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. 2023 Jul 11;22(8):2558–2569. doi: 10.1021/acs.jproteome.2c00788

Metabolomics Study Revealing Purines as Potential Diagnostic Biomarkers of Acute Respiratory Distress Syndrome in Patients with Community—Acquired Pneumonia

Fen Xiong †,‡, Kaiyuan Jiang †, Jianuo Chen ‡, Yongqin Yan ‡, Yiyang Zhou †, Zihao Chen †, Hong Zheng †, Yuping Li ‡,*, Hongchang Gao †,‡,§,*
PMCID: PMC10407924  PMID: 37432907

Abstract

graphic file with name pr2c00788_0008.jpg

Community-acquired pneumonia (CAP) is a significant threat to human health and the leading cause of acute respiratory distress syndrome (ARDS). We aimed to reveal the metabolic profiling whether can be used for assessing CAP with or without ARDS (nARDS) and therapeutic effects on CAP patients after treatment. Urine samples were collected at the onset and recovery periods, and metabolomics was employed to identify robust biomarkers. 19 metabolites were significantly changed in the ARDS relative to nARDS, mainly involving purines and fatty acids. After treatment, 7 metabolites in the nARDS and 14 in the ARDS were found to be significantly dysregulated, including fatty acids and amino acids. In the validation cohort, we observed that the biomarker panel consisted of N2,N2-dimethylguanosine, 1-methyladenosine, 3-methylguanine, 1-methyladenosine, and uric acid exhibited better AUCs of 0.900 than pneumonia severity index and acute physiology and chronic health evaluation II (APACHE II) scores between the ARDS and nARDS. Combining L-phenylalanine, phytosphingosine, and N-acetylaspartylglutamate as biomarkers for discriminating the nARDS and ARDS patients after treatment exhibited good AUCs of 0.811 and 0.821, respectively. The metabolic pathway and defined biomarkers may serve as crucial indicators for predicting the development of ARDS in CAP patients and for assessing therapeutic effects.

Keywords: acute respiratory distress syndrome, community-acquired pneumonia, metabolomics, purines, biomarker

Introduction

Community-acquired pneumonia (CAP) is an acute disease caused by viruses, bacteria, chlamydia, and mycoplasma outside of healthcare facilities.1 It is one of the common causes of mortality in patients who are immunocompromised.2 Failure to provide effective treatment on time might result in acute respiratory distress syndrome (ARDS), which occurs in ∼10 to 13% of intensive care unit patients with CAP, and cause multiple organ dysfunction, septic shock, and even death.3,4 However, CAP-induced ARDS is frequently underdiagnosed or diagnosed late, which increases the risk of inappropriate treatment.5,6 Therefore, a prompt diagnosis, along with the clinical assessment of the CAP-induced ARDS, and the initiation of suitable therapy could improve the patient’s prognosis.

In recent years, studies focusing on changes in metabolic profiling and the discovery of biomarkers in CAP patients have been increasing.7,8 An important goal of the metabolic profiling and biomarker discovery is to study pathogenesis, i.e., elucidating molecular pathways and mechanisms that are strongly related to a disease state.9−12 Ning et al. used a metabolomics method to reveal fifteen metabolites that were dysregulated in CAP patients and combined lactate, sphinganine, and dehydroepiandrosterone sulfate as a metabolite biomarker panel for discriminating severe CAP from non-severe CAP.13 This study greatly promoted metabolomics in elucidating the molecular mechanisms and biomarkers involved in CAP patients relative to healthy humans. However, it is equally important to comprehend the metabolic changes during the recovery phase of CAP patients. Furthermore, several metabolomics researches have provided powerful approaches to discover biomarkers and revealed the underlying mechanisms of ARDS,14,15 but few of them have focused on metabolic changes in CAP-induced ARDS. Thus, elucidating metabolic alterations of CAP patients developing into ARDS is very significant.

Urine is an ideal biological medium for disease research because it is easily obtained and less complex than other body fluids.16 Besides, urine sample collection is non-invasive, and no special preparation is required from patients. In this study, we collected a cohort of patients with CAP (47 without ARDS, nARDS, and 49 with ARDS, ARDS) at the onset and recovery periods and divided them into the discovery and validation cohorts. Urine samples from the discovery cohort were studied by untargeted metabolomics based on liquid chromatography with tandem mass spectrometry (LC–MS) to obtain metabolic profiling and elucidate the abnormal pathway between ARDS and nARDS, nARDS before and after treatment, as well as ARDS. The important metabolites were quantified absolutely in the validation cohort by targeted MS assay, which found reliable urine biomarkers for diagnosing CAP with or without ARDS, nARDS before and after treatment, as well as ARDS. Besides, the relationships between the potential biomarkers and clinical parameters were investigated.

Experimental Procedures

Study Design

The study samples were obtained from the general ward and respiratory intensive care unit of the First Affiliated Hospital, Wenzhou Medical University, from May 2020 to October 2021. The diagnosis criteria for CAP were based on the 2016 CAP guideline from China.17 Briefly, the onset of the disease in the community was marked by clinical manifestations of pneumonia: recent onset of cough, expectoration, or aggravation of existing symptoms of respiratory tract diseases, with or without chest pain, purulent sputum, hemoptysis, or dyspnea; fever; signs of pulmonary consolidation and/or moist rales; peripheral white blood cell count (WBC) > 10 × 109/L or < 4 × 109/L, with or without a left shift. A CAP patient with the following symptoms was termed CAP developing into ARDS: acute onset of hypoxemia (arterial partial pressure of oxygen to fraction of inspired oxygen (PaO2/FiO2) < 300 mmHg), with bilateral infiltrates on frontal chest radiograph, with no evidence of left atrial hypertension.18,19 The Berlin definition of ARDS was applied and categorized as mild (200 mmHg < PaO2/FiO2 < 300 mmHg), moderate (100 mmHg < PaO2/FiO2 < 200 mmHg), and severe (PaO2/FiO2 < 100 mmHg).19 In the discovery cohort, 9 patients had mild ARDS, 9 had moderate ARDS, and 3 had severe ARDS. The validation cohort consisted of 14 patients with mild ARDS, 11 with moderate ARDS, and 3 with severe ARDS. All patients were treated in the hospital, and urine samples were collected at the onset of CAP and the post-treatment period. The treatment protocol included the administration of antimicrobic drugs and support treatment, included: oxygen therapy or high-flow nasal oxygen, or even mechanical ventilation with moderate or severe ARDS patients. The onset period was defined as the first day of admission to the ward, while the post-treatment period referred to the phase after ten days to two weeks of treatment, during which the patients’ clinical symptoms and laboratory indicators improved. Patients were diagnosed as CAP according to the above symptoms and were excluded if they had tuberculosis, non-infectious interstitial lung disease, pulmonary edema, atelectasis, pulmonary tumors, pulmonary embolism, eosinophilia, or vasculitis. Moreover, patients who died or failed to provide urine samples after treatment were excluded from the study. Baseline clinical parameters were obtained from electronic medical records. This study received approval from the Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University (No. 2020-111) and followed the Declaration of Helsinki (as revised in 2013). Written informed consent was obtained from each participant.

Sample Collection and Preparation

All CAP patients were inpatients and were kept in an overnight fasting state. Urine samples were collected in the next morning and stored at −80 °C until metabolomics analysis using LC–MS. 100 μL of urine sample was added to a 1.5 mL Eppendorf tube and mixed with 300 μL water containing L-2-chlorolphenylalanine (internal standard, 0.03 mg/mL). The mixture was vortexed for 1 min. The supernatant was centrifuged for 15 min at 15,000 g at 4 °C before applying for LC–MS analysis.

Untargeted LC–MS Based Metabolomics Analysis

Urine sample analysis was carried out on a SHIMADZU CBM-30A Lite LC system (Shimadzu Corporation, Kyoto, Japan) coupled to a 6600 Triple TOF (AB SCIEX, Foster City, CA, USA) mass detector within an 80–1000 m/z mass range under both ESI positive and negative ion modes. A Waters Acquity HSS T3 column (2.1 × 100 mm, 1.8 μm) was utilized for metabolite separation and the mobile phase consisted of 5 mM ammonium acetate and 0.1% formic acid in water (A) and acetonitrile (B) at a flow rate of 0.4 mL/min. The gradient elution was as follows: 0–0.5 min, 2% B; 0.5–10 min, 2–40% B; 10–14 min, 40–98% B; 14.1–18 min, 98–2% B. The injection volume was 2 μL, the column temperature was 45 °C, and the collision energy of the fragment was 40 V.

Targeted LC–MS Analysis

Targeted analysis was performed on a SHIMADZU CBM-30A Lite LC system coupled to an API 6500 Q-TRAP (AB SCIEX, Foster City, CA, USA) mass detector, which was operated in ESI positive ion mode. The column, injection volume, column temperature, and flow rate were kept the same as mentioned above. The mobile phase A consisted of 0.1% formic acid in water and phase B was acetonitrile. The gradient was as follows: 0–4 min, 10–40% B; 4–10 min, 40–90% B; 10–12 min, 90–10% B. The data was acquired through multiple reaction monitoring (MRM).

LC–MS Data Analysis and Metabolite Identification

The untargeted LC–MS data were analyzed by the XC-MS software (XC-MS plus, CA, USA). The data were converted into a table of time-aligned detected features with their m/z, RT, and intensity from each urine sample. The peaks with intensity below 500 were removed for data analysis and the coefficient of variation (CV, standard deviation/average intensity × 100%) of the quality control (QC) sample was set to below 20%. Metabolites were identified using MS-DIAL 4.0 software (http://metabolomics_software/MS-DIAL/index), MetDNA2 (http://metdna.zhulab.cn/), and HMDB 5.0 (https://hmdb.ca/) based on accurate m/z value and MS/MS characteristic fragments.

Multivariate data analyses were performed on SMICA-P software (version 14.0, Umetrics, Umea, Sweden). The data were normalized using internal standards and Pareto scaling. Unsupervised principal component analysis (PCA) was used to evaluate the quality and main trends of group separation inherent in the dataset. A supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was applied to distinguish categories and identify the differentially expressed variables. Further, we used OPLS-DA to evaluate the metabolic differences between nARDS and ARDS, nARDS before and after treatment, as well as ARDS, and select important metabolites through the variable importance in the projection (VIP) scores. The metabolites were further analyzed via the unpaired Student’s t test between nARDS and ARDS patients, and the paired Student’s t test in nARDS before and after treatment, as well as ARDS. All the P values were corrected for false discovery rates (FDR). Volcano plots were drawn from the online website (https://www.omicshare.com/).

Concentrations of metabolites were calculated from the area-response ratio of independent calibration curves for each metabolite using AB SciexMultiQuant software (version 2.1, AB SCIEX, CA, USA) based on LC–MS targeted analysis. Data were analyzed using an unpaired Student’s t test between nARDS and ARDS patients and the paired Student’s t test was used for nARDS before and after treatment, as well as ARDS, performed on SPSS software (version 19.0, IBM Corp, Armonk, New York).

Statistical Analysis

The laboratory results between nARDS and ARDS, nARDS before and after treatment, as well as ARDS are shown in Table S1, including white blood cell count (WBC), absolute neutrophil count (NEUT#), absolute lymphocytes count (LYMPH#), interleukin (IL), tumor necrosis factor (TNF), CD4+ T, CD4+ 8, C-reactive protein (CRP), procalcitonin (PCT). Statistical analyses were done as mentioned above and the significance level was defined at P < 0.05. The relationship between metabolites and the laboratory results was calculated using Spearman’s correlation in the R package (version 4.1.2) and illustrated as heatmaps. The receiver operating characteristic (ROC) curve and the area generated under the ROC curve (AUC) were analyzed using the R package.

Results

Demographic and Clinical Characteristics of Participants

The design of the current study is shown in Figure 1. We collected urine samples from a cohort of patients with CAP, which included 21 nARDS and 21 ARDS in the discovery cohort, and 26 nARDS and 28 ARDS in the validation cohort at the onset and recovery periods. The detailed clinical characteristics of the participants are shown in Table 1. There were no significant differences in terms of age, sex, smoking history, or underlying diseases, including diabetes mellitus, cardiovascular disease, cerebrovascular disease, and chronic kidney disease between the nARDS and ARDS patients in both the discovery and validation cohorts. Pneumonia severity index (PSI) and acute physiology and chronic health evaluation II (APACHE II) scores were significantly increased, while PaO2/FiO2 was decreased in the discovery and validation cohorts of the ARDS relative to nARDS. The laboratory results are depicted in Table S1. In the discovery cohort, we observed higher levels of WBC, NEUT#, and CRP in the ARDS relative to the nARDS. After treatment, nARDS showed significant increases in CD4+ T and CD8+ T, and a decrease in CRP. TNF-α was significantly increased in the ARDS after treatment, while WBC, NEUT#, CD4+ T, and CRP were reduced relative to nARDS. In the validation cohort, CD4+ T was found to decrease in the ARDS relative to nARDS, while IL-10, CRP, and PCT showed higher levels. After treatment, WBC, NEUT#, CRP, and PCT were significantly reduced in the nARDS and ARDS, while LYMPH# was increased. Besides, ARDS showed a lower level of IL-10 and higher levels of CD4+ T and CD8+ T after treatment.

Figure 1.

Figure 1

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Design of the study. This study involved 96 patients with the CAP, including the discovery and validation cohorts. CAP: community-acquired pneumonia, ARDS: acute respiratory distress syndrome; nARDS: CAP without ARDS; nARDS-pre: nARDS patients before treatment; nARDS-post: nARDS patients after treatment; ARDS-pre: ARDS patients before treatment; ARDS-post: ARDS patients after treatment.

Table 1. Demographic and Clinical Characteristics of the 96 CAP Patients Enrolled in This Studya.

characteristics discovery cohort validation cohort
nARDS (n = 21) ARDS (n = 21) P nARDS (n = 26) ARDS (n = 28) P
age 62.4 ± 11.6 66.3 ± 10.9 0.270 62.4 ± 12.0 63.7 ± 13.4 0.837
sex, male 13(61.9%) 14(66.6%) 0.754 22(84.6%) 19(67.8%) 0.644
smoking history 7(33.3%) 8(25.8%) 0.754 12(46.1%) 13(46.4%) 0.607
diabetes mellitus 8(25.8%) 9(42.8%) 0.760 8(30.7%) 4(14.2%) 0.252
cardiovascular disease 8(25.8%) 13(61.9%) 0.128 15(53.6%) 13.(46.4%) 0.797
cerebrovascular disease 1(4.7%) 2((9.5%) 0.643 1(3.8%) 1(3.5%) 0.958
chronic kidney disease 3(14.2%) 2(9.5%) 0.560 4(15.3%) 1(3.6%) 0.193
PSI score 9.4 ± 12.5 19.5 ± 12.0 0.014 9.0 ± 16.8 20.5 ± 14.0 0.008
APACHE II score 7.44 ± 4.67 13.95 ± 5.95 <0.001 7.6 ± 3.8 14.8 ± 6.0 <0.001
PaO2/FiO2 (mmHg) 365.4 ± 56.9 200.6 ± 60.7 <0.001 401.6 ± 46.8 173.7 ± 98.0 <0.001
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a

Abbreviation: PSI: pneumonia severity index; APACHE II: acute physiology and chronic health evaluation II score; PaO2/FiO2: arterial partial pressure of oxygen to fraction of inspired oxygen. CAP: community-acquired pneumonia; ARDS: acute respiratory distress syndrome; nARDS: CAP without ARDS; nARDS-pre: nARDS patients before treatment; nARDS-post: nARDS after treatment; ARDS-pre: ARDS patients before treatment; ARDS-post: ARDS patients after treatment.

Metabolomics Profiling in the Urine of CAP Patients

In this study, we utilized a method of untargeted metabolomics based on LC–MS for global metabolic profiling of urine samples, and the base peak chromatogram is shown in Figure S1. We detected more than 5000 metabolite features in the urine samples and subsequently performed PCA and OPLS-DA analysis. A total of 736 metabolites in the ESI+ mode and 431 metabolites in the ESI– mode were identified via MS/MS spectral matching. The QC samples were tightly clustered on the PCA plot, indicating good analytical repeatability and stability of the instrument (Figures 2A and S2A). PCA was performed on all subjects to indicate the difference in metabolic profiles of patients with nARDS and ARDS before and after treatment (Figures 2B and S2B). The OPLS-DA score plots showed a clear separation between the nARDS and ARDS in ESI+ and ESI– mode (Figures 2C and S2C) with good fitting and predictive performances (ESI+: R2Y = 0.72; Q2 = 0.50; ESI–: R2Y = 0.69; Q2 = 0.48). Other comparison between two groups of nARDS before and after treatment (Figure 2D, ESI+ mode, and Figure S2D, ESI– mode), as well as ARDS (Figure 2E, ESI+ mode, and Figure S2E, ESI– mode), showed clear discrimination. Subsequently, volcano plots were used for analyzing the P value of metabolites, as shown in Figures 2F–H and S2F–H. To assess whether the OPLS-DA model was overfitting, we conducted 20 permutation tests using SMICA-P and generated the permutation plots. The results indicated that there was no risk of overfitting, as the replaced R2 and Q2 values were lower than the original values and the intercept of the regression line on the y-axis was less than zero (Figure S3).

Figure 2.

Figure 2

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Urine untargeted metabolomics analysis of nARDS and ARDS patients. (A) Evaluation of analytical stability by PCA score plot (ESI+). (B) PCA score plot showing the metabolic phenotype changes in nARDS-pre, nARDS-post, ARDS-pre, and ARDS-post (ESI+). The OPLS-DA score plot reveals the differences in metabolic phenotypes between ARDS-pre and nARDS-pre (C, ESI+), nARDS-pre and nARDS-post (D, ESI+), and ARDS-pre and ARDS-post (E, ESI+). Subsequently, volcano plot analysis has been used to identify important metabolites (FDR < 0.05) as highlighted in F–H. ARDS: acute respiratory distress syndrome; nARDS: CAP without ARDS; nARDS-pre: nARDS patients before treatment; nARDS-post: nARDS patients after treatment; ARDS-pre: ARDS patients before treatment; ARDS-post: ARDS patients after treatment.

Metabolites with VIP > 1 and FDR < 0.05 were used to identify critical differential metabolites, as highlighted in Tables S2–S4. Pathway analysis of the selected metabolites in ARDS compared to nARDS showed that three pathways, namely nucleotide metabolism, fatty acid metabolism, and amino acid metabolism, were predominantly affected (Figure 3A). Besides, we found that the pathways significantly altered in the nARDS and ARDS patients after treatment, were involved in the metabolism of fatty acid, lipid, and amino acid (Figure 3B,C). Notably, we found that disordered levels of purines and pyrimidines occurred only in the ARDS relative to nARDS. The levels of uric acid, 1-methyladenosine, 3-methylguanine, 7-methylguanine, N2,N2-dimethylguanosine (m22g), pseudouridine, and 1-methylinosine were significantly decreased in the ARDS compared with nARDS (Figure 3A).

Figure 3.

Figure 3

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Metabolic changes in the urine of nARDS and ARDS patients. The pathway analysis of differential metabolites between the ARDS-pre and nARDS-pre (A), nARDS-pre and nARDS-post (B), ARDS-pre and ARDS-post (C). The color in the heatmap from red to blue indicates high to low in each metabolite level via data normalization. ARDS: acute respiratory distress syndrome; nARDS: CAP without ARDS; nARDS-pre: nARDS patients before treatment; nARDS-post: nARDS patients after treatment; ARDS-pre: ARDS patients before treatment; ARDS-post: ARDS patients after treatment.

As shown in Figure 3A,C, two eicosanoids, including methyl arachidonate and ethyl arachidonate, were significantly reduced in the ARDS compared with nARDS, while three eicosanoids, including ethyl arachidonate, leukotriene B4 ethanolamide, and leukotriene B5, were increased in the ARDS after treatment. However, in the nARDS, we only identified a higher level of 10,11-dihydro-12R-hydroxy-leukotriene E4 after treatment (Figure 3B). The levels of two carnitines, namely 2-hydroxylauroylcarnitine and 3-hydroxytridecanoylcarnitine, were decreased in the ARDS relative to nARDS (Figure 3A), but increased in the ARDS after treatment (Figure 3C). We also detected a lower level of methyl-dodecadienoate in the ARDS relative to nARDS and a higher level in ARDS after treatment. Phytosphingosine and palmitic acid were significantly increased in both the ARDS and nARDS after treatment. Besides, we identified that several lysophosphate (LysoPA) and lysophosphatidylcholine (LysoPC) levels were improved in nARDS and ARDS patients after treatment. The relative higher abundances of LysoPC 14:0 and LysoPC 18:2 were observed in the nARDS patients (Figure 3B), and LysoPA 19:0 was increased in the ARDS patients (Figure 3C).

Amino acid metabolism was also disrupted between the nARDS and ARDS, nARDS before and after treatment, as well as ARDS. Lower levels of L-tryptophan, alanylleucine, hydroxyprolyl-alanine, and Nε,Nε,Nε-trimethyllysine were observed in the ARDS compared with nARDS (Figure 3A). We also identified lower levels of L-phenylalanine and N-acetylaspartylglutamate (NAAG) in the nARDS and ARDS after treatment (Figure 3B,C). Additionally, we observed that N-acetylneuraminate 9-phosphate, pantothenic acid, choline, and creatinine were reduced in the ARDS relative to nARDS (Table S2). N-Acetylneuraminate 9-phosphate and phenol sulfate were increased in the ARDS after treatment (Table S4).

Potential Diagnostic Biomarkers for CAP-Induced ARDS and Therapeutic Biomarkers of CAP Patients

The aforementioned results indicated that the progression of CAP-induced ARDS was associated with metabolic shifts. For differential diagnosis of CAP with and without ARDS, the criteria of metabolomics based biomarkers are VIP >1 and P value <0.001. There were twelve specific metabolic biomarkers for distinguishing CAP with and without ARDS, as shown in the volcano plot (Figure S4). These metabolites included pantothenic acid, creatinine, methyl arachidonate, ethyl arachidonate, choline, pseudouridine, 1-methyladenosine, 7-methylguanine, 3-methylguanine, 1-methylinosine, m22g, and uric acid. Targeted analysis base on a LC–MS was performed in another population to validate the diagnostic capacity of twelve metabolites between the ARDS and nARDS groups (Figure S5 and Table S5). The result showed that ethyl arachidonate had a minimal response between the ARDS and nARDS groups (Table S6), hence, the remaining eleven metabolites were validated and quantified in the ARDS and nARDS groups. Subsequently, statistical analysis of critical metabolites concentration was calculated between the ARDS and nARDS groups in the validation cohort. The result showed that 6 metabolites, including pantothenic acid, 1-methyladenosine, 7-methylguanine, 3-methylguanine, 1-methylinosine, and uric acid were significantly decreased in the ARDS relative to nARDS (Table S6). ROC analysis with AUC value was performed on these metabolites and showed that m22g (AUC 0.771, P = 0.0030), 1-methylinosine (AUC 0.762, P = 0.0166), 3-methylguanine (AUC 0.755, P = 0.0005), 1-methyladenosine (AUC 0.723, P = 0.0079), pantothenic acid (AUC 0.706, P = 0.0187), and uric acid (AUC 0.755, P = 0.0101) exhibited good performances to predict CAP with and without ARDS (Figure 4A and Table 2). The AUC values of only the first three metabolites were better than PSI score (AUC 0.723, P = 0.0191), but none of them were superior to APACHE II score (AUC 0.853, P = 3.1 × 10–5) (Figure 4A,B and Table 2). Interestingly, all metabolites, except for pantothenic acid, were classified as purines. A combination of these purines using binary logistic regression analysis showed a good AUC value (AUC 0.900, P = 9.3 × 10–6) (Figure 4B and Table 2), indicating that they can act as a metabolite panel to assess CAP patients developing into ARDS.

Figure 4.

Figure 4

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The potential diagnostic biomarkers for assessing CAP-induced ARDS and evaluation of therapeutic efficacy in nARDS and ARDS after treatment. (A) ROC curve analysis of selected metabolites for discrimination of ARDS relative to nARDS. (B) ROC curve of PSI score, APACHE II score, and combining purines between ARDS and nARDS patients. (C) ROC curve analysis of various parameters for assessing therapeutic efficacy in nARDS after treatment. (D) ROC curve analysis of various parameters for assessing therapeutic efficacy in ARDS after treatment. M22g: N2,N2-dimethylguanosine; NAAG: N-acetylaspartylglutamate; PSI: pneumonia severity index; APACHE II: acute physiology and chronic health evaluation II; CRP: c-reactive protein; PCT, procalcitonin; ROC: receiver operating characteristic; AUC: area under the curve. ARDS: acute respiratory distress syndrome; nARDS: CAP without ARDS; nARDS-pre: nARDS patients before treatment; nARDS-post: nARDS patients after treatment; ARDS-pre: ARDS patients before treatment; ARDS-post: ARDS patients after treatment.

Table 2. Areas under the Curve of Variable Parameters for Determining the CAP-Induced ARDS and Evaluating Effect after CAP with and without ARDS Treatmenta.

name AUC sensitivity specificity P 95% CI
lower limit higher limit
nARDS-pre vs ARDS-pre
N2,N2-dimethylguanosine 0.771 0.607 0.961 0.0030 0.623 0.893
1-methylinosine 0.762 0.785 0.769 0.0166 0.615 0.887
3-methylguanine 0.755 0.678 0.692 0.0005 0.591 0.881
1-methyladenosine 0.724 0.678 0.807 0.0079 0.578 0.865
pantothenic acid 0.706 0.785 0.538 0.0187 0.562 0.839
uric acid 0.688 0.571 0.693 0.0101 0.531 0.825
7-methylguanine 0.638 0.571 0.692 0.0812 0.555 0.831
5 purines combination 0.900 0.821 0.885 9.3 × 10–6 0.810 0.988
PSI score 0.723 0.714 0.730 0.0191 0.582 0.861
APACHE II score 0.853 0.751 0.846 3.1 × 10–5 0.761 0.942
nARDS-pre vs nARDS-post
phytosphingosine 0.701 0.538 0.846 0.0150 0.562 0.838
L-phenylalanine 0.692 0.807 0.615 0.0110 0.550 0.833
N-acetylaspartylglutamate 0.681 0.692 0.538 0.0100 0.531 0.818
3 metabolites combination 0.811 0.730 0.769 0.0002 0.688 0.912
CRP 0.909 0.961 0.807 6.18 × 10–9 0.811 0.987
PCT 0.796 0.692 0.769 0.0028 0.677 0.905
ARDS-pre vs ARDS-post
L-phenylalanine 0.725 0.607 0.786 0.0040 0.580 0.850
N-acetylaspartylglutamate 0.721 0.857 0.571 0.0030 0.578 0.839
phytosphingosine 0.677 0.571 0.714 0.0320 0.538 0.811
3 metabolites combination 0.821 0.714 0.821 6.2 × 10–5 0.702 0.822
CRP 0.897 0.857 0.982 6.37 × 10–9 0.797 0.98
PCT 0.905 0.964 0.785 0.0033 0.804 0.971
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a

Abbreviation: PSI: pneumonia severity index; APACHE II: acute physiology and chronic health evaluation II score; CAP: community-acquired pneumonia; ARDS: acute respiratory distress syndrome; nARDS: CAP without ARDS; nARDS-pre: nARDS patients before treatment; nARDS-post: nARDS patients after treatment; ARDS-pre: ARDS patients before treatment; ARDS-post: ARDS patients after treatment; AUC: area under the curve. CRP, c-reactive protein; PCT, procalcitonin.

To evaluate the therapeutic effects on ARDS and nARDS, we examined all differential metabolites between before and after treatment in these two disease groups. We discovered four metabolites that were common between the two groups, namely phytosphingosine, L-phenylalanine, palmitic acid, and NAAG (Table S3–S4). Consequently, we intended to investigate these four metabolites further as potential biomarkers for assessing therapeutic effects between before and after treatment in the nARDS and ARDS groups. Similarly, four common metabolites in both pre- and post-treatment groups were accurately determined by targeted analysis. In the validation cohort, we found that palmitic acid was unaltered and phytosphingosine, L-phenylalanine, and NAAG were significantly changed in nARDS and ARDS patients after treatment (Table S6). As shown in Figure 4C,D and Table 2, these metabolites exhibited good performances in discriminating nARDS (phytosphingosine: AUC 0.701, P = 0.0150; L-phenylalanine: AUC 0.692, P = 0.0110; NAAG: AUC 0.681, P = 0.0100) and ARDS (L-phenylalanine: AUC 0.725, P = 0.0040; NAAG: AUC 0.721, P = 0.0030; phytosphingosine: AUC 0.677, P = 0.0320) after the treatment. However, a combination of these metabolites showed better AUC values of 0.811 (P = 0.0002) and 0.821 (P = 6.2 × 10–5) in the nARDS and ARDS after treatment, respectively. We also calculated the diagnostic performance of CRP and PCT, as two clinical parameters usually used to judge treatment effects. In the nARDS, 3 metabolites combination performed better than PCT (AUC 0.796, P = 0.0028) and worse than CRP (AUC 0.909, P = 6.18 × 10–9), but 3 metabolites combination were not superior to both CRP (AUC 0.897, P = 6.37 × 10–9) and PCT (AUC 0.905, P = 0.0033) in the ARDS (Figure 4C,D and Table 2).

Next, in the validation cohorts, we compared the discrimination accuracy of the machine learning (ML)-based metabolites combination score and clinical parameters for assessing CAP-induced ARDS and evaluation of therapeutic efficacy in nARDS and ARDS after treatment. The cutoff values of ML-based metabolite combination score and clinical parameters were selected based on the Youden index. The sensitivities and specificities of predictors of CAP-induced ARDS were 75 and 84% for APACHE II score (cutoff = 11.5), 71 and 73% for PSI score (cutoff = 17.5), and 82 and 92% for ML-based 5 purines combination score (cutoff = 0.59). The sensitivities and specificities of predictors between the before and after treatment of nARDS were 96 and 80% for CRP (cutoff = 73.6 mg/L), 65 and 84% for PCT (cutoff = 0.1 ng/L), and 85 and 69% for ML-based 3 metabolites combination score (cutoff = 0.38). Besides, in the acute phase and the remission phase of ARDS, the sensitivities and specificities of predictors were 86 and 89% for CRP (cutoff = 67.5 mg/L), 96 and 78% for PCT (cutoff = 1.2 ng/L), and 85 and 71% for ML-based 3 metabolites combination score (cutoff = 0.44). Applying the cutoff for APACHE II and PSI scores, 4 (15.38%) and 7 (26.92%) ARDS patients were falsely classified as nARDS patients (false-positive), 8 (28.57%) and 8 (28.48%) nARDS patients were falsely classified as ARDS patients (false-negative), respectively, whereas only 2 (7.69%) (false-positive) and 5 (17.85%) (false-negative) cases were misclassified using ML-based 5 purines combination score (Figure 5A,B and Table S7). We also explored the availability of the ML-based 3 metabolites combination score in reclassifying the acute phase and the remission phase of nARDS and ARDS compared with CRP and PCT. Based on the cutoff value of 3 metabolites combination score, the nARDS and ARDS in the acute phase were 18 (69.24%) and 20 (71.25%) patients and in the remission phase were 22 (84.62%) and 24 (85.72%) cases, respectively (Figure 5C–F and Table S7). However, in the nARDS and ARDS, 21 (80.77%) and 25 (89.29%) in the acute phase, 25 (89.29%) and 24 (85.72%) patients in the remission phase were correctly reclassified by using the CRP, and 22 (84.62%) and 22 (78.58%) in the acute phase, 17 (65.39%) and 27 (96.43%) patients in the remission phase were correctly reclassified by using the PCT respectively (Figure 5C–F and Table S7). Taken together, compared with PSI and APACHE II scores, ML-based 5 purines combination scores were more beneficial for classifying nARDS and ARDS patients, but ML-based 3 metabolites combination scores were worse than CRP and PCT for estimating nARDS and ARDS patients in the acute phase and the remission phase.

Figure 5.

Figure 5

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Machine learning (ML)-based metabolites combination score and clinical parameters for assessing CAP-induced ARDS and evaluation of therapeutic efficacy in nARDS and ARDS after treatment in the validation cohorts. The two-dimensional plot of the (A) APACHE II score (x-axis, cutoff values: 11.5) and (B) PSI score (x-axis, cutoff values: 17.5) and ML-based 5 purines combination score (y-axis Youden index: 0.59) between nARDS-pre and ARDS-pre in the validation cohort. The two-dimensional plot of the (C) CRP levels (x-axis, cutoff values: 73.6 mg/L) and (D) PCT levels (x-axis, cutoff values: 0.10 ng/L) and ML-based 3 metabolites combination score (y-axis, cutoff values: 0.38) between nARDS-pre and nARDS-post in the validation cohort. The two-dimensional plot of the (E) CRP levels (x-axis, cutoff values: 67.5 mg/L) and (F) PCT levels (x-axis, cutoff values: 1.2 ng/L) and ML-based 3 metabolites combination score (y-axis, cutoff values: 0.38) between ARDS-pre and ARDS-post in the validation cohort. The cutoff values of ML-based metabolites combination score were selected based on the Youden index and indicated by the dashed line. PSI: pneumonia severity index; APACHE II: acute physiology and chronic health evaluation II score; CRP: C-reactive protein; PCT: procalcitonin; ARDS: acute respiratory distress syndrome; nARDS: CAP without ARDS; nARDS-pre: nARDS patients before treatment; nARDS-post: nARDS patients after treatment; ARDS-pre: ARDS patients before treatment; ARDS-post: ARDS patients after treatment.

Relationship between Clinical Parameters and Metabolic Changes

To calculate the relationship between clinical parameters and critical biomarkers, Spearman’s correlation analysis was performed and correlations with |R| > 0.25 and P < 0.05 only were listed in heatmaps (Figure 6). In the ARDS relative to nARDS, CRP was found to be negatively associated with 3-methylguanine and uric acid, while PCT was negatively relate to 1-methyladenosine (Figure 6A). m22g showed negative correlations with PCT, IL-6, and IL-10 (Figure 6A). Interestingly, we also observed that these purines exhibit good relationships (R > 0.28, P < 0.05) with PaO2/FiO2 value, a major indicator for the diagnosis of ARDS (Figure S6). The changes of purines are closely related to clinical indicators of CAP developing into ARDS, suggesting their highly reliability as diagnostic biomarkers.

Figure 6.

Figure 6

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Correlation heatmap between potential biomarker and clinical parameters. The relationships between biomarker and clinical parameters in the ARDS-pre and nARDS-pre (A), nARDS-pre and nARDS-post (B), ARDS-pre and ARDS-post (C). Spearman’s correlation analysis has been carried out to assess the associations between metabolic changes and clinical parameters, and only correlations with |R| > 0.25 and P < 0.05 are highlighted. WBC: white blood cell count; NEUT#: absolute neutrophil count; LYMPH#: absolute lymphocytes count; CRP: C-reactive protein; PCT: procalcitonin; IL: interleukin, TNF: tumor necrosis factor. ARDS: acute respiratory distress syndrome; nARDS: CAP without ARDS; nARDS-pre: nARDS patients before treatment; nARDS-post: nARDS patients after treatment; ARDS-pre: ARDS patients before treatment; ARDS-post: ARDS patients after treatment.

After treatment, we also found that NEUT# was negatively correlated with phytosphingosine and positively with L-phenylalanine, while NAAG showed a negative relationship with LYMPH# and a positive correlation with CRP in the nARDS (Figure 6B). On the other hand, in the ARDS after treatment, L-phenylalanine and NAAG were positively correlated with CRP and negatively with CD4+ T and CD8+ T (Figure 6C). Besides, we found that L-phenylalanine showed positive relationships with WBC, NEUT#, and IL-10 (Figure 6C). The changes in the levels of these clinical parameters could present a resolution of inflammation. Here we suggested that the regulation of these metabolites might help in the treatment of CAP patients.

Discussion

The study successfully described the application of metabolomics in CAP patients at the onset and recovery periods. Compared with nARDS, the ARDS showed significant changes in 19 metabolites, and metabolic disturbances were found related mainly to nucleotides, fatty acids, and amino acids. After treatment, 7 and 14 metabolites were identified in nARDS and ARDS, respectively, and were associated with fatty acids, lipids, and amino acids. Some studies have demonstrated that fatty acids can suppress inflammation and thereby treat viral infection.20,21 We also found significant reductions in fatty acids, including eicosanoids and carnitines, in CAP patients, especially in those with ARDS. Recent studies have suggested that arachidonate may function as an endogenous antiviral metabolite and play a role in inactivating enveloped viruses, such as influenza virus.22,23 Carnitines have also been revealed to suppress inflammation and protect against lung injury.24,25 Therefore, we speculated that lower levels of eicosanoids and carnitines might increase inflammation in CAP patients. Glycerophosphorylcholines (GPCs)-mediated signaling has also been associated with the regulation of inflammation processes.26 Here we observed that some GPCs were significantly increased in the nARDS and ARDS after treatment, indicating that lipid metabolism might be activated during the resolution of inflammation. Amino acids are closely associated with inflammation and act as potential targets in infection control.27,28 We found that some amino acids were significantly dysregulated in the ARDS relative to nARDS and in CAP patients after treatment. Another interesting finding was that ARDS patients exhibited lower levels of nucleotides than the nARDS patients. Nucleotides, including purines and pyrimidines, are important in many cellular functions and sometimes by acting as signaling molecules during inflammation.29,30 Recently studies have reported that nucleotides, such as inosine and adenosine, are naturally occurring anti-inflammatory agents.31,32 Therefore, the severity of CAP and metabolic alterations associated with pneumonia need to be considered during the diagnosis and treatment of patients.

Metabolomics combined with targeted analysis is a very useful method to find the diagnostic and therapeutic biomarkers in the disease.11 In this study, interestingly, we found that purines (e.g., uric acid, 1-methyladenosine, 3-methylguanine, m22g, and 1-methylinosine) act as important metabolite panels for discriminating CAP-induced ARDS and showed negative relationships with indicators of inflammation. These biomarkers, except for uric acid, are classified as methyl-purines. Some methyl-purines, such as N6-methyladenosine have been reported to effectively inhibit the immune inflammation through modification TGM2 mRNA.33 The results indicated that decreasing methyl-purines are associated with mRNA and might act as a pathogenic factor in CAP patients who will develop ARDS. Among the potential metabolic biomarkers in CAP patients after treatment, the level of phytosphingosine was significantly decreased both in the nARDS and ARDS. It was inversely correlated with NEUT# in the ARDS. Phytosphingosine is a sphingolipid that is reported to be one of the structural membrane components for lung cells and plays an important role in inflammation-regulatory.34−36 We hypothesized that phytosphingosine level could be a rough indicator of lung cell viability. Also, we observed the reduced levels of L-phenylalanine and NAAG in the nARDS and ARDS after treatment and showed good relationships with inflammatory indicators. In the COVID-19 patients, the level of plasma L-phenylalanine was found to be higher compared with control, suggesting its potential use as a differential diagnostic marker.37 NAAG is derived from N-acetylaspartate and glutamate. Some evidence have revealed that glutamate can regulate pathophysiological functions in peripheral tissues, including the lung and immune systems.38 Lou et al. reported that N-acetylaspartate acts as a unique circulating biomarker for distinguishing non-small-cell lung cancer from controls.39 Thus, we suggested the clinical potential of L-phenylalanine and NAAG in evaluating the therapeutic’s efficacy of CAP.

In this study, we revealed some special potential biomarkers for distinguishing CAP with and without ARDS and some metabolites for the evaluation of therapeutic efficacy in the nARDS and ARDS after treatment. Nevertheless, this study is clearly limited by the relatively small sample sizes both in the discovery and validation cohort, it is just a small step for assessing CAP-induced ARDS and therapeutic effects using a metabolomics approach and targeted analysis. Next, expanding the validation cohort was needed before the potential biomarkers can be applied to clinical application.

Conclusions

The current study demonstrates the feasibility of using an untargeted metabolomics approach based on LC–MS to discover metabolic pathways and biomarkers in the CAP patients with and without ARDS, both at onset and recovery period. Using targeted analysis, purines (e.g., uric acid, 1-methyladenosine, 3-methylguanine, m22g, and 1-methylinosine) are suggested as diagnostic biomarkers between the ARDS and nARDS. Meanwhile, phytosphingosine, L-phenylalanine, and NAAG are suggested for evaluating the efficacy of therapeutics in the nARDS and ARDS after treatment. Also, the correlation between potential biomarker and clinical parameters, was established in this study. The suggested metabolic pathway may provide valuable clues for treating CAP patients, and potential biomarkers can be utilized as a reference for further clinical examination.

Acknowledgments

We would like to thank all participants for collecting the data of this clinical study. The Scientific Research Center of Wenzhou Medical University is acknowledged for technical services.

Glossary

Abbreviations

CAP

community-acquired pneumonia

ARDS

acute respiratory distress syndrome

nARDS

CAP without ARDS

PSI

pneumonia severity index; pneumonia severity index

APACHE II

acute physiology and chronic health evaluation II

AUC

area under the curve

LC–MS

liquid chromatography with tandem mass spectrometry

MRM

multiple reaction monitoring

CV

coefficient of variation

QC

quality control

PCA

unsupervised principal component analysis

OPLS-DA

supervised orthogonal partial least squares discriminant analysis

VIP

variable importance in the projection

FDR

false discovery rates

WBC

white blood cell

NEUT#

absolute neutrophil count

LYMPH#

absolute value of lymphoc-ytes

IL

interleukin

TNF

tumor necrosis factor

CRP

C-reactive protein

PCT

procalcitonin

ROC

receiver operating characteristic

LysoPA

lysophosphate

LysoPC

lysophosphatidylcholine

NAAG

N-acetylaspartylglutamate

m22g

N2,N2-dimethylguanosine

ML

machine learning

GPCs

glycerophosphorylcholines

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.2c00788.

  • Base peak chromatogram of untargeted metabolomics in the urine sample; urine untargeted metabolomics analysis of nARDS and ARDS patients; the permutation test charts were used to tested whether the OPLS-DA model was overfitting; volcano plot showing most discriminant metabolites between the ARDS and nARDS; 16 metabolites detected in multiple reaction monitoring (MRM) by targeted analysis; the relationships between purines and clinical parameters in the ARDS and nARDS by Spearman’s correlation analysis; laboratory results of before and after treatment in CAP patients with and without ARDS enrolled in this study; list of differentiated metabolites between CAP patients with and without ARDS; list of differentiated metabolites between before and after treatment in the nARDS of CAP patients; list of differentiated metabolites between before and after treatment in the ARDS of CAP patients; multiple reaction monitoring of critical metabolites in the validation cohort; statistical analysis of critical metabolites concentration in the validation cohort; the percentage of false-positive and false-negative cases in the validation cohort (PDF)

Author Contributions

∥ F.X. and K.J. contributed equally to this work.

Author Contributions

F.X., H.G., and Y.L. design the experimental. Y.L. and H.G. provision of study materials and patients. J.C., Y.Y., and Z.C. contributed to sample collection and clinical information collection. F.X., K.J., and Y.Z. contributed to sample preparation and metabolomic analysis. F.X. and K.J. contributed to the data analysis and manuscript writing. F.X., K.J., H.G. contributed to result discussion and interpretation. All authors have read and approved the final manuscript.

This study was supported by the Key Research and Development Program of Zhejiang Province (No. 2019C03030) and Health Innovation Talent Project of Zhejiang Province.

The authors declare no competing financial interest.

Notes

All subjects gave their informed consent for inclusion before they participated in the study. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University (No. 2020-111).

Supplementary Material

pr2c00788_si_001.pdf (772.4KB, pdf)

References

  1. Musher D. M.; Thorner A. R. Community-acquired pneumonia. N. Engl. J. Med. 2014, 371, 1619–1628. 10.1056/NEJMra1312885. [DOI] [PubMed] [Google Scholar]
  2. Stefano A.; Charles S. D. C.; Francesco A.; Giovanni S.; Marcos I. R. Community-acquired pneumonia. Lancet 2021, 398, 906–919. 10.1016/S0140-6736(21)00630-9. [DOI] [PubMed] [Google Scholar]
  3. Bellani G.; Laffey J. G.; Pham T.; Fan E.; Brochard L.; Esteban A.; et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA 2016, 315, 788–800. 10.1001/jama.2016.0291. [DOI] [PubMed] [Google Scholar]
  4. Ceccato A.; Torres A.; Cilloniz C.; Amaro R.; Gabarrus A.; Polverino E.; et al. Invasive disease versus urinary antigen confirmed pneumococcal community-acquired pneumonia. Chest 2017, 151, 1311–1319. 10.1016/j.chest.2017.01.005. [DOI] [PubMed] [Google Scholar]
  5. Meyer N. J.; Gattinoni L.; Calfee C. S. Acute respiratory distress syndrome. Lancet 2021, 398, 622–637. 10.1016/S0140-6736(21)00439-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Chen C.; Shi L.; Li Y.; Wang X.; Yang S. Disease-specific dynamic biomarkers selected by integrating inflammatory mediators with clinical informatics in ARDS patients with severe pneumonia. Cell Biol. Toxicol. 2016, 32, 169–184. 10.1007/s10565-016-9322-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Zhou B.; Lou B.; Liu J.; She J. Serum metabolite profiles as potential biochemical markers in young adults with community-acquired pneumonia cured by moxifloxacin therapy. Sci. Rep. 2020, 10, 4436. 10.1038/s41598-020-61290-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Arshad H.; Alfonso J. C. L.; Franke R.; Michaelis K.; Araujo L.; Habib A.; et al. Decreased plasma phospholipid concentrations and increased acid sphingomyelinase activity are accurate biomarkers for community-acquired pneumonia. J. Transl. Med. 2019, 17, 365. 10.1186/s12967-019-2112-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Christ-Crain M.; Opal S. M. Clinical review: The role of biomarkers in the diagnosis and management of community-acquired pneumonia. Crit. Care 2010, 14, 203. 10.1186/cc8155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Biron B. M.; Ayala A.; Lomas-Neira J. L. Biomarkers for sepsis: what is and what might be?. Biomarker Insights 2015, 10, 7–17. 10.4137/BMI.S29519. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Nicholson J. K.; Lindon J. C. Systems biology: Metabonomics. Nature 2008, 455, 1054–1056. 10.1038/4551054a. [DOI] [PubMed] [Google Scholar]
  12. Yanes O.; Tautenhahn R.; Patti G. J.; Siuzdak G. Expanding coverage of the metabolome for global metabolite profiling. Anal. Chem. 2011, 83, 2152–2161. 10.1021/ac102981k. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Ning P.; Zheng Y.; Luo Q.; Liu X.; Kang Y.; Zhang Y.; et al. Metabolic profiles in community-acquired pneumonia: developing assessment tools for disease severity. Crit. Care 2018, 22, 130. 10.1186/s13054-018-2049-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Yan Y.; Chen J.; Liang Q.; Zheng H.; Ye Y.; Nan W.; Zhang X.; et al. Metabolomics profile in acute respiratory distress syndrome by nuclear magnetic resonance spectroscopy in patients with community-acquired pneumonia. Respir. Res. 2022, 23, 172. 10.1186/s12931-022-02075-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Evans C. R.; Karnovsky A.; Kovach M. A.; Standiford T. J.; Burant C. F.; Stringer K. A. Untargeted LC-MS metabolomics of bronchoalveolar lavage fluid differentiates acute respiratory distress syndrome from health. J. Proteome Res. 2014, 13, 640–649. 10.1021/pr4007624. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Khamis M. M.; Adamko D. J.; El-Aneed A. Mass spectrometric based approaches in urine metabolomics and biomarker discovery. Mass Spectrom. Rev. 2017, 36, 115–134. 10.1002/mas.21455. [DOI] [PubMed] [Google Scholar]
  17. Cao B.; Huang Y.; She D. Y.; Cheng Q. J.; Fan H.; Tian X. L.; et al. Diagnosis and treatment of community-acquired pneumonia in adults: 2016 clinical practice guidelines by the Chinese Thoracic Society Chinese Medical Association. Clin. Respir. J. 2018, 12, 1320–1360. 10.1111/crj.12674. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Hernu R.; Wallet F.; Thiollière F.; Martin O.; Richard J. C.; Schmitt Z.; et al. An attempt to validate the modification of the American-European consensus definition of acute lung injury/acute respiratory distress syndrome by the Berlin definition in a university hospital. Intensive Care Med. 2013, 39, 2161–2170. 10.1007/s00134-013-3122-6. [DOI] [PubMed] [Google Scholar]
  19. Ranieri V. M.; Rubenfeld G. D.; Thompson B. T.; Ferguson N. D.; Caldwell E.; Fan E.; et al. Acute respiratory distress syndrome: the Berlin Definition. JAMA. J. Am. Med. Assoc. 2012, 307, 2526–2533. 10.1001/jama.2012.5669. [DOI] [PubMed] [Google Scholar]
  20. Calder P. C. Fatty acids and inflammation: the cutting edge between food and pharma. Eur. J. Pharmacol. 2011, 668, S50–S58. 10.1016/j.ejphar.2011.05.085. [DOI] [PubMed] [Google Scholar]
  21. Namgaladze D.; Brüne B. Macrophage fatty acid oxidation and its roles in macrophage polarization and fatty acid-induced inflammation. Biochim. Biophys. Acta, Mol. Cell Biol. Lipids 2016, 1861, 1796–1807. 10.1016/j.bbalip.2016.09.002. [DOI] [PubMed] [Google Scholar]
  22. Das U. N. Arachidonic acid and other unsaturated fatty acids and some of their metabolites function as endogenous antimicrobial molecules: A review. J. Adv. Res. 2018, 11, 57–66. 10.1016/j.jare.2018.01.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hoxha M. What about COVID-19 and arachidonic acid pathway?. Eur. J. Clin. Pharmacol. 2020, 76, 1501–1504. 10.1007/s00228-020-02941-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Haghighatdoost F.; Jabbari M.; Hariri M. The effect of L-carnitine on inflammatory mediators: a systematic review and meta-analysis of randomized clinical trials. Eur. J. Clin. Pharmacol. 2019, 75, 1037–1046. 10.1007/s00228-019-02666-5. [DOI] [PubMed] [Google Scholar]
  25. Tousson E.; Hafez E.; Zaki S.; Gad A. P53, Bcl-2 and CD68 expression in response to amethopterin-induced lung injury and ameliorating role of l-carnitine. Biomed. Pharmacother. 2014, 68, 631–639. 10.1016/j.biopha.2014.05.007. [DOI] [PubMed] [Google Scholar]
  26. Ruiz M.; Jové M.; Schlüter A.; Casasnovas C.; Villarroya F.; Guilera C.; et al. Altered glycolipid and glycerophospholipid signaling drive inflammatory cascades in adrenomyeloneuropathy. Hum. Mol. Genet. 2015, 24, 6861–6876. 10.1093/hmg/ddv375. [DOI] [PubMed] [Google Scholar]
  27. Tomé D. Amino acid metabolism and signalling pathways: potential targets in the control of infection and immunity. Nutr. Diabetes 2021, 11, 20. 10.1038/s41387-021-00164-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. He F.; Wu C.; Li P.; Li N.; Zhang D.; Zhu Q.; et al. Functions and signaling pathways of amino acids in intestinal inflammation. Biomed. Res. Int. 2018, 2018, 9171905 10.1155/2018/9171905. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Linden J.; Koch-Nolte F.; Dahl G. Purine release, metabolism, and signaling in the inflammatory response. Annu. Rev. Immunol. 2019, 37, 325–347. 10.1146/annurev-immunol-051116-052406. [DOI] [PubMed] [Google Scholar]
  30. Gidlöf O.; Sathanoori R.; Magistri M.; Faghihi M. A.; Wahlestedt C.; Olde B.; et al. Extracellular uridine triphosphate and adenosine triphosphate attenuate endothelial inflammation through miR-22-Mediated ICAM-1 Inhibition. J. Vasc. Res. 2015, 52, 71–80. 10.1159/000431367. [DOI] [PubMed] [Google Scholar]
  31. Hasko G.; Sitkovsky M. V.; Szabo C. Immunomodulatory and neuroprotective effects of inosine. Trends Pharmacol. Sci. 2004, 25, 152–157. 10.1016/j.tips.2004.01.006. [DOI] [PubMed] [Google Scholar]
  32. Sitkovsky M. V.; Lukashev D.; Apasov S.; Kojima H.; Koshiba M.; Caldwell C.; et al. Physiological control of immune response and inflammatory tissue damage by hypoxia-inducible factors and adenosine A2A receptors. Annu. Rev. Immunol. 2004, 22, 657–682. 10.1146/annurev.immunol.22.012703.104731. [DOI] [PubMed] [Google Scholar]
  33. Lin X.; Tao C.; Zhang R.; Zhang M.; Wang Q.; Chen J. N6-methyladenosine modification of TGM2 mRNA contributes to the inhibitory activity of sarsasapogenin in rheumatoid arthritis fibroblast-like synoviocytes. Phytomedicine 2022, 95, 153871 10.1016/j.phymed.2021.153871. [DOI] [PubMed] [Google Scholar]
  34. Park M. T.; Kang J. A.; Choi J. A.; Kang C. M.; Kim T. H.; Bae S.; et al. Phytosphingosine induces apoptotic cell death via caspase 8 activation and Bax translocation in human cancer cells. Clin. Cancer Res. 2003, 9, 878–885. [PubMed] [Google Scholar]
  35. Gomez-Larrauri A.; Presa N.; Dominguez-Herrera A.; Ouro A.; Trueba M.; Gomez-Muñoz A. Role of bioactive sphingolipids in physiology and pathology. Essays Biochem. 2020, 64, 579–589. 10.1042/EBC20190091. [DOI] [PubMed] [Google Scholar]
  36. Sharma L.; Prakash H. Sphingolipids are dual specific drug targets for the management of pulmonary infections: perspective. Front. Immunol. 2017, 8, 378. 10.3389/fimmu.2017.00378. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Atila A.; Alay H.; Yaman M. E.; Akman T. C.; Cadirci E.; Bayrak B.; et al. The serum amino acid profile in COVID-19. Amino Acids 2021, 53, 1569–1588. 10.1007/s00726-021-03081-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Du J.; Li X. H.; Li Y. J. Glutamate in peripheral organs: Biology and pharmacology. Eur. J. Pharmacol. 2016, 784, 42–48. 10.1016/j.ejphar.2016.05.009. [DOI] [PubMed] [Google Scholar]
  39. Lou T. F.; Sethuraman D.; Dospoy P.; Srivastva P.; Kim H. S.; Kim J.; et al. Cancer-specific production of N-acetylaspartate via NAT8L overexpression in non-small cell lung cancer and its potential as a circulating biomarker. Cancer Prev. Res. 2016, 9, 43–52. 10.1158/1940-6207.CAPR-14-0287. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

pr2c00788_si_001.pdf (772.4KB, pdf)

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Articles from Journal of Proteome Research are provided here courtesy of American Chemical Society

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