tryptophan levels and catabolism relative to healthy controls or COPD patients who do not experience exacerbations. Therefore, the role of tryptophan catabolism in frequent or prolonged exacerbations warrants future investigation.

Conclusion

Patients with an AECOPD have a unique plasma metabolomic signature at the initiation of their exacerbation. This signature includes an increase in the Kyn/Trp ratio consistent with an increase in IDO activity. The role of tryptophan catabolism during AECOPD warrants further investigation.

Supplementary materials

Table S1.

Transitions for tryptophan and kynurenine

Metabolite Q1 m/z Q2 m/z
Tryptophan 204.892 188
Tryptophan 204.892 169.9
Tryptophan 204.892 158.96
Tryptophan 204.892 117.908
Kynurenine 208.92 191.904
Kynurenine 208.92 145.943
Kynurenine 208.92 135.957
Kynurenine 208.92 94.049
Tryptophan 13C11 216 199
Tryptophan 13C11 216 169
Tryptophan 13C11 216 154
Tryptophan 13C11 216 140.9
Kynurenine D6 215 198
Kynurenine D6 215 150.9
Kynurenine D6 215 142
Kynurenine D6 215 98.2

Note: Q1 and Q2, first and second mass analyzers.

Abbreviation: m/z, mass/charge.

Table S2.

Analytes differentially expressed comparing day 0 to day 30

RT m/z Putative identification
0.4092 192.0339
0.3653 227.1242
1.5818 229.1532 Peptide
0.6904 229.1537 Peptide
0.6979 251.1345 Peptide
3.5796 285.6607
1.0920 293.0517
0.6929 319.1215
1.0963 343.0335
1.0904 395.0595
1.0927 401.0747
1.0978 411.0227
2.2749 416.2397
2.2383 436.2067 Peptide
2.2804 438.2229
1.9731 440.2379
0.3585 448.668
1.6195 456.2284 Peptide
2.2801 479.2485 Peptide
6.5000 480.3403 Lipid
6.4761 482.3572 Lipid
6.5022 502.3207 Peptide
6.4766 504.3396
5.2860 509.3309
1.0933 531.0327
13.1409 531.4049
2.2757 541.2188 Peptide
13.1434 594.4145 Lipid
6.4827 606.3066 Peptide
1.6767 709.0683
13.1658 940.523

Abbreviations: RT, retention time; m/z, mass/charge.

Table S3.

Analytes differentially expressed comparing day 0 to controls

RT m/z Putative identification
1.8662 163.1318
1.6614 171.0983
1.86 185.1138
1.5791 191.1498
0.4092 192.0339
0.3588 193.1538
0.4057 198.0943
3.0522 199.1797
0.3585 203.0523
0.5741 204.1223
9.8983 208.0385
1.6615 212.1249
2.5382 213.1435
0.3442 219.026
3.0513 221.1605
3.4716 223.0953
1.8588 226.1407
1.1927 229.1528
1.5818 229.1532 Peptide
0.6904 229.1537 Peptide
1.0901 230.0336
1.5848 240.1584
13.7318 243.968
0.3551 244.0788
3.0993 244.1537
3.4708 245.078
1.0961 246.0052
0.7777 246.0236
1.6071 246.1656
8.6263 247.1305 Peptide
3.0976 249.1076
0.6979 251.1345 Peptide
1.5881 251.1353
13.7623 251.3842
1.0976 253.0267
2.5361 254.1701
1.6966 255.1202
0.4398 256.0572
1.6824 257.0866
8.8146 259.6639
0.3524 263.0841
0.8497 267.0591
3.8807 269.1376
0.3485 271.0384
1.6376 274.0912
1.0971 276.0376
0.3572 276.9842
3.2302 281.1351
5.1828 283.152
6.6211 283.2227
3.475 286.1041
0.3485 287.0129
1.0906 287.0312
2.2739 289.1319
3.0952 290.1338 Peptide
1.7269 292.0288
1.6814 292.0944
1.092 293.0517
2.0077 295.1871
1.6749 297.0732
5.7355 300.1562 Peptide
6.6352 305.2672
1.0892 309.0231
2.2719 309.6462
1.0864 315.036
4.693 316.1872 Peptide
1.6716 319.0568
0.6929 319.1215
1.6643 319.2067
3.2243 322.1631
6.6409 322.2928
7.4187 322.6846
6.6068 324.2496
6.6439 327.2493
0.3577 330.7515
7.4216 331.6843
0.3551 332.7515
1.0892 337.0185
4.1134 338.2654 Sphingosine
0.6137 339.0849
1.0963 343.0335
3.5422 343.2918
0.3486 344.9705
7.4247 345.688
1.6143 346.0417
5.1858 346.1605 Peptide
1.1105 346.9641
0.3591 349.1201 Peptide
6.6633 349.2919
13.0941 352.2866
3.8858 354.127 Peptide
0.3356 355.0009
7.9032 355.2823
2.269 357.2031 Lipid
8.8104 357.2973
1.5944 363.05
3.5481 365.2758
6.6604 366.3192
1.72 367.9704
0.3604 371.1011
6.6651 371.2745
4.726 371.3242
13.1143 372.7997
1.0931 373.074
7.4528 376.3162
7.901 377.2642
8.8109 379.2798
1.6829 381.0287
0.3593 383.114
1.6636 387.1926
2.1815 389.1957
0.3536 390.7096
4.7366 393.3058
1.0904 395.0595
1.6779 395.0655
0.3563 399.0878
6.2208 400.34
1.0927 401.0747
1.9865 402.2247
0.3465 402.9335
2.0137 404.2393
1.09 405
0.3504 405.096
1.6048 410.0573
6.6635 410.3434
1.0978 411.0227
0.9505 411.1445
10.2998 411.2636 Peptide
2.2901 413.1735 Peptide
6.6685 415.2991
2.2749 416.2397
1.9486 418.2552 Peptide
1.0939 420.9697 Myo-inositol
0.4032 421.0134
13.056 422.1534 Peptide
1.986 424.2069 Peptide
2.0219 426.2226 Peptide
3.3009 430.3147
3.8793 435.1404 Peptide
2.2383 436.2067 Peptide
1.9411 438.2215
2.2804 438.2229
7.9047 439.2336 Peptide
1.9731 440.2379 Peptide
0.3602 441.0728
8.8391 441.2505
0.361 443.07 Peptide
1.6293 443.2068 Peptide
7.9101 445.253 Peptide
1.6022 446.0764
0.3583 446.6695
0.4907 447.1107
0.3585 448.668
1.6604 450.0504
0.352 451.1009
13.1007 451.3301
2.1912 454.2176
6.6619 454.3717
6.6553 455.4541
0.571 456.0049
1.6195 456.2284 Peptide
6.67 459.3263 Peptide
1.7063 459.9791
0.4394 463.0131
0.7459 463.0142
1.6884 463.0538
1.5937 464.0727 Peptide
0.3345 464.9774
1.9822 465.2359 Peptide
12.737 467.0998
3.473 467.1661 Peptide
10.3003 467.3253
4.9923 468.3055
0.987 469.0545
13.1096 469.3385
1.7225 470.9654
1.6195 472.0268
1.0919 479.012
1.6744 479.1225 Peptide
2.2801 479.2485 Peptide
8.8117 482.2728 Peptide
5.5895 482.3202
7.9175 485.1119
4.993 490.2884 Peptide
6.1727 496.3386 Lipid
6.6598 498.3931
0.3607 499.0317
0.3661 501.0279
5.5152 502.2903 Peptide
6.5022 502.3207 Peptide
6.6618 503.3524
5.588 504.3023 Peptide
9.342 505.1728 Peptide
7.906 507.2218 Peptide
0.3502 509.0611
8.8148 509.2378 Peptide
6.6027 510.3528 Lipid
0.4088 511.1077
10.3056 512.3832
1.6796 514.1279 Peptide
4.3737 514.3133 Peptide
0.3597 515.0057
0.374 517.1219
6.1755 518.3203 Lipid
0.3419 518.8442
5.7313 520.3393
10.2989 521.3353 Peptide
6.4842 522.3544 Lipid
6.6752 522.355
7.4224 524.37 Lipid
1.9592 527.2105 Peptide
10.3044 528.3798 Peptide
5.902 530.3202
10.3126 530.3374 Peptide
0.3349 530.8702
1.0933 531.0327
0.3529 532.9185
13.773 536.1627
10.7768 537.3705
0.3335 538.905
0.3693 539.1055
5.7297 539.3104
13.7723 541.1272 Peptide
2.2757 541.2188 Peptide
5.7333 542.3218
6.6533 542.4236
1.952 543.2365 Peptide
2.2779 546.1989 Peptide
7.4211 546.3526 Lipid
6.6677 547.3805
0.4231 548.0531
9.2218 554.1747 Peptide
0.3499 554.8997
1.6083 555.0536
0.37 555.0773
2.2743 557.1907
9.2188 559.1309
0.3603 560.9874
7.4221 562.328 Peptide
13.6 563.393
2.2754 568.1795 Peptide
5.5875 572.2921 Peptide
1.5933 572.3243
5.7311 573.3019 Peptide
1.6231 574.0328
1.6091 577.0354
7.4223 577.3347 Peptide
13.1318 577.4431
1.6266 585.0671
2.606 585.2705 Peptide
6.6434 586.4511
10.3024 590.4088
6.932 590.4251
6.6434 591.4084
6.4549 592.2653 Peptide
1.6239 598.3267
6.8086 600.3238 Peptide
1.6056 601.1917
1.7112 603.939
5.7322 604.2916
6.4827 606.3066 Peptide
0.3469 607.0939
7.415 608.3224 Peptide
5.392 611.2865 Peptide
6.6809 612.3241 Peptide
7.4196 614.3404
0.3622 619.0479
4.3825 620.3067 Peptide
0.3506 626.9787
13.6773 627.453
2.2746 630.1508
6.6362 630.4782
0.5446 633.0673
10.5995 633.1485
3.4554 633.2536 Peptide
0.3616 635.0202
13.6383 635.3657
6.6358 635.4317
10.725 637.4437
1.6034 639.0026
0.359 644.7984
7.4254 646.2822
1.6088 649.9961
0.3528 655.0219
5.7638 665.2701 Peptide
0.3547 670.9943
1.7155 673.9541
6.6237 674.5011
7.4111 676.3079
0.3492 676.9997
12.0071 677.554
0.3161 678.6746
6.6254 679.4573 Lipid
1.7114 679.9699
0.3429 688.7822
1.7173 689.9271
0.3507 692.97
0.3477 694.9721
13.6232 703.4517 Lipid
13.1358 703.5737
1.6767 709.0683
11.7477 712.5443
7.4314 713.3025
9.9864 717.632 Lipid
6.6167 718.5284
13.4943 722.551 Ceramide
6.6094 723.4853
5.7355 725.4297 Lipid
13.1381 725.5549
7.4214 729.4603
13.5057 729.5885
0.3477 734.953
1.6269 735.4944 Lipid
11.4089 739.5345
13.8137 740.5197 Lipid
10.8899 741.5508
1.9897 743.3704
7.4197 744.2956
13.493 751.567 Lipid
13.1615 754.0566
10.4529 758.5868
0.5736 759.0603
7.7516 760.5809 Lipid
13.0793 764.0389
13.1581 765.0463
6.5956 767.509
2.2768 771.4027
13.132 771.5631
0.3524 772.9186
12.8808 774.5722
12.293 778.5329 Lipid
13.1639 782.5684 Lipid
13.1358 793.5442
10.3346 794.5649 Lipid
13.7571 796.561
7.4286 797.4589
1.599 798.3899
5.7361 798.9721
5.7311 801.9803
13.1657 804.5514 Lipid
14.361 806.5633 Lipid
12.9319 806.5706 Lipid/ceramide
13.2426 810.6704
0.3509 810.8878
2.2829 812.4288
8.6836 820.4116
12.6246 820.5522 Lipid
13.0707 827.5999
12.9375 828.5527 Lipid
13.9946 830.568 Lipid
6.1864 830.961
5.7348 835.9755
0.3123 836.6608
6.4852 839.497
1.7063 841.9379
7.4234 842.023
10.3171 847.4601
11.7131 856.5728
5.7373 866.9606
5.7347 869.9697
13.1642 872.5377 Lipid
8.8102 876.5694
11.9 882.5886
13.0421 884.6029
12.9953 893.0155
12.9321 896.5377
5.7506 900.2364
5.7392 900.9573
7.4281 910.0114
13.1352 929.5189 Lipid
11.7452 939.4679
13.1658 940.523
1.6171 954.6025
11.4692 955.5848
13.0992 963.574
3.5652 979.9296

Abbreviations: m/z, mass/charge; RT, retention time.

Acknowledgments

We thank the University of Minnesota Supercomputing Institute for technical support and the University of Minnesota Chemistry Department for use of the Waters Synapt G2 HDMS quadrupole orthogonal acceleration time of flight mass spectrometer that was used in the discovery phase, and Dr Connett and Helen Voekler of the MACRO and LEUKO data coordinating center for samples and database information. We thank Drs Richard Albert and Stephen Lazarus for critical review of the manuscript. This work was supported by grants from the National Heart, Lung, and Blood Institute of the National Institutes of Health (U10 HL074407, U10 HL074408, U10HL074409, U10 HL074416, U10 HL074418, U10 HL074422, U10 HL074424, U10 HL074428, U10 HL074431, U10 HL074439, and U10 HL074441) and NIH T32 HL07741 (Sandri).

Footnotes

Author contributions

CW, MG, and TG conceptualized the project; MG, CB, BW, SH, and BS carried out experiments; CW, MG, CR, and CB carried out data analysis, CW and MG participated in writing the manuscript, and TG, PW, CB, BW, and BS critically reviewed the manuscript. All authors contributed toward data analysis, drafting and critically revising the paper and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interests in this work.

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1.

Transitions for tryptophan and kynurenine

Metabolite Q1 m/z Q2 m/z
Tryptophan 204.892 188
Tryptophan 204.892 169.9
Tryptophan 204.892 158.96
Tryptophan 204.892 117.908
Kynurenine 208.92 191.904
Kynurenine 208.92 145.943
Kynurenine 208.92 135.957
Kynurenine 208.92 94.049
Tryptophan 13C11 216 199
Tryptophan 13C11 216 169
Tryptophan 13C11 216 154
Tryptophan 13C11 216 140.9
Kynurenine D6 215 198
Kynurenine D6 215 150.9
Kynurenine D6 215 142
Kynurenine D6 215 98.2

Note: Q1 and Q2, first and second mass analyzers.

Abbreviation: m/z, mass/charge.

Table S2.

Analytes differentially expressed comparing day 0 to day 30

RT m/z Putative identification
0.4092 192.0339
0.3653 227.1242
1.5818 229.1532 Peptide
0.6904 229.1537 Peptide
0.6979 251.1345 Peptide
3.5796 285.6607
1.0920 293.0517
0.6929 319.1215
1.0963 343.0335
1.0904 395.0595
1.0927 401.0747
1.0978 411.0227
2.2749 416.2397
2.2383 436.2067 Peptide
2.2804 438.2229
1.9731 440.2379
0.3585 448.668
1.6195 456.2284 Peptide
2.2801 479.2485 Peptide
6.5000 480.3403 Lipid
6.4761 482.3572 Lipid
6.5022 502.3207 Peptide
6.4766 504.3396
5.2860 509.3309
1.0933 531.0327
13.1409 531.4049
2.2757 541.2188 Peptide
13.1434 594.4145 Lipid
6.4827 606.3066 Peptide
1.6767 709.0683
13.1658 940.523

Abbreviations: RT, retention time; m/z, mass/charge.

Table S3.

Analytes differentially expressed comparing day 0 to controls

RT m/z Putative identification
1.8662 163.1318
1.6614 171.0983
1.86 185.1138
1.5791 191.1498
0.4092 192.0339
0.3588 193.1538
0.4057 198.0943
3.0522 199.1797
0.3585 203.0523
0.5741 204.1223
9.8983 208.0385
1.6615 212.1249
2.5382 213.1435
0.3442 219.026
3.0513 221.1605
3.4716 223.0953
1.8588 226.1407
1.1927 229.1528
1.5818 229.1532 Peptide
0.6904 229.1537 Peptide
1.0901 230.0336
1.5848 240.1584
13.7318 243.968
0.3551 244.0788
3.0993 244.1537
3.4708 245.078
1.0961 246.0052
0.7777 246.0236
1.6071 246.1656
8.6263 247.1305 Peptide
3.0976 249.1076
0.6979 251.1345 Peptide
1.5881 251.1353
13.7623 251.3842
1.0976 253.0267
2.5361 254.1701
1.6966 255.1202
0.4398 256.0572
1.6824 257.0866
8.8146 259.6639
0.3524 263.0841
0.8497 267.0591
3.8807 269.1376
0.3485 271.0384
1.6376 274.0912
1.0971 276.0376
0.3572 276.9842
3.2302 281.1351
5.1828 283.152
6.6211 283.2227
3.475 286.1041
0.3485 287.0129
1.0906 287.0312
2.2739 289.1319
3.0952 290.1338 Peptide
1.7269 292.0288
1.6814 292.0944
1.092 293.0517
2.0077 295.1871
1.6749 297.0732
5.7355 300.1562 Peptide
6.6352 305.2672
1.0892 309.0231
2.2719 309.6462
1.0864 315.036
4.693 316.1872 Peptide
1.6716 319.0568
0.6929 319.1215
1.6643 319.2067
3.2243 322.1631
6.6409 322.2928
7.4187 322.6846
6.6068 324.2496
6.6439 327.2493
0.3577 330.7515
7.4216 331.6843
0.3551 332.7515
1.0892 337.0185
4.1134 338.2654 Sphingosine
0.6137 339.0849
1.0963 343.0335
3.5422 343.2918
0.3486 344.9705
7.4247 345.688
1.6143 346.0417
5.1858 346.1605 Peptide
1.1105 346.9641
0.3591 349.1201 Peptide
6.6633 349.2919
13.0941 352.2866
3.8858 354.127 Peptide
0.3356 355.0009
7.9032 355.2823
2.269 357.2031 Lipid
8.8104 357.2973
1.5944 363.05
3.5481 365.2758
6.6604 366.3192
1.72 367.9704
0.3604 371.1011
6.6651 371.2745
4.726 371.3242
13.1143 372.7997
1.0931 373.074
7.4528 376.3162
7.901 377.2642
8.8109 379.2798
1.6829 381.0287
0.3593 383.114
1.6636 387.1926
2.1815 389.1957
0.3536 390.7096
4.7366 393.3058
1.0904 395.0595
1.6779 395.0655
0.3563 399.0878
6.2208 400.34
1.0927 401.0747
1.9865 402.2247
0.3465 402.9335
2.0137 404.2393
1.09 405
0.3504 405.096
1.6048 410.0573
6.6635 410.3434
1.0978 411.0227
0.9505 411.1445
10.2998 411.2636 Peptide
2.2901 413.1735 Peptide
6.6685 415.2991
2.2749 416.2397
1.9486 418.2552 Peptide
1.0939 420.9697 Myo-inositol
0.4032 421.0134
13.056 422.1534 Peptide
1.986 424.2069 Peptide
2.0219 426.2226 Peptide
3.3009 430.3147
3.8793 435.1404 Peptide
2.2383 436.2067 Peptide
1.9411 438.2215
2.2804 438.2229
7.9047 439.2336 Peptide
1.9731 440.2379 Peptide
0.3602 441.0728
8.8391 441.2505
0.361 443.07 Peptide
1.6293 443.2068 Peptide
7.9101 445.253 Peptide
1.6022 446.0764
0.3583 446.6695
0.4907 447.1107
0.3585 448.668
1.6604 450.0504
0.352 451.1009
13.1007 451.3301
2.1912 454.2176
6.6619 454.3717
6.6553 455.4541
0.571 456.0049
1.6195 456.2284 Peptide
6.67 459.3263 Peptide
1.7063 459.9791
0.4394 463.0131
0.7459 463.0142
1.6884 463.0538
1.5937 464.0727 Peptide
0.3345 464.9774
1.9822 465.2359 Peptide
12.737 467.0998
3.473 467.1661 Peptide
10.3003 467.3253
4.9923 468.3055
0.987 469.0545
13.1096 469.3385
1.7225 470.9654
1.6195 472.0268
1.0919 479.012
1.6744 479.1225 Peptide
2.2801 479.2485 Peptide
8.8117 482.2728 Peptide
5.5895 482.3202
7.9175 485.1119
4.993 490.2884 Peptide
6.1727 496.3386 Lipid
6.6598 498.3931
0.3607 499.0317
0.3661 501.0279
5.5152 502.2903 Peptide
6.5022 502.3207 Peptide
6.6618 503.3524
5.588 504.3023 Peptide
9.342 505.1728 Peptide
7.906 507.2218 Peptide
0.3502 509.0611
8.8148 509.2378 Peptide
6.6027 510.3528 Lipid
0.4088 511.1077
10.3056 512.3832
1.6796 514.1279 Peptide
4.3737 514.3133 Peptide
0.3597 515.0057
0.374 517.1219
6.1755 518.3203 Lipid
0.3419 518.8442
5.7313 520.3393
10.2989 521.3353 Peptide
6.4842 522.3544 Lipid
6.6752 522.355
7.4224 524.37 Lipid
1.9592 527.2105 Peptide
10.3044 528.3798 Peptide
5.902 530.3202
10.3126 530.3374 Peptide
0.3349 530.8702
1.0933 531.0327
0.3529 532.9185
13.773 536.1627
10.7768 537.3705
0.3335 538.905
0.3693 539.1055
5.7297 539.3104
13.7723 541.1272 Peptide
2.2757 541.2188 Peptide
5.7333 542.3218
6.6533 542.4236
1.952 543.2365 Peptide
2.2779 546.1989 Peptide
7.4211 546.3526 Lipid
6.6677 547.3805
0.4231 548.0531
9.2218 554.1747 Peptide
0.3499 554.8997
1.6083 555.0536
0.37 555.0773
2.2743 557.1907
9.2188 559.1309
0.3603 560.9874
7.4221 562.328 Peptide
13.6 563.393
2.2754 568.1795 Peptide
5.5875 572.2921 Peptide
1.5933 572.3243
5.7311 573.3019 Peptide
1.6231 574.0328
1.6091 577.0354
7.4223 577.3347 Peptide
13.1318 577.4431
1.6266 585.0671
2.606 585.2705 Peptide
6.6434 586.4511
10.3024 590.4088
6.932 590.4251
6.6434 591.4084
6.4549 592.2653 Peptide
1.6239 598.3267
6.8086 600.3238 Peptide
1.6056 601.1917
1.7112 603.939
5.7322 604.2916
6.4827 606.3066 Peptide
0.3469 607.0939
7.415 608.3224 Peptide
5.392 611.2865 Peptide
6.6809 612.3241 Peptide
7.4196 614.3404
0.3622 619.0479
4.3825 620.3067 Peptide
0.3506 626.9787
13.6773 627.453
2.2746 630.1508
6.6362 630.4782
0.5446 633.0673
10.5995 633.1485
3.4554 633.2536 Peptide
0.3616 635.0202
13.6383 635.3657
6.6358 635.4317
10.725 637.4437
1.6034 639.0026
0.359 644.7984
7.4254 646.2822
1.6088 649.9961
0.3528 655.0219
5.7638 665.2701 Peptide
0.3547 670.9943
1.7155 673.9541
6.6237 674.5011
7.4111 676.3079
0.3492 676.9997
12.0071 677.554
0.3161 678.6746
6.6254 679.4573 Lipid
1.7114 679.9699
0.3429 688.7822
1.7173 689.9271
0.3507 692.97
0.3477 694.9721
13.6232 703.4517 Lipid
13.1358 703.5737
1.6767 709.0683
11.7477 712.5443
7.4314 713.3025
9.9864 717.632 Lipid
6.6167 718.5284
13.4943 722.551 Ceramide
6.6094 723.4853
5.7355 725.4297 Lipid
13.1381 725.5549
7.4214 729.4603
13.5057 729.5885
0.3477 734.953
1.6269 735.4944 Lipid
11.4089 739.5345
13.8137 740.5197 Lipid
10.8899 741.5508
1.9897 743.3704
7.4197 744.2956
13.493 751.567 Lipid
13.1615 754.0566
10.4529 758.5868
0.5736 759.0603
7.7516 760.5809 Lipid
13.0793 764.0389
13.1581 765.0463
6.5956 767.509
2.2768 771.4027
13.132 771.5631
0.3524 772.9186
12.8808 774.5722
12.293 778.5329 Lipid
13.1639 782.5684 Lipid
13.1358 793.5442
10.3346 794.5649 Lipid
13.7571 796.561
7.4286 797.4589
1.599 798.3899
5.7361 798.9721
5.7311 801.9803
13.1657 804.5514 Lipid
14.361 806.5633 Lipid
12.9319 806.5706 Lipid/ceramide
13.2426 810.6704
0.3509 810.8878
2.2829 812.4288
8.6836 820.4116
12.6246 820.5522 Lipid
13.0707 827.5999
12.9375 828.5527 Lipid
13.9946 830.568 Lipid
6.1864 830.961
5.7348 835.9755
0.3123 836.6608
6.4852 839.497
1.7063 841.9379
7.4234 842.023
10.3171 847.4601
11.7131 856.5728
5.7373 866.9606
5.7347 869.9697
13.1642 872.5377 Lipid
8.8102 876.5694
11.9 882.5886
13.0421 884.6029
12.9953 893.0155
12.9321 896.5377
5.7506 900.2364
5.7392 900.9573
7.4281 910.0114
13.1352 929.5189 Lipid
11.7452 939.4679
13.1658 940.523
1.6171 954.6025
11.4692 955.5848
13.0992 963.574
3.5652 979.9296

Abbreviations: m/z, mass/charge; RT, retention time.


Articles from International Journal of Chronic Obstructive Pulmonary Disease are provided here courtesy of Dove Press

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