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Identification and characterization of lysophosphatidylcholine 14:0 as a biomarker for drug-induced lung disease

Drug-induced interstitial lung disease (DILD) occurs when drug exposure causes inflammation of the lung interstitium. DILD can be caused by different types of drugs, and some DILD patterns results in a high mortality rate; hence, DILD poses a serious problem in clinical practice as well as drug development, and strategies to diagnose and distinguish DILD from other lung diseases are necessary. We aimed to identify novel biomarkers for DILD by performing lipidomics analysis on plasma samples from patients with acute and recovery phase DILD. Having identified lysophosphatidylcholines (LPCs) as candidate biomarkers for DILD, we determined their concentrations using validated liquid chromatography/mass spectrometry biomarker assays. In addition, we evaluated the ability of LPCs to discriminate patients with acute phase DILD from those with recovery phase DILD, DILD-tolerant, or other lung diseases, and characterized their association with clinical characteristics. Lipidomics analysis revealed a clear decrease in LPC concentrations in the plasma of patients with acute phase DILD. In particular, LPC(14:0) had the highest discriminative index against recovery phase and DILD-tolerant patients. LPC(14:0) displayed no clear association with causal drugs, or subjects’ backgrounds, but was associated with disease severity. Furthermore, LPC(14:0) was able to discriminate between patients with DILD and other lung diseases, including idiopathic interstitial pneumonia and lung disease associated with connective tissue disease. LPC(14:0) is a promising biomarker for DILD that could improve the diagnosis of DILD and help to differentiate DILD from other lung diseases, such as idiopathic interstitial pneumonia and connective tissue disease.


Materials and methods
Subjects. All patients were recruited from Shinshu University, Nippon Medical School, Chiba University, and Hiroshima University. Healthy volunteers were recruited from the Yaesu Sakura-Dori Clinic. DILD was diagnosed by respiratory specialists according to the following Japanese diagnostic criteria 6 that ameliorated the diagnostic criteria reported by Camus et al. 5 : 1) History of ingestion of a drug known to induce lung injury; 2) the clinical manifestations have been reported to be induced by a drug; 3) other causes of the clinical manifestations could be ruled out; and 4) improvement of the clinical manifestations after drug discontinuation.
Based on HRCT findings, DILD was classified into four patterns: DAD, NSIP, OP, and others, by respiratory specialists at each hospital. Some patients displayed multiple patterns.
DILD recovery was determined at least two weeks after the onset of DILD by respiratory specialists at each hospital, based on the improvement of clinical symptoms, lung imaging findings (e.g., HRCT), and oxygenation status (e.g., SpO 2 ). Patients who were prescribed DILD-causing drugs without DILD for at least 12 weeks were enrolled in the DILD-tolerant group. Differences in general and background diseases and clinical characteristics Sample collection. Blood samples were collected into vacuum blood collection tubes containing 7 mL EDTA-2Na by venipuncture (Venoject II, Terumo, Tokyo, Japan) and were immediately centrifuged at 2500 × g and 4 °C for 10 min. Plasma was dispensed into screw-capped polypropylene tubes and stored at − 80 °C within 2-4 h of drawing blood.
Lipidomics and candidate biomarker discovery. Lipidomics was performed as described previously 20 .
The processed lipid level data are presented in Supplementary information 2. Lipids confirmed by fragment ions were used for data analysis. Significant differences in lipid levels were assessed using the false discovery rate (FDR)-adjusted Welch's t-test, and candidate biomarkers were identified using the effect size (Hedge's g).

LPC validated liquid chromatography/mass spectrometry (LC/MS) biomarker assay and biomarker validation and characterization.
A validated LC/MS biomarker assay was performed on the LPCs as described previously 21 , with some modifications. The modified details and validated assay parameters and performance are described in Supplementary information 1 and Supplementary information 3. The determined LPC concentrations and associated clinical characteristics of patients with and without DILD are presented in Supplementary information 4.
Statistical analysis. LPC concentrations were compared between two or more groups using Student's t-tests or ANOVA with Tukey's post-hoc test in GraphPad Prism 9 (GraphPad Software, San Diego, CA, USA). The discrimination ability of different LPCs was assessed using the area under the curve (AUC) of receiver operating characteristic (ROC) analysis using GraphPad Prism 9. The correlation between LPC concentration and severity of symptoms in DILD was calculated using Pearson's correlation coefficient. Multivariable logistic regression analysis of LPC concentration and associated clinical characteristics was performed using R 3.6.0 software (R Foundation for Statistical Computing, Vienna, Austria).  www.nature.com/scientificreports/ lipids exceeded the stringent threshold (FDR-adjusted adjusted p < 0.05 and effect size (|g|) > 1) level of difference between the acute and recovery phase samples, including 37 and 18 in the screening and validation sets, respectively (Fig. 1B). Then, lipids exceeding the threshold in both the screening and validation studies were extracted, which resulted in nine lipids being identified as candidate biomarkers (Fig. 1B), all of which were expressed at lower levels in the acute phase samples compared to that in the recovery phase samples. Since six of the nine candidate biomarkers were LPCs, we focused on LPCs in subsequent experiments.

Validation of LPCs as DILD biomarkers.
To validate LPCs as candidate biomarkers for DILD, we estab-  Table 1 and listed individually in Supplementary information 4. Next, we compared plasma LPC concentrations between acute and recovery phase of DILD patients and DILD-tolerant patients ( Fig. 2A). All eight LPCs were expressed at significantly lower levels in patients with DILD in the acute phase than in the recovery phase or in DILD-tolerant patients. ROC analysis was then performed to evaluate the discriminative performance of the individual LPCs between acute and recovery phase DILD patients ( Furthermore, we examined the association between plasma LPC(14:0) concentration and severity of symptoms in DILD, by assessing parameters such as cough, breathlessness, body temperature, oxygen administration, and SpO 2 levels. The severity of cough and breathlessness were evaluated by the attending physician based on the classification criteria for severity of adverse drug reactions 22 and scored from 0 (no symptoms) to 3 (severe symptoms). SpO 2 levels were only evaluated in patients without oxygen administration, since oxygen administration results in amelioration of SpO 2 levels. As shown in Fig. 3C, all severity scores of DILD symptoms correlated significantly with plasma LPC(14:0) levels, with breathlessness levels exceeding the absolute correlation coefficient of > 0.4. In addition, patients with oxygen administration demonstrated significantly lower plasma LPC(14:0) concentrations than those without (Fig. 3D). These results suggest that plasma LPC(14:0) concentration is associated with those symptom severities in DILD that reflect disease severeity.
Along with DILD pattern, causal drugs, and symptom severities, we examined the association between plasma LPC(14:0) concentrations and other clinical chemistry/biomarker levels in patients with and without DILD. As shown in Fig. 3E, plasma LPC(14:0) concentration correlated significantly and negatively with blood C-reactive protein (CRP) levels (r = -0.60). Significant correlations were also observed with other biomarker levels, except KL-6, but with absolute correlation coefficients of < 0.4.
Using multiple regression analysis, we examined the effects of the physical and clinical background characteristics of the patients, which may modulate the blood lipid profiles [23][24][25] . The physical characteristics examined were sex, age, body mass index (BMI), and smoking experience, and the clinical characteristics were chest radiation and associated diseases (LuCa, other cancers, high blood pressure, diabetes, lipid disorder, and heart disease). As shown in Fig. 4A, DILD was the primary factor contributing to plasma LPC(14:0) concentrations (p = 9.7E -13 ), and BMI was the only other factor that contributed significantly (p = 6.3E -4 ; Fig. 4B). To exclude the effect of BMI on plasma LPC(14:0) concentrations and evaluate the effect of DILD, we adjusted plasma LPC(14:0) concentrations for BMI using the beta obtained from multiple regression analysis and compared concentrations between acute and recovery phase DILD patients or DILD-tolerant patients. As shown in Fig. 4C, adjusted LPC(14:0) concentrations were significantly lower in patients with acute phase DILD than that in the recovery phase or DILD-tolerant patients. ROC analysis between acute and recovery phase DILD patients (Fig. 4D) and between acute phase DILD patients and DILD-tolerant patients (Fig. 4E) Table 2.
Although there were several differences in background characteristics between patients with DILD and other lung diseases, multiple regression analyses demonstrated that background characteristics had a negligible effect (Fig. 4A). As shown in Fig. 5A, plasma LPC(14:0) concentrations were significantly lower in patients with acute   www.nature.com/scientificreports/ KL-6 and SP-D biomarkers in LuCa, IIP, and CTD cases. Therefore, this study demonstrated that LPCs, particularly LPC(14:0), could be a novel and effective biomarker for DILD. Blood LPC levels have previously been proposed as candidate biomarkers for acute inflammatory diseases. For instance, serum LPC levels are decreased in patients with community-acquired pneumonia and are associated with their prognosis and mortality 26,27 . Consistently, we found that plasma LPC(14:0) concentrations were decreased in patients with BaPn. LPCs have also been proposed as useful biomarkers for diagnosing sepsis and predicting patient mortality 28 , while metabolomics and lipidomics screening have recently revealed decreased serum LPC levels in patients with COVID-19 with lung injury 29,30 . Our results further strengthen the association between blood LPC levels and acute inflammation. Moreover, LPCs have been reported as candidate biomarkers for cancer and diabetes, with which chronic inflammation is associated 25,31,32 . However, we found that plasma LPC(14:0) concentrations were higher in patients with the chronic inflammatory lung diseases IIP, CTD, COPD, and BrAs, than that in patients with DILD. In addition, we demonstrated the negligible impact of cancers and   www.nature.com/scientificreports/ diabetes on blood LPC(14:0) concentrations. Therefore, the association between chronic inflammation and blood LPC levels may be to a lesser extent than that of acute inflammation and blood LPC levels.

Discussion
Notably, the estimated cut-off values of LPC(14:0) for discriminating between acute DILD and IIP were compatible with recovery phase DILD, or DILD tolerance, which was approximately 275 ng/mL. Consequently, LPC(14:0) could be useful for specifically diagnosing DILD compared to IIP, which have different treatment strategies. For LuCa and CTD, the estimated cut-off values of LPC(14:0) were around 380 ng/mL, which is over 100 ng/mL above the cut-off for DILD diagnosis. Whilst LPC(14:0) was not applicable for discriminating DILD from BaPn, culture-, antigen-, or gene-based tests for bacteria can be used for diagnosing BaPn.
To date, no direct mechanism-based evidence of decreased plasma LPC concentrations has been provided in patients with DILD. However, the roles of LPCs and their metabolizing enzyme autotaxin in inflammation and immune response have been well characterized 33,34 . Autotaxin acts as phospholipase D to cleave choline from LPC and produce lysophosphatidic acid (LPA), which activates various LPA receptors to induce inflammatory and anti-inflammatory responses 33,34 . Serum autotoxin levels have been reported to correlate with the severity and mortality of acute respiratory distress syndrome 35 , and increased serum autotoxin levels have been detected in patients with severe COVID-19 36 . Plasma autotaxin levels also correlate with mortality in patients with severe sepsis 37 , suggesting that autotaxin may play a role in systemic hyper-inflammation and decreased plasma LPC concentrations in DILD. However, autotaxin also exerts important effects in chronic inflammatory diseases,   34 , and is present at increased levels in the bronchoalveolar lavage fluid of IPF models 38 . Since our study demonstrated a lesser impact of chronic inflammation on plasma LPC levels than that of acute inflammation, the role of autotaxin on the plasma LPC levels during acute and chronic inflammation may be different and/or could involve other factors. Along with the mechanism regulating the decreased in plasma LPC concentrations in patients with DILD, there is no direct evidence for the role of decreased LPCs themselves on DILD pathogenesis. However, LPC has been reported to activate multiple signaling pathways involved in oxidative stress and inflammatory responses 39 . The roles of LPCs in endothelial cells, including vascular endothelium, and immune cells were characterized in association with worsened inflammation. For example, LPCs induce the production of cytokines in monocyte recruitment, cytotoxicity, apoptosis, and oxidative stress in endothelial cells [40][41][42][43] as well as those involved in macrophage and B cell activation, and apoptosis in immune cells [44][45][46] . Therefore, one possible role of reduced LPCs in DILD pathogenesis is the counteraction of lung inflammation.
Despite our novel findings, this study has several limitations. First, the sample sizes for patients with DILD and related lung diseases were relatively small for verified clinical assessment. Second, although we demonstrated that causal drugs had a limited impact on plasma LPC concentrations, we were unable to fully exclude the possibility that the primary effects of causal drugs and their associated diseases may have influenced our outcomes. Third, we recruited patients with DILD from four core hospitals using the same sampling protocol; however, hospital-to-hospital variation in sample preparation may have yielded slightly different plasma LPC concentrations. Fourth, we did not control the alcohol and food habits of the patients included in this study, which might have affected postprandial metabolite responses since alcohol intake and a high-fat diet have been shown to alter the expression of hepatic genes, including those related to energy homeostasis and diet metabolism 47,48 . Fifth, this study only included Japanese patients, and thus our findings may not be applicable to other ethnic groups. Finally, we did not examine the effects of different radiological patterns, such as the distributed area of the affected lung lesions, which may be associated with different inflammatory pathways as well as lipid alteration. Therefore, these limitations should be addressed in further studies before applying LPCs as practical biomarkers for diagnosing DILD in a clinical setting.
In conclusion, this study identified LPC(14:0) as a general biomarker of DILD, which was superior to KL-6 and SP-D when discriminating between acute and recovery phase DILD and tolerant controls. In addition, we characterized an association between LPC(14:0) and disease severity and demonstrated its ability to discriminate between DILD and IIP or CTD. Together, the findings of this study suggest that LPC(14:0) could help diagnose DILD before and during the administration of drugs indicated to treat the onset of DILD or in patients with suspected DILD having shortness of breath or dyspnea.

Data availability
All data generated or analysed during this study are included in this published article and its supplementary information files.