Serum metabolites as biomarkers in systemic sclerosis-associated interstitial lung disease

Systemic sclerosis (SSc) is a severe multi-organ disease with interstitial lung disease (ILD) being the major cause of death. While targeted therapies are emerging, biomarkers for sub-stratifying patients based on individual profiles are lacking. Herein, we investigated how levels of serum metabolites correlated with different stages of SSc and SSc-ILD. Serum samples of patients with SSc without ILD, stable and progressive SSc-ILD as well as of healthy controls (HC) were analysed using liquid targeted tandem mass spectrometry. The best discriminating profile consisted of 4 amino acids (AA) and 3 purine metabolites. l-tyrosine, l-tryptophan, and 1-methyl-adenosine distinguished HC from SSc patients. l-leucine, l-isoleucine, xanthosine, and adenosine monophosphate differentiated between progressing and stable SSc-ILD. In SSc-ILD, both, l-leucine and xanthosine negatively correlated with changes in FVC% predicted. Additionally, xanthosine was negatively correlated with changes in DLco% predicted and positively with the prognostic GAP index. Validation of l-leucine and l-isoleucine by an enzymatic assay confirmed both the sub-stratification of SSc-ILD patients and correlation with lung function and prognosis score. Serum metabolites may have potential as biomarkers for discriminating SSc patients based on the presence and severity of ILD. Confirmation in larger cohorts will be needed to appreciate their value for routine clinical care.


Patients with progressing ILD show different clinical features compared with patients with stable ILD.
With respect to autoantibody profiles, a higher prevalence of anti-Centromere antibodies occurred in non-ILD patients (n = 9, 75%; p = 0.0045), of which the majority had only sclerodactyly or no skin involvement (n = 7, 58.3%) ( Table 1). Anti-topoisomerase1-positivity, previously reported in the context of severe skin and lung involvement, was predominantly found in progressive SSc-ILD patients (n = 7, 58.3%, p = 0.0045, Fig. 1a) ( Table 1, Fig. 1b).
While no difference was seen in time from onset from first non-Raynaud's symptoms between the different patients' groups, progressive patients had a significantly longer average duration of ILD than stable patients (mean ± SD = 4.7 ± 2.7 vs. 1.7 ± 1.0 years, p = 0.0028) and a more severe skin fibrosis as assessed by modified Rodnan skin score (mRSS) than both stable SSc-ILD and non-ILD patients ( Table 1; Fig. 1c, mean ± SD = 10.3 ± 7.7 vs. 4.5 ± 5.8 and 3.7 ± 4.1, respectively; p = 0.024 and 0.011).
While there was a tendency towards a higher prevalence of immunosuppressive treatment, higher erythrocyte sedimentation rate (ESR) and higher C-reactive protein (CRP) levels in progressive SSc-ILD patients, changes were not statistically significant (Table 1). In addition, there was a non-significant tendency towards a lower body mass index (BMI) in progressive SSc-ILD compared to stable SSc-ILD and non-ILD patients ( Metabolic serum profiling detects differences between disease subtypes. To identify differentially regulated serum metabolites as potential discriminators between healthy individuals and different SSc subgroups, targeted metabolic profiling for 110 metabolites (Supplementary Table S1) using targeted LC-MS/ MS analysis was performed. After data processing and filtration, a total of 85 serum metabolites was detected, 56 in ESI (electron spray ionization) positive, 24 in ESI negative mode, and 5 in both modes (Supplementary  Table S2). To test our hypothesis of distinct and discriminating metabolite patterns we performed multivariate analysis (hierarchical clustering and PLS-DA), followed by univariate analysis. Performance of significant metabolites from both analyses was then assessed by ROC analysis.
Hierarchical clustering of the targeted serum profiles suggested differences between the four groups ( Fig. 2), which became more apparent in the subsequent PLS-DA (Fig. 3). Interestingly, the clearest separation was observed when comparing patients with progressing and stable ILD (Fig. 3b).

Serum metabolites show potential as biomarkers.
To define the best discriminating metabolites we used variable importance in projection (VIP) scores (Supplementary Tables S3a,b) with a rather strict threshold set at ≥ 2. Based on this approach, we found distinct metabolite signatures for the different group comparisons (Table 2). Healthy individuals and SSc patients were best distinguished by changes in the amino acids (AAs) l-tyrosine and l-tryptophan, whereas SSc patients with and without ILD were best classified by dysregulation of l-threonine, xanthosine, 3-aminoisobutyric acid, and adenosine monophosphate. Progressors compared to stable ILD patients were characterized by alterations of l-leucine, l-isoleucine, xanthosine, and adenosine monophosphate.
In accordance with the results from the multivariate analysis, ANOVA-based univariate analysis with FDR correction for multiple testing identified l-leucine (p = 0.028), l-tyrosine (p = 0.077), xanthosine (p = 0.032), Table 1. Clinical characteristics of SSc patients of the Zurich cohort at time point of serum collection. BMI body mass index, CPI composite physiologic index, CRP C-reactive protein, dcSS diffuse cutaneous SSc, DLco carbon dioxide diffusion capacity, ESR erythrocyte sedimentation rate, FEV1 forced expiratory volume in one second, FVC forced vital capacity, lcSSc limited cutaneous SSc, GAP gender-age-physiology, mRSS modified Rodnan skin score, SD standard deviation, TLC total lung capacity. a n dependent on available datasets. b Disease duration after onset of first non-Raynaud's symptoms, c Symptoms reported by patients d Disease duration from first diagnosis of SSc-ILD c Including treatment with following medications: azathioprine, corticosteroids, hydroxychloroquine, leflunomide, methotrexate, mycophenolate mofetil, rituximab, and tocilizumab.

Characteristics
Non-ILD SSc (n = 12) Stable SSc-ILD (n = 12) Progressive SSc-ILD (n = 12) All (n = 24-36 a ) General Sex, n (%) www.nature.com/scientificreports/ l-tryptophan (p = 0.028 for ESI+ and ESI− modes), and 1-methyladenosine (p = 0.077) as significantly altered between groups. Analysis of peak areas showed that levels of l-leucine and l-isoleucine were highest in healthy individuals and gradually decreased from SSc patients without ILD to those with stable ILD. Interestingly, patients with progressing ILD had increased levels compared with stable SSc-ILD patients (Fig. 4a,b). For xanthosine, we observed significantly lower levels in healthy individuals compared with non-ILD SSc patients as well as for stable SSc-ILD patients compared with progressing SSc-ILD patients (Fig. 4c).
Notably, in patients with SSc-ILD, both, l-leucine and xanthosine negatively correlated with changes in the lung function parameter FVC% predicted (r = − 0.48 and − 0.51; p = 0.016 and 0.011, respectively), while xanthosine also negatively correlated with changes in DLco% predicted (r = − 0.58; p = 0.0038) as well as with the GAP index (r = 0.67, p = 0.0005) (Fig. 6). The correlations with changes in absolute values of FVC and DLco% predicted are shown in Supplementary Fig. S1a,b.
Validation of higher values of the BCAAs l-leucine and l-isoleucine in progressive SSc-ILD compared to stable disease with the use of an enzymatic assay resulted in similar results as LC-MS/MS, with significantly higher values detected in progressive patients (mean = 286.5 and 235.5 µM, for progressive and stable patients, respectively; p = 0.005) (Fig. 7a). In ROC analysis (AUC 0.818, 95% CI 0.631-1.032), a cut-off value of 250.3 µM separated stable from progressive patients with a sensitivity of 72.7% and a specificity of 83.3% (Fig. 7b). Furthermore, BCAA levels negatively correlated with changes in FVC (r = − 0.55; p = 0.0063) and DLco% predicted (r = − 0.56; p = 0.0064) and positively with the GAP index (r = 0.66, p = 0.0005) (Fig. 7c-e). The correlations with changes in absolute values of FVC and DLco% predicted are shown in Supplementary Fig. S1c.
For external validation, we assessed BCAA levels in an independent, prospectively followed cohort of SSc-ILD patients from Paris. Herein, we found a similar trend towards higher BCAA levels in patients with future progression of ILD as compared with patients, who remained stable during follow-up (mean = 311.8 ± 26.5 and 289.6 ± 49.4 µM, respectively; p = 0.26). In ROC analysis, BCAA levels correlated negatively with DLco% predicted (r = − 0.38, p = 0.027), and positively with the mortality-predicting GAP index (r = 0.39, p = 0.022). The results are shown in Supplementary Fig. S2, for details including patients' characteristics refer to data supplement.
In order to assess whether elevated BCAA levels specifically reflected fibrotic and/or pulmonary processes, we measured BCAA serum levels in 29 patients with non-fibrotic, primary myositis 26 without concomitant ILD. Interestingly, BCAA levels significantly correlated with disease activity (Spearman's rho = 0.52, p = 0.0037). Patients with CK levels exceeding the defined upper limit of normal (170 U/l) had significantly higher BCAA levels than patients with low CK levels (mean = 309.5 ± 88.2 and 253.4 ± 41.6 U/l for active and inactive disease, respectively; p = 0.032). No correlation with systemic inflammation, measured by CRP as a surrogate marker, was observed (Pearson's r = − 0.15, p = 0.46). All results are visualized in Supplementary Fig. S3, for details including patients' characteristics refer to data supplement. www.nature.com/scientificreports/

Discussion
Our study assessed the potential of serum metabolites as circulating biomarkers for disease stage and severity of SSc(-ILD). Serum metabolite profiling yielded a final set of 4 amino acids and 3 purine metabolites. Changes in the levels of l-tyrosine, l-tryptophan, and 1-methyl-adenosine distinguished HC from SSc and alterations in l-leucine, l-isoleucine, xanthosine, and adenosine monophosphate profiles differentiated between progressing and stable SSc-ILD with l-tryptophan and l-leucine being the best performing discriminators in the respective groups. Increased serum levels of l-leucine and l-isoleucine in progressing compared with stable SSc-ILD patients were confirmed with an independent enzymatic assay with definition of a critical threshold.
Our results are in accordance with the previously reported changes in energy metabolism in fibrotic conditions [13][14][15] . Similar to our findings, previous studies in SSc and/or fibrosing ILD reported decreased serum levels of l-tryptophan 27,28 or upregulation in IPF lungs 14 . Serotonin 29 , a downstream product of l-tryptophan, was upregulated in fibrotic conditions 30 including SSc 31 . Inhibition of the serotonin receptors had anti-fibrotic effects in experimental conditions and in a first in human proof-of-concept study [31][32][33][34] . Elevated levels of 1-methyladenosine 29 have so far been associated with proliferative and/or metastatic tumors 35,36 . Non-modified adenosine, generated extracellularly from ATP/ADP breakdown 29 , is a well-known pro-fibrotic mediator [37][38][39][40][41] and its inhibition was beneficial in in fibrotic animal models 37,39,40 . As previously observed in lung fibrosis and SSc 14 , there was a tendency towards lower adenosine monophosphate (AMP) levels in progressing SSc-ILD patients. Interestingly, AMP-activated protein kinase (AMPK), a critical sensor of energy sufficiency, acts as central metabolic switch in cell metabolism and thereby opposes mTOR (mechanistic target of rapamycin) signaling 42 . Activation of AMPK causes a shift from anabolism to catabolism to generate ATP to restore energy homeostasis. In fibrotic conditions, AMPK activity was decreased 43 . Activation of AMPK reversed established lung fibrosis in the bleomycininduced lung fibrosis model 16,44 . Patients with progressing compared with stable ILD displayed increased levels of l-leucine and l-isoleucine. Both essential AA are highly abundant in elastin 45 , a major extracellular matrix (ECM) protein, and could thus reflect increased ECM degradation 46 . Both AAs were upregulated in the lung tissue and the exhaled breath condensate of patients with IPF 14,19 . BCAAs, particularly l-leucine, stimulate protein synthesis and reduce protein breakdown via the phosphorylation of mTOR 47 . mTOR plays an important role in anabolic processes by causing cells to switch from oxidative phosphorylation to aerobic glycolysis 48 . In SSc und pulmonary fibrosis, mTOR activity is increased 49 . Its inhibition by rapamycin prevented experimental fibrosis and showed some benefit in diffuse cutaneous SSc patients in a small, randomized, phase 1 study 48   www.nature.com/scientificreports/ of xanthosine, a purine nucleoside, were higher in progressing compared with stable SSc-ILD. Purine receptors were suggested to play a role in fibrosis 50 . Clinically, in our study, progressive SSc-ILD patients were characterized by a higher prevalence of antitopoisomerase 1 antibodies, more severe skin fibrosis, worse lung function and worse prognostic scores. In addition, they showed a substantial decline of lung function in the observation period. Of note, both l-leucine and xanthosine negatively correlated with pulmonary function (changes in FVC% and DLco% predicted respectively) and one ILD-mortality prediction score (GAP index), which underlines their relevance as biomarker candidates. Most importantly, we could validate l-leucine and l-isoleucine, the best discriminators of progressing vs. stable SSc-ILD, in an independent experiment. In the BCAA assay, a defined cut-off value of 250. www.nature.com/scientificreports/ with changes of lung function parameters and positively with the GAP index. Similar results were obtained by analysing an independent, external validation cohort of SSc-ILD patients from Paris. The limitations of our study mainly arise from the limited number of patients. Validation in external multicentre cohorts will be needed to assess the future usefulness in clinical routine. Furthermore, potential correlations with other protein biomarkers should be assessed. Prediction modelling for progression of ILD with circulating biomarkers, clinical, functional, and imaging parameters would be ideal to test the performance of circulating biomarkers only models versus mixed models. This, however, again warrants large datasets and multi-centre cohorts.
Disturbances in AA metabolism have been reported in other studies on SSc and IPF. Differences in identified metabolite profiles might arise from different analysis and detection methodologies (i.e. mass spectrometry or ion exchange chromatography) or differences in sample collection, storage and processing. We decided on a large-scale targeted analysis on a triple quadrupole mass spectrometer rather than an untargeted full-scan approach applying high-resolution mass spectrometry due to the increased sensitivity, linearity, reproducibility and straight-forward metabolite identification of targeted LC-MS/MS acquisition 51 . Although the applied assay covered multiple differentially regulated pathways, it is limited to the tested 110 metabolites. Further research could be carried out using untargeted metabolic profiling or a targeted assay covering a wider range of metabolites such as carbohydrates or phospholipids.
Additionally, given the real-life scenario, we cannot exclude that the fasting state and/or the diet might have some influence especially on the measured AA levels. Supplementation with micronutrients and/or vitamins did not occur in our patients' cohort and we draw blood at approximately the same time in the morning to eliminate potentially confounding factors as well as possible. The fact, however, that changes in (BC)AA levels were reported consistently in other studies of lung fibrosis argues against a strong or exclusively dietary influence.
Furthermore, we have to take into account that SSc is a multi-organ disease. In our study, patients with progressive ILD also had more extensive skin disease pointing towards a more severe disease state. We can therefore not assume that changes in BCAA serum levels exclusively reflect lung pathology. Taking into account the correlation between CK and BCAA levels in primary myositis patients, it seems likely that in both myositis and SSc(-ILD), high serum BCAA levels reflect overall disease activity characterized by a switch to an anabolic state with subsequent changes in AA metabolism. Thus, in these complex diseases, changes in BCAA levels can probably not be attributed to either pro-fibrotic or immune processes since they may rather reflect the disturbed metabolism that arises from global tissue remodelling with varying contributions of different cell types. This argues, however, not against the usefulness of BCAA as progression markers in a given disease context.
In conclusion, our study suggests that serum metabolites might have potential as circulating biomarkers for discriminating stable and progressive SSc patients. Confirmation in larger multi-cohorts will be needed to fully appreciate their value for routine clinical care.

Methods
Patients and controls. For this study, SSc patients from the University Hospital Zurich's prospective SSc patients cohort were divided in the following three groups: patients without ILD (non-ILD), patients with stable ILD and patients with progressive ILD (n = 12 per group).
Progressive ILD was defined as either a relative decrease in FVC% predicted of ≥ 10% independent of changes in DLco% predicted, a decrease in FVC% predicted of 5-9% combined with a decrease in DLco% predicted of ≥ 15%, or an increase of pathologic lung involvement in high resolution computed tomography (HRCT) from < 20% to > 20% compared to the previous visit [mean follow-up interval = 14 months (range = 9-26)] 52,53 . Stable ILD was defined as the absence of the above-mentioned criteria for progression in any of the visits recorded in the EUSTAR database 54 . Non-ILD patients were defined as having no evidence of lung involvement on HRCT scans.
Progressive SSc-ILD patients were matched with stable SSc-ILD and non-ILD SSc patients as well as healthy controls (HC, n = 12) for age, sex, and time point of blood withdrawal (morning).
In addition, serum BCAA levels were analyzed in two additional cohorts of SSc-ILD and primary myositis patients. Detailed information on these patients can be found in the Supplementary Methods, data supplement pp. 15.
Serum collection and processing was performed following a standardized protocol in accordance with international guidelines 55 . Aliquots of serum were stored at − 80 °C until further processing.
Written informed consent was obtained from all enrolled individuals. Ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS). Serum profiling of 110 metabolites of SSc patients and HC was performed using a targeted ultra-high performance liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) assay as described previously 56 .
For metabolite extraction, frozen serum samples were thawed at room temperature and 300 μL of ice-cold acetonitrile:ethanol (1:1) were added to 100 μL of each sample. Samples as well as an equally treated water-only control were vortexed, incubated at − 80 °C for 30 min, centrifuged at 14,000g and 4 °C for 15 min in order to pellet precipitated proteins. 350 μL of supernatant of each sample were transferred to fresh tubes and dried using a speed vacuum centrifuge at room temperature and minimum pressure 5.1 Torr (Savant SPD1010, Thermo). www.nature.com/scientificreports/ Dried samples were stored at − 80 °C until further processing. Before data acquisition, samples were reconstituted in 300 μL 10% methanol, resulting in a final dilution of 1:3 in respect to the initial serum volume, sonicated for 1 min in a water bath in order to ensure complete reconstitution, and centrifuged at 14,000g and 4 °C for 20 min. Supernatants were transferred into glass vials and a quality control (QC) sample was generated by pooling 10 µL of all extracted samples. After, samples were stored at 10 °C until analysis. Extracted samples were analysed on a Xevo TQ-S triple quadrupole mass spectrometer interfaced with an electrospray ionization source and coupled to an ACQUITY UPLC I-Class system (both Waters, USA). Chromatographic separation of 1 µL extract was performed using reversed-phase chromatography (ACQUITY UPLC HSS T3, 2.1 × 100 mm, 1.8 µm column, Waters, USA) with mobile phases composed of (A) 0.1% formic acid in H 2 O and (B) 0.1% formic acid in methanol. Further LC conditions and MS-specific parameters were previously described 56 . LC-MS/MS analysis was performed in negative electrospray ionization (ESI−) and positive electrospray ionization (ESI+) mode by two independent injections.
Before analysis of the first sample, the instrument was equilibrated by injecting the QC sample 10 times. Samples were then injected in a randomized block design order with intermittent analysis of the QC sample after every fifth sample in order to observe instrumental fluctuations.
Branched chain amino acid assay. For quantification of the branched chain amino acids (BCAAs) leucine, isoleucine and valine, a commercial colorimetric analysis kit (ab83374, Abcam, United Kingdom) was used following the manufacturer's instructions. Briefly, leucine standards and 2.5 times diluted serum samples were incubated for 30 min at room temperature with an equal volume of enzyme and substrate mix, initiating the colorimetric reaction. Absorption at 450 nm was measured using a GloMax-Multi Detection System microplate reader (Promega, USA) and sample BCAA concentration was calculated in relation to the standard curve.

Data analysis.
Raw metabolomics data were processed in Skyline 4.2 (MacCoss Lab Software, USA).
Metabolites were selected for statistical analysis if the peak area decreased linearly in diluted samples, no detection of background noise in extracted blank sample was observed, the coefficient of variation was ≤ 20% in QC samples, as well as, a proper peak shape was detected. Peak areas were then used for further statistical analysis as specified below.
Statistical data analysis and graphical visualization was performed using R 3.6 with the mixOmics package and GraphPad Prism 8.0.0 (GraphPad Software, USA).
For univariate analysis and hierarchical clustering, metabolite peak areas were Z-score transformed for normalization.
Univariate analysis of metabolomics data was performed by applying one-way ANOVA as well as Tukey's post-hoc test for multi-group data. Two-group data was analyzed by Student's t-test or Mann-Whitney U test for parametric and nonparametric data, respectively. Categorical data were analysed using a Chi-Square test. Performance of potential biomarkers was assessed by Receiver Operating Characteristic (ROC) curve analysis. Pearson's (parametric) and Spearman's (non-parametric) correlation was used to assess linear relationships between metabolites and clinical parameters. For multivariate analysis, data were subjected to partial least-square discriminant analysis (PLS-DA) with variable importance in projection (VIP) scores of ≥ 2 being considered statistically significant.
Data are presented either as medians with interquartile range (boxplots; horizontal line = median, boxes = interquartile range) or as means with standard deviation (SD; tables). For false discovery rate (FDR)corrected univariate analysis of metabolites (excluding post-hoc testing), the significance level was set to 0.1, while for all other analyses p-values < 0.05 were considered statistically significant.

Data availability
All data are presented either in the main text or in the data supplement. The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.