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