Abstract:
Objective: To explore the characteristic lipid signature in placentas collected from normal pregnancies and those with mild and severe intrahepatic cholestasis of pregnancy (ICP). This research aims to clarify the pathogenesis and identify lipid biomarker for ICP through LC-MS/MS based lipidomic analysis.
Design: Cross-sectional study, including normal pregnancy women and women with mild and severe ICP.
Setting: Chengdu Women’s and Children’s Center Hospital.
Population: Placenta samples collected from 30 normal pregnancy women and 30 mild and severe ICP women respectively. Women with normal pregnancy and ICP were recruit from April 2021 to July 2022 in Chengdu, China.
Main outcome measures: Differentially expressed lipids.
Results: Fourty-four lipids were differentially expressed both in mild and severe ICP placenta. The pathway analysis revealed these lipids are mainly enriched in glycerophospholipid metabolism and autophagy pathway. Weighted correlation network analysis (WGCNA) identified the correlation network module of lipids highly related to ICP. Using multiple logistic regression analysis, we identified three and four combined metabolites that had an area under receiver operating characteristic curves (AUC) ≥ 0.90.
Conclusion: Our results systematically revealed the lipid signature in mild and severe ICP placenta. The results may provide new insight into the treatment and early prediction of ICP.
Keywords: Intrahepatic cholestasis of pregnancy, lipidomics, phosphatidylethanolamine, sphingolipids, autophagy
Introduction
Intrahepatic cholestasis of pregnancy (ICP) is the most common pregnancy-specific liver disease that usually occurs in the second or third trimester of pregnancy. Its clinical manifestations are mild to severe persistent pruritus, abnormal liver function, and elevated total bile acid (TBA) levels1, 2. Although ICP is rarely harmful to the mother, it poses a significant risk to the fetus of perinatal complications such as preterm birth, meconium-stained amniotic fluid, neonatal depression, respiratory distress syndrome and stillbirth3. The incidence of ICP varies geographically and demographically from 0.1 to 15.6%4. Nowadays, there is no effective treatment for ICP, administration of ursodeoxycholic acid (UDCA) is one of the choices. UDCA was proven to relieve pruritus and decrease bile acid levels, though it is of unproven benefit in preventing adverse effects on the fetus5, 6. At present, there is a lack of consensus on the diagnostic criteria for ICP. Most guidelines agree on the requirement of pruritus and abnormal liver enzymes, the most sensitive biochemical index used in the diagnostics of ICP is the level of total bile acids. The European Association for the Study of the Liver (EASL) and the Society for Maternal-Fetal Medicine (SMFM) propose persistent pruritus that resolves with delivery and bile acid concentrations > 10μmol/L for diagnosis7. Many prospective studies revealed that TBA level ≥ 40μmol/L was associated with an increased risk of adverse neonatal outcomes in ICP8, 9. However, evidence proved TBA levels alone are not a sufficiently sensitive and specific marker for this disease. According to a retrospective study conducted by Kondrackiene et al, concentrations of cholic acid (CA), chenodeoxycholic acid (CDCA), and the CA/CDCA ratio are better markers10, 11. Accordingly, there is growing interest in identifying new markers with diagnostic and prognostic value in ICP.
The pathogenesis of ICP remains unknown, there are several theories about the causes of ICP, including the estrogen-bile acid axis, placental hypoxia, lipid metabolism disorders and trophoblast autophagy12-14. As a subcategory of metabolic profiling, lipidomics can efficiently analyze lipid molecule changes in various pathophysiological processes15. More and more evidence proved that abnormal lipid metabolism is closely related to various pregnancy-related diseases such as preeclampsia, gestational diabetes mellitus, and preterm birth16-18. Since ICP is closely related to bile acid metabolism, metabolomic technology has become an effective method for finding diagnostic markers in serum, urine, and even hair samples of ICP patients in recent years. Metabolomic signatures from serum and urine can be used as biomarkers for the diagnosis of ICP19, 20. In recent years, more and more studies believe that lipid metabolism is closely related to the occurrence and development of ICP, the dysregulation of bile acid in serum and placenta will lead to abnormal lipid metabolism to a large extent, and then increase the risk of ICP21. High triglyceride concentrations in the second and third trimesters have been reported to be associated with an increased risk of ICP, possibly due to reduced activity of bile acid receptors FXR and TGR522. A more recent untargeted lipidomics study revealed the abnormal lipid profiles of plasma collected from ICP patients and suggests that sphingolipid metabolism dysregulation may be associated with the development of ICP23. Therefore, using lipid metabolites as clues may help us to further explore the molecular mechanisms underlying the occurrence of intrahepatic cholestasis of pregnancy.
In this study, LC-MS/MS based untargeted lipidomics was applied to investigate lipidomics profiling of placentas from women with normal pregnancies and those with mild or severe ICP. And we also uncovered a group of lipids with high accuracy in identifying and diagnosing ICP. Our findings may provide new insights into the pathogenesis and prediction of ICP.
Materials and methods
Clinical specimen collection and preparation
The placenta tissue was obtained from Chengdu Women’s and Children’s Central Hospital from April 2021 to July 2022 in Chengdu, China. The Ethics Committee of the Chengdu Women’s and Children’s Central Hospital approved the study documents and placenta collection (Permission Number: 2022(49)-2).
The inclusion criteria of ICP were as follows: 1) new-onset pruritus and elevated levels of TBA, TBA level > 10 but < 40 μmol/l were categorized as mild, whereas the TBA level ≥ 40 μmol/l were categorized as severe; 2) age between 18 and 35 years; 3) singleton pregnancy; 4) providing informed consent. In this study, 30 severe ICP patients (ICP-S group), 30 mild ICP patients (ICP-M group) and 30 normal pregnant women (control group) who delivered via cesarean section were recruited from the Chengdu Women’s and Children’s Central Hospital. Patients who had received infertility treatment (such as in vitro fertilization or intrauterine insemination), serious illnesses before and during pregnancy (such as chronic liver and gall bladder diseases, skin disease, hypertension, preeclampsia, diabetes, hematological diseases, kidney, and nervous system disease), and infectious diseases (such as viral hepatitis, syphilis, or acquired immune deficiency syndrome) were excluded. The villous tissue of maternal placental samples (3 × 3 × 3 cm) was obtained immediately after delivery. The samples were flash-frozen in liquid nitrogen and stored at -80℃ for lipidomics analysis.
Sample preparation
Lipids were extracted as follows: 300μL of methanol: water (1/1, vol/vol) were added to each placental sample (30mg), and 20μL of LysoPC-17:0 (0.1 mg/mL, dissolved in methanol) was added as an internal standard to reflect the repeatability of the extraction, then placed into 2 steel balls and grind with a grinder (60Hz, 2min). Subsequently, 300μL of chloroform was added to each sample and the whole samples were extracted by ultrasonic for 10 min in an ice-water bath, stored at -20 °C for 20 min. And then centrifuged at 4°C (13,000 rpm) for 10 min before decanting 200μL of subnatant to sample vials. 300μL chloroform: methanol (2/1, vol/vol, containing 0.1mM BHT) was added into the residue samples, samples vortexed for 30 s, extracted by ultrasonic for 10 min in ice-water bath, then placed at -20°C for 20 min and then centrifuged at 4°C (13,000 rpm) for 10 min prior to decanting of subnatant to sample vials. Combined two subnatants and mixed well. Then the mixed subnatant (400μL) was dried under a nitrogen stream and re-dissolved in 300 μL of isopropanol: methanol (1/1, vol/vol), vortexed for 30 s and extracted by ultrasonic for 3 min in ice-water bath, and then filtered through a 0.22 μm organic phase pinhole filter for subsequent analysis. QC samples were prepared by mixing aliquot of all samples to be a pooled sample.
Untargeted lipidomics analysis
LC-MS/MS analyses were performed using a Dionex Ultimate 3000 RS UHPLC fitted with Q-Exactive quadrupole-Orbitrap mass spectrometer equipped with heated electrospray ionization (ESI) source (Thermo Fisher Scientific, Waltham, MA, USA). An ACQUITY UPLC BEH C18 column (1.7 μm, 2.1 × 100 mm) was employed in both positive and negative modes. The binary gradient elution system consisted of (A) acetonitrile:water (60:40, v:v, containing 10mmol/L ammonium formate) and (B) acetonitrile: isopropanol(10:90, v:v, containing 10mmol/L ammonium formate) and separation was achieved using the following gradient:0 min, 5% B; 0.5 min, 5% B; 2 min, 43% B; 32.1 min, 52% B; 8.5 min, 53% B; 8.6 min,75% B;114 min, 90% B, 14.5in, 100% B, 15.5min, 100% B, 15.7min, 5% B and 18min, 5%B. The flow rate was 0.4 mL/min and the column temperature was 60°C. All the samples were kept at 4 °C during the analysis. The injection volume was 5 μL. The mass spectrometer was operated in both positive ESI+ mode and negative ESI- mode. The QCs were injected at regular intervals throughout the analytical run to provide a set of data from which repeatability can be assessed.
Data preprocessing and statistical analysis
The original Q Exactive LC-MS/MS data in raw format were processed by the software Lipid Search for MSn and the exact mass-to-charge ratio (m/z) of parent ions. The molecular structure of lipids and the additive mode of its positive and negative ions were identified according to the parent ions and multi-stage mass spectrometry data of each sample. The results were aligned according to a certain retention time range and combined into a single report to sort out the original data matrix. In each sample, all peak signals were normalized (that is, the signal intensity of each peak is converted to the relative intensity in the spectrum, and then multiplied by 10000). The extracted data were then further processed by removing any peaks with a missing value (ion intensity = 0) in more than 50% in groups and by replacing the zero value by half of the minimum value. A data matrix was combined from the positive and negative ion data. The matrix was imported in R to carry out Principle Component Analysis (PCA) to observe the overall distribution among the samples and the stability of the whole analysis process. Orthogonal Partial Least-Squares-Discriminant Analysis (OPLS-DA) and Partial Least-Squares-Discriminant Analysis (PLS-DA) were utilized to distinguish the metabolites that differ between groups. To prevent overfitting, 7-fold cross-validation and 200 Response Permutation Testing (RPT) were used to evaluate the quality of the model.
Variable Importance of Projection (VIP) values obtained from the OPLS-DA model was used to rank the overall contribution of each variable to group discrimination. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database was used to analyze metabolic pathway enrichment. A weighted metabolite co-expression network was built with R package24. The co-expression module is a collection of metabolites with high topological overlap similarity. Pearson correlation analysis was used to calculate the correlation between the modules and the clinical traits to identify significant clinical modules, P < 0.05 was considered as modules with significant difference. We also constructed a diagnostic model through receiver operating characteristic (ROC) curves and multiple logistic regression was used to merge multiple indicators. The statistical graphs were generated by GraphPad Prism 9 software, and a two-tailed Student’s T-test was used for statistical analysis. All data are presented as the means ± standard errors of the means (SEM). The lipids with a VIP >1.0 and P < 0.05 were considered as significantly changed.
Results