life Article The Interplay between Pathophysiological Pathways in Early-Onset Severe Preeclampsia Unveiled by Metabolomics Lina Youssef 1 , Francesca Crovetto 1 , Rui Vasco Simoes 1,2 , Jezid Miranda 1 , Cristina Paules 1 , Miquel Blasco 3 , Marta Palomo 4,5,6 , Héctor García-Calderó 7,8 , Olga Tura-Ceide 9,10,11 , Ana Paula Dantas 12 , Virginia Hernandez-Gea 7,8 , Pol Herrero 13 , Núria Canela 13 , Josep Maria Campistol 3,14 , Joan Carles Garcia-Pagan 7,8 , Maribel Diaz-Ricart 5,6 , Eduard Gratacos 1,14 and Fatima Crispi 1,14, * 1 BCNatal|Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, 08036 Barcelona, Spain; lyoussef@clinic.cat (L.Y.); fcrovetto@clinic.cat (F.C.); rui.simoes@research.fchampalimaud.org (R.V.S.); jezidmiranda@gmail.com (J.M.); cristinapaules@hotmail.com (C.P.); gratacos@clinic.cat (E.G.) 2 Champalimaud Research, Champalimaud Centre for the Unknown, 1400-038 Lisbon, Portugal 3 Nephrology and Renal Transplantation Department, Hospital Clínic, Centro de Referencia en Enfermedad Glomerular Compleja del Sistema Nacional de Salud (CSUR), University of Barcelona, 08036 Barcelona, Spain; MIBLASCO@clinic.cat (M.B.); JMCAMPIS@clinic.cat (J.M.C.) 4 Josep Carreras Leukaemia Research Institute, Hospital Clinic, University of Barcelona, 08036 Barcelona, Spain; mpalomo@carrerasresearch.org  5  Hematopathology, Centre Diagnòstic Biomèdic (CDB), Hemotherapy-Hemostasis Department, Hospital Clinic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Citation: Youssef, L.; Crovetto, F.; University of Barcelona, 08036 Barcelona, Spain; mdiaz@clinic.cat Simoes, R.V.; Miranda, J.; Paules, C.; 6 Barcelona Endothelium Team (BET), 08036 Barcelona, Spain Blasco, M.; Palomo, M.; 7 Barcelona Hepatic Hemodynamics Laboratory, Liver Unit, Hospital Clinic, Institut d’Investigacions García-Calderó, H.; Tura-Ceide, O.; Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, 08036 Barcelona, Spain; Dantas, A.P.; et al. The Interplay HGARCIA@clinic.cat (H.G.-C.); VIHERNANDEZ@clinic.cat (V.H.-G.); JCGARCIA@clinic.cat (J.C.G.-P.) 8 Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), between Pathophysiological Pathways in Early-Onset Severe Health Care Provider of the European Reference Network on Rare Liver Disorders (ERN-Liver), Preeclampsia Unveiled by 08036 Barcelona, Spain 9 Department of Pulmonary Medicine, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Metabolomics. Life 2022, 12, 86. Sunyer (IDIBAPS), University of Barcelona, 08036 Barcelona, Spain; TURA@clinic.cat https://doi.org/10.3390/ 10 Biomedical Research Networking Center on Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, life12010086 28029 Madrid, Spain 11 Girona Biomedical Research Institute—IDIBGI, 17190 Girona, Spain Academic Editors: Ilona 12 Cardiovascular Institute, Hospital Clinic, Institut d’Investigacions Biomèdiques August Pi i Hromadnikova and Katerina Sunyer (IDIBAPS), University of Barcelona, 08036 Barcelona, Spain; ADANTAS@clinic.cat Kotlabova 13 Eurecat, Centre Tecnològic de Catalunya, Centre for Omic Sciences (COS), Joint Unit Universitat Rovira i Received: 21 December 2021 Virgili-EURECAT, Unique Scientific and Technical Infrastructures (ICTS), 43204 Reus, Spain; Accepted: 4 January 2022 pol.herrero@agilent.com (P.H.); nuria.canela@eurecat.org (N.C.) 14 Centre for Biomedical Research on Rare Diseases (CIBER-ER), Instituto de Salud Carlos III, Published: 7 January 2022 28029 Madrid, Spain Publisher’s Note: MDPI stays neutral * Correspondence: fcrispi@clinic.cat with regard to jurisdictional claims in published maps and institutional affil- Abstract: Introduction: Preeclampsia is a multi-system disorder unique to pregnancy responsible for a iations. great part of maternal and perinatal morbidity and mortality. The precise pathogenesis of this complex disorder is still unrevealed. Methods: We examined the pathophysiological pathways involved in early-onset preeclampsia, a specific subgroup representing its most severe presentation, using LC- MS/MS metabolomic analysis based on multi-level extraction of lipids and small metabolites from Copyright: © 2022 by the authors. maternal blood samples, collected at the time of diagnosis from 14 preeclamptic and six matched Licensee MDPI, Basel, Switzerland. healthy pregnancies. Statistical analysis comprised multivariate and univariate approaches with This article is an open access article distributed under the terms and the application of over representation analysis to identify differential pathways. Results: A clear conditions of the Creative Commons difference between preeclamptic and control pregnancies was observed in principal component Attribution (CC BY) license (https:// analysis. Supervised multivariate analysis using orthogonal partial least square discriminant analysis creativecommons.org/licenses/by/ provided a robust model with goodness of fit (R2 X = 0.91, p = 0.002) and predictive ability (Q2 Y = 0.72, 4.0/). p < 0.001). Finally, univariate analysis followed by 5% false discovery rate correction indicated Life 2022, 12, 86. https://doi.org/10.3390/life12010086 https://www.mdpi.com/journal/life Life 2022, 12, 86 2 of 21 82 metabolites significantly altered, corresponding to six overrepresented pathways: (1) aminoacyl- tRNA biosynthesis; (2) arginine biosynthesis; (3) alanine, aspartate and glutamate metabolism; (4) D-glutamine and D-glutamate metabolism; (5) arginine and proline metabolism; and (6) histidine metabolism. Conclusion: Metabolomic analysis focusing specifically on the early-onset severe form of preeclampsia reveals the interplay between pathophysiological pathways involved in this form. Future studies are required to explore new therapeutic approaches targeting these altered metabolic pathways in early-onset preeclampsia. Keywords: early-onset preeclampsia; fetal growth restriction; LC-MS/MS; maternal serum; preeclamp- sia; metabolomics; severe preeclampsia 1. Introduction Preeclampsia is a multi-system disorder unique to pregnancy characterized by new- onset elevated blood pressure associated with proteinuria occurring after 20 weeks of gestation [1]. This complex disease affects 2–5% of pregnancies and is a major cause of maternal and perinatal morbidity and mortality [2,3]. However, the precise pathogenesis of preeclampsia is still unrevealed. Although abnormal placentation is known to play a central role especially in severe cases of preeclampsia [4,5], it is assumed that preeclampsia is a spectral disorder with different underlying etiologies, all converging into a seemingly identical clinical presentation that affects maternal health [6]. This heterogeneity strongly limits our understanding of the underlying etiology and the identification of therapeutic targets. The distinct forms of this disorder are recognized as early and late-onset preeclamp- sia before or after 34 weeks of gestation, respectively. Early-onset preeclampsia is less common than the late-onset form. However, it is more severe and frequently associated with maternal and perinatal complications [7]. Understanding the pathophysiology of early-onset severe preeclampsia is paramount for improving its clinical management, as well as detecting potential therapeutic targets for this subtype of preeclampsia. Metabolomic-focused studies can contribute considerably to our understanding of complex multi-organ disorders by identifying biochemical mechanisms that are involved in their pathophysiology. Indeed, investigating and quantifying single metabolites offer a quite sensitive measure of the biochemical status of the studied subjects [8]. In addition, the interaction between these metabolites might uncover metabolic signatures of a disease or disease subtype [8]. Few studies have been conducted so far to examine the metabolomic profile of preeclampsia through maternal blood samples collected near delivery [9–15], but their results are difficult to interpret due to the high degree of sample heterogeneity and due to a focus on individual metabolites rather than examining the whole picture of activated pathways. In a previous study, we analyzed blood metabolomic profiles of mothers and fetuses at delivery using nuclear magnetic resonance approach and demonstrated that cases of preeclampsia with fetal growth restriction exhibit the most disturbed metabolomic profiles [16]. To further explore these complex alterations and pathophysiological pathways involved, we have now studied a well-defined subgroup of early-onset severe preeclampsia, typically associated with fetal growth restriction, using liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics–a highly sensitive technique performed with multi-level extraction of lipids and small metabolites from maternal blood samples. Thus, our aim in this study was to identify the metabolomic fingerprint of early-onset severe preeclampsia and associated pathways underlying the pathogenesis of this disorder. 2. Results 2.1. Baseline and Perinatal Characteristics Table 1 displays baseline characteristics and perinatal outcomes of the pregnancies included in this study. Baseline characteristics of pregnant women were comparable among the study groups. Ovum donation had not been used to achieve pregnancy in any of the Life 2022, 12, 86 3 of 21 three preeclamptic pregnancies achieved by assisted reproductive technologies. As ex- pected, pregnancies complicated by preeclampsia presented altered feto-placental Doppler parameters. Liver enzymes including aspartate aminotransferase (AST), alanine amino- transferase (ALT), and gamma-glutamyl transferase (GGT) were higher in preeclampsia. One preeclamptic patient presented abnormal creatinine concentration, four patients pre- sented abnormal AST and ALT concentrations and one patient presented high bilirubin and low platelets according to the cutoffs used clinically. In preeclampsia, average gestational age at the time of delivery was 32 weeks, up to 80% of deliveries were achieved through cesarean sections and all neonates were admitted to the neonatal intensive care unit, ex- hibiting subsequently one case of neonatal mortality. Two cases of preeclampsia had the diagnosis of hemolysis, elevated liver enzymes and low platelets (HELLP) syndrome. Table 1. Baseline characteristics of the study population. Controls Preeclampsia p-Value n=6 n = 14 Maternal characteristics Age (years) 36.5 ± 2.9 34.1 ± 3.8 0.28 * Caucasian ethnicity 5 (83.3) 6 (42.9) 0.10 ƒ Pre-gestational BMI (kg/m2 ) 22.2 ± 2.7 24.1 ± 4.1 0.38 * Nulliparity 5 (83.3) 9 (64.3) 0.39 ƒ Previous preeclampsia 0 (0) 1 (7.1) 0.50 ƒ Chronic hypertension 0 (0) 1 (7.1) 0.50 ƒ Assisted reproductive technologies 0 (0) 3 (21.4) 0.22 ƒ Smoking during pregnancy 1 (16.7) 1 (7.1) 0.52 ƒ Fetal ultrasound assessment Gestational age at ultrasound (weeks) 26.6 ± 3.7 30.5 ± 2.6 0.013 * Estimated fetal weight (g) 994 ± 496 1187 ± 328 0.30 * Estimated fetal weight centile 34 (29–66) 1 (0–3) <0.001 ω Uterine arteries mean PI (z score) −0.53 ± 1.77 2.60 ± 2.08 0.002 * Umbilical artery PI (z score) −0.40 ± 0.32 1.27 ± 1.71 0.07 * Middle cerebral artery PI (z score) 0.13 ± 1.09 −1.16 ± 0.99 0.027 * Cerebroplacental ratio (z score) −0.06 ± 0.89 −1.93 ± 1.6 0.027 * Ductus venosus PI (z score) −1 ± 0.79 −0.34 ± 1.29 0.44 * Maternal biophysical and biochemical profile at the time of sampling Gestational age at blood draw (weeks) 30 ± 1.8 31.3 ± 1.8 0.16 * Systolic blood pressure (mmHg) 150.5 ± 18.1 107.7 ± 8.3 <0.001 * Diastolic blood pressure (mmHg) 89.3 ± 8 70.7 ± 3.3 <0.001 * Creatinine (mg/dL) 0.42 ± 0.07 0.69 ± 0.17 0.001 * Urea (mg/dL) 15.4 ± 4.2 40.4 ± 16.7 0.002 * Glomerular filtration rate (mL/min) 85 ± 12.2 62.1 ± 8 <0.001 * Sodium (mEq/L) 138.3 ± 1.2 136.4 ± 2.1 0.048 * Potassium (mEq/L) 4.13 ± 0.21 4.61 ± 0.23 0.001 * AST (u/L) 17 (15–23) 25 (16–48) 0.16 ω ALT (u/L) 15 (9–21) 27 (14–65) 0.07 ω GGT (u/L) 10 (7–11) 19 (15–28) 0.013 ω Uric acid (mg/dL) 3.33 ± 0.43 6.09 ± 1.25 <0.001 * Glucose (mg/dL) 75.5 ± 7.1 87.8 ± 19.7 0.13 * Triglycerides (mg/dL) 178.8 ± 67.7 260.6 ± 97.8 0.06 * Total cholesterol (mg/dL) 277.2 ± 50.1 241.9 ± 54 0.16 * Fibrinogen (g/L) 4.57 ± 0.98 3.44 ± 1.05 0.07 * Platelets (103 /L) 271.8 ± 70.6 209.1 ± 71.9 0.09 * Proteinuria (g/24 h) - 2.6 (0.6–5.8) - Protein/creatinine ratio - 2.2 (0.4–4.2) - Life 2022, 12, 86 4 of 21 Table 1. Cont. Controls Preeclampsia p-Value n=6 n = 14 Perinatal outcomes Gestational age at delivery (weeks) 40.3 ± 1.1 32 ± 1.9 <0.001 * Cesarean section 1 (16.7) 11 (78.6) 0.018 ƒ Female sex 4 (66.7) 8 (57.1) 0.69 ƒ Birthweight (g) 3531 ± 410 1287 ± 267 <0.001 * Birthweight centile 58 (41–80) 0 (0–1) <0.001 ω APGAR score 5 min < 7 0 (0) 1 (7.1) 0.50 ƒ Umbilical artery pH 7.20 ± 0.07 7.20 ± 0.09 0.97 * Data are presented as mean ± standard deviation, median (interquartile range) or n (%) as appropriate. BMI, body mass index; PI, pulsatility index; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GGT, gamma-glutamyl transferase. p value was calculated by student t (*), Mann Whitney U (ω ) or Fisher exact (ƒ ) tests as appropriate. 2.2. Metabolomics Results 383 metabolites (out of 400) were present in ≥70% of the samples (complete results dataset is provided in Supplementary Materials Table S1). Figure 1a,b shows a clear separation between preeclampsia and controls through the principal component analysis (PCA). The first and second components explained 34.9% and 13% of the variance between cases and controls, respectively. Figure 1c demonstrated the separation between the study groups by the partial least squares discriminant analysis (PLS-DA). Figure 1d demonstrates the top 15 most important metabolites responsible for class separation. The contribution of each metabolite is demonstrated by the distance from the Y-axis (the greater the distance, the greater the contribution). The heatmap on the right side of this plot also indicates if the concentration of this metabolite is higher or lower in preeclampsia compared to controls. The model obtained by orthogonal projection to latent structures discriminant analysis (OPLS-DA) showed a high goodness of fit (R2X = 0.915, p = 0.003) and a strong predictive ability (Q2 Y = 0.718, p = 0.001, 1 predictive + 1 orthogonal components), i.e., the model explains more than 90% of the variation between the study groups with a predictive ability of 72%). The two cases of HELLP syndrome were not separated from other severe preeclamptic pregnancies. Univariate analysis revealed 82 statistically different metabolites in preeclampsia vs. controls (Table 2), in agreement with the multivariate analysis. Hierarchical clustering analysis (HCA) considering the top 25 metabolites showed 2 clusters in correspondence with the study groups (Figure 2). Table 2. The 82 metabolites significantly different in preeclampsia vs. controls by univariate analysis considering false discovery rate correction (q-value < 0.05). Controls Preeclampsia Preeclampsia/ HMDB Metabolite Name p-Value q-Value Mean (SD) Mean (SD) Controls HMDB00192 Cystine −0.553 (0.167) 0.237 (0.251) <0.0001 < 0.0001 Up HMDB00149 Ethanolamine −0.397 (0.160) 0.170 (0.174) <0.0001 < 0.0001 Up HMDB00158 Tyrosine −0.431 (0.070) 0.185 (0.238) <0.0001 < 0.0001 Up HMDB00062 Free carnitine −0.572 (0.138) 0.245 (0.393) <0.0001 < 0.0001 Up HMDB00194 Anserine −0.361 (0.130) 0.155 (0.183) <0.0001 0.0006 Up HMDB00824 Propionyl-carnitine −0.581 (0.079) 0.249 (0.464) <0.0001 0.0006 Up Iso-butyryl-carnitine/ HMDB02013 −0.565 (0.120) 0.242 (0.465) <0.0001 0.0011 Up Butyryl-carnitine HMDB0000138 Glyco-cholic acid-iso3 0.819 (0.401) −0.351 (0.450) <0.0001 0.0014 Down HMDB00679 Homo-citrulline −0.614 (0.158) 0.263 (0.318) <0.0001 0.0021 Up HMDB00243 Pyruvic acid −0.505 (0.282) 0.216 (0.300) <0.0001 0.0034 Up Life 2022, 12, 86 5 of 21 Table 2. Cont. Controls Preeclampsia Preeclampsia/ HMDB Metabolite Name p-Value q-Value Mean (SD) Mean (SD) Controls HMDB00774 Pregnenolone sulfate 0.708 (0.395) −0.303 (0.426) 0.0001 0.0035 Down HMDB00696 Methionine −0.512 (0.144) 0.219 (0.351) 0.0001 0.0038 Up HMDB00510 L-2-Aminoadipic acid −0.561 (0.334) 0.240 (0.339) 0.0001 0.0038 Up HMDB00673 Linolenic acid-iso2 −0.567 (0.368) 0.243 (0.341) 0.0002 0.0044 Up HMDB00450 Hydroxylysine −0.490 (0.292) 0.210 (0.354) 0.0002 0.0054 Up 2-methyl-butyryl- HMDB00688 carnitine/Isovaleryl- −0.562 (0.219) 0.241 (0.403) 0.0002 0.0060 Up carnitine/Valeryl-carnitine HMDB0000138 Glyco-cholic acid-iso2 0.719 (0.650) −0.308 (0.374) 0.0003 0.0061 Down HMDB00001 1-Methylhistidine −0.524 (0.168) 0.224 (0.390) 0.0003 0.0064 Up HMDB00182 Lysine −0.374 (0.181) 0.160 (0.271) 0.0004 0.0069 Up HMDB06547 Stearidonic acid −0.650 (0.508) 0.278 (0.534) 0.0004 0.0069 Up HMDB00904 Citrulline −0.300 (0.080) 0.129 (0.341) 0.0004 0.0075 Up HMDB00641 Glutamine −0.396 (0.169) 0.170 (0.301) 0.0005 0.0075 Up HMDB11483 LPE 20:2-sn1 0.616 (0.513) −0.264 (0.380) 0.0005 0.0075 Down HMDB00063 Cortisol 0.979 (0.168) −0.420 (0.561) 0.0006 0.0089 Down HMDB00168 Asparagine −0.311 (0.127) 0.133 (0.352) 0.0006 0.0089 Up HMDB00177 Histidine −0.372 (0.132) 0.160 (0.299) 0.0006 0.0089 Up HMDB00234 Testosterone 1.018 (0.787) −0.436 (0.695) 0.0006 0.0089 Down HMDB0000138 Glyco-cholic acid-iso1 0.716 (0.543) −0.307 (0.500) 0.0007 0.0094 Down HMDB00687 Leucine −0.376 (0.195) 0.161 (0.294) 0.0007 0.0096 Up HMDB00008 2-Hydroxybutyric acid −0.611 (0.395) 0.262 (0.460) 0.0008 0.0098 Up NA LPI 18:1 0.631 (0.252) −0.271 (0.740) 0.0008 0.0098 Down HMDB00159 Phenylalanine −0.366 (0.170) 0.157 (0.298) 0.0009 0.0104 Up HMDB10383 LPC 16:1 e −0.294 (0.166) 0.126 (0.235) 0.0009 0.0108 Up HMDB00208 alpha-Ketoglutaric acid −0.368 (0.208) 0.158 (0.306) 0.0013 0.0139 Up NA 11.13-Eicosadienoic acid −0.530 (0.377) 0.227 (0.418) 0.0013 0.0139 Up HMDB00517 Arginine −0.297 (0.132) 0.127 (0.367) 0.0013 0.0139 Up NA 17-HDHA −0.648 (0.384) 0.278 (0.443) 0.0013 0.0139 Up HMDB00167 Threonine −0.415 (0.295) 0.178 (0.353) 0.0015 0.0156 Up HMDB00883 Valine −0.344 (0.265) 0.147 (0.274) 0.0016 0.0157 Up HMDB06528 w3-docosapentaenoic acid −0.524 (0.416) 0.225 (0.435) 0.0022 0.0203 Up HMDB00172 Isoleucine −0.309 (0.123) 0.133 (0.289) 0.0022 0.0203 Up HMDB00148 Glutamic acid −0.262 (0.267) 0.112 (0.191) 0.0022 0.0203 Up HMDB00214 Ornithine −0.439 (0.261) 0.188 (0.394) 0.0023 0.0203 Up Urso-deoxycholic/ HMDB00946 0.650 (0.394) −0.279 (0.584) 0.0024 0.0204 Down Hyo-deoxycholic acid HMDB07132 HMDB07105 HMDB07051 DG 34:3 −0.411 (0.335) 0.176 (0.344) 0.0024 0.0204 Up HMDB07023 HMDB07024 HMDB00190 Lactic acid −0.295 (0.151) 0.126 (0.273) 0.0024 0.0204 Up HMDB00826 Pentadecanoic acid −0.440 (0.236) 0.189 (0.407) 0.0025 0.0206 Up HMDB05474 TG 54:6 −0.517 (0.475) 0.221 (0.415) 0.0026 0.0206 Up HMDB02231 Eico-senoic acid −0.521 (0.332) 0.223 (0.474) 0.0027 0.0213 Up HMDB00271 Sarcosine −0.481 (0.338) 0.206 (0.436) 0.0030 0.0230 Up Life 2022, 12, 86 6 of 21 Table 2. Cont. Controls Preeclampsia Preeclampsia/ HMDB Metabolite Name p-Value q-Value Mean (SD) Mean (SD) Controls HMDB00774 Pregnenolone sulfate 0.708 (0.395) −0.303 (0.426) 0.0001 0.0035 Down HMDB00696 Methionine −0.512 (0.144) 0.219 (0.351) 0.0001 0.0038 Up HMDB00510 L-2-Aminoadipic acid −0.561 (0.334) 0.240 (0.339) 0.0001 0.0038 Up HMDB00673 Linolenic acid-iso2 −0.567 (0.368) 0.243 (0.341) 0.0002 0.0044 Up HMDB00450 Hydroxylysine −0.490 (0.292) 0.210 (0.354) 0.0002 0.0054 Up 2-methyl-butyryl- HMDB00688 carnitine/Isovaleryl- −0.562 (0.219) 0.241 (0.403) 0.0002 0.0060 Up carnitine/Valeryl-carnitine HMDB0000138 Glyco-cholic acid-iso2 0.719 (0.650) −0.308 (0.374) 0.0003 0.0061 Down HMDB00001 1-Methylhistidine −0.524 (0.168) 0.224 (0.390) 0.0003 0.0064 Up HMDB00182 Lysine −0.374 (0.181) 0.160 (0.271) 0.0004 0.0069 Up HMDB06547 Stearidonic acid −0.650 (0.508) 0.278 (0.534) 0.0004 0.0069 Up HMDB00904 Citrulline −0.300 (0.080) 0.129 (0.341) 0.0004 0.0075 Up HMDB00641 Glutamine −0.396 (0.169) 0.170 (0.301) 0.0005 0.0075 Up HMDB11483 LPE 20:2-sn1 0.616 (0.513) −0.264 (0.380) 0.0005 0.0075 Down HMDB00063 Cortisol 0.979 (0.168) −0.420 (0.561) 0.0006 0.0089 Down HMDB00168 Asparagine −0.311 (0.127) 0.133 (0.352) 0.0006 0.0089 Up HMDB00177 Histidine −0.372 (0.132) 0.160 (0.299) 0.0006 0.0089 Up HMDB00234 Testosterone 1.018 (0.787) −0.436 (0.695) 0.0006 0.0089 Down HMDB0000138 Glyco-cholic acid-iso1 0.716 (0.543) −0.307 (0.500) 0.0007 0.0094 Down HMDB00687 Leucine −0.376 (0.195) 0.161 (0.294) 0.0007 0.0096 Up HMDB00008 2-Hydroxybutyric acid −0.611 (0.395) 0.262 (0.460) 0.0008 0.0098 Up NA LPI 18:1 0.631 (0.252) −0.271 (0.740) 0.0008 0.0098 Down HMDB00159 Phenylalanine −0.366 (0.170) 0.157 (0.298) 0.0009 0.0104 Up HMDB10383 LPC 16:1 e −0.294 (0.166) 0.126 (0.235) 0.0009 0.0108 Up HMDB00208 alpha-Ketoglutaric acid −0.368 (0.208) 0.158 (0.306) 0.0013 0.0139 Up NA 11.13-Eicosadienoic acid −0.530 (0.377) 0.227 (0.418) 0.0013 0.0139 Up HMDB00517 Arginine −0.297 (0.132) 0.127 (0.367) 0.0013 0.0139 Up NA 17-HDHA −0.648 (0.384) 0.278 (0.443) 0.0013 0.0139 Up HMDB00167 Threonine −0.415 (0.295) 0.178 (0.353) 0.0015 0.0156 Up HMDB00883 Valine −0.344 (0.265) 0.147 (0.274) 0.0016 0.0157 Up HMDB06528 w3-docosapentaenoic acid −0.524 (0.416) 0.225 (0.435) 0.0022 0.0203 Up HMDB00172 Isoleucine −0.309 (0.123) 0.133 (0.289) 0.0022 0.0203 Up HMDB00148 Glutamic acid −0.262 (0.267) 0.112 (0.191) 0.0022 0.0203 Up HMDB00214 Ornithine −0.439 (0.261) 0.188 (0.394) 0.0023 0.0203 Up Urso-deoxycholic/ HMDB00946 0.650 (0.394) −0.279 (0.584) 0.0024 0.0204 Down Hyo-deoxycholic acid HMDB07132 HMDB07105 HMDB07051 DG 34:3 −0.411 (0.335) 0.176 (0.344) 0.0024 0.0204 Up HMDB07023 HMDB07024 HMDB00190 Lactic acid −0.295 (0.151) 0.126 (0.273) 0.0024 0.0204 Up HMDB00826 Pentadecanoic acid −0.440 (0.236) 0.189 (0.407) 0.0025 0.0206 Up HMDB05474 TG 54:6 −0.517 (0.475) 0.221 (0.415) 0.0026 0.0206 Up HMDB02231 Eico-senoic acid −0.521 (0.332) 0.223 (0.474) 0.0027 0.0213 Up HMDB00271 Sarcosine −0.481 (0.338) 0.206 (0.436) 0.0030 0.0230 Up Life 2022, 12, 86 7 of 21 Table 2. Cont. Controls Preeclampsia Preeclampsia/ HMDB Metabolite Name p-Value q-Value Mean (SD) Mean (SD) Controls HMDB00161 Alanine −0.378 (0.185) 0.162 (0.367) 0.0033 0.0243 Up HMDB12328 Trans-palmitoleic acid −0.497 (0.244) 0.213 (0.607) 0.0033 0.0243 Up HMDB06734 ChoE 202 0.320 (0.336) −0.137 (0.252) 0.0034 0.0243 Down NA LPC 22:3-sn1 0.629 (0.343) −0.269 (0.607) 0.0034 0.0243 Down HMDB05447 HMDB10490 HMDB10502 TG 54:7 −0.543 (0.565) 0.233 (0.433) 0.0035 0.0243 Up HMDB10475 HMDB10489 HMDB07972 PC 34:1 e 0.350 (0.286) −0.150 (0.315) 0.0037 0.0249 Down NA LPI 18:2 0.624 (0.343) −0.267 (0.609) 0.0037 0.0249 Down HMDB04702 12.13-EpOME(9) −0.461 (0.293) 0.198 (0.443) 0.0039 0.0254 Up HMDB10392 LPC 20:2-sn2 0.516 (0.470) −0.221 (0.455) 0.0041 0.0264 Down HMDB0240261 LPI 18:0 0.633 (0.280) −0.271 (0.643) 0.0042 0.0267 Down HMDB31654 3-Aminobutanoic acid −0.415 (0.227) 0.178 (0.414) 0.0042 0.0267 Up HMDB10221 HODE-iso1 −0.512 (0.440) 0.219 (0.475) 0.0048 0.0295 Up NA LPI 22:4 0.548 (0.358) −0.235 (0.546) 0.0049 0.0300 Down NA LPI 20:4 0.590 (0.305) −0.253 (0.613) 0.0053 0.0320 Down HMDB10386 LPC 18:2-sn2 0.453 (0.348) −0.194 (0.446) 0.0056 0.0330 Down HMDB00694 2-hydroxyglutaric acid −0.346 (0.099) 0.148 (0.557) 0.0060 0.0349 Up HMDB08287 HMDB08257 HMDB08318 PC 42:5 e 0.339 (0.292) −0.145 (0.333) 0.0064 0.0364 Down HMDB08383 HMDB08286 NA LPI 14:0 0.660 (0.510) −0.283 (0.689) 0.0077 0.0432 Down HMDB06547 Stearidonic acid-iso1 −0.405 (0.284) 0.173 (0.431) 0.0078 0.0432 Up HMDB01383 Sphingosine-1-P 0.402 (0.341) −0.172 (0.414) 0.0081 0.0440 Down Dehydro-epiandrost-erone HMDB01032 0.607 (0.361) −0.260 (0.670) 0.0085 0.0440 Down sulfate HMDB00123 Glycine −0.260 (0.085) 0.111 (0.305) 0.0087 0.0440 Up HMDB00575 Homo-cystine −0.472 (0.373) 0.202 (0.649) 0.0087 0.0440 Up HMDB05461 TG 54:5 −0.324 (0.164) 0.139 (0.614) 0.0087 0.0440 Up HMDB02802 Cortisone 0.715 (0.138) −0.307 (0.574) 0.0087 0.0440 Down HMDB02226 Adrenic acid −0.421 (0.335) 0.180 (0.447) 0.0087 0.0440 Up HMDB00112 gamma-Aminobutyric acid −0.449 (0.609) 0.193 (0.373) 0.0092 0.0460 Up HMDB00162 Proline −0.308 (0.256) 0.132 (0.329) 0.0096 0.0471 Up HMDB00673 Linolenic acid-iso1 −0.414 (0.387) 0.178 (0.432) 0.0098 0.0474 Up HMDB10391 LPC 20:1-sn2 0.526 (0.403) −0.225 (0.578) 0.0100 0.0480 Down NA LPC 22:3-sn2 0.498 (0.458) −0.214 (0.530) 0.0105 0.0497 Down NA LPI 16:0 0.577 (0.324) −0.247 (0.668) 0.0107 0.0498 Down DG, diglycerides; ChoE, cholesteryl ester; HDHA, hydroxy-docosahexaenoic acid; LPE, lyso-phosphatidy- lethanolamine; LPI, lyso-phosphatidylinositol; LPC, lyso-phosphatidylcholines; PC, phosphatidylcholines; TG, triglycerides. Life 2021, 11, x FOR PEER REVIEW 5 of 20 Life 2022, 12, 86 8 of 21 Figure Figure 1. 1.(a) (a)2-dimensional 2-dimensionaland and(b) (b)3-dimensional 3-dimensionalprincipal principalcomponent componentanalysis analysis(PCA) (PCA)scores scoresplots. plots. (c) (c) Partial Partial least leastsquares squaresdiscriminant discriminantanalysis analysis(PLS-DA) (PLS-DA)scores scoresplot between plot betweencomponents components 1 and 2. 1 and The explained 2. The explainedvariance is shown variance is shown in in brackets. brackets.Controls (CT) Controls (CT)are arepresented presentediningreen greencircles circles and and preeclampsia preeclampsiacasescases(PE) (PE)ininviolet violettriangles. triangles.(d)(d) Top 1515 Top most important most importantmetabolites metabolitesthat contribute that contributeto group separation identified through PLS-DA ranked by variable importance in projection (VIP) to group separation identified through PLS-DA ranked by variable importance in projection (VIP) scores. The right heatmap shows the mean intensity variable in the respective group, with red and scores. The right heatmap shows the mean intensity variable in the respective group, with red and blue indicating high and low metabolite levels, respectively. GA, glyco-cholic acid; HDHA, hy- blue indicating high andacid; droxy-docosahexaenoic low metabolite levels, respectively. GA, glyco-cholic LPC, lyso-phosphatidylcholines; acid; HDHA, hydroxy- LPE, Lyso-phosphatidylethanola- docosahexaenoic acid; LPC, lyso-phosphatidylcholines; LPE, Lyso-phosphatidylethanolamine; mine; LPI, lyso-phosphatidylinositol; NA, non-applicable; Pregnenolone s, pregnenolone sulfate; LPI, lyso-phosphatidylinositol; Stearidonic NA, non-applicable; ac, stearidonic acid; Pregnenolone s, pregnenolone sulfate; Urso/Hyo ac, urso-deoxycholic/hyo-deoxycholic acid.Stearidonic ac, stearidonic acid; Urso/Hyo ac, urso-deoxycholic/hyo-deoxycholic acid. Univariate analysis revealed 82 statistically different metabolites in preeclampsia vs. controls (Table 2), in agreement with the multivariate analysis. Hierarchical clustering analysis (HCA) considering the top 25 metabolites showed 2 clusters in correspondence with the study groups (Figure 2). HMDB00162 Proline −0.308 (0.256) 0.132 (0.329) 0.0096 0.0471 Up HMDB00673 Linolenic acid-iso1 −0.414 (0.387) 0.178 (0.432) 0.0098 0.0474 Up HMDB10391 LPC 20:1-sn2 0.526 (0.403) −0.225 (0.578) 0.0100 0.0480 Down NA LPC 22:3-sn2 0.498 (0.458) −0.214 (0.530) 0.0105 0.0497 Down NA LPI 16:0 0.577 (0.324) −0.247 (0.668) 0.0107 0.0498 Down Life 2022, 12, 86 9 of 21 DG, diglycerides; ChoE, cholesteryl ester; HDHA, hydroxy-docosahexaenoic acid; LPE, lyso-phosphatidylethanolamine; LPI, lyso-phosphatidylinositol; LPC, lyso-phosphatidylcholines; PC, phosphatidylcholines; TG, triglycerides. Figure 2. Unsupervised hierarchical clustering considering the top 25 metabolites selected from uni- Figure 2. Unsupervised hierarchical clustering considering the top 25 metabolites selected from variate analysis. A z-score transformation was performed on the intensity of each metabolite across univariate analysis. A z-score transformation was performed on the intensity of each metabo- all samples and the heatmap displays each sample z-score. Rows represent metabolites and columns lite across samples. represent all samples and Both the heatmap metabolites anddisplays samples each sample z-score. are clustered Rows distance by Euclidean representand metabo- Ward lites and columns represent samples. Both metabolites and samples are clustered by Euclidean linkage. 1-Methylhistidine (HMDB00001); 2-MIV-carnitine, 2-methyl-butyryl-carnitine/isovaleryl- distance and Ward linkage.(HMDB00688); carnitine/valeryl-carnitine 1-Methylhistidine (HMDB00001); Aminoadipic 2-MIV-carnitine, ac, aminoadipic 2-methyl-butyryl- acid (HMDB00510); Anser- carnitine/isovaleryl-carnitine/valeryl-carnitine ine (HMDB00194); Cortisol (HMDB00063); Cortisone (HMDB00688); Aminoadipic (HMDB02802); ac, aminoadipic Cystine (HMDB00192); acid Ethan- olamine (HMDB00149); (HMDB00510); AnserineFree carnitine (HMDB00062); (HMDB00194); GA, glyco-cholic Cortisol (HMDB00063); acid (HMDB0000138); Cortisone (HMDB02802); Cys-Glu- tamine tine (HMDB00641); (HMDB00192); HDHA, hydroxy-docosahexaenoic Ethanolamine acid (NA); (HMDB00149); Free carnitine Histidine (HMDB00177); (HMDB00062); IB- GA, glyco-cholic carnitine, iso-butyryl-carnitine/butyryl-carnitine (HMDB02013); Homo-citrulline acid (HMDB0000138); Glutamine (HMDB00641); HDHA, hydroxy-docosahexaenoic acid (NA); His-(HMDB00679); Hydroxylysine (HMDB00450); L-2-Aminoadipic ac, L-2-Aminoadipic acid (HMDB00510); Linolenic tidine (HMDB00177); IB-carnitine, iso-butyryl-carnitine/butyryl-carnitine (HMDB02013); Homo- citrulline (HMDB00679); Hydroxylysine (HMDB00450); L-2-Aminoadipic ac, L-2-Aminoadipic acid (HMDB00510); Linolenic ac, linolenic acid (HMDB00673); LPE, lyso-phosphatidylethanolamine (HMDB11483); Lysine (HMDB00182); Methionine (HMDB00696); Pregnenolone s, pregnenolone sulfate (HMDB00774); P-carnitine, propionyl-carnitine (HMDB00824); Pyruvic acid (HMDB00243); Tyrosine (HMDB00158). The over representation analysis based on the 82 differentially expressed metabolites resulted in the significant over representation of eight pathways out of 40 pathways iden- tified: (1) aminoacyl-tRNA biosynthesis; (2) arginine biosynthesis; (3) alanine, aspartate and glutamate metabolism; (4) valine, leucine and isoleucine biosynthesis; (5) linoleic Life 2022, 12, 86 10 of 21 acid metabolism; (6) D-glutamine and D-glutamate metabolism; (7) arginine and proline metabolism; and (8) histidine metabolism (Table 3 and Figure 3). However, pathways 4 and 5 (valine, leucine and isoleucine biosynthesis and linoleic acid metabolism) correspond to es- sential amino and fatty acids, and therefore are not functional in humans. Thus, pathways 4 and 5 were further excluded from the results leaving six differential pathways in early-onset severe preeclampsia compared to controls. Lipid data showed higher triglycerides and re- duced glyco-cholic acid, lyso-phosphatidylethanolamines, lyso-phosphatidylcholines, lyso- phosphatidylinositols, pregnenolone sulfate, dehydroepiandrosterone sulfate, testosterone, cortisol and cortisone. The cortisol:cortisone ratio was significantly lower in preeclampsia (2.63 ± 0.7 vs. 7.09 ± 1.39 in controls, p < 0.001). Table 3. Over representation analysis of different metabolites in preeclampsia vs controls based on the Kyoto Encyclopedia of Genes and Genome (KEGG). Pathway Total Expected Hits Raw p −log(10)p Adjusted p FDR Impact Aminoacyl-tRNA 48 17.03 16 0.0006 × 10−9 12.20 0.05 × 10−9 0.05 × 10−9 0 biosynthesis Arginine 14 0.49 6 0.004 × 10−3 54.37 0.0003 0.0002 0.48 biosynthesis Alanine, aspartate and glutamate 28 0.99 7 0.03 × 10−3 44.95 0.003 0.0009 0.45 metabolism Valine, leucine and isoleucine 8 0.28 4 0.09 × 10−3 40.48 0.007 0.002 0 biosynthesis Linoleic acid 5 0.18 3 0.0004 33.95 0.032 0.007 1 metabolism D-Glutamine and D-glutamate 6 0.21 3 0.0008 31.05 0.06 0.011 0.50 metabolism Arginine and 38 13.48 6 0.0017 27.60 0.14 0.020 0.36 proline metabolism Histidine 16 0.57 4 0.0018 27.25 0.14 0.020 0.27 metabolism Phenylalanine, tyrosine and 4 0.14 2 0.007 21.50 0.54 0.07 1 tryptophan biosynthesis Butanoate 15 0.53 3 0.014 18.47 1 0.12 0.03 metabolism Nitrogen 6 0.21 2 0.017 17.72 1 0.13 0 metabolism Glyoxylate and dicarboxylate 32 11.36 4 0.024 1.61 1 0.17 0.11 metabolism Glycine, serine and threonine 33 1.17 4 0.027 15.68 1 0.17 0.34 metabolism Phenylalanine 10 0.35 2 0.05 13.34 1 0.28 0.36 metabolism Lysine degradation 25 0.89 3 0.06 1.25 1 0.31 0.14 Glutathione 28 0.99 3 0.07 11.31 1 0.37 0.11 metabolism alpha-Linolenic 13 0.46 2 0.08 11.24 1 0.37 0 acid metabolism Cysteine and methionine 33 1.17 3 0.11 0.96 1 0.51 0.10 metabolism Life 2022, 12, 86 11 of 21 Table 3. Cont. Pathway Total Expected Hits Raw p −log(10)p Adjusted p FDR Impact Glycerophospholipid 36 12.77 3 0.13 0.88 1 0.59 0.13 metabolism Citrate cycle 20 0.71 2 0.16 0.81 1 0.65 0.10 (TCA cycle) Valine, leucine and isoleucine 40 14.19 3 0.17 0.78 1 0.65 0 degradation beta-Alanine 21 0.75 2 0.17 0.77 1 0.65 0 metabolism Pyruvate 22 0.78 2 0.18 0.74 1 0.67 0.29 metabolism Ubiquinone and other 9 0.32 1 0.28 0.56 1 0.97 0 terpenoid-quinone biosynthesis Porphyrin and chlorophyll 30 10.65 2 0.29 0.54 1 0.97 0 metabolism Biotin metabolism 10 0.35 1 0.30 0.52 1 0.98 0 Steroid hormone 85 30.16 4 0.36 0.45 1 1 0.08 biosynthesis Glycero-lipid 16 0.57 1 0.44 0.36 1 1 0.01 metabolism Tyrosine 42 14.90 2 0.44 0.35 1 1 0.14 metabolism Primary bile acid 46 16.32 2 0.49 0.31 1 1 0.02 biosynthesis Pantothenate and 19 0.67 1 0.50 0.30 1 1 0 CoA biosynthesis Seleno-compound 20 0.71 1 0.52 0.29 1 1 0 metabolism Sphingolipid 21 0.75 1 0.53 0.27 1 1 0.02 metabolism Propanoate 23 0.82 1 0.57 0.25 1 1 0 metabolism Glycolysis/ 26 0.92 1 0.61 0.21 1 1 0.10 Gluconeogenesis Arachidonic acid 36 12.77 1 0.73 0.14 1 1 0 metabolism Biosynthesis of unsaturated fatty 36 12.77 1 0.73 0.14 1 1 0 acids Pyrimidine 39 13.84 1 0.76 0.12 1 1 0 metabolism Steroid 42 14.90 1 0.79 0.11 1 1 0 biosynthesis Purine metabolism 65 23.07 1 0.91 0.04 1 1 0 Life 2021, 11, x FOR PEER REVIEW 11 of 20 Life 2022, 12, 86 12 of 21 Histidine metabolism Alanine, aspartate and glutamate metabolism Asparagine 1-methyl- Glycolysis/ Pyruvate Alanine Aspartate Fumarate Anserine histidine Citrate cycle Gluconeogenesis D-glutamine and D-glutamate metabolism argininosucc- Arginine biosynthesis Carnosine Nitrogen inate Histidine metabolism Glutamine Urocanate Citrate cycle Carbonyl N-acetyl Citrulline Arginine Ammonia phosphate citrulline Urea cycle 4-Imidazolone- 5-propionate Nitric oxide 2-oxoglutarate Glutamate N-acetyl N-acetyl glutamate ornithine Ornithine N-formimino- 4-aminobutyr- 4-aminobutanoate Putrescine glutamate aldehyde Succinate semialdehyde (S)-1-pyrroline- Proline Hydroxyproline 5-carboxylate Succinate Arginine and proline metabolism Figure3. Figure Proposed metabolome 3. Proposed metabolome view viewwith withsignificant significantpathways pathways according according to the pathway to the pathwayenrichment enrich- analysis. The meaning of compound colors within the pathways is as follows: ment analysis. The meaning of compound colors within the pathways is as follows: grey means grey means those metabolites are not detected as significantly different in our data and are used as background those metabolites are not detected as significantly different in our data and are used as background for enrichment for enrichmentanalysis; red red analysis; means that the means thatmetabolites are in are the metabolites the in data thewith datasignificantly higher levels with significantly higherin levels in severe early-onset early-onset severecompared preeclampsia preeclampsia compared to controls. to controls. KEGG database KEGG database (http://www.kegg.jp/kegg/ (http://www.kegg.jp/kegg/kegg1.html, kegg1.html, accessed on 11 August 2021) accessed was theonsource 11 August of the2021) was the proposed source of metabolic the proposed pathways. metabolic pathways. 3. Discussion 3. Discussion This study displays the significant changes in the metabolome of pregnant women withThis early-onset severe preeclampsia, study displays the significant showing changes forinthe thefirst time a comprehensive metabolome of pregnant set of cir- women culating metabolites and associated pathophysiological pathways. The with early-onset severe preeclampsia, showing for the first time a comprehensive set of most discriminative pathways were circulating related and metabolites to the metabolism associated of specific amino pathways. pathophysiological acids such The as arginine, alanine, most discrimi- aspartate, glutamate, proline and histidine, in addition to an altered native pathways were related to the metabolism of specific amino acids such as arginine, lipid profile. Among alanine, the main aspartate, pathways glutamate, identified proline in the current and histidine, study to in addition arean the arginine altered biosynthe- lipid profile. sis and metabolism. Among the mainIndeed, pathways arginine is anin identified essential molecule the current studyinare thethe pathophysiology arginine biosyn- of preeclampsia since it is the precursor of nitric oxide, a potent endothelial-derived thesis and metabolism. Indeed, arginine is an essential molecule in the pathophysiology vasodila- torpreeclampsia of [17]. A reduced sinceactivity it is theofprecursor the nitricof oxide nitricmight oxide, contribute to the clinical features a potent endothelial-derived of vas- preeclampsia such as vasoconstriction and endothelial dysfunction [17]. odilator [17]. A reduced activity of the nitric oxide might contribute to the clinical features In the literature, controversial of preeclampsia results suchhave been reported forand as vasoconstriction arginine levels in endothelial preeclampsia, dysfunction [17].likely to litera- In the be due to differences in the studied population and the methodology used [18,19]. ture, controversial results have been reported for arginine levels in preeclampsia, likely Few trials have to reported a potential role for arginine in the prevention and treatment be due to differences in the studied population and the methodology used [18,19]. Few of preeclampsia [20]. Our data trials haveshow higher reported levels of arginine a potential role for in early-onset arginine severe in the preeclampsia prevention and further and treatment of preeclampsia [20]. Our data show higher levels of arginine in early-onsetmight demonstrate its interaction with other altered and non-altered metabolites that severe be targeted to stop the vicious cycle of activated pathophysiological pathways (Figure 3). preeclampsia and further demonstrate its interaction with other altered and non-altered In the present study, alanine, aspartate, glutamate and glutamine metabolism were metabolites that might be targeted to stop the vicious cycle of activated pathophysiologi- also identified among the principal metabolic pathways in early-onset severe preeclamp- cal pathways (Figure 3). sia. Essentially, the glutamine-cycling pathway plays a crucial role in the development of In the present study, alanine, aspartate, glutamate and glutamine metabolism were metabolic risk [21]. Abnormal glutamate metabolism suggests liver involvement in the also identified among the principal metabolic pathways in early-onset severe preeclamp- global metabolic modulation since glutamate metabolism is linked to aminotransferase sia. Essentially, the glutamine-cycling pathway plays a crucial role in the development of reactions [22]. Indeed, the metabolism of almost all amino acids is initiated by aminotrans- metabolic risk [21]. Abnormal glutamate metabolism suggests liver involvement in the ferases, and glutamate is produced by the transfer of the amino group [23]. AST and ALT global metabolic modulation since glutamate metabolism is linked to aminotransferase are two aminotransferases widely used in monitoring preeclamptic patients since they reactions [22]. Indeed, the metabolism of almost all amino acids is initiated by aminotrans- become elevated in severe cases such as the most extreme phenotype of preeclampsia, the ferases, and glutamate is produced by the transfer of the amino group [23]. AST and ALT HELLP syndrome. Furthermore, it is known that women exposed to preeclampsia during are two aminotransferases pregnancy have an increased widely risk ofused in monitoring metabolic syndrome preeclamptic patients since they later in life [24]. become elevated in severe cases such as the most extreme phenotype of preeclampsia, the Life 2022, 12, 86 13 of 21 In addition, our data suggest that histidine metabolism is also disturbed in early-onset severe preeclampsia with high levels of histidine, 1-methylhistidine and other metabolites in line with the literature [12,18]. However, no detailed profiling of this pathway has been done previously in preeclampsia. Histidine is the precursor of carnosine which functions as an antioxidant and scavenger of reactive oxygen species and unsaturated aldehydes of cell membrane fatty acids formed due to peroxidation during oxidative stress [25]. Therefore, high levels of histidine might reflect exacerbated oxidative stress in early-onset severe preeclampsia [1,26]. Moreover, patients with early-onset severe preeclampsia exhibited higher concen- trations of valine, leucine and isoleucine in agreement with our previous findings in a population of preeclamptic mothers with growth-restricted fetuses [16]. These three metabo- lites are essential amino acids that play a crucial role in energy metabolism. A combined profile of isoleucine, leucine, valine, tyrosine and phenylalanine is a good predictor of future diabetes [27]. These results might explain the link between preeclampsia and the two-fold increased risk of diabetes throughout life [28]. Furthermore, this study demonstrates the involvement of the essential fatty acid linoleic acid in early-onset severe preeclampsia. It is known that endothelial dysfunction during preeclampsia might be enhanced by the imbalance of vasoactive prostaglandins (prostacyclin:thromboxane ratio) causing vasoconstriction of small arteries and platelets activation [29]. Linoleic acid is the main precursor of arachidonic acid, the origin com- pound of prostaglandins and prostanoids. Prior trials investigated whether daily use of linoleic acid and calcium supplementation during the third trimester effectively prevents preeclampsia in high-risk women. No evidence was found that this supplementation influences the incidence of preeclampsia, the length-of-gestation, or the need for preterm delivery [30]. However, it has been demonstrated that daily treatment with aspirin, in- hibits prostaglandin synthesis from the arachidonic acid, in high-risk women decreases the incidence of preterm preeclampsia [31]. On the other hand, lipid data seem to be highly disturbed in early-onset severe preeclamp- sia. It is well established that preeclampsia is a disease characterized by inflammation, com- plement activation, altered lipid metabolism and related oxidative stress [16,32–34]. Lipid classes are linked tightly during their synthesis, which may explain the similarity of observed alterations in most of them. Low phospholipid levels play a role in promoting cell membrane damage and inflammation. In preeclampsia, the decrease in pregnenolone sulfate, dehy- droepiandrosterone sulfate and testosterone might be related to impaired placental steroido- genesis [35]. Moreover, low cortisol and cortisone are most likely to be due to enhanced glucocorticoid metabolism, in addition to intensified conversion of cortisol to its inactive form cortisone since the cortisol:cortisone ratio was also significantly lower in preeclamp- sia compared to controls [36]. This imbalance might contribute to high blood pressure in preeclampsia, obesity, renal disease and other hypertensive disorders [37]. Thus, based on previously published data we can speculate that disturbed lipid metabolism might be involved in the development of preeclampsia while altered pathways of amino acid metabolism are mainly involved in the clinical manifestation of this disease. The prospective design of the current study and the recruitment of a well-characterized homogenous group of early-onset severe preeclampsia associated with fetal growth re- striction with no chronic disorders are the principal strengths of this study. Moreover, the collection of maternal blood samples was achieved directly upon confirming the diagnosis in the cases and at matched gestational age in the controls. Fasting status was assured in all the participating patients. The processing, storage and analysis of the samples followed rigorous protocols. Among the limitations, we declare the relatively small number of cases and controls in this study and the importance of validating our results in larger cohorts and exploring the detected pathways in other phenotypes of preeclampsia such as late-onset preeclampsia and preeclampsia with no associated fetal growth restriction. Life 2022, 12, 86 14 of 21 4. Materials and Methods 4.1. Study Population This was a nested case-control study within the project “Targeting endothelial dys- function in highly prevalent diseases–PIE15/00027”. Singleton pregnancies with a di- agnosis of early-onset severe preeclampsia associated with fetal growth restriction who attended the Departments of Maternal-Fetal Medicine at BCNatal (Barcelona, Spain) be- tween July 2016 and December 2017 were recruited prospectively. Preeclampsia was defined as high blood pressure (systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg on two occasions, at least four hours apart) with protein- uria (≥300 mg/24 h or protein/creatinine ratio ≥ 0.3) developed after 20 weeks of gesta- tion [38,39]. Early-onset preeclampsia was defined as disease onset before 34 weeks of gestation [7]. To include a homogenous group of complicated pregnancies, we recruited early-onset cases that needed elective delivery before 34 weeks of gestation indicated for severe preeclampsia which was considered upon presenting one or more of the following severity criteria [39]: blood pressure ≥160 mmHg systolic or ≥110 mmHg diastolic on two occasions at least 4 h apart, thrombocytopenia (<100,000/mm3 ), impaired liver function (elevated blood concentrations of liver enzymes to twice normal concentration and/or se- vere persistent right upper quadrant or epigastric pain unresponsive to medication and not accounted for by alternative diagnoses), progressive renal insufficiency (serum creatinine concentration > 1.1 mg/dL), pulmonary edema, new-onset cerebral or visual disturbances. HELLP syndrome was defined as preeclampsia associated with lactate dehydrogenase elevated to 600 u/L or more, AST and ALT elevated more than twice the upper limit of normal, and platelets count < 100,000/mm3 [39]. Pregnancy termination was by labor in- duction or cesarean section upon obstetric indication. Fetal growth restriction was defined as estimated fetal weight and birthweight below the 10th centile associated with either abnormal cerebroplacental ratio (<5th centile) or abnormal uterine arteries mean pulsatility index (>95th centile), or birthweight below the 3rd centile. Uncomplicated pregnancies with normotensive mothers and an appropriate for gestational age fetus -defined as estimated fetal weight and birthweight above the 10th centile- were randomly selected from our general population to be included as controls and matched with cases by maternal age, ethnicity, pre-gestational body mass index and gestational age at maternal blood draw (±2 weeks). Estimated fetal weight and birthweight centiles were calculated according to local standards [40]. In all pregnancies, gestational age was calculated based on the crown- rump length at first trimester ultrasound [41]. Pregnancies with chromosomal/structural anomalies or intrauterine infection were excluded. The history of pregestational diabetes, autoimmune, renal or coagulation disorders were also considered excluding criteria. 4.2. Data Collection Maternal age, ethnicity, pregestational body mass index, chronic hypertension, parity, obstetric history, mode of conception and smoking status were collected at enrolment. Estimated fetal weight and feto-placental Doppler assessment was achieved in all the study participants. Ultrasound studies were performed using a Siemens Sonoline Antares (Siemens Medical Systems, Malvern, PA, USA) or a Voluson 730 Expert (GE Medical Sys- tems, Milwaukee, WI, USA) with 6–4-MHz linear curved-array probes. Estimated fetal weight was calculated using the Hadlock formula [42] and centile based on local reference curves [40]. Fetoplacental Doppler examination followed standardized guidelines [43] in- cluded the uterine arteries [44], the umbilical artery [45], the fetal middle cerebral artery [45] and the ductus venosus [46] with the calculation of the cerebroplacental ratio [47]. Mater- nal biochemical profile was also assessed at the time of maternal blood draw, including the evaluation of renal (creatinine, urea, sodium, potassium) and liver (AST, ALT, GGT) function, uric acid, glucose, triglycerides, total cholesterol, fibrinogen and platelets count. At the time of delivery, gestational age, birthweight, Apgar scores, umbilical artery pH, neonatal intensive care unit admissions and perinatal mortality were recorded. Life 2022, 12, 86 15 of 21 4.3. Maternal Blood Sampling Peripheral maternal blood was obtained by venipuncture within 24–48 h of preeclamp- sia diagnosis and at matched gestational age for controls. Sampling was done in the morning after fasting for 6–8 h at least. The samples were incubated for 30 min at room temperature to allow clotting and subsequently centrifuged at 1500 g for 10 min at 4 ◦ C to separate the serum from clots. Afterwards, serum samples were transferred to acetonitrile treated tubes and immediately stored at −80 ◦ C until assayed. 4.4. Metabolomic Analysis The metabolomic analysis comprised the determination of lipids (lipidomics), amino acids and polar metabolites, achieved through a rigorous and well-established four-level extraction protocol. Lipidomic analysis was achieved by two methods, with methanol extraction and with choloform:methanol extraction (Folch method). Detailed methodology is provided in the Appendix A. This analysis was achieved by investigators blinded to the study group of each sample. 4.5. Statistical Analysis Clinical data were analyzed with the statistical software STATA 14.2 (StataCorp LLC, College Station, TX, USA). Categorical data are displayed as percentages and continuous data as mean ± standard deviation or median (interquartile range) according to their distribution (normality was assessed using the Kolmogorov-Smirnov test). Statistical analysis for continuous variables included the use of student t-tests for normally distributed data or Mann Whitney U tests in non-normally distributed data. Fisher exact test was used for categorical variables. All reported p-values are two-sided. p < 0.05 was set for statistical significance. For metabolomics data, statistical approach was performed using Metaboanalyst 4.0 (http://www.metaboanalyst.ca/, accessed on 11 August 2021). Initially, a multivariate modeling was applied including the use of unsupervised methods such as PCA, and super- vised methods such as PLS-DA and OPLS-DA. A variable importance in projection (VIP) plot, which is a visual representation of the importance of the metabolites in discriminating the groups of interest, is provided. Secondly, for each protein a univariate Student’s t-test was performed and the Benjamini-Hochberg method was used to adjust p values for multi- ple testing with consideration of a 5% false discovery rate. An additional unsupervised HCA was performed based on the univariate results. Last, differential pathways were iden- tified from an over representation analysis performed in Metaboanalyst using the Pathway Analysis option. This analysis was performed using a Fisher’s exact test to calculate the probability of finding at least a particular number of metabolites containing a biological term of interest in the given compound list based on the Kyoto Encyclopedia of Genes and Genome (KEGG) [48]. Detailed statistical approach is provided in the Appendix A. 5. Conclusions In conclusion, we identified a complex mix of pathophysiological pathways in early- onset severe preeclampsia in this study. The main changes were attributed to amino acids, specifically arginine, alanine, aspartate, glutamate, valine, leucine, isoleucine, proline and histidine, and linoleic acid metabolism. The metabolomic profile observed in early-onset severe preeclampsia suggests the involvement of multiple pathways in its clinical manifes- tations, such as vasoconstriction, endothelial dysfunction, oxidative stress, complement activation, inflammation and predisposition to metabolic syndrome and diabetes. The interplay between the detected pathways may provide a better understanding of the un- derlying etiology for this specific phenotype of preeclampsia and unveil its impending therapeutic targets. Further research is warranted to develop effective therapeutics and investigate their usefulness in reducing maternal and perinatal complications in early-onset severe preeclampsia. Life 2022, 12, 86 16 of 21 Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/life12010086/s1, Table S1: Univariate analysis results for each metabolite. Author Contributions: L.Y., E.G. and F.C. (Fatima Crispi) conceived the hypothesis and the study design. M.B., M.P., H.G.-C., O.T.-C., A.P.D., V.H.-G., J.M.C., J.C.G.-P., M.D.-R., E.G. and F.C. (Fatima Crispi) designed the methods for the project “Targeting endothelial dysfunction in highly prevalent diseases–PIE15/00027”. L.Y., J.M., C.P., F.C. (Francesca Crovetto) were involved in recruiting the patients. L.Y. established the database and collected the blood samples. P.H. and N.C. performed the metabolomics data acquisition. L.Y., P.H. and N.C. analyzed the data. L.Y., R.V.S., E.G. and F.C. (Fatima Crispi) drafted the manuscript. All the authors revised the manuscript and approved this submitted final version. All authors have read and agreed to the published version of the manuscript. Funding: This project has been partially funded with the support of the Erasmus+ Programme of the European Union (Framework Agreement number: 2013-0040). This publication reflects the views only of the author, and the Commission cannot be held responsible for any use, which may be made of the information contained therein. Additionally, the research leading to these results has received funding form “la Caixa” Foundation under grant agreements LCF/PR/GN14/10270005 and LCF/PR/GN18/10310003, the Instituto de Salud Carlos III (PI14/00226, PI15/00130, PIE15/00027, PI17/00675, PI18/00073, PI18/00960, PI19/00888, PI20/00569) integrados en el Plan Nacional de I+D+I y cofinanciados por el ISCIII-Subdirección General de Evaluación y el Fondo Europeo de Desarrollo Regional (FEDER) “Una manera de hacer Europa”, Cerebra Foundation for the Brain Injured Child (Carmarthen, Wales, UK), Ministerio de Economía y Competitividad (SAF PID2019- 105/48RB-100), Fundació La Marató de TV3 (202026-10), Instituto Juan Carlos III (JR19/00006), European Reference Network (ERN) RARE-Liver (hepatic diseases) and AGAUR 2017 SGR grant nº 1531 and 517 from Generalitat de Catalunya. O.T.C. was supported by Miguel Servet grant from the Instituto de Salud Carlos III (CP17/00114). Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the local ethics committee of the Hospital Clinic of Barcelona (HCB/2015/0585). Informed Consent Statement: Written informed consent was obtained from all subjects involved in the study. Data Availability Statement: The metabolomics data reported in this study are available as supple- mentary information. Acknowledgments: The Hospital Clínic-IDIBAPS Biobank resource has been used in this research. We thank the patients for joining this study and the nurses from the Departments of Maternal-Fetal Medicine at BCNatal (Barcelona, Spain) for their assistance in gathering human samples. We also thank Jordi Mayneris from the Proteomics facility of the Centre for Omic Sciences Joint Unit of the Universitat Rovira i Virgili-Eurecat for their contribution to the metabolomics and bioinformatics analysis. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Appendix A.1. Methods Appendix A.1.1. Metabolomics Technique Method 1: Lipidomic Analysis with Methanol Extraction For the extraction of less hydrophobic lipids, a protein precipitation extraction was performed by adding four volumes of methanol containing internal standard mixture (myristic acid-d27, arachidonic acid-d8, Cholic acid-d5, Taurocholic acid-d5 LysoPC 18:1- d7, and labelled Carnitine Mix from Cambridge Isotopes) to serum. Then, the samples were mixed and incubated at −20 ◦ C for 30 min., centrifuged at 15,000 rpm and supernatant was analysed by UHPLC-qTOF (model 6550 of Agilent, Agilent, Santa Clara, CA USA) in both positive and negative electrospray ionization mode. The chromatographic method was the same for both ionization modes. A gradient elution with water and acetonitrile with 0.05% formic acid as mobile phase and C18 column (ACQUITY UPLC BEH C18 Column, 1.7 µm, 2.1 mm × 100 mm) that allowed the sequential Life 2022, 12, 86 17 of 21 elution of the less hydrophobic lipids such as non-esterified fatty acids, acyl carnitines, bile acids, steroids and lysophospholipids among others. The identification of lipid species was performed by matching their accurate mass and tandem mass spectrum, when available, to Metlin-PCDL from Agilent containing more than 40,000 metabolites and lipids. In addition, chromatographic behavior of pure standards for each family and bibliographic information was used to ensure their putative identification. After putative identification of lipids, these were semi quantified depending on their family similarity by an internal standard calibration curves using the pure chemical stan- dards indicated in Table A1. Table A1. Standards used for methanol lipidomic extraction. Standards 15-HETE lysoPC 18:1 arachidonic acid lysoPE18:1 Cholic acid lysoPI 18:1 Deoxycholic acid sphingosine-1-P Dehydroisoandosterone-3-sulfate Taurocholic acid Hydrocortisone (cortisol) Carnitine Mix Method 2: Lipidomic Analysis with Choloform:Methanol (Folch Method) Extraction For the extraction of more hydrophobic lipids, a liquid-liquid extraction with chlo- roform:methanol (2:1) based on Folch procedure was performed by adding four volumes of chloroform:methanol (2:1) containing internal standard mixture (Lipidomic SPLASH®, Avanti Polar Lipids, Birmingham, AL, USA) to serum. Then, the samples were mixed and incubated at −20 ◦ C for 30 min. Afterwards, water with NaCl (0.8%) was added and mixture was centrifuged at 15,000 rpm. Lower phase was recovered, evaporated to dryness and reconstituted with methanol:methyl-tert-butyl ether (9:1) and analysed by UHPLC-qTOF (model 6550 of Agilent, USA) in both positive and negative electrospray ionization modes. As method 1, the chromatographic method was the same for both ionizations. Gra- dient consist in an elution with a ternary mobile phase containing water, methanol and 2-propanol with 10mM ammonium formate and 0.1% formic acid. The stationary phase was a C18 column (Kinetex EVO C18 Column, 2.6 µm, 2.1 mm × 100 mm) that allows the se- quential elution of the more hydrophobic lipids such as lysophospholipids, sphingomyelins, phospholipids, diglycerides, triglycerides and cholesteryl esters, among others. The identification of lipid species was performed by matching their accurate mass and tandem mass spectrum, when available, to Metlin-PCDL from Agilent containing more than 40,000 metabolites and lipids. In addition, chromatographic behaviour of pure standards for each family and bibliographic information was used to ensure their putative identification. After putative identification of lipids, these were semi quantified depending on their family similarity by and internal standard calibration curves using the pure chemical standards indicated in Table A2. Table A2. Standards used for Folch based lipidomic analysis. Standards Sphingomyelin d18:1/16:0 Stearoyl-rac-glycerol Oelamide Dilynoleyl-rac-glycerol Palmitoyl-oleyl-lynoleyl-rac-glycerol Dipalmitoyl-phosphoethanolamine Dioleyl-phosphocholine Cholesteryl linoleate Life 2022, 12, 86 18 of 21 Method 3: Aminoacids Analysis For the extraction of aminoacids, a protein precipitation extraction was performed by adding four volumes of methanol containing internal standards (Metabolomics labelled aminoacid mixture from Cambridge Isotopes) to serum samples. Then, the samples were mixed and incubated at −20 ◦ C for 30 min., centrifuged at 15,000 rpm and supernatant was derivatized using AccQ-Tag reagent from Waters®(Milford, MA, USA) following manufacturing protocol. Then, derivatized aminoacids were analysed by UHPLC-QqQ (model 6490 of Agilent, USA) in Multiple Reaction Monitoring acquisition. The chromatographic separation consists of a gradient elution with water and ace- tonitrile with 0.1% formic acid as mobile phase and C18 column (ACQUITY UPLC HSS T3 Column, 1.7 µm, 2.1 mm × 150 mm) that allows the determination and separation of aminoacids and derivatives. Their semi-quantification was done with pure chemical standard curves for all compounds. Method 4: Polar Metabolites and Central Carbon Metabolism For the extraction of polar metabolites and central carbon metabolism, a protein precipitation extraction was performed by adding eight volumes of methanol:water (8:2) containing internal standard mixture (succinic acid-d4, myristic acid-d27, glicerol-13C3 and D-glucose-13C6) to serum samples. Then, the samples were mixed and incubated at 4ºC for 10 min., centrifuged at 15,000 rpm and supernatant was evaporated to dryness and freeze dried in a liophylizator before compound derivatization (metoximation and silylation). The derivatized compounds were analysed by GC-qTOF (model 7200 of Agilent, USA). The chromatographic separation was based on Fiehn Method, using a J&W Scientific HP5-MS (30 m × 0.25 mm i.d., 0.25 µm film capillary column and helium as carrier gas using an oven program from 60 to 325 ◦ C. Ionization was done by electronic impact (EI), with electron energy of 70 eV and operated in full Scan mode. In addition of targeted compounds from central carbon metabolism, a screening for the identification of more metabolites was performed by matching their EI mass spectrum and retention time to metabolomic Fiehn library (from Agilent) which contains more than 1.400 metabolites. After putative identification of metabolites, these and target compounds were semi- quantified depending on their family similarity by an internal standard calibration curves using the pure chemical standards indicated in Table A3. Table A3. Standards used for central carbon metabolism analysis. Compound Piruvic acid Aconitic acid Lactic acid 3-Phosphoglyceric acid Glycolic acid Citric/isocitric acid 2-Hydroxybutyric acid Myristic acid Oxalic Acid Glucose 3-Hydroxybutiric acid Palmitic acid Glycerol Ribose-5-phosphate Succinic acid Linoleic acid Glyceric acid Oleic Acid Fumaric acid Stearic acid Malic acid Fructose-6-phosphate Threonic acid Glucose-6-phosphate 2-hydroxyglutaric acid Fructose-1,6-bisphosphate alpha-Ketoglutaric acid 6-Phosphogluconic acid Phosphoenolpyruvate Sucrose Ornithine alpha-Tocopherol Glyceraldehyde-3-phosphate Cholesterol Life 2022, 12, 86 19 of 21 Appendix A.2. Statistical Analysis For metabolomics data, statistical approach was performed using Metaboanalyst 4.0 (http://www.metaboanalyst.ca/, accessed on 11 August 2021). Only those metabolites that were present in ≥ 70% of the samples in at least one group were considered. To estimate missing values in the included metabolites, we used a Bayesian principal component analysis approach. Then, a log base 2 transformation was applied, and data were mean- centered for univariate analyses and Pareto scaled for multivariate analyses. Initially, a multivariate modelling was performed including the use of unsupervised methods such as principal component analysis (PCA), and supervised methods like partial least squares discriminant analysis (PLS-DA) and an orthogonal projection to latent structures discriminant analysis (OPLS-DA). A variable importance in projection (VIP) plot, which is a visual representation of the importance of the particular metabolites in discriminating the groups of interest, is provided. To assess the significance of class discrimination whether it could be due to chance, a permutation test was performed applying 2000 permutations. For the selected model, the goodness of fit (R2X) and the predictive performance (Q2Y), which relate to the explained and predicted variance respectively, were calculated. All these methods were applied with a Pareto scaling. Secondly, for each metabolite a univariate Student’s t-test was performed and Benjamini- Hochberg method was used to adjust p values for multiple testing with consideration of 5% false discovery rate. An additional unsupervised hierarchical clustering analysis (HCA) was performed based on the univariate results. 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