Plasma and urine metabolomic analyses in aortic valve stenosis reveal shared and biofluid-specific changes in metabolite levels.

Aortic valve stenosis (AVS) is a prevalent condition among the elderly population that eventually requires aortic valve replacement. The lack of reliable biomarkers for AVS poses a challenge for its early diagnosis and the application of preventive measures. Untargeted gas chromatography mass spectrometry (GC-MS) metabolomics was applied in 46 AVS cases and 46 controls to identify plasma and urine metabolites underlying AVS risk. Multivariate data analyses were performed on pre-processed data (e.g. spectral peak alignment), in order to detect changes in metabolite levels in AVS patients and to evaluate their performance in group separation and sensitivity of AVS prediction, followed by regression analyses to test for their association with AVS. Through untargeted analysis of 190 urine and 130 plasma features that could be detected and quantified in the GC-MS spectra, we identified contrasting levels of 22 urine and 21 plasma features between AVS patients and control subjects. Following metabolite assignment, we observed significant changes in the concentration of known metabolites in urine (n = 14) and plasma (n = 15) that distinguish the metabolomic profiles of AVS patients from healthy controls. Associations with AVS were replicated in both plasma and urine for about half of these metabolites. Among these, 2-Oxovaleric acid, elaidic acid, myristic acid, palmitic acid, estrone, myo-inositol showed contrasting trends of regulation in the two biofluids. Only trans-Aconitic acid and 2,4-Di-tert-butylphenol showed consistent patterns of regulation in both plasma and urine. These results illustrate the power of metabolomics in identifying potential disease-associated biomarkers and provide a foundation for further studies towards early diagnostic applications in severe heart conditions that may prevent surgery in the elderly.

Introduction

Aortic valve stenosis (AVS) results from inflammation caused by mechanical stress, lipid infiltration leading to fibrosis, leaflet thickening, and eventually calcification [1, 2]. The risk of AVS increases with age with 10% of AVS patients being above the age of 80 [3]. Pharmacologic treatments of AVS are often ineffective [4] and surgical valve repair or replacement is eventually needed in the elderly when surgery is often problematic [5]. Elevated concentration of plasma lipopotein(a) remains the most robust marker for AVS that may account for disease pathophysiology and valve molecular anomalies described in AVS [6]. Nevertheless, early diagnosis and prognosis of AVS can be greatly improved by the high-throughput measurement of reliable molecular biomarkers in easily accessible biospecimens.

Metabolomics provides a platform for biomedical discovery, as well as clinical and pharmaceutical applications, which has been extensively used for biomarker discovery, drug response ascertainment and disease pathway identification [7]. It relies on the qualitative and quantitative analysis of small molecular weight metabolites, which are end products of genome expression while integrating consequences of environmental exposures [8]. It has been successfully used for in depth characterisation of metabolic changes in health and disease [9] and particularly powerful to identify metabolites associated with increased risk of metabolic and vascular diseases [10-12]. The application of metabolomics to test associations between AVS and many metabolites simultaneously represents a prospect of significant advances for early disease diagnosis and improved treatment efficacy.

Here, we applied highly sensitive untargeted metabolomics based on gas chromatography mass spectrometry to identify metabolites associated with AVS. Through plasma and urine paralleled metabolomic profiling of AVS patients and control subjects, we sought to identify a series of metabolic features associated with the disease. We also investigated the existence of metabolites showing either shared or biofluid-specific association with AVS. These results underline the power of metabolomics to identify potential biomarkers for early AVS diagnosis and targets for therapeutic applications that may prevent or anticipate the need for cardiac surgery in the elderly.

Material and methods

Study subjects and AVS diagnosis

Subjects were recruited as part of a comprehensive study on coronary artery disease between 2007 and 2009 [13]. They were selected based on the presence (cases) or absence (controls) of AVS as clinically determined by the occurrence of a systolic murmur in the aortic valve area, which was subsequently confirmed in cases by echocardiography. Urine and plasma samples from 46 AVS patients and 46 healthy controls (matched for sex and age ± 5 years) were used in this study. About 30 ml of urine and 20 ml of arterial blood were collected in subjects after 12 hours fasting. Blood was collected on EDTA and plasma was separated by centrifugation at room temperature. Urine samples were centrifuged at room temperature. Plasma and urine aliquots were stored at -80°C until metabolomic analysis.

All subjects provided a written informed consent, and the study protocol was approved by the International Review Board (IRB) at the Lebanese American University. All protocols were performed according to the Helsinki Declaration of 1975.

Gas chromatography coupled with mass spectrometry

Samples were prepared for metabolite extraction using methods optimized for urine [14] and plasma [15]. The internal standard 2-isopropylmalic acid was used for quality control. Trimethylsilylation was applied in sample preparation of urine and plasma extracts for gas chromatography mass spectrometry (GC-MS) acquisition. Samples were subjected to GC-MS HP6890 (Agilent Technologies, Santa Clara, CA) equipped with a capillary column HP-5MS 5% phenyl methyl siloxane of 30m nominal length, 250μm nominal diameter and 0.25μm nominal film thickness (Agilent Technologies, Santa Clara, CA). A 1μL aliquot of the derivatized solution was injected under split mode with a ratio 3:1 using Helium gas. GC-MS raw chromatograms were exported in CDF format for data pre-processing, and CSV files were obtained which included peak retention time, peak height, peak area and metabolites identification using the NIST08 library (https://chemdata.nist.gov/). Metabolite annotation was manually checked using a similarity criterion of ≥80%. Data from negative controls (same reagents and conditions excluding sample) were acquired with GC-MS in order to remove artifact peaks from the solvents used in extraction and derivatization.

Metabolomic data pre-processing

GC-MS raw data were pre-processed to generate a comprehensive peak table that included all detected peaks characterized by a specific retention time (RT), mass to charge ratio (m/z) and the intensity of each peak across multiple samples from multiple sample groups. The XCMS (v 3.6.1) tool in R statistical language (through Bioconductor v 1.30.4) was used for GC-MS data pre-processing where RT were aligned, and signal drift and batch effect were corrected. XCMS uses CDF format as input, and gives a data Matrix table as output. Using XCMS, peak detection was performed while the peak width parameter was set visually after assessing the chromatographic peaks belonging to the internal standard. Thus, based on the internal standard, the range of RT values was set between 450 and 460 seconds, the m/z range was between 275 and 278 and the value of maximum expected deviation of m/z values was set to 3ppm. Then, peak alignment was performed so that all RTs can be adjusted to match across all samples.

Metabolomic data processing

The MetaboAnalyst tool (v 2.0.1) in R package was used for statistical analysis. Each spectral feature was normalized to the internal standard 2-isopropylmalic acid. In a separate analysis, data generated from urine samples were normalized to creatinine, which is proposed as an alternative method to internal standard to account for urine dilution [16]. A generalized logarithm transformation was subsequently applied for data transformation. Then univariate analysis using volcano plot and multivariate analysis using principle component analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal PLS-DA (OPLS-DA) [17] were performed. Volcano plot showed the combination between the fold-change (log 2(FC)) of the relative abundance of each spectral feature in AVS cases and controls and the statistical significance of the FC. Model cross validation with R2 and Q2 was used to assess the goodness of fit and predictability of the OPLS-DA model respectively. The index of Variable Importance in Projection (VIP), which measures the importance of individual metabolite features in the PLS-DA model, was used to weigh their contribution to the separation between cases and controls. Since OPLS-DA tends to over fit data a permutation test with 1,000 iterations was performed to validate the model and understand the significance of class discrimination. To decrease the rate of false positives in the selection, q-values were calculated using Benjamini-Hochberg method [18] and the threshold was set at 0.05. Metabolites were selected as candidates when VIP>1, False Discovery Rate (FDR) <0.05 and q-values <0.05. Receiver operating characteristic (ROC) analysis was developed using the Biomarker Analysis tool in MetaboAnalyst (www.metaboanalyst.ca) to evaluate the performance of each candidate metabolite to separate cases and controls. Area under the ROC curve (AUC) for each metabolite, as well as their 95% confidence intervals, were used to assess the utility of the candidate metabolites according to criteria [19] designed to rank candidate biomarkers as excellent (AUC = 0.9–1.0), good (AUC = 0.8–0.9), fair (AUC = 0.7–0.8), poor (AUC = 0.6–0.7) or failed (AUC = 0.5–0.6).

Generalized linear models (GLMs) were used to determine the metabolomic peaks significantly associated with AVS. After adjusting for age, sex, body mass index (BMI), hyperlipidemia and diabetes, logistic regression was used to assess the association of the metabolite peaks with AVS. The p-values obtained corresponded to the p-value of the peak in each model. These values were then corrected using the Benjamini-Hochberg method [18]. Peaks were considered to be statistically significant when their adjusted p-values (q-values) were less than 0.05.

Biological pathway analysis

Analysis of the biological pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG, www.genome.jp/kegg) underlying AVS risk was carried out with data from urine and plasma metabolites that were significantly associated with AVS using the web-tool MetaboAnalyst (www.metaboanalyst.ca). Both over-representation of significantly altered metabolites within pathways (P-values based on hypergeometric test) and the impact of metabolite changes on the function of the pathway through alterations in critical junction points of the pathway (relative betweenness centrality) were assessed.

Results

Clinical and biochemical features of AVS patients and control individuals

The 92 subjects were phenotypically well characterized with a mean age of 59.1 (±1.3) years, a mean body weight of 81.9 kg (±1.7), a mean BMI of 30.8 kg/m2 (±0.5), a mean blood glucose 112.3 mg/dL (±5.3), a mean triglyceride of 186.5 mg/dL (±9.0), a mean HDL-cholesterol of 40.1 mg/dL (±1.4), a mean of LDL-cholesterol of 115.0 mg/dL (±4.3) and mean of total cholesterol of 188.3 mg/dL (±5.1) (. A total of 63 subjects were hypertensive (68.5%), 68 had family history of hypertension (73.9%), 18 were diagnosed with diabetes (19.6%), 40 were hyperlipidemic (43.5%), 52 had family history of diabetes (56.5%) and 37 had family history of hyperlipidemia (40.2%). There were no significant differences between AVS patients and control individuals for biochemical variables. Markedly reduced serum LDL-cholesterol in AVS subjects when compared to controls was not statistically significant (p = 0.215). There were no significant differences between males and females in any of these variables (

General features of metabolomic profiling data

Using a signal to noise ratio of 6 applied to peak detection on the GC-MS chromatograms, a total of 190 and 130 peaks have been confidently detected in urine and plasma, respectively. Using the NIST08 library, a total of 112 and 70 metabolites possessing a similarity index ≥ 80% were detected with GC-MS in urine and plasma extracts respectively (). The intensity of each peak was measured and normalized to the internal standard (2-isopropylmalic acid).

Metabolomic analysis of urine and plasma samples in AVS patients

Using volcano plots, we identified 30 features showing evidence of difference in urine levels (nominal p<0.05) between AVS patients and controls, including 21 features which were more abundant in AVS patients than in controls (). In the PCA score plots derived from GC-MS spectra, a clear separation was obtained to differentiate metabolome profiles of AVS patients and controls (). The principal components PC1, PC2, and PC3 described 26.8%, 16.6% and 9.2% of the variation, respectively. The OPLS-DA score plot also provided a clear separation of AVS patients and controls (). The goodness of fit values of the OPLS-DA model were 0.113 (R2X) and 0.76 (R2Y), with a predictive ability value of 0.735 (Q2) (). This model explained 11.3% of the variation in metabolites levels and 76.0% of the variation between the groups, and the average prediction capability was 73.5%. The difference between R2Y and Q2 was less than 0.2 and the Q2 value was greater than 50%, revealing an excellent predictive capability. Permutation tests were performed (1000 iterations) to verify that this OPLS-DA model was not random or due to over fitting (p<0.001) ().

Discrimination analysis of AVS patients and control individuals through metabolomic profiling of biofluids.

Metabolomic data were derived from GC-MS spectra of urine (a-c) and plasma (d-f) samples from AVS patients (n = 46) and healthy controls (n = 46). Univariate analysis of GC-MS spectral data in the 92 samples was performed to identify metabolomic features significantly separating cases and controls (nominal p<0.05), which are plotted in pink dots in the upper part of the volcano plots (a, d). Multivariate statistics were applied to perform principle component analysis (PCA) (b, e) and orthogonal partial least squares-discriminant analysis (OPLS-DA) (c, f) and assess sample classification in cases and controls. The 95% confidence regions are displayed by shaded ellipses in AVS patients (red) and controls (green).

To improve the metabolic profile of AVS patients, we complemented urine metabolic analyses with GC-MS metabolomic profiling of plasma samples from the same panel of AVS and control subjects. As previously observed with urine metabolic features, volcano plot analysis identified 23 significantly contributing features (nominal p<0.05), including 10 features that were more abundant in AVS patients than in controls (). The PCA score plots of plasma metabolomics provided some evidence of clustering of AVS and control groups that was however inferior to that achieved with urine data (). The PC1, PC2 and PC3 described 42.4%, 16.4%, and 7.7% of the variation, respectively. The OPLS-DA plot also indicated that the two groups are well separated into 2 clusters (). OPLS-DA showed significantly good predictability (Q2 = 0.649), and good capability to explain the metabolic variation between AVS patients and controls (R2Y = 0.684), with goodness of fit values of 0.180 (R2X) and 0.684 (R2Y) (). The model explained 18.0% of the variation in metabolite levels and 68.4% of the variation between the groups, and a higher average prediction capability (94.9%) than urine data (73.5%). The difference between R2Y and Q2 (<0.2) and the Q2 value (>50%) confirmed the excellent predictive capability of the model (0.684). The permutation test indicated that AVS had significant impacts on the plasma metabolic profiling (p<0.001; 1,000 iterations) ().

Urine and plasma metabolomic profiling in AVS underlines biofluid-specific changes in metabolite abundance

We used the index of Variable Importance in Projection (VIP) derived from the PLS-DA models of urine and plasma metabolomic datasets to weigh the impact of each individual metabolite feature to separate AVS cases and controls (). Following feature annotations using the NIST08 library, we identified a total of 16 known urine metabolites significantly contributing to the separation between AVS and controls (VIP>1, nominal p<0.05, q<0.05). These include trans-Aconitic acid, myristic acid, methylmalonic acid, 7-Dehydrocholesterol, 2,4-Di-tert-butylphenol, malonic acid, 2-Hydroxyhippuric acid, 3-Hydroxyhippuric acid, succinic acid, glycerol, quinic acid, uric acid, stearic acid, 4-Deoxyerythronic acid, 3-(3-Hydroxyphenyl)-3-Hydroxypropanoic acid (HPHPA) and myo-inositol (). We included elaidic acid in the list of annotated metabolites even though it is not reported as yet in human urine in the Human Metabolome Database (HMDB).

Contribution of metabolites in the separation of AVS cases and controls.

The Variable Importance in Projection (VIP) was used to weigh the contribution of urine (a) and plasma (b) metabolomic features to the separation between cases and controls in the PLS-DA model. Data were normalized to the internal standard 2-isopropylmalic acid. Upregulated (blue bars) and downregulated (red bars) features are shown. Metabolites found associated with AVS in both urine and plasma are underlined. Details of metabolite features are given in .

Results from association analysis to AVS were generally conserved when urine metabolomic data were normalized to creatinine (). The metabolites salicyluric acid (2-Hydroxyhippuric acid), myo-inositol, glycerol, 4-Deoxyerythronic acid, uric acid and two unknown metabolites at RT 8.1 and 15.8mins were associated to AVS only following normalization to the internal standard. On the other hand, associations between AVS and the metabolites p-Hydroxyphenylacetic acid, palmitic acid, oxoadipic acid, hypoxanthine, estrone, D-Glucose and two unknown metabolites at RT 13.3 and 13.5mins were significant only when data were normalized to creatinine. The remaining metabolites consistently associated to AVS using the two normalization procedures showed similar VIP, similar magnitude and direction of changes between AVS and controls and consistent magnitude statistical significance of association. Strong conservation of our results derived through two different normalization methods underlines the robustness of our findings.

Among the plasma metabolic features significantly contributing to the separation between AVS and controls, 14 could be attributed to known metabolites (elaidic acid, palmitic acid, oleic acid, myristic acid, trans-Aconitic acid, D-Mannose, estrone, L-Alanine, L-Valine, myo-inositol, ornithine, hydroxylamine, 2,4-Di-tert-butylphenol, glycine) (, ). According to HMDB, 2,4-Di-tert-butylphenol has not been previously reported in human plasma.

In the urine dataset normalized to the internal standard, levels of myristic acid, trans-Aconitic acid, myo-inositol and 2,4-Di-tert-butylphenol were different between AVS patients and controls in both plasma and urine, but myristic acid and myo-inositol showed discordant direction of changes in the two biofluids. In addition when urine metabolomic data were normalized to creatinine, estrone and palmitic acid also showed opposite direction of changes in AVS in plasma and urine.

Biofluid metabolomic profiling data suggest novel candidate metabolite biomarkers for AVS

The performance of each urine and plasma candidate metabolite to separate AVS cases and controls was evaluated by ROC curve analysis (). AUC values with their p-values and FC for each urine and plasma metabolite associated with AVS are summarized in . The majority of potential metabolite biomarkers showed good to excellent (AUC>0.8) discriminant capability. In urine, trans-Aconitic acid, myristic acid, methylmalonic acid, 7-Dehydrocholesterol, 2,4-Di-tert-butylphenol, succinic acid, malonic acid were excellent potential biomarkers (AUC = 0.90–1.00). The known metabolites 3-(3-Hydroxyphenyl)-3-Hydroxypropanoic acid (HPHPA), quinic acid, 4-Deoxyerythronic acid and uric acid were good potential biomarkers (AUC = 0.80–0.90). The remaining candidates (2-Hydroxyhippuric acid, 3-Hydroxyhippuric acid, stearic acid, glycerol, and myo-inositol) were fair biomarkers (AUC = 0.7–0.8) ().

Excellent potential plasma biomarkers showing AUC above 0.90 include elaidic acid, estrone, palmitic acid, myristic acid, 2,4-Di-tert-butylphenol, oleic acid and myo-inositol. Glycine, hydroxylamine, trans-Aconitic acid, L-Alanine and L-Valine were good biomarkers (AUC = 0.80–0.90), whereas ornithine and D-Mannose were not considered as good biomarkers ().

These analyses also pointed to GC-MS signals associated with AVS that correspond to unknown metabolites in urine (n = 9) and plasma (n = 9) and show fair to excellent capacity to separate AVS patients to control individuals.

Statistical association of urine and plasma GC-MS features identifies metabolites underlying AVS risk

Following adjustment for age, sex, BMI, hyperlipidemia and diabetes, we identified statistically significant associations between AVS and 30 features in urine () and 35 features in plasma (). In several instances, several independent features were attributed to a single metabolite. Among these features, 14 urine metabolites and 15 plasma metabolites could be identified based on available information in the NIST08 data repository (Tables ). Urine metabolites significantly associated with AVS included myristic acid, palmitic acid, methylmalonic acid, succinic acid, 2-Oxovaleric acid, elaidic acid, ribonolactone, oxoadipic acid, myo-inositol, estrone, α-lactose, trans-Aconitic acid, 7-Dehydrocholesterol and 2,4-Di-tert-butylphenol (). With the exception of methylmalonic acid, myo-inositol, 7-Dehydrocholesterol and two unknown metabolites at RT 12.2 and 20.3mins, statistically significant associations between AVS and urine metabolites were replicated when metabolomic data normalized to creatinine were used for statistical analysis (). Normalization of urine data to creatinine allowed the identification of several additional associations between AVS and metabolites, including 4-Deoxyerythronic acid, erythronic acid, 2-Deoxypentonic acid, D-Fructose, HPHPA, quinic acid, stearic acid, D-Glucose, 7-Dehydrocholesterol and nine unknown metabolites ().

Association analysis of urine GC-MS spectral data in AVS patients and control individuals.

Data were derived by GC-MS analysis of urine samples from 46 patients and 46 controls. Generalized linear models were used to determine significant associations between metabolomic peaks and AVS after adjusting for age, sex, body mass index, hyperlipidemia and diabetes, and correcting for multiple testing. Signal intensities normalized to the internal standard 2-isopropylmalic acid are plotted against the Q-values. Features showing evidence of statistically significant association with AVS (q values<0.05) are shown with red dots.

Association analysis of plasma GC-MS spectral data in AVS patients and control individuals.

Data were derived by GC-MS analysis of plasma samples from 46 patients and 46 controls. Generalized linear models were used to determine significant associations between metabolomic peaks and AVS after adjusting for age, sex, body mass index, hyperlipidemia and diabetes, and correcting for multiple testing. Signal intensities normalized to the internal standard 2-isopropylmalic acid are plotted against the Q-values. Features showing evidence of statistically significant association with AVS (q values<0.05) are shown with red dots.

We identified statistically significant associations between plasma features and AVS for glycine, D-Glucose, trans-Aconitic acid, myristic acid, palmitic acid, elaidic acid, L-Valine, 2,4-Di-tert-butylphenol, phosphoric acid, 2-Oxovaleric acid, dodecanoic acid, quinic acid, galactose, myo-inositol and estrone (.

AVS patients showed significantly elevated urinary concentrations of myristic acid, trans-Aconitic acid, methylmalonic acid, 2-Oxovaleric acid, oxoadipic acid, palmitic acid, elaidic acid, α-lactose, estrone, ribonolactone, succinic acid and myo-inositol (, ). By contrast, urine concentration of the remaining metabolites (2,4-Di-tert-butylphenol and 7-Dehydrocholesterol) were lower in AVS patients than in controls. Plasma concentration of the saturated fatty acids myristic acid and palmitic acid, dodecanoic acid, the unsaturated fatty acid elaidic acid, the essential amino acid L-Valine, estrone, phosphoric acid, 2-Oxovaleric acid, quinic acid, 2,4-Di-tert-butylphenol, myo-inositol and galactose were significantly lower in AVS patients than in controls (, ). In contrast, plasma concentration of the amino acid glycine, D-Glucose and trans-Aconitic acid were higher in AVS patients than in controls (). Interestingly, evidence of replicated association to AVS in both urine and plasma was observed for a series of 9 metabolites (myristic acid, trans-Aconitic acid, palmitic acid, estrone, 2-Oxovaleric acid, elaidic acid, 2,4-Di-tert-butylphenol, myo-inositol, quinic acid and an unknown metabolite at RT 14.5mins) (Tables ). However, only 2,4-Di-tert-butylphenol and trans-Aconitic acid displayed concordant pattern of up- or down-regulation in the two biofluids in AVS patients.

Regulatory pattern of urine metabolites associated with AVS in patients and control individuals.

Data from urine candidate metabolites in the 46 AVS cases (orange boxes) and 46 controls (blue boxes) are shown. Data are normalized to the internal standard (2-isopropylmalic acid) and log transformed. The boxplots show the median and the inter-quartile range for each metabolite in the two groups.

Regulatory pattern of plasma metabolites associated with AVS in patients and control individuals.

Data from plasma candidate metabolites in the 46 AVS cases (orange boxes) and 46 controls (blue boxes) are shown. Data are normalized to the internal standard (2-isopropylmalic acid) and log transformed. The boxplots show the median and the inter-quartile range for each metabolite in the two groups.

Fatty acid biosynthesis and galactose metabolism are altered in AVS

To elucidate the biological relevance of metabolites associated with AVS, we carried out pathway analysis using data from urine and plasma metabolites associated with AVS. These metabolites are involved in 28 pathways in KEGG. The most significant pathways underlying AVS risk were the metabolism of galactose (α-lactose, galactose, glucose, myo-inositol) (p = 0.0003; FDR p = 0.025), the biosynthesis of fatty acids and, to a lesser extent, the metabolism of branched chain amino acids (valine, leucine, isoleucine) (). The metabolism of starch and sucrose and the metabolism of glycine, serine and threonine were also detected.

Pathway analysis of metabolites associated with AVS.

Outputs of urine and plasma metabolomic profiling in AVS patients and control subjects were used to identify changes in biological pathways in the human Kyoto Encyclopedia of Genes and Genomes (KEGG, www.genome.jp/kegg) using the MetaboAnalyst web-tool (www.metaboanalyst.ca). Data are plotted to illustrate the most significantly altered pathways in terms of p-values derived from hypergeometric test on the vertical axis and impact on the horizontal axis.

Discussion

We report results from paralleled untargeted metabolomic profile analyses of urine and plasma in a cohort of patients with AVS and control subjects that identified individual metabolites, metabolite patterns and biological pathways underlying disease risk. Known and unknown metabolites showed biofluid-specific changes between AVS and control individuals or either conserved or discordant regulation patterns in the two biofluids. Our data provide a solid foundation for the definition of metabolites and biological pathways that may be used as potential biomarkers for AVS diagnosis and prevention, as well as targets for therapeutic applications.

Metabolomic profiling is a powerful hypothesis-free strategy that we applied to the identification of a repository of unknown and known urine and plasma metabolites associated with AVS. Several of these associations concern individual metabolites that show evidence of pathophysiological relevance to heart diseases and therefore potentially to AVS. Metabolomic studies have shown that plasma levels of glycine and myo-inositol are increased in patients with heart failure [20]. Elevated plasma myo-inositol was also reported in primary dilated cardiomyopathy [21] and in a preclinical model of myocardial infarction [22]. Elaidic acid is an unsaturated trans-fatty acid, which was found elevated in the serum of patients with coronary artery disease and positively associated with LDL-cholesterol and triglyceride [23, 24].

Biological pathway analysis pointed to fatty acid metabolism as a prominent mechanism associated with AVS in our study. The underlying metabolites that are the most relevant to cardiovascular diseases were the saturated fatty acids dodecanoic, myristic, stearic and palmitic acids and the unsaturated trans fatty acid elaidic acid. Examples of dietary sources of these fatty acids are hydrogenated vegetable oils (elaidic acid), palm oil, meats, cheeses, butter and dairy products (palmitic acid), animal and vegetable fats, coconut and nutmeg oils (myristic acid). Palmitic acid can also be synthesized in the liver through fatty acid biosynthesis or elongation from myristic acid in the mitochondria. Fatty acids are central to the function of the heart since 50 to 70% of its energy is produced by mitochondrial fatty acid β-oxidation [25]. Epidemiological studies and clinical trials have suggested an association between the intake of saturated fatty acids and the risk of coronary heart disease (CHD) [26-28], even though this link remains contested [29]. For instance, the intake of palmitic, stearic and elaidic acids was correlated to the progression of CHD [30] and was associated with a 9–24% increased risk of CHD [31]. Reducing the dietary palmitic and myristic acid decreased the risk for CHD [32], whereas high consumption of myristic acid was correlated with high mortality due to CHD [33], presumably through the effect of myristic, palmitic and stearic acids on increased platelet aggregation [34]. Palmitic, dodecanoic and myristic acids are the major cholesterol-raising saturated fatty acids [35] and diets rich in these metabolites result in high LDL-cholesterol level and low HDL/LDL cholesterol [36]. Plasma levels of myristic acid are negatively associated with HDL-cholesterol in a population characterized with obesity and metabolic syndrome [37]. These fatty acids may therefore contribute to AVS in our study through their role in lowering HDL and increasing LDL-cholesterol levels [38]. The lack of association between AVS and fatty acids despite non-significant differences in lipoprotein levels between cases and controls may be explained by the treatment of many patients with lipid lowering medications (statins).

Paralleled metabolomic analysis of plasma and urine samples provides information about biofluid-specific changes in AVS. It also identifies conserved associations of metabolites to AVS in the two biofluids, which may suggest dysregulation in relevant biological pathways and allows prediction of the level of plasma metabolites based on their urine concentration in patients. Intriguingly, over 75% of GC-MS features and over 80% of known metabolites associated with AVS were up-regulated in urine and downregulated in plasma. In addition, even though the level of half of these known metabolites was different between patients and controls in both plasma and urine, they were almost all upregulated in urine and downregulated in plasma in the AVS group, suggesting a stimulation of their elimination in urine of patients. Only 2,4-Di-tert-butylphenol and trans-Aconitic acid (TAA) displayed consistent trend of regulation in the two biofluids.

2,4-Di-tert-butylphenol is a lipophilic phenol present in the environment and a product of bacterial metabolism [39]. It shows antioxidant properties against LDL-oxidation [40], thus potentially preventing atherosclerosis, and anti-inflammatory properties by decreasing the expression of TNF-α, interleukins IL-6 and IL-1b in a mouse macrophage cell line [41]. TAA is an unsaturated tricarboxylic acid and an isomer of the tricarboxylic acid cycle intermediate cis-Aconitic acid. TAA is mainly obtained from the sugar cane molasses [42] and is also metabolized by bacteria [43]. Metabolomic studies in humans indicated that the isomer cis-Aconitic acid is downregulated in the plasma of patients with CHD [44] and that aconitic acid is a marker of myocardial injury [45]. These data support a role of 2,4-Di-tert-butylphenol and TAA in AVS, which requires experimental validation.

Conclusions

Our findings provide initial evidence of candidate metabolite biomarkers of AVS and raise novel hypotheses regarding their contribution to the disease and the relevant pathophysiological mechanisms involved. Many metabolites associated with AVS in our study are involved in biological pathways that do not have obvious relevance to heart diseases, and therefore open new research avenues to test their implication in AVS etiopathogenesis. In addition, several urine and plasma metabolomic features associated with AVS remain unknown and require chemical attribution for unambiguous identification of the underlying metabolites. Further investigations are warranted to replicate association between metabolites and AVS in larger population studies. In addition, future work is required to determine whether the candidate metabolites identified affect aortic valve either directly or indirectly through factors known to contribute to AVS risk, including for example cholesterol metabolism and Lpa. We have verified that metabolites associated with AVS do not show evidence of significant association with plasma LDL and HDL (). Along the same line, changes in these metabolites may be reactive to AVS pathology and drug treatments. Assessment of causal relationships between the candidate metabolites that we have identified and AVS can be tested through extended genetic association analyses and application of Mendelian randomization methods in large genetic studies.

Clinical and biochemical features in males and females selected for presence or absence of aortic valve stenosis.

Data are means ± SEM.

(DOCX)

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Metabolites identified using the NIST08 library (https://chemdata.nist.gov/) after analysis of gas chromatography mass spectrometry (GC-MS) of urine and plasma samples of patients with aortic valve stenosis and controls.

RT, Retention Time; HMDB, Human Metabolome Database; KEGG, Kyoto Encyclopedia of Genes and Genomes.

(XLSX)

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Urinary and plasma metabolites contributing to the separation between the AVS patients and healthy controls.

Data were derived by GC-MS analysis of urine and plasma samples from 46 patients and 46 controls. Data were normalized to the internal standard 2-isopropylmalic acid. Variable importance in the projection (VIP) was obtained from PLS-DA with a threshold of 1.0; p-values are calculated from a volcano plot; q-values are the adjusted p-value with Benjamini-Hochberg method. Area Under the Curve (AUC) was calculated using the online tool MetaboAnalyst to determine biomarker utility. Regulation gives information on up- or down-regulation of the features in AVS patients. RT, Retention time; FDR, False Discovery Rate.

(XLSX)

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Urinary metabolites contributing to the separation between the AVS patients and controls.

Data were derived by GC-MS analysis of urine samples from 46 patients and 46 controls. Data were normalized to creatinine and logTranformed. Variable importance in the projection (VIP) was obtained from PLS-DA with a threshold of 1.0; q-values are the adjusted p-value with Benjamini-Hochberg method. Regulation gives information on up- or down-regulation of the features in AVS patients. RT, Retention time; FDR, False Discovery Rate.

(XLSX)

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Urine metabolic fingerprint of AVS patients and healthy controls.

Intensity values derived by GC-MS analysis of urine samples in AVS cases and controls were normalized to the internal standard 2-isopropylmalic acid.

(XLSX)

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Plasma metabolic fingerprint of AVS patients and healthy controls.

Intensity values derived by GC-MS analysis of plasma samples in AVS cases and controls were normalized to the internal standard 2-isopropylmalic acid.

(XLSX)

Click here for additional data file.

Validation of the OPLS-DA model of biofluid metabolomic data from patients with aortic valve stenosis (AVS) and control individuals.

Data were derived from GC-MS spectra of urine (a, b) and plasma (c, d) samples from AVS patients (n = 46) and control individuals (n = 46). Model validation was performed using permutation test with 1000 iterations on the OPLS-DA model. Empirical p-values Q2: p<0.001 and R2Y: p<0.001.

(TIF)

Click here for additional data file.

ROC analysis of candidate urine metabolites separating AVS patients and control individuals.

Each of the 16 candidate metabolites (VIP>1, nominal p<0.05, q<0.05) has a ROC curve where the sensitivity is on the y-axis and the specificity is on the x-axis. The AUROC is shown in blue and the AUC values with their 95% confidence intervals are presented in the curves.

(TIF)

Click here for additional data file.

ROC analysis of candidate plasma metabolites separating AVS patients and control individuals.

Each of the 14 candidate metabolites (VIP>1, nominal p<0.05, q<0.05) has a ROC curve where the sensitivity is on the y-axis and the specificity is on the x-axis. The AUROC is shown in blue and the AUC values with their 95% confidence intervals are presented in the curves.

(TIF)

Click here for additional data file.

Association analysis of plasma GC-MS spectral data with HDL and LDL in patients and controls.

Data were derived by GC-MS analysis of plasma samples from 46 AVS patients and 46 control individuals. Generalized linear models were used to determine significant associations between metabolomic peaks and HDL (a) and LDL (b) and correcting for multiple testing. Signal intensities normalized to the internal standard are plotted against the Q-values.

(TIF)

Click here for additional data file.

9 Sep 2020

PONE-D-20-24273

Plasma and urine metabolomic analyses in aortic valve stenosis reveal shared and biofluid-specific metabolic regulation

PLOS ONE

Dear Dr. Gauguier,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

The paper is of quite some interest, but, as evident from the reviewer comments, multiple detail require attention, on several occasions more clarity/a (better) explanation appears necessary. However, due to the excellent comments, this should not present a major obstacle.

==============================

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Reviewer #2: Yes

Reviewer #3: Partly

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

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5. Review Comments to the Author

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Reviewer #1: Overall, the manuscript is well written presenting results and conclusions supported by the data. Few minor comments:

- The text in figure 1 is not readable. It would be helpful if the authors could use a bigger font in all parts. Specifically, it would be better if parts d and h could be moved to another figure(s), as the bar graphs would be more clear in this case and the rest of the plots would be bigger.

- Figure 4 could be separated in two or more figures so that the bars and the text are more clear.

- All data should be provided as part of the manuscript or its supporting information, or deposited to a public repository.

Reviewer #2: The article from Al Hageh et al. describes the significant changes in plasma and urine metabolomics in patients with aortic valve stenosis, compared to matched controls. The study is methodologically sound, statistically appropriate and results are cautiously interpreted. However, the article appears to fall in between two study types that are biomarker discovery and physiopathology. By focusing on one or the other, the study would have more impact. The results on 2,4-di-tert-butylphenol and Trans aconitic acid are of the greatest interest (fig 4a).

A major missing information in the study is a technical definition of AVS diagnosis (in general and in the study) and a summary of AVS-related echography results from cases and controls. Could a severity score be obtained and correlated to metabolomics results ?

The result section shows a promising discriminating urinary pattern in PCA and OPLS-DA. This could define a multidimensional biomarker of AVS, to be tested in a validation cohort or compared to an AVS severity score. Correlations between individual candidate biomarker levels and AVS severity scores could also be interesting.

The discussion on the biological interpretation of observed changes would gain from being more in depth. What is the link between dietary and circulating fatty acids ? Could a pathway analysis be relevant to the study?

The authors report changes in urine levels of many substances between AVS patients and controls, and conclude to changes in renal elimination. Did the authors control the presence of CKD (eGFR to add to table S1) and its influence by normalizing to urine creatinine level?

One can get lost in the large number of data, tables, figures and supplements that are provided (which should be simplified). For instance, all AUCs (p11-12) and estimates (from logistic regression? For what unit change?) could be placed in a table, possibly containing results from different successive steps. This would help readers go through the result section and provide a general view of results.

Other comments:

- How were the blood and urine samples obtained (At what time of the day ? In fasting conditions ?), prepared and stored?

- For molecules with discriminant ability, the cutoff values could be reported

- The wording “differential (metabolic) regulations” is largely used while misleading, particularly in the title. Using simple terms such as “different levels” would be better.

- The figures quality is low and prevents any interpretation of their content (blurry, unreadable). The figure legends should explain all the items depicted in figures. In addition:

• Figure 1a/e: What are the different colours?

• Figure 1f: The overlapping of clusters in PCA and OPLS-DA is in contrast with the statement of “clearly independent clustering” (line 181, p10)

• Figure 2-3: Please explain why a given metabolite can have several features. Shouldn’t metabolites with various TMS adducts be summed up ?

• Figure 4. Please reword/simplify the figure title to match its content. What are the units of metabolite levels?

- Table S1: The percentages in different columns must be related to the sample size of that column. This table (or parts of it) deserve to be in the main manuscript.

- Line 272: genomic strategy?

Reviewer #3: This interesting article by Gauguier et al describe both plasma and urinary metabolite profiles associated with Aortic Valve Stenosis. Contrasting trends in metabolic regulation were noted between the two bio-fluids. I do have a few comments:

1. In the abstract reference is made to “pre-processed data” what is meant with this?

2. I think the abstract can benefit from adding specifics regarding the metabolites that stood out in this study. In the current state you have to read all the way to the results sections of the paper to get an idea about the metabolic pathways affected by AVS.

3. Spacing before in text references should be corrected.

4. In the introduction line 43-44: I think the two sentences can be combined. The second statement needs a reference.

5. Try to avoid repetition e.g. line 51-52 “Metabolomics has been extensively used for biomarker discovery, drug response ascertainment and disease pathway identification” and line 54-55 “ Metabolomics provides a platform for biomedical discovery as well as clinical and pharmaceutical applications”

6. The end of the introduction reads as an abstract in that it is ending with a vague idea of what was found in the study (line 61-68). I think it would be better to just end the introduction with the aim of the study.

7. In the methods section a lot of basic results is given. I would suggest that his is moved to the results sections.

8. Normally a sentence would not start with a number (e.g. 63 subjects), but rather with the number written in words.

9. It is stated in the text that there is no differences between AVS patients and controls for biochemical variables (line 82-84 page 5) but it think Table S1 should also indicate p-values between groups to indicate this.

10. Some abbreviations should be explained upon first mention, and normally a sentence should not start with an abbreviation.

11. In the results section (page 9 line 145) it is stated that 190 and 130 peaks have been confidently detected in urine and plasma. Perhaps just add a measure of confidence there.

12. The levels of urinary metabolites is determined by the concentration of urine. Did you adjust these levels of creatinine?

13. Also the opposing findings in plasma vs urine may have something to do with renal function. Do you have any information on renal function of the AVS vs controls?

14. The results section on page 13 (line 235 – 248) is difficult to follow. Also reference is made to Table 1a and Table 1b, but Table 1 as included in the article does not have a “a” and “b” part.

15. Apart from just comparing metabolic profiles (urine and plasma) between AVS and controls perhaps you can also look into correlations of significantly different metabolites with echo parameters?

16. Figure 4 is nice to indicate the differences between the bio-samples and AVS vs controls. But perhaps you can also consider a Venn diagram to indicate differences and similarities?

17. I think the discussion can benefit greatly from a figure of the metabolic pathways identified.

18. The involvement of fatty acids is interesting but also expected as fatty acids are the main source of energy in the heart, perhaps you can just add some reference to that in the discussion.

19. In this study the lipid profile (Table S1) did not differ between the AVS and controls but yet the fatty acids are quite prominent. Also when considering that you mentioned in the Conclusions that there was no associations between LDL and HDL (results not shown) Perhaps you can comment on that?

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Catharina M.C. Mels

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1 Oct 2020

Detailed responses are in the file "PONE-D-20-24273_Response to Reviewers" and below:

************************************************************************

Reviewer #1: Overall, the manuscript is well written presenting results and conclusions supported by the data. Few minor comments:

We thank the Reviewer for her/his review of our manuscript.

- The text in figure 1 is not readable. It would be helpful if the authors could use a bigger font in all parts. Specifically, it would be better if parts d and h could be moved to another figure(s), as the bar graphs would be more clear in this case and the rest of the plots would be bigger.

Response: We thank the Reviewer for this suggestion. We have split Figure 1 in two figures (new Fig1= old Fig 1a,b,c,e,f,g; new Fig2= old Fig1d+h) and revised the text accordingly.

- Figure 4 could be separated in two or more figures so that the bars and the text are more clear.

Response: As suggested by the Reviewer, the text on the figure has been enhanced and Figure 4 has been split in two figures (Figs 5 and 6 in the revised version of the manuscript). Reference to the figures has been amended in the text accordingly.

- All data should be provided as part of the manuscript or its supporting information, or deposited to a public repository.

Response: As requested by the Reviewer, the raw datafiles of the metabolomic profiling experiments in urine and plasma samples (GC/MS data normalised to the internal standard) have been included in Supplementary tables 4 (urine) and 5 (plasma).

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Reviewer #2: The article from Al Hageh et al. describes the significant changes in plasma and urine metabolomics in patients with aortic valve stenosis, compared to matched controls. The study is methodologically sound, statistically appropriate and results are cautiously interpreted. However, the article appears to fall in between two study types that are biomarker discovery and physiopathology. By focusing on one or the other, the study would have more impact. The results on 2,4-di-tert-butylphenol and Trans aconitic acid are of the greatest interest (fig 4a).

We thank the Reviewer for her/his positive evaluation of our manuscript and her/his useful suggestions.

A major missing information in the study is a technical definition of AVS diagnosis (in general and in the study) and a summary of AVS-related echography results from cases and controls. Could a severity score be obtained and correlated to metabolomics results ?

Response: The Reviewer raises an important point that we now address in the Methods section of the paper. Aortic valve stenosis was diagnosed initially on physical examination by finding a systolic murmur in the aortic valve area. It was subsequently confirmed by Echocardiography. In our study, AVS was considered present if reported by the clinician in the Medical Record. However, the degree and severity of AVS were not recorded and could not be included in our database. This is now clarified in the Methods section, page 5, lines 76-78 of the revised version of the manuscript.

The result section shows a promising discriminating urinary pattern in PCA and OPLS-DA. This could define a multidimensional biomarker of AVS, to be tested in a validation cohort or compared to an AVS severity score. Correlations between individual candidate biomarker levels and AVS severity scores could also be interesting.

Response: As above mentioned, only presence or absence of AVS was reported in subjects showing systolic murmur in the aortic valve area. Unfortunately, the degree and severity of AVS were not recorded at the time of clinical examination and were therefore not available in our database.

The discussion on the biological interpretation of observed changes would gain from being more in depth. What is the link between dietary and circulating fatty acids ? Could a pathway analysis be relevant to the study?

Response: As suggested by the Reviewer, we have addressed the point about dietary fatty acids in the discussion (Page 15-16, lines 299-303).

The second point made by Reviewer 2 about the need to carry out pathway analysis was also raised by Reviewer 3. To comply with these suggestions, we carried out this analysis using MetaboAnalyst and the KEGG database and identified predominantly changes in fatty acid biosynthesis and galactose metabolism are altered in AVS. Data are now reported at the end of the result section of the revised version of the manuscript (Page 14, lines 270-277). Methods are described on Page 8 (lines 143-150). An additional figure (Figure 7) illustrates results from pathway analysis.

The occurrence of fatty acid metabolism in results from pathway analysis is relevant to the point made by the Reviewer about dietary and circulating fatty acids. As suggested by Reviewer 2, we developed the discussion with biological interpretation of the data, with a specific focus on fatty acids (page 15-16, lines 296-304).

The authors report changes in urine levels of many substances between AVS patients and controls, and conclude to changes in renal elimination. Did the authors control the presence of CKD (eGFR to add to table S1) and its influence by normalizing to urine creatinine level?

Response: The Reviewer is correct that adjustment for several confounding factors is required to analyse urine metabolome data. We could not control for the presence of CKD since eGFR values were missing for many patients, hence if included would have significantly reduced the power of the analyses. Urine data were normalised to the internal standard 2-isopropylmalic acid, which prevented subsequent normalisation to creatinine.

One can get lost in the large number of data, tables, figures and supplements that are provided (which should be simplified). For instance, all AUCs (p11-12) and estimates (from logistic regression? For what unit change?) could be placed in a table, possibly containing results from different successive steps. This would help readers go through the result section and provide a general view of results.

Response: As suggested by the Reviewer, we have simplified the report of data in the result section and that of technical details in the supplementary material. We have merged supplementary Tables 2 and 3 in a single table (now supplementary Table 2). Along the same line, supplementary Tables 4 and 5 have been merged in a single table (now supplementary Table 3). In the latter, the metabolites have been ranked according to their VIP instead of their retention time, in order to underline those contributing the most significantly to the separation between cases and controls. The text on page 11 has been simplified by moving the AUC and 95% CI for associated metabolites in supplementary table 3. The text on page 12 has also been simplified for metabolites associated with AVS after correction for confounders: we removed from the text values of the regression coefficients and confidence intervals which are already given in Tables 2 (urine) and 3 (plasma) (Table 1 has been split in two tables in response to a comment from Reviewer 3.

- How were the blood and urine samples obtained (At what time of the day ? In fasting conditions ?), prepared and stored?

Response: As requested by the Reviewer, information about sample collection and storage has been added in the methods section of the revised version of the manuscript, page 5, lines 80-83. About 30 ml of urine and 20 ml of arterial blood was collected after 12 hours fasting. Blood collected in EDTA tubes was spun for plasma separation. Plasma and urine aliquots were stored at -80oC until metabolomic analysis.

- For molecules with discriminant ability, the cutoff values could be reported.

Response: As mentioned in the methods section page 7, lines 134-136, metabolites with discriminant potential were ranked as excellent (AUC=0.9-1.0), good (AUC=0.8-0.9), fair (AUC=0.7-0.8), poor (AUC=0.6-0.7) or failed (AUC=0.5-0.6). For clarity, this information has been added in the text on page 12, lines 227-240.

- The wording “differential (metabolic) regulations” is largely used while misleading, particularly in the title. Using simple terms such as “different levels” would be better.

Response: As requested by the Reviewer, the suggested changes have been made throughout the text where appropriate, as well as in the title of the paper which now reads: “Plasma and urine metabolomic analyses in aortic valve stenosis reveal shared and biofluid-specific changes in metabolite levels”.

- The figures quality is low and prevents any interpretation of their content (blurry, unreadable). The figure legends should explain all the items depicted in figures. In addition:

Response: As suggested by the Reviewer, the text on figures has been enhanced and their resolution has been increased.

• Figure 1a/e: What are the different colours?

Response: We apologise to the Reviewer if the color code on fig1a/e (now fig1a/d) was unclear. We did mention in the figure legend that pink dots in the upper part of the volcano plots indicated metabolomic features that significantly separated cases and controls (nominal p<0.05). We have rephrased the text of the legend to clarify this point.

• Figure 1f: The overlapping of clusters in PCA and OPLS-DA is in contrast with the statement of “clearly independent clustering” (line 181, p10)

Response: We thank the Reviewer for pointing this. We have amended the text to underline that PCA-based clustering of plasma data was inferior to that of urine data.

• Figure 2-3: Please explain why a given metabolite can have several features. Shouldn’t metabolites with various TMS adducts be summed up?

Response: In this study we used the XCMS tool for data pre-processing to correct deviations in the retention time from one sample to the next. XCMS uses two pieces of information to do the alignment: RT and the highest point in MS/MS spectrum of each RT. Thus, the feature detected for example at RT=7.6 has a MS/MS spectrum with three major peaks at m/z 72, 147 and 275. Thus, if in one sample the highest peak in the MS/MS spectrum is at m/z 72 and in another sample the highest peak is at m/z 275, in this case XCMS considers them as two separate peaks and it doesn’t align them as one peak. For that reason we can find many peaks for the same metabolite.

• Figure 4. Please reword/simplify the figure title to match its content. What are the units of metabolite levels?

Response: We thank the Reviewer for pointing to this detail. The units are intensities of MS signals for each metabolite normalised by that of the internal standard 2-isopropylmalic acid. As requested, the title of figure 4 (now Figures 5 and 6) has been simplified. We have clarified in the legend to the figure that the unit are log transformed intensities normalised to the internal standard.

- Table S1: The percentages in different columns must be related to the sample size of that column. This table (or parts of it) deserve to be in the main manuscript.

Response: We thank the Reviewer for spotting errors in the calculation of percentages for cases and controls, which have been rectified in Table S1 and in the new Table 1. As suggested by the Reviewer, part of Table S1 has been moved to the main manuscript. It is now Table 1 in the revised version of the manuscript. This table reports data in cases and controls, whereas data in males and females remain in Table S1.

- Line 272: genomic strategy?

Response: “genomic” has been withdrawn.

-----------------------------------------------------------------------------------------------------------

Reviewer #3: This interesting article by Gauguier et al describe both plasma and urinary metabolite profiles associated with Aortic Valve Stenosis. Contrasting trends in metabolic regulation were noted between the two bio-fluids.

The authors thank the Reviewer for her thorough evaluation of our manuscript and her useful comments and suggestions.

I do have a few comments:

1. In the abstract reference is made to “pre-processed data” what is meant with this?

Response: The pre-processing stage involved merging all the raw MS data in one peak table, which requires detection and alignment of the same peaks for all the sample prior to statistical analysis. Technically, the treatment of the raw data in order to get one table (dataMatrix) that contain all the samples with the aligned peaks is called pre-processing. After this step comes the processing where the chemometric analysis is applied such as PCA, PLS-DA, This point has been clarified in the abstract of the revised version of the manuscript.

2. I think the abstract can benefit from adding specifics regarding the metabolites that stood out in this study. In the current state you have to read all the way to the results sections of the paper to get an idea about the metabolic pathways affected by AVS.

Response: We agree with the Reviewer that the abstract lacks details about metabolites associated with AVS. As suggested, we have added towards the end of the abstract details of metabolites associated with AVS that show shared or discordant pattern of regulation in plasma and urine.

3. Spacing before in text references should be corrected.

Response: This has been corrected throughout the text.

4. In the introduction line 43-44: I think the two sentences can be combined. The second statement needs a reference.

Response: We thank the Reviewer for this suggestion. We have changed the text accordingly (Page 3, Lines 47-48).

5. Try to avoid repetition e.g. line 51-52 “Metabolomics has been extensively used for biomarker discovery, drug response ascertainment and disease pathway identification” and line 54-55 “Metabolomics provides a platform for biomedical discovery as well as clinical and pharmaceutical applications”

Response: We agree with the Reviewer’s comment. As suggested we have merged the two sentences in the revised version of the manuscript (Page 3, Lines 55-57).

6. The end of the introduction reads as an abstract in that it is ending with a vague idea of what was found in the study (line 61-68). I think it would be better to just end the introduction with the aim of the study.

Response: As suggested we have changed the end of the introduction by providing the general objectives of the study rather than results.

7. In the methods section a lot of basic results is given. I would suggest that his is moved to the results sections.

Response: This point was also pointed by Reviewer 2. To follow the recommendation of the two Reviewers, we have moved the description of the biochemical and clinical data in patients and control subjects from the methods to results section. Also part of Table S1 (data in cases and controls) has been moved to the main text as a new table (Table 1). Data in males and females remain in Table S1.

8. Normally a sentence would not start with a number (e.g. 63 subjects), but rather with the number written in words.

Response: This has been rectified (Page 9, line 157).

9. It is stated in the text that there is no differences between AVS patients and controls for biochemical variables (line 82-84 page 5) but it think Table S1 should also indicate p-values between groups to indicate this.

Response: As requested, a column with P-values has been added to TableS1 (Table 1 in the revised version of the manuscript).

10. Some abbreviations should be explained upon first mention, and normally a sentence should not start with an abbreviation.

Response: we have been through the manuscript and made corrections accordingly.

11. In the results section (page 9 line 145) it is stated that 190 and 130 peaks have been confidently detected in urine and plasma. Perhaps just add a measure of confidence there.

Response: The Reviewer is correct that threshold of detection of peaks should be mentioned in the manuscript. Following verifications with the equipment supplier and the platform, we can confirm that a signal to noise ratio of 6 was used to as detection threshold. This is now reported in the methods section (Page 9, line 165).

12. The levels of urinary metabolites is determined by the concentration of urine. Did you adjust these levels of creatinine?

Response: All urine and plasma MS data were systematically normalised to the internal standard 2-isopropylmalic acid. To ensure consistency in statistical analysis of plasma and urine data, we did not adjust the urine data for creatinine levels. Adjusting urine data for creatinine would have resulted in a normalisation for two variables. In our hands, this has resulted to very low values and was problematic for statistical analysis.

13. Also the opposing findings in plasma vs urine may have something to do with renal function. Do you have any information on renal function of the AVS vs controls?

Response: This important point was also raised by Reviewer 2. eGFR values were missing for many patients in our dataset, which prevented their use in the analysis since it would have significantly reduced the power of the analyses, and hampered the reliability of results.

14. The results section on page 13 (line 235 – 248) is difficult to follow. Also reference is made to Table 1a and Table 1b, but Table 1 as included in the article does not have a “a” and “b” part.

Response: To clarify statistical results in urine and plasma initially reported in Tables 1a and 1b, the table has been split in two tables (table 1 for urine data and table 2 for plasma data).

15. Apart from just comparing metabolic profiles (urine and plasma) between AVS and controls perhaps you can also look into correlations of significantly different metabolites with echo parameters?

Response: The Reviewer raises and important point. Unfortunately, in our database, AVS was considered present if reported by the clinician in the Medical Record. We had no access to the echo parameters of the patients.

16. Figure 4 is nice to indicate the differences between the bio-samples and AVS vs controls. But perhaps you can also consider a Venn diagram to indicate differences and similarities?

Response: We agree with the Reviewer that a Venn diagram would be a useful illustration of our results. On the other hand, plotting the intensity values provides the reader with better estimates of means and inter-individual variability, which is the reason we chose to represent the results as box plots.

17. I think the discussion can benefit greatly from a figure of the metabolic pathways identified.

Response: We agree with the Reviewer that pathway analysis would be an interesting addition in the manuscript. We carried out this analysis using MetaboAnalyst and the KEGG database and identified predominantly changes in fatty acid biosynthesis and galactose metabolism are altered in AVS. This is now reported at the end of the result section (Page 14, lines 270-277). Methods are described in the relevant section on Page 8 (lines 143-150). An additional figure (Figure 7) illustrates results from pathway analysis.

18. The involvement of fatty acids is interesting but also expected as fatty acids are the main source of energy in the heart, perhaps you can just add some reference to that in the discussion.

Response: The point raised by Reviewer 3 about the need to improve the discussion with additional information on fatty acids was also raised by Reviewer 2. We agree that this is an important aspect in the article, which is supported by results from pathway analysis. As requested we have developed the discussion along these lines (Page 16, lines 303-304). A reference (24- Lopaschuk et al. Myocardial Fatty Acid Metabolism in Health and Disease. Physiological Reviews 90, 207–258,2010) has been added.

19. In this study the lipid profile (Table S1) did not differ between the AVS and controls but yet the fatty acids are quite prominent. Also when considering that you mentioned in the Conclusions that there was no associations between LDL and HDL (results not shown) Perhaps you can comment on that?

Response: The Reviewer is correct that association between AVS and fatty acids are quite prominent in our study despite lack of associations with lipoproteins. This lack of association maybe partly due to the fact that most of the patients were under lipid lowering medications (Statins), hence any association would have been very difficult to ascertain, especially that adjusting for medication use could not be performed due to loss of power. As suggested by the Reviewer, we have revised the discussion accordingly (Page 16, lines 317-319). Also, as requested by the journal we have added a supplementary figure (S4 Fig) to show the lack of association between metabolite features and LDL and HDL.

19 Oct 2020

PONE-D-20-24273R1

Plasma and urine metabolomic analyses in aortic valve stenosis reveal shared and biofluid-specific changes in metabolite levels

PLOS ONE

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Please perform the minor changes requested by reviewer 2 and resubmit, so the paper can be accepted.

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Reviewer #2: The authors have answered all comments, but one: the addition of a correction for urine dilution is feasible and would have been highly relevant to the study.

Minor comments:

*Lines 264-268: the two sentences appear contradictory. Please edit the first one to clarify that the series of metabolites are consistent within (not across) fluids.

*Figures 5 and 6:

-What is the y-axis legend ? what units? what numbers (impossible to read)?

-Display the controls on the left and cases on the right to help readers

*Figure 7:

-identify starch metabolism

-display backtransformed P-values on the y-axis (and color legend).

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23 Oct 2020

Comments to the Author

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Reviewer #2: The authors have answered all comments, but one: the addition of a correction for urine dilution is feasible and would have been highly relevant to the study.

Response: We agree with the Reviewer that urine dilution is an important criterion when analysing urine metabolomics. As a rule, we also have to deal with correction for technical deviation between samples and runs using the internal standard in order to consistently normalise the urine and plasma datasets with exactly the same method.

As requested by the Reviewer, we have recalculated the statistics using urine data normalised to creatinine instead of the internal standard. We have mentioned this in the methods section.

The results from association studies between urine metabolites and AVS are very concordant in both cases of data normalisation. We now report in a new table in the revised version of the manuscript (Supplementary Table 4) results from the association between AVS and urine metabolites normalised to creatinine before adjustment for age, sex, body mass index, hyperlipidemia and diabetes.

Also, we have changed the format of Table 2 to show in parallel association results, following adjustment for age, sex, body mass index, hyperlipidemia and diabetes, based on urine metabolomic data independently normalised to the internal standard and to creatinine. This provides the reader with elements to directly compare association data using these two methods, and to be able to visualise consistency in the outputs. Essentially, almost all associations identified following normalisation to the internal standard are replicated when the data are normalised to creatinine, suggesting higher stringency upon data normalisation to the internal standard.

Details of these new findings are given in the results section, pages 11 to 14.

A reference (Fiehn O. Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and Untargeted Profiling. Curr Protoc Mol Biol. 2016;114:30.4.1-.4.2) was added.

Minor comments:

*Lines 264-268: the two sentences appear contradictory. Please edit the first one to clarify that the series of metabolites are consistent within (not across) fluids.

Response: The reviewer is correct. The sentences have been changed (lines 288-293 of the revised manuscript).

*Figures 5 and 6:

-What is the y-axis legend ? what units? what numbers (impossible to read)?

-Display the controls on the left and cases on the right to help readers

Response: Figures 5 and 6 have been redrawn as requested by the Reviewer. Font has been increased and data from controls and cases are shown on the left and right, respectively, for each panel. Details of the unit of the Y-axis are given in the legends to the figures.

*Figure 7:

-identify starch metabolism

Response: As requested, starch metabolism, which was omitted on the original version of Figure 7, was added in the revised version of this Figure. The text on page 15 was amended. We apologise for this mistake.

-display backtransformed P-values on the y-axis (and color legend).

Response: As requested, the revised version of Figure 7 displays backtransformed P-values on the y-axis.

Reviewer #3: Thank you for taking the time to make the requested amendments. I have nothing further to add.

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Reviewer #1: No

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Reviewer #3: No

Submitted filename: PONE-D-20-24273_R2_Response to Reviewers.docx

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26 Oct 2020

Plasma and urine metabolomic analyses in aortic valve stenosis reveal shared and biofluid-specific changes in metabolite levels

PONE-D-20-24273R2

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10 Nov 2020

PONE-D-20-24273R2

Plasma and urine metabolomic analyses in aortic valve stenosis reveal shared and biofluid-specific changes in metabolite levels

Dear Dr. Gauguier:

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Table 1

Clinical and biochemical features of the 92 subjects selected for presence or absence of aortic valve stenosis.

	Total (92)	Controls (46)	Cases (46)	p-value
Age	59.1 ± 1.3	59.5 ± 1.9	58.8 ± 1.9	0.81
Body weight (Kg)	81.9 ± 1.7	82.2 ± 2.4	81.6 ± 2.3	0.86
Body mass index (Kg/m2)	30.8 ± 0.5	30.9 ± 0.8	30.8 ± 0.7	0.93
Plasma glucose (mg/dL)	112.3 ± 5.3 (49)	112.2 ± 6.6 (36)	112.6 ± 7.5 (13)	0.97
Triglycerides (mg/dL)	186.5 ± 9.0 (80)	186.6 ± 13.2 (42)	186.4 ± 12.2 (38)	0.99
HDL cholesterol (mg/dL)	40.1 ± 1.4 (82)	40.1 ± 1.9 (43)	40.0 ± 2.1 (39)	0.98
LDL cholesterol (mg/dL)	115.0 ± 4.3 (80)	120.0 ± 6.2 (42)	109.4 ± 5.7 (38)	0.22
Total cholesterol (mg/dL)	188.3 ± 5.1 (82)	191.0 ± 8.0 (43)	185.3 ± 6.3 (39)	0.58
Diagnosed diabetic (%)	18 (19.6%)	7 (15.2%)	11 (23.9%)
Diagnosed hypertensive (%)	63 (68.5%)	27 (58.7%)	36 (78.3%)
Diagnosed hyperlipidemic (%)	40 (43.5%)	12 (26.1%)	28 (60.9%)
FH diabetes (%)	52 (56.5%)	29 (63.0%)	23 (50.0%)
FH hypertension (%)	68 (73.9%)	34 (73.9%)	34 (73.9%)
FH hyperlipidemia (%)	37 (40.2%)	16 (34.8%)	21 (45.7%)

FH, Family History. Data are means ± SEM.

Table 2

Urinary metabolites contributing to the separation between the AVS patients and healthy controls.

		Normalization internal standard				Normalization creatinine				Regulation
Metabolite	RT	q-value	RC	CI 2.5	CI 97.5	q-value	RC	CI 2.5	CI 97.5	in AVS
Methylmalonic acid	2.7	0.0301	4.78	2.78	7.24	NS	-	-	-	Positive
Unknown	3.4	0.0183	4.98	3.10	7.66	0.0099	9.95	6.25	15.1	Positive
Unknown	3.5	NS	-	-	-	0.0291	5.41	3.20	8.24	Positive
Succinic acid	5.5	0.0040	4.82	3.02	7.10	0.0273	6.88	4.06	10.44	Positive
4-Deoxyerythronic acid	6.3	NS	-	-	-	0.0384	4.15	2.41	6.35	Positive
2-Oxovaleric acid	6.8	0.0376	3.83	2.22	5.82	0.0131	7.50	4.51	11.21	Positive
Unknown	7.1	NS	-	-	-	0.0382	6.94	4.05	10.63	Positive
2,4-Di-tert-butylphenol	7.3	0.0041	-2.15	-3.27	-1.41	0.0009	-5.52	-8.02	-3.61	Negative
Erythronic acid	7.4	NS	-	-	-	0.0372	3.29	1.90	5.02	Positive
Unknown	7.9	NS	-	-	-	0.0388	4.07	2.36	6.23	Positive
2-Deoxypentonic acid	8.2	NS	-	-	-	0.0346	5.61	3.30	8.58	Positive
D-Fructose	8.3	NS	-	-	-	0.0342	5.07	2.98	7.75	Positive
Unknown	8.5	NS	-	-	-	0.0291	6.46	3.78	9.79	Positive
Ribonolactone	9.1	0.0343	1.40	0.82	2.13	0.0289	8.84	5.34	13.62	Positive
Myristic acid	9.4	0.0209	3.91	2.41	6.01	0.0111	4.69	2.97	7.16	Positive
HPHPA	9.5	NS	-	-	-	0.0153	5.84	3.58	8.88	Positive
Quinic acid	9.7	NS	-	-	-	0.008	3.94	2.40	5.83	Positive
Oxoadipic acid	9.84	0.0439	5.42	3.11	8.27	0.0468	7.37	4.22	11.28	Positive
Palmitic acid	10.6	0.0014	5.88	3.84	8.64	0.0048	8.07	5.04	11.93	Positive
Myo-inositol	11.1	0.0284	7.57	4.53	11.59	NS	-	-	-	Positive
Elaidic acid	11.7	0.0125	7.24	4.44	10.92	0.0251	7.36	4.33	11.13	Positive
Stearic acid	11.8	NS	-	-	-	0.048	8.32	4.82	12.80	Positive
Unknown	12.2	0.0032	3.24	2.07	4.80	NS	-	-	-	Positive
D-Glucose	12.4	NS	-	-	-	0.0186	4.86	2.93	7.37	Positive
Unknown	12.5	NS	-	-	-	0.0206	5.38	3.18	8.10	Positive
Estrone	12.7	0.0190	3.40	2.11	5.22	0.0234	8.55	5.13	13.03	Positive
Unknown	13	0.0079	-1.58	-2.44	-1.02	0.0109	-4.47	-6.79	-2.78	Negative
Unknown	13.3	NS	-	-	-	0.0057	4.83	2.98	7.13	Positive
Unknown	13.4	NS	-	-	-	0.031	4.29	2.54	6.57	Positive
Unknown	13.5	0.0087	1.74	1.05	2.58	0.01	6.10	3.72	9.12	Positive
Unknown	13.6	0.0094	5.02	3.23	7.75	0.006	6.75	4.29	10.17	Positive
7-Dehydrocholesterol	14	0.0475	-0.77	-1.17	-0.43	NS	-	-	-	Negative
Unknown	14.1	NS	-	-	-	0.031	5.89	3.50	9.02	Positive
Alpha-Lactose	14.4	0.0058	4.21	2.69	6.35	0.0224	11.95	7.44	18.58	Positive
Unknown	14.5	0.0018	4.16	2.67	6.08	0.0074	6.48	4.03	9.68	Positive
Trans-Aconitic acid	15.2	0.0007	1.57	1.05	2.30	0.0015	3.28	2.15	4.86	Positive
Unknown	16.4	0.0011	-1.23	-1.85	-0.83	0.0012	-4.11	-6.13	-2.74	Negative
Unknown	20.3	0.0094	-2.00	-2.98	-1.22	NS	-	-	-	Negative

Data were derived by GC-MS analysis of urine samples from 46 patients and 46 controls. P-values were adjusted for age, sex, body mass index, hyperlipidemia and diabetes. Data are shown for urine metabolomic signals normalized to either the internal standard 2-isopropylmalic acid or creatinine. The regression coefficient (RC) illustrates the magnitude of the statistical effect on the increased or decreased concentration of the metabolites in AVS patients. RT, Retention Time; CI, Confidence Interval. Positive and negative regulation indicates up- and down-regulation of the metabolic features in AVS patients, respectively. HPHPA, 3-(3-Hydroxyphenyl)-3-hydroxypropanoic acid.

Table 3

Plasma metabolites contributing to the separation between the AVS patients and healthy controls.

Metabolite	RT	q-value	Regression coefficient	CI 2.5	CI 97.5	Regulation in AVS
L-Valine	4.4	0.0035	-0.77	-1.14	-0.48	Negative
Phosphate/phosphoric acid	5.1	0.0014	-1.81	-2.64	-1.14	Negative
Glycine	5.2	0.0031	0.91	0.56	1.34	Positive
Unknown	5.4	0.0167	2.08	1.27	3.21	Positive
2-Oxovaleric acid	6.8	0.0021	-1.86	-2.74	-1.17	Negative
2,4-Di-tert-butylphenol	7.3	0.0002	-0.98	-1.40	-0.65	Negative
Dodecanoic acid	8.0	0.0452	-1.12	-1.71	-0.61	Negative
Unknown	8.4	0.0026	-1.37	-2.01	-0.85	Negative
Myristic acid	9.4	0.0001	-0.80	-1.13	-0.53	Negative
Galactose	9.5	0.0012	-0.82	-1.20	-0.52	Negative
Quinic acid	9.7	0.0004	-0.89	-1.30	-0.59	Negative
D-Glucose	9.9	0.0176	4.62	2.73	7.03	Positive
Palmitic acid	10.6	0.0006	-1.71	-2.50	-1.13	Negative
Myo-inositol	11.1	0.0013	-1.22	-1.78	-0.78	Negative
Elaidic acid	11.7	<0.001	-0.93	-1.30	-0.63	Negative
Estrone	12.7	0.0014	-0.66	-1.01	-0.44	Negative
Unknown	12.9	0.0007	-0.68	-1.00	-0.44	Negative
Unknown	14.5	0.0211	-0.48	-0.74	-0.28	Negative
Trans-Aconitic acid	15.2	0.0056	0.90	0.55	1.34	Positive
Unknown	16.9	0.0455	0.63	0.35	0.97	Positive
Unknown	17.2	0.0382	-0.33	-0.50	-0.18	Negative

Data were derived by GC-MS analysis of plasma samples from 46 patients and 46 controls. P-values were adjusted for age, sex, body mass index, hyperlipidemia and diabetes. The regression coefficient illustrates the magnitude of the statistical effect on the increased or decreased concentration of the metabolites in AVS patients. RT, Retention Time; CI, Confidence Interval. Positive and negative regulation indicates up- and down-regulation of the metabolic features in AVS patients, respectively.