Preterm birth (birth prior to 37 completed weeks of gestation) is a massive global health burden: it leads all causes of death in neonates and children to the age of 5 worldwide (Liu et al., 2015), and survivors face a broad array of short- and long-term health challenges (Blencowe et al., 2013). Most preterm births in the United States are due to spontaneous onset of preterm labor, with unknown underlying cause (Goldenberg et al., 2008). Preterm labor has proven difficult to treat; there currently exist no highly effective interventions that prevent spontaneous preterm birth (SPTB) (Smith et al., 2009; Romero et al., 2014). The dearth of effective treatments stems from our lack of understanding about the pathways regulating spontaneous onset of labor, both preterm and at term.

The placenta defines the maternal-fetal interface; it is capable of profoundly influencing both maternal and fetal physiology (Kiserud et al., 2006; Shaut et al., 2008; Lykke et al., 2009), and it is subject to remodeling in response to its local environment (Genbacev et al., 1996). Placental disease is known to contribute to other disorders of pregnancy including preeclampsia (Plaks et al., 2013; Li et al., 2016; Romero and Chaiworapongsa, 2013) and intrauterine growth restriction (McIntyre et al., 2020; Xu et al., 2021) and there is increasing interest in its potential role as a driver of preterm labor (Koga et al., 2009; Pique-Regi et al., 2019; Beharier et al., 2020).

Recent studies have found that placentas from pregnancies that resulted in SPTB have unique metabolomic and transcriptomic signatures from term counterparts (Elshenawy et al., 2020; Lien et al., 2021; Paquette et al., 2018). The reported differences broadly implicate the stress response, inflammation, and various metabolic pathways. It has been challenging to narrow these findings or translate them for mechanistic relevance, given the lack of suitable culture systems that integrate experimental manipulation of placental cells with the uterine myocyte response. Furthermore, interpretation studies profiling human placentas from SPTB is sharply limited by the lack of gestational age-matched controls. Without these, the effects of gestational age cannot be distinguished from factors driving premature labor. The placenta encounters a dynamic local environment across its lifetime and faces evolving needs of the growing fetus, so the context of normal gestational age-related changes is vital for the correct interpretation of molecular characteristics distinguishing an SPTB placenta. Defining the molecular-level changes in the healthy placenta as it approaches the end of gestation, and their potential effects on the timing of labor onset would therefore address important knowledge gaps.

Here, we report findings from unbiased transcriptomic analysis of healthy mouse placenta, which highlighted hypoxia-inducible factor 1 (HIF-1) signaling as a hallmark of advanced gestational timepoints, accompanied by mitochondrial dysfunction and cellular senescence. We detected some similar effects in human placentas, then modeled HIF-1 induction with two different stimuli and in two trophoblast cell models, demonstrating that mitochondrial dysfunction and cellular senescence arise secondary to HIF-1 stabilization. Whole transcriptome analysis revealed that upon HIF-1 stabilization, a trophoblast cell line acquires signatures that recapitulate the aged placenta. Finally, we show that conditioned media from these cells is sufficient to potentiate a contractile phenotype in uterine myocytes, implicating a mechanism by which the aging placenta may help drive the transition from uterine quiescence to contractility at the onset of labor. This mechanism is further reinforced by an in vivo model in which administration of the prolyl hydroxylase inhibitor dimethyloxalyl glycine (DMOG) to pregnant mice induces HIF-1 signaling in the placenta and causes SPTB.

To illuminate gestational age-dependent transcriptional changes in across a healthy pregnancy, we collected whole mouse placentas at 48 hr intervals spanning embryonic day 13.5–17.5 (e13.5–e17.5) and quantified mRNA and protein targets that emerged from network analysis of an independently published microarray dataset from healthy mouse pregnancies. Upon searching Gene Expression Omnibus (GEO) (Edgar et al., 2002) datasets for ‘placenta AND transcriptome’, we were surprised that among 326 results, only 9 included data from normal placenta across a series of gestational timepoints extending to late pregnancy (Knox and Baker, 2008; Zhou et al., 2009; Loux et al., 2019; Soncin et al., 2018; Maeda et al., 2019; Morey et al., 2021; Steinhauser et al., 2021; Figure 1). We applied weighted gene correlation network analysis (WGCNA) to a microarray study of mouse placenta (Knox and Baker, 2008) (GEO accession GSE11224) spanning e8.5 to postnatal day 0 (p0) to assess the mRNA signature of the aging mouse placenta, using the dataset with the best temporal resolution across gestation. Distinct clusters emerged, each reflecting a group of genes whose expression changes across timepoints in a unified way (Figure 2A; accompanying statistics available at Mendeley Data, doi: 10.17632/g6vrw9jjn4.1). Genes in the ‘blue’ cluster showed increasingly positive correlation with advancing gestational timepoints; KEGG functional pathways significantly overrepresented among these genes include HIF-1 signaling, AMPK signaling, and cellular senescence. Genes in the ‘turquoise’ cluster showed increasingly negative correlation to advancing timepoints; KEGG functional pathways significantly overrepresented among these genes include the citric acid cycle, mitochondrial complex I biogenesis, and the mitotic cell cycle.

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To validate the dynamic pathway activity suggested by the WGCNA, we used qPCR to quantify mRNA expression of the senescence marker Glb1 (Lee et al., 2006) in our mouse placentas, which mirrored the WGCNA finding that cellular senescence in the placenta peaks in the final days of gestation (Figure 2B). HIF-1 protein abundance was found to peak at e17.5 (Figure 2C) and HIF-1 targets Hk2 and Slc2a1 likewise confirmed increasing HIF-1 activation with advancing gestational age through e17.5 (Figure 2D). Of note, a modest but significant fetal sex-dependent difference in HIF-1 target expression (but not Glb1) was also observed across timepoints (Figure 2—figure supplement 1). To assess changes in mitochondrial abundance across gestation, we measured COX IV protein expression and found it declined progressively across mouse gestation (Figure 2E) as predicted by the WGCNA.

Having observed a pattern of declining mitochondrial abundance in the placenta with advancing gestational age, we next investigated the mitochondrial DNA (mtDNA) lesion rate. The mitochondrial genome is particularly vulnerable to reactive oxygen species (ROS) insults, and mtDNA damage participates in a vicious cycle with mitochondrial dysfunction and further ROS production; these effects are observed in a number of age-related diseases in various tissues (Jang et al., 2018) and may drive age-associated loss of function (Trifunovic et al., 2004). We employed a semi-long run real-time qPCR approach (Rothfuss et al., 2010) to quantify relative mtDNA lesion rates in mouse placentas, normalized to e13.5. There was a measurable increase in the mtDNA lesion rate in the D-loop and COII/ATPase 6 regions of the mitochondrial genome at e17.5 (Figure 2F). Together, these results reflect a series of coordinated changes as gestation progresses—namely HIF-1 signaling induction, decreasing mitochondrial abundance, accumulating mtDNA damage, and escalating cellular senescence—confirming the patterns we discovered through reanalysis of published transcriptomic data.

We next probed human placentas for the same gestational age-dependent changes. Studying normal human placenta at a range of gestational ages beyond the second trimester is challenging, as placental sampling is usually only possible after delivery, and most deliveries associated with healthy pregnancies occur at term. We therefore sought to capitalize on rare exceptions such as cases of placenta previa, vasa previa, or uterine dehiscence, where iatrogenic preterm delivery is indicated for reasons unrelated to placental health. With an objective to compare term versus preterm placentas, yet minimize confounding factors that accompany labor and disease states that could be expected to affect placental health, we designed a case-control study using the following exclusion criteria: onset of labor prior to delivery (spontaneous or induced); maternal history of hypertension, asthma, diabetes, or autoimmune disease; pregnancy complicated by gestational hypertension, gestational diabetes, preeclampsia, multiples, fetal anomalies, placenta accreta spectrum disorder, or smoking during pregnancy.

Placentas from 9 cesarean deliveries occurring before 35 weeks’ gestation and 11 cesarean deliveries occurring after 39 weeks’ gestation were studied (Table 1). Maternal characteristics including race, nulliparity, and obesity were not statistically different across the early versus late groups; there was a small but statistically significant difference in maternal age at the time of delivery (31.9±0.9 years versus 36.2±1.0 for early gestation versus late, p=0.008). The distribution of fetal sex was not different among groups.

qPCR revealed a trend toward increased mRNA expression of GLB1 and HIF-1 targets HK2 and SLC2A1 in the >39-week cohort (Figure 3A), consistent with the gestational age-dependent effect seen in mouse placenta. We also examined mitochondrial abundance in the two groups and found that mitochondrial RNA transcripts ATP6 and COX2 were significantly decreased (Figure 3B) and COX IV protein abundance was lower at the later timepoint (Figure 3C), mirroring the mouse findings. Of note, power calculation to reject the null hypothesis for difference between means for some of these measurements (with α=0.05 and β=0.2) suggests a sample size of greater than 35 per group is required, assuming a similar effect size as seen in mouse data (e.g. expecting GLB1 fold change [FC] difference of 40% across groups), and greater variability than seen for mouse data (e.g. expected standard deviation of GLB1 FC equal to 0.6, vs 0.3 in mice). Practical constraints, especially given our strict exclusion criteria, make a study of this size unfeasible; nonetheless, we have included analysis of 20 human placentas here in recognition of the vital importance of translating mouse findings to human biology, even preliminarily. The data should be interpreted in the context of these statistical realities.

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The co-appearance of cellular senescence, HIF-1 signaling, and mitochondrial dysregulation in the placenta as it approaches the end of gestation led us to hypothesize that in aging placental cells, HIF-1 induction could be upstream of mitochondrial dysregulation and cellular senescence, as is seen in other systems in emerging aging research (Bratic and Larsson, 2013; Wiley and Campisi, 2016; Wiley and Campisi, 2021). To test this hypothesis, we established a pharmacological model of HIF-1 induction in primary mouse trophoblasts using cobalt chloride, a prolyl hydroxylase inhibitor that stabilizes HIF-1α (Maxwell et al., 1999; Jaakkola et al., 2001) and has been widely used to model hypoxia. After 6 hr of CoCl₂ exposure, we confirmed HIF-1 protein accumulation in cultured trophoblasts (Figure 4A). After 48 hr of CoCl₂ exposure, mouse trophoblasts exhibit decreased mitochondrial abundance, by Cox2 mRNA expression (Figure 4B) and Cox IV protein abundance (Figure 4C), and an increase in senescence-associated beta galactosidase (SA-βGal), encoded by Glb1 (Figure 4D) and detected as a blue stain in an X-gal assay for senescence (Figure 4E). These findings suggest that HIF-1 stabilization induces subsequent mitochondrial dysfunction and senescence in trophoblasts.

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Primary trophoblasts undergo spontaneous syncytialization in culture, a phenomenon that limits the duration of study and may also confound the interpretation of experimental changes in key metabolic factors (Nursalim et al., 2020). We therefore also modeled HIF-1 activation in JAR cells, a trophoblast cell line that does not undergo syncytialization (Rothbauer et al., 2017). Consistent with our results in primary cells, we found that HIF-1 is stabilized in CoCl₂-treated JAR cells (Figure 5A), and after 6 days of exposure, mtDNA and protein abundance declined (Figure 5B–C). We further defined the time course of mitochondrial downregulation: by 72 hr the effect began to appear (Figure 5—figure supplement 1). Furthermore, at the 6-day timepoint we found that CoCl₂ exposure leads to accumulation of mitochondrial ROS, as measured by mtSOX, a fluorescent mt-superoxide indicator dye (Figure 5D), and impairs mitochondrial polarization, as measured by tetramethylrhodamine ethyl ester (TMRE) staining, a cell-permeant fluorescent dye that accumulates in polarized mitochondria (Figure 5E). Additionally, we observed pronounced signs of cellular senescence: morphological hallmarks (cellular swelling) and β-galactosidase overexpression (Figure 5F); growth arrest which persists for days after removal of the HIF-1 stabilizing compound (Figure 5G); and a senescence-associated secretory phenotype (SASP, Figure 5H) reflected by increases in mRNA expression of the genes encoding VEGF, TNFα, and IL-1α and a decrease in mRNA expression of the gene encoding anti-inflammatory cytokine IL-10.

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We conducted additional studies to confirm that cell death was not a primary contributor to the lack of proliferation. Propidium iodide staining with quantitative fluorescence cytometry indicated that CoCl₂ treatment only increased cell death by 0–3% (Figure 5—figure supplement 2). Importantly, cells remained adherent and continued to acidify culture medium beyond 14 days of CoCl₂ exposure, providing confidence that HIF-1 stabilization induces a phenotype characterized by predominantly viable cells that are no longer proliferating, namely cellular senescence. Finally, to assess whether the effects we observed were attributable to HIF-1 stabilization and not an off-target effect of CoCl₂, we also evaluated an alternative prolyl hydroxylase inhibitor, dimethyloxalylglycine (DMOG) (Epstein et al., 2001), and found similar effects on HIF-1, mitochondrial abundance and cellular senescence (Figure 5—figure supplement 3).

To determine if other features of the aged placenta phenotype were recapitulated in this model, we performed whole transcriptome analysis via RNA-Seq. Differential expression (log2 FC>1; false discovery rate [FDR] <0.05) was found for 2188 upregulated and 1389 downregulated genes (Figure 5I). Gene set enrichment analysis revealed that chemical hypoxia led to upregulation of inflammatory signaling, and downregulation of the TCA cycle, mitochondrial biogenesis, and respiratory electron transport. These data therefore recapitulated metabolic patterns that emerged from time course transcriptomic analysis of intact placentas (Figure 2A).

The onset of labor is accompanied by a transformation of the uterus from quiescence into a distinct physiologic state in which it generates powerful, coordinated contractions. This phenotypic change is characterized by upregulation of a cadre of contraction-associated proteins (CAPs), notably cyclooxygenase-2 (COX-2, encoded by PTGS2), prostaglandin F2α receptor (encoded by PTGFR), interleukin-6 (IL-6), and connexin 43 (Cx43, encoded by GJA1). Phenotypic switching can be modeled in primary and immortalized uterine myocytes upon stimulation with inflammatory mediators such as IL-1β, PGF2α, and thrombin (Nishimura et al., 2020; Rauk and Chiao, 2000; Leimert et al., 2019).

To test whether primary placental metabolic disruptions could crosstalk with uterine myocytes to trigger the contractile phenotype, we collected conditioned media from our JAR cell model of HIF-1-driven senescence and measured the effect on expression of CAPs in uterine myocytes (hTERT-HM cells). We observed a robust and specific effect: co-stimulation of uterine myocytes with IL-1β plus conditioned media from JAR cells treated with CoCl₂ (but not from untreated JAR cells) potentiated the induction of PTGS2, GJA1, PTGFR, and IL6 mRNA expression (Figure 6A–D). We next employed a collagen lattice assay previously described for assessing contractility of myometrial cells in vitro (Nishimura et al., 2020; Devost and Zingg, 2007), and demonstrated that myocyte contraction is augmented upon exposure to conditioned media from JAR cells treated with CoCl₂ but not from untreated JAR cells (Figure 6E). These transcriptional and functional results collectively demonstrate that uterine myocytes are responsive to the secretome of JAR cells driven to senescence via HIF-1 induction.

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To test whether HIF-1 induction drives labor onset in vivo, we administered DMOG intraperitoneally to pregnant mice on gestational day e16.5. Analysis of placentas recovered 12 hr following injection indicated that HIF-1 protein is stabilized in placenta following maternal DMOG injection (Figure 7A), and transcription of HIF-1 target genes Hk2 and Slc2a1 was significantly increased (Figure 7B). Following injection of DMOG, gestational length was significantly shortened compared to injection of vehicle alone (Figure 7C–D).

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The subject of whether the healthy placenta undergoes aging (with accompanying dysfunction) within its 40-week lifespan has been of great interest and debate for many years. Age-related functional decline is a near-universal phenomenon affecting many tissues, but the underlying biochemical driving factors vary widely depending on the context of cell type and local stressors. Mechanisms well described in other tissues are rooted in genomic instability, mitochondrial dysfunction and oxidative stress, nutrient deprivation and metabolic insufficiency, or loss of proteostasis (Campisi et al., 2019). While characterization of the placenta as an aging organ appeared in the literature beginning nearly 50 years ago (Rosso, 1976; Martin and Spicer, 1973), a counter-argument has been offered that there is no logical reason for the placenta to undergo accelerated aging relative to the fetus, since both share the same genes and environment (Fox, 1997). A third viewpoint proposes that aging in the placenta is indicative of a disease state—not normal progression of healthy gestation—one usually reflective of maladaptive responses to oxidative stress (Sultana et al., 2017; Cindrova-Davies et al., 2018; Biron-Shental et al., 2010; Chen et al., 2011; Maiti et al., 2017). Finally, recent reports have noted prominent signs of aging in another fetally derived tissue, the chorioamniotic membranes, in healthy pregnancies (Bonney et al., 2016), and have linked aging of the fetal membranes to the onset of labor (Menon et al., 2016; Menon et al., 2019). The results presented here span mouse and human placentas to provide evidence for placental aging in normal pregnancy. Mechanistic dissection of this phenomenon further demonstrates how hypoxia in late gestation may be both a causal determinant of mitochondrial dysfunction and senescence observed within the trophoblast as well as a trigger to induce uterine contraction.

To our knowledge, this is the first report to propose that placental aging in healthy pregnancy is characterized by induction of HIF-1 signaling, accompanied by downstream effects including mitochondrial dysfunction and cellular senescence. These findings have implications for the role of the placenta in signaling that promotes the onset of labor, particularly given our demonstration that upon HIF-1 stabilization, trophoblasts can induce inflammatory changes in uterine myocytes and potentiate their contractility, and our in vivo demonstration that administration of DMOG stabilizes placental HIF-1 and leads to preterm labor in mice. Important next steps prompted by this work include determining the specific components of the secretome of HIF-1-activated trophoblasts which are responsible for inducing myocyte transformation, anticipating overlap with findings of prior studies delineating paracrine signals that mediate labor onset (Sheller-Miller et al., 2019; Migale et al., 2015; Srikhajon et al., 2014; Gomez-Lopez et al., 2014). Additionally, having established that HIF-1 signaling is upstream of mitochondrial dysfunction and cellular senescence in late-gestation placenta in normal pregnancies, future studies can investigate whether factors that modulate this pathway could increase or decrease vulnerability to preterm labor. In studies targeted to uterine decidua, a link between cellular senescence and labor onset has previously been established, with evidence implicating phospho-Akt and mTORC1 signaling upstream of prostaglandin synthesis as a key mechanism in this model (Hirota et al., 2010; Hirota et al., 2011). It is possible that senescence signaling from the placenta and decidua converge, and labor is provoked upon these convergent endpoint signals surpassing a threshold, regardless of their origin within the gestational compartment. Future studies using targeted manipulation of senescence and metabolism in these compartments individually will help clarify their specific contributions to labor onset.

Our results suggest that placental HIF-1 becomes stabilized late in gestation, and this could be secondary to the onset of hypoxia sensing as oxygen demand outstrips supply in the fetoplacental unit. However, placental hypoxia has been difficult to examine in vivo. Despite evolving approaches for measurement of placental oxygenation, findings have varied widely and adequate spatiotemporal resolution has so far been challenging (Nye et al., 2018). However, it is apparent that due to arteriovenous shunting and high metabolic extraction rates, the placenta experiences hypoxia throughout pregnancy with a reported partial pressure of oxygen 30–50 mm Hg in the intervillous space, so perhaps a relevant question is what prevents HIF-1 stabilization and its downstream effects early in gestation. It is possible that the gestational age-dependent HIF-1 induction we observed reflects not a change in oxygen availability, but rather a change in other HIF-regulatory factors such as nicotinamide adenine dinucleotide (NAD⁺) depletion, as has been demonstrated to occur in muscle and is accompanied by metabolic reprogramming and mitochondrial decline (Gomes et al., 2013). Importantly, replenishment of NAD⁺ was shown to restore markers of mitochondrial function in these aged cells, suggesting that mitochondrial effects of HIF-1-driven aging are reversible in some systems. Separately, a recent study which compared the miR-nomes of first- versus third-trimester human placenta highlighted differential expression of miRNAs with functional links to silencing of hypoxia response and cellular senescence pathways in the first trimester (Gonzalez et al., 2021). Future investigations into the impact of placental HIF-1 regulation will require a more precise understanding of placental oxygenation in vivo and a sophisticated approach to model these factors in placental explants or other cellular systems.

The shift in metabolic phenotype away from oxidative respiration in late pregnancy as demonstrated by our transcriptomics analysis is consistent with earlier findings from respirometry studies in mouse placenta (Sferruzzi-Perri et al., 2019), where rates of oxidative phosphorylation were noted to decline from e14 to e19, particularly in the labyrinthine zone. It is still unclear if this shift reflects changing substrate availability, or oxygen content, or perhaps the metabolic needs of the developing fetus. A better understanding of the dynamic metabolism of the placenta across gestation will be crucial for developing strategies for optimizing placental health, with major implications for both maternal and fetal outcomes. For example, placental metabolomics studies with a gestational time series could help corroborate the metabolic implications interpreted from our transcriptomic data. And while our study stemmed from analysis of the whole-organ placental transcriptome, single-cell transcriptomics across advancing gestational age in healthy placentas would offer critical cell-type localization of specific metabolic events noted to occur late in pregnancy.

Our data have implications for other adverse pregnancy outcomes beyond preterm birth, including intrauterine growth restriction and preeclampsia. Two important recent studies have examined separate mouse models with constitutively active placental HIF-1 and found that when induced from the beginning of gestation, placental HIF-1 signaling drives abnormal placentogenesis and impaired spiral artery remodeling, leading to placental hypoperfusion, fetal growth restriction, and a preeclampsia-like clinical syndrome (Albers et al., 2019; Sallais et al., 2022). In the context of these earlier results, our study highlights the critical gestational age dependence of HIF-1 effects, having demonstrated that in a normally developed mouse placenta, induction of HIF-1 signaling during the final days of gestation leads to labor onset. Timing may be key to understanding how two distinct pregnancy complications—preterm labor and preeclampsia—appear to emerge separately from overlapping pathophysiologic mechanisms (Rasmussen et al., 2017; Mandò et al., 2014; Fujimaki et al., 2011; Davy et al., 2009; Burton et al., 2009).

In summary, we report a molecular characterization of placental aging phenomena occurring in normal pregnancies, stemming from induction of HIF-1 signaling and downstream mitochondrial dysfunction and cellular senescence. These findings establish aging in the placenta as a feature of normal gestation; identify HIF-1 signaling as an upstream trigger leading to mitochondrial dysfunction and senescence in the placenta; demonstrate that the secretome of senescent trophoblasts is sufficient to potentiate uterine myocyte transformation and contractility; and establish that HIF-1 induction in vivo can induce preterm labor. These findings may have important implications for illuminating the factors that determine gestational length both in health and disease.

Statistics accompanying WGCNA (Figure 2) are accessible through Mendeley Data, doi: https://doi.org/10.17632/g6vrw9jjn4.1. The RNA Seq dataset has been deposited in NCBI's Gene Expression Omnibus21 and is accessible through GEO Series accession number GSE199278 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE199278).

1. 1. Ciampa EJ
   2. Parikh SM

   (2022) NCBI Gene Expression Omnibus

   ID GSE199278. Hypoxia-inducible factor 1 signaling drives placental aging and can elicit inflammatory changes in uterine myocytes.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE199278
2. 1. Ciampa EJ

   (2022) Mendeley Data

   WGCNA supplement.

   https://doi.org/10.17632/g6vrw9jjn4.1

1. 1. Knox K
   2. Baker JC

   (2008) NCBI Gene Expression Omnibus

   ID GSE11224. Expression data from developing mouse placenta.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11224

1. 1. Albers RE
   2. Kaufman MR
   3. Natale BV
   4. Keoni C
   5. Kulkarni-Datar K
   6. Min S
   7. Williams CR
   8. Natale DRC
   9. Brown TL

   (2019) Trophoblast-Specific Expression of Hif-1α Results in Preeclampsia-Like symptoms and fetal growth restriction

   Scientific Reports 9:2742.

   https://doi.org/10.1038/s41598-019-39426-5

   • PubMed
   • Google Scholar
2. 1. Beharier O
   2. Tyurin VA
   3. Goff JP
   4. Guerrero-Santoro J
   5. Kajiwara K
   6. Chu T
   7. Tyurina YY
   8. St Croix CM
   9. Wallace CT
   10. Parry S
   11. Parks WT
   12. Kagan VE
   13. Sadovsky Y

   (2020) PLA2G6 guards placental trophoblasts against ferroptotic injury

   PNAS 117:27319–27328.

   https://doi.org/10.1073/pnas.2009201117

   • PubMed
   • Google Scholar
3. 1. Bentsen MA
   2. Rausch DM
   3. Mirzadeh Z
   4. Muta K
   5. Scarlett JM
   6. Brown JM
   7. Herranz-Pérez V
   8. Baquero AF
   9. Thompson J
   10. Alonge KM
   11. Faber CL
   12. Kaiyala KJ
   13. Bennett C
   14. Pyke C
   15. Ratner C
   16. Egerod KL
   17. Holst B
   18. Meek TH
   19. Kutlu B
   20. Zhang Y
   21. Sparso T
   22. Grove KL
   23. Morton GJ
   24. Kornum BR
   25. García-Verdugo J-M
   26. Secher A
   27. Jorgensen R
   28. Schwartz MW
   29. Pers TH

   (2020) Transcriptomic analysis links diverse hypothalamic cell types to fibroblast growth factor 1-induced sustained diabetes remission

   Nature Communications 11:4458.

   https://doi.org/10.1038/s41467-020-17720-5

   • PubMed
   • Google Scholar
4. 1. Biron-Shental T
   2. Sukenik-Halevy R
   3. Sharon Y
   4. Goldberg-Bittman L
   5. Kidron D
   6. Fejgin MD
   7. Amiel A

   (2010) Short telomeres may play a role in placental dysfunction in preeclampsia and intrauterine growth restriction

   American Journal of Obstetrics and Gynecology 202:381.

   https://doi.org/10.1016/j.ajog.2010.01.036

   • PubMed
   • Google Scholar
5. 1. Blencowe H
   2. Cousens S
   3. Chou D
   4. Oestergaard M
   5. Say L
   6. Moller AB
   7. Kinney M
   8. Lawn J
   9. Born Too Soon Preterm Birth Action Group

   (2013) Born too soon: the global epidemiology of 15 million preterm births

   Reproductive Health 10 Suppl 1:S2.

   https://doi.org/10.1186/1742-4755-10-S1-S2

   • PubMed
   • Google Scholar
6. 1. Bonney EA
   2. Krebs K
   3. Saade G
   4. Kechichian T
   5. Trivedi J
   6. Huaizhi Y
   7. Menon R

   (2016) Differential senescence in feto-maternal tissues during mouse pregnancy

   Placenta 43:26–34.

   https://doi.org/10.1016/j.placenta.2016.04.018

   • PubMed
   • Google Scholar
7. 1. Bratic A
   2. Larsson NG

   (2013) The role of mitochondria in aging

   The Journal of Clinical Investigation 123:951–957.

   https://doi.org/10.1172/JCI64125

   • PubMed
   • Google Scholar
8. 1. Burton GJ
   2. Yung HW
   3. Cindrova-Davies T
   4. Charnock-Jones DS

   (2009) Placental endoplasmic reticulum stress and oxidative stress in the pathophysiology of unexplained intrauterine growth restriction and early onset preeclampsia

   Placenta 30 Suppl A:S43–S48.

   https://doi.org/10.1016/j.placenta.2008.11.003

   • PubMed
   • Google Scholar
9. 1. Campisi J
   2. Kapahi P
   3. Lithgow GJ
   4. Melov S
   5. Newman JC
   6. Verdin E

   (2019) From discoveries in ageing research to therapeutics for healthy ageing

   Nature 571:183–192.

   https://doi.org/10.1038/s41586-019-1365-2

   • PubMed
   • Google Scholar
10. 1. Chen KH
    2. Chen LR
    3. Lee YH

    (2011) Exploring the relationship between preterm placental calcification and adverse maternal and fetal outcome

    Ultrasound in Obstetrics & Gynecology 37:328–334.

    https://doi.org/10.1002/uog.7733

    • PubMed
    • Google Scholar
11. 1. Chen Y
    2. Lun ATL
    3. Smyth GK

    (2016) From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline

    F1000Research 5:1438.

    https://doi.org/10.12688/f1000research.8987.2

    • PubMed
    • Google Scholar
12. 1. Chen S
    2. Zhou Y
    3. Chen Y
    4. Gu J

    (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor

    Bioinformatics 34:i884–i890.

    https://doi.org/10.1093/bioinformatics/bty560

    • Google Scholar
13. 1. Cindrova-Davies T
    2. Fogarty NME
    3. Jones CJP
    4. Kingdom J
    5. Burton GJ

    (2018) Evidence of oxidative stress-induced senescence in mature, post-mature and pathological human placentas

    Placenta 68:15–22.

    https://doi.org/10.1016/j.placenta.2018.06.307

    • PubMed
    • Google Scholar
14. 1. Davy P
    2. Nagata M
    3. Bullard P
    4. Fogelson NS
    5. Allsopp R

    (2009) Fetal growth restriction is associated with accelerated telomere shortening and increased expression of cell senescence markers in the placenta

    Placenta 30:539–542.

    https://doi.org/10.1016/j.placenta.2009.03.005

    • PubMed
    • Google Scholar
15. 1. Devost D
    2. Zingg HH

    (2007) Novel in vitro system for functional assessment of oxytocin action

    American Journal of Physiology. Endocrinology and Metabolism 292:E1–E6.

    https://doi.org/10.1152/ajpendo.00529.2005

    • PubMed
    • Google Scholar
16. 1. Edgar R
    2. Domrachev M
    3. Lash AE

    (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

    Nucleic Acids Research 30:207–210.

    https://doi.org/10.1093/nar/30.1.207

    • PubMed
    • Google Scholar
17. 1. Elshenawy S
    2. Pinney SE
    3. Stuart T
    4. Doulias PT
    5. Zura G
    6. Parry S
    7. Elovitz MA
    8. Bennett MJ
    9. Bansal A
    10. Strauss JF
    11. Ischiropoulos H
    12. Simmons RA

    (2020) The metabolomic signature of the placenta in spontaneous preterm birth

    International Journal of Molecular Sciences 21:1043.

    https://doi.org/10.3390/ijms21031043

    • PubMed
    • Google Scholar
18. 1. Epstein AC
    2. Gleadle JM
    3. McNeill LA
    4. Hewitson KS
    5. O’Rourke J
    6. Mole DR
    7. Mukherji M
    8. Metzen E
    9. Wilson MI
    10. Dhanda A
    11. Tian YM
    12. Masson N
    13. Hamilton DL
    14. Jaakkola P
    15. Barstead R
    16. Hodgkin J
    17. Maxwell PH
    18. Pugh CW
    19. Schofield CJ
    20. Ratcliffe PJ

    (2001) C. elegans EGL-9 and mammalian homologs define a family of dioxygenases that regulate HIF by prolyl hydroxylation

    Cell 107:43–54.

    https://doi.org/10.1016/s0092-8674(01)00507-4

    • PubMed
    • Google Scholar
19. 1. Fox H

    (1997) Aging of the placenta

    Archives of Disease in Childhood. Fetal and Neonatal Edition 77:F171–F175.

    https://doi.org/10.1136/fn.77.3.f171

    • PubMed
    • Google Scholar
20. 1. Fujimaki A
    2. Watanabe K
    3. Mori T
    4. Kimura C
    5. Shinohara K
    6. Wakatsuki A

    (2011) Placental oxidative DNA damage and its repair in preeclamptic women with fetal growth restriction

    Placenta 32:367–372.

    https://doi.org/10.1016/j.placenta.2011.02.004

    • PubMed
    • Google Scholar
21. 1. Genbacev O
    2. Joslin R
    3. Damsky CH
    4. Polliotti BM
    5. Fisher SJ

    (1996) Hypoxia alters early gestation human cytotrophoblast differentiation/invasion in vitro and models the placental defects that occur in preeclampsia

    The Journal of Clinical Investigation 97:540–550.

    https://doi.org/10.1172/JCI118447

    • PubMed
    • Google Scholar
22. 1. Goldenberg RL
    2. Culhane JF
    3. Iams JD
    4. Romero R

    (2008) Epidemiology and causes of preterm birth

    The Lancet 371:75–84.

    https://doi.org/10.1016/S0140-6736(08)60074-4

    • Google Scholar
23. 1. Gomes AP
    2. Price NL
    3. Ling AJY
    4. Moslehi JJ
    5. Montgomery MK
    6. Rajman L
    7. White JP
    8. Teodoro JS
    9. Wrann CD
    10. Hubbard BP
    11. Mercken EM
    12. Palmeira CM
    13. de Cabo R
    14. Rolo AP
    15. Turner N
    16. Bell EL
    17. Sinclair DA

    (2013) Declining NAD(+) induces a pseudohypoxic state disrupting nuclear-mitochondrial communication during aging

    Cell 155:1624–1638.

    https://doi.org/10.1016/j.cell.2013.11.037

    • PubMed
    • Google Scholar
24. 1. Gomez-Lopez N
    2. StLouis D
    3. Lehr MA
    4. Sanchez-Rodriguez EN
    5. Arenas-Hernandez M

    (2014) Immune cells in term and preterm labor

    Cellular & Molecular Immunology 11:571–581.

    https://doi.org/10.1038/cmi.2014.46

    • PubMed
    • Google Scholar
25. 1. Gonzalez TL
    2. Eisman LE
    3. Joshi NV
    4. Flowers AE
    5. Wu D
    6. Wang Y
    7. Santiskulvong C
    8. Tang J
    9. Buttle RA
    10. Sauro E
    11. Clark EL
    12. DiPentino R
    13. Jefferies CA
    14. Chan JL
    15. Lin Y
    16. Zhu Y
    17. Afshar Y
    18. Tseng HR
    19. Taylor K
    20. Williams J
    21. Pisarska MD

    (2021) High-throughput miRNA sequencing of the human placenta: expression throughout gestation

    Epigenomics 13:995–1012.

    https://doi.org/10.2217/epi-2021-0055

    • PubMed
    • Google Scholar
26. 1. Hirota Y
    2. Daikoku T
    3. Tranguch S
    4. Xie H
    5. Bradshaw HB
    6. Dey SK

    (2010) Uterine-specific p53 deficiency confers premature uterine senescence and promotes preterm birth in mice

    The Journal of Clinical Investigation 120:803–815.

    https://doi.org/10.1172/JCI40051

    • PubMed
    • Google Scholar
27. 1. Hirota Y
    2. Cha J
    3. Yoshie M
    4. Daikoku T
    5. Dey SK

    (2011) Heightened uterine mammalian target of rapamycin complex 1 (mTORC1) signaling provokes preterm birth in mice

    PNAS 108:18073–18078.

    https://doi.org/10.1073/pnas.1108180108

    • PubMed
    • Google Scholar
28. 1. Jaakkola P
    2. Mole DR
    3. Tian YM
    4. Wilson MI
    5. Gielbert J
    6. Gaskell SJ
    7. von Kriegsheim A
    8. Hebestreit HF
    9. Mukherji M
    10. Schofield CJ
    11. Maxwell PH
    12. Pugh CW
    13. Ratcliffe PJ

    (2001) Targeting of HIF-alpha to the von Hippel-Lindau ubiquitylation complex by O2-regulated prolyl hydroxylation

    Science 292:468–472.

    https://doi.org/10.1126/science.1059796

    • PubMed
    • Google Scholar
29. 1. Jang JY
    2. Blum A
    3. Liu J
    4. Finkel T

    (2018) The role of mitochondria in aging

    The Journal of Clinical Investigation 128:3662–3670.

    https://doi.org/10.1172/JCI120842

    • PubMed
    • Google Scholar
30. 1. Kiserud T
    2. Ebbing C
    3. Kessler J
    4. Rasmussen S

    (2006) Fetal cardiac output, distribution to the placenta and impact of placental compromise

    Ultrasound in Obstetrics & Gynecology 28:126–136.

    https://doi.org/10.1002/uog.2832

    • PubMed
    • Google Scholar
31. 1. Knox K
    2. Baker JC

    (2008) Genomic evolution of the placenta using co-option and duplication and divergence

    Genome Research 18:695–705.

    https://doi.org/10.1101/gr.071407.107

    • PubMed
    • Google Scholar
32. 1. Koga K
    2. Cardenas I
    3. Aldo P
    4. Abrahams VM
    5. Peng B
    6. Fill S
    7. Romero R
    8. Mor G

    (2009) Activation of TLR3 in the trophoblast is associated with preterm delivery

    American Journal of Reproductive Immunology 61:196–212.

    https://doi.org/10.1111/j.1600-0897.2008.00682.x

    • PubMed
    • Google Scholar
33. 1. Langfelder P
    2. Horvath S

    (2008) WGCNA: an R package for weighted correlation network analysis

    BMC Bioinformatics 9:559.

    https://doi.org/10.1186/1471-2105-9-559

    • PubMed
    • Google Scholar
34. 1. Lee BY
    2. Han JA
    3. Im JS
    4. Morrone A
    5. Johung K
    6. Goodwin EC
    7. Kleijer WJ
    8. DiMaio D
    9. Hwang ES

    (2006) Senescence-associated beta-galactosidase is lysosomal beta-galactosidase

    Aging Cell 5:187–195.

    https://doi.org/10.1111/j.1474-9726.2006.00199.x

    • PubMed
    • Google Scholar
35. 1. Leimert KB
    2. Verstraeten BSE
    3. Messer A
    4. Nemati R
    5. Blackadar K
    6. Fang X
    7. Robertson SA
    8. Chemtob S
    9. Olson DM

    (2019) Cooperative effects of sequential PGF2α and IL-1β on IL-6 and COX-2 expression in human myometrial cells†

    Biology of Reproduction 100:1370–1385.

    https://doi.org/10.1093/biolre/ioz029

    • PubMed
    • Google Scholar
36. 1. Li F
    2. Fushima T
    3. Oyanagi G
    4. Townley-Tilson HWD
    5. Sato E
    6. Nakada H
    7. Oe Y
    8. Hagaman JR
    9. Wilder J
    10. Li M
    11. Sekimoto A
    12. Saigusa D
    13. Sato H
    14. Ito S
    15. Jennette JC
    16. Maeda N
    17. Karumanchi SA
    18. Smithies O
    19. Takahashi N

    (2016) Nicotinamide benefits both mothers and pups in two contrasting mouse models of preeclampsia

    PNAS 113:13450–13455.

    https://doi.org/10.1073/pnas.1614947113

    • Google Scholar
37. 1. Lien YC
    2. Zhang Z
    3. Cheng Y
    4. Polyak E
    5. Sillers L
    6. Falk MJ
    7. Ischiropoulos H
    8. Parry S
    9. Simmons RA

    (2021) Human placental transcriptome reveals critical alterations in inflammation and energy metabolism with fetal sex differences in spontaneous preterm birth

    International Journal of Molecular Sciences 22:7899.

    https://doi.org/10.3390/ijms22157899

    • PubMed
    • Google Scholar
38. 1. Liu L
    2. Oza S
    3. Hogan D
    4. Perin J
    5. Rudan I
    6. Lawn JE
    7. Cousens S
    8. Mathers C
    9. Black RE

    (2015) Global, regional, and national causes of child mortality in 2000–13, with projections to inform post-2015 priorities: an updated systematic analysis

    The Lancet 385:430–440.

    https://doi.org/10.1016/S0140-6736(14)61698-6

    • Google Scholar
39. 1. Loux SC
    2. Dini P
    3. El-Sheikh Ali H
    4. Kalbfleisch T
    5. Ball BA

    (2019) Characterization of the placental transcriptome through mid to late gestation in the mare

    PLOS ONE 14:e0224497.

    https://doi.org/10.1371/journal.pone.0224497

    • PubMed
    • Google Scholar
40. 1. Lykke JA
    2. Langhoff-Roos J
    3. Sibai BM
    4. Funai EF
    5. Triche EW
    6. Paidas MJ

    (2009) Hypertensive pregnancy disorders and subsequent cardiovascular morbidity and type 2 diabetes mellitus in the mother

    Hypertension 53:944–951.

    https://doi.org/10.1161/HYPERTENSIONAHA.109.130765

    • PubMed
    • Google Scholar
41. 1. Maeda KJ
    2. Showmaker KC
    3. Johnson AC
    4. Garrett MR
    5. Sasser JM

    (2019) Spontaneous superimposed preeclampsia: chronology and expression unveiled by temporal transcriptomic analysis

    Physiological Genomics 51:342–355.

    https://doi.org/10.1152/physiolgenomics.00020.2019

    • PubMed
    • Google Scholar
42. 1. Maiti K
    2. Sultana Z
    3. Aitken RJ
    4. Morris J
    5. Park F
    6. Andrew B
    7. Riley SC
    8. Smith R

    (2017) Evidence that fetal death is associated with placental aging

    American Journal of Obstetrics and Gynecology 217:441.

    https://doi.org/10.1016/j.ajog.2017.06.015

    • PubMed
    • Google Scholar
43. 1. Mandò C
    2. De Palma C
    3. Stampalija T
    4. Anelli GM
    5. Figus M
    6. Novielli C
    7. Parisi F
    8. Clementi E
    9. Ferrazzi E
    10. Cetin I

    (2014) Placental mitochondrial content and function in intrauterine growth restriction and preeclampsia

    American Journal of Physiology. Endocrinology and Metabolism 306:E404–E413.

    https://doi.org/10.1152/ajpendo.00426.2013

    • PubMed
    • Google Scholar
44. 1. Martin BJ
    2. Spicer SS

    (1973) Ultrastructural features of cellular maturation and aging in human trophoblast

    Journal of Ultrastructure Research 43:133–149.

    https://doi.org/10.1016/s0022-5320(73)90074-9

    • PubMed
    • Google Scholar
45. 1. Maxwell PH
    2. Wiesener MS
    3. Chang GW
    4. Clifford SC
    5. Vaux EC
    6. Cockman ME
    7. Wykoff CC
    8. Pugh CW
    9. Maher ER
    10. Ratcliffe PJ

    (1999) The tumour suppressor protein VHL targets hypoxia-inducible factors for oxygen-dependent proteolysis

    Nature 399:271–275.

    https://doi.org/10.1038/20459

    • PubMed
    • Google Scholar
46. 1. McIntyre KR
    2. Vincent KMM
    3. Hayward CE
    4. Li X
    5. Sibley CP
    6. Desforges M
    7. Greenwood SL
    8. Dilworth MR

    (2020) Human placental uptake of glutamine and glutamate is reduced in fetal growth restriction

    Scientific Reports 10:16197.

    https://doi.org/10.1038/s41598-020-72930-7

    • PubMed
    • Google Scholar
47. 1. Menon R
    2. Behnia F
    3. Polettini J
    4. Saade GR
    5. Campisi J
    6. Velarde M

    (2016) Placental membrane aging and HMGB1 signaling associated with human parturition

    Aging 8:216–230.

    https://doi.org/10.18632/aging.100891

    • PubMed
    • Google Scholar
48. 1. Menon R
    2. Richardson LS
    3. Lappas M

    (2019) Fetal membrane architecture, aging and inflammation in pregnancy and parturition

    Placenta 79:40–45.

    https://doi.org/10.1016/j.placenta.2018.11.003

    • PubMed
    • Google Scholar
49. 1. Migale R
    2. Herbert BR
    3. Lee YS
    4. Sykes L
    5. Waddington SN
    6. Peebles D
    7. Hagberg H
    8. Johnson MR
    9. Bennett PR
    10. MacIntyre DA

    (2015) Specific lipopolysaccharide serotypes induce differential maternal and neonatal inflammatory responses in a murine model of preterm labor

    The American Journal of Pathology 185:2390–2401.

    https://doi.org/10.1016/j.ajpath.2015.05.015

    • PubMed
    • Google Scholar
50. 1. Morey R
    2. Farah O
    3. Kallol S
    4. Requena DF
    5. Meads M
    6. Moretto-Zita M
    7. Soncin F
    8. Laurent LC
    9. Parast MM

    (2021) Transcriptomic drivers of differentiation, maturation, and polyploidy in human extravillous trophoblast

    Frontiers in Cell and Developmental Biology 9:702046.

    https://doi.org/10.3389/fcell.2021.702046

    • PubMed
    • Google Scholar
51. 1. Nishimura F
    2. Mogami H
    3. Moriuchi K
    4. Chigusa Y
    5. Mandai M
    6. Kondoh E

    (2020) Mechanisms of thrombin-Induced myometrial contractions: Potential targets of progesterone

    PLOS ONE 15:e0231944.

    https://doi.org/10.1371/journal.pone.0231944

    • PubMed
    • Google Scholar
52. 1. Nursalim YNS
    2. Blenkiron C
    3. Groom KM
    4. Chamley LW

    (2020) Growing human trophoblasts in vitro: a review of the media commonly used in trophoblast cell culture

    Reproduction 160:R119–R128.

    https://doi.org/10.1530/REP-19-0605

    • PubMed
    • Google Scholar
53. 1. Nye GA
    2. Ingram E
    3. Johnstone ED
    4. Jensen OE
    5. Schneider H
    6. Lewis RM
    7. Chernyavsky IL
    8. Brownbill P

    (2018) Human placental oxygenation in late gestation: experimental and theoretical approaches

    The Journal of Physiology 596:5523–5534.

    https://doi.org/10.1113/JP275633

    • PubMed
    • Google Scholar
54. 1. Paquette AG
    2. Brockway HM
    3. Price ND
    4. Muglia LJ

    (2018) Comparative transcriptomic analysis of human placentae at term and preterm delivery

    Biology of Reproduction 98:89–101.

    https://doi.org/10.1093/biolre/iox163

    • PubMed
    • Google Scholar
55. 1. Patro R
    2. Duggal G
    3. Love MI
    4. Irizarry RA
    5. Kingsford C

    (2017) Salmon provides fast and bias-aware quantification of transcript expression

    Nature Methods 14:417–419.

    https://doi.org/10.1038/nmeth.4197

    • PubMed
    • Google Scholar
56. 1. Pennington KA
    2. Schlitt JM
    3. Schulz LC

    (2012) Isolation of primary mouse trophoblast cells and trophoblast invasion assay

    Journal of Visualized Experiments 1:e3202.

    https://doi.org/10.3791/3202

    • PubMed
    • Google Scholar
57. 1. Pique-Regi R
    2. Romero R
    3. Tarca AL
    4. Sendler ED
    5. Xu Y
    6. Garcia-Flores V
    7. Leng Y
    8. Luca F
    9. Hassan SS
    10. Gomez-Lopez N

    (2019) Single cell transcriptional signatures of the human placenta in term and preterm parturition

    eLife 8:e52004.

    https://doi.org/10.7554/eLife.52004

    • PubMed
    • Google Scholar
58. 1. Plaks V
    2. Rinkenberger J
    3. Dai J
    4. Flannery M
    5. Sund M
    6. Kanasaki K
    7. Ni W
    8. Kalluri R
    9. Werb Z

    (2013) Matrix metalloproteinase-9 deficiency phenocopies features of preeclampsia and intrauterine growth restriction

    PNAS 110:11109–11114.

    https://doi.org/10.1073/pnas.1309561110

    • PubMed
    • Google Scholar
59. 1. Rasmussen S
    2. Ebbing C
    3. Irgens LM

    (2017) Predicting preeclampsia from a history of preterm birth

    PLOS ONE 12:e0181016.

    https://doi.org/10.1371/journal.pone.0181016

    • PubMed
    • Google Scholar
60. 1. Raudvere U
    2. Kolberg L
    3. Kuzmin I
    4. Arak T
    5. Adler P
    6. Peterson H
    7. Vilo J

    (2019) g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update)

    Nucleic Acids Research 47:W191–W198.

    https://doi.org/10.1093/nar/gkz369

    • PubMed
    • Google Scholar
61. 1. Rauk PN
    2. Chiao JP

    (2000) Interleukin-1 stimulates human uterine prostaglandin production through induction of cyclooxygenase-2 expression

    American Journal of Reproductive Immunology 43:152–159.

    https://doi.org/10.1111/j.8755-8920.2000.430304.x

    • PubMed
    • Google Scholar
62. 1. Robinson MD
    2. McCarthy DJ
    3. Smyth GK

    (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

    Bioinformatics 26:139–140.

    https://doi.org/10.1093/bioinformatics/btp616

    • PubMed
    • Google Scholar
63. 1. Romero R
    2. Chaiworapongsa T

    (2013) Preeclampsia: a link between trophoblast dysregulation and an antiangiogenic state

    The Journal of Clinical Investigation 123:2775–2777.

    https://doi.org/10.1172/JCI70431

    • PubMed
    • Google Scholar
64. 1. Romero R
    2. Dey SK
    3. Fisher SJ

    (2014) Preterm labor: one syndrome, many causes

    Science 345:760–765.

    https://doi.org/10.1126/science.1251816

    • PubMed
    • Google Scholar
65. 1. Rosso P

    (1976)

    Placenta as an aging organ

    Current Concepts in Nutrition 4:23–41.

    • PubMed
    • Google Scholar
66. 1. Rothbauer M
    2. Patel N
    3. Gondola H
    4. Siwetz M
    5. Huppertz B
    6. Ertl P

    (2017) A comparative study of five physiological key parameters between four different human trophoblast-derived cell lines

    Scientific Reports 7:5892.

    https://doi.org/10.1038/s41598-017-06364-z

    • PubMed
    • Google Scholar
67. 1. Rothfuss O
    2. Gasser T
    3. Patenge N

    (2010) Analysis of differential DNA damage in the mitochondrial genome employing a semi-long run real-time PCR approach

    Nucleic Acids Research 38:e24.

    https://doi.org/10.1093/nar/gkp1082

    • PubMed
    • Google Scholar
68. 1. Sallais J
    2. Park C
    3. Alahari S
    4. Porter T
    5. Liu R
    6. Kurt M
    7. Farrell A
    8. Post M
    9. Caniggia I

    (2022) HIF1 inhibitor acriflavine rescues early-onset preeclampsia phenotype in mice lacking placental prolyl hydroxylase domain protein 2

    JCI Insight 7:e158908.

    https://doi.org/10.1172/jci.insight.158908

    • PubMed
    • Google Scholar
69. 1. Sferruzzi-Perri AN
    2. Higgins JS
    3. Vaughan OR
    4. Murray AJ
    5. Fowden AL

    (2019) Placental mitochondria adapt developmentally and in response to hypoxia to support fetal growth

    PNAS 116:1621–1626.

    https://doi.org/10.1073/pnas.1816056116

    • PubMed
    • Google Scholar
70. 1. Shaut CAE
    2. Keene DR
    3. Sorensen LK
    4. Li DY
    5. Stadler HS

    (2008) HOXA13 Is essential for placental vascular patterning and labyrinth endothelial specification

    PLOS Genetics 4:e1000073.

    https://doi.org/10.1371/journal.pgen.1000073

    • PubMed
    • Google Scholar
71. 1. Sheller-Miller S
    2. Trivedi J
    3. Yellon SM
    4. Menon R

    (2019) Exosomes cause preterm birth in mice: Evidence for paracrine signaling in pregnancy

    Scientific Reports 9:608.

    https://doi.org/10.1038/s41598-018-37002-x

    • PubMed
    • Google Scholar
72. 1. Smith V
    2. Devane D
    3. Begley CM
    4. Clarke M
    5. Higgins S

    (2009) A systematic review and quality assessment of systematic reviews of randomised trials of interventions for preventing and treating preterm birth

    European Journal of Obstetrics, Gynecology, and Reproductive Biology 142:3–11.

    https://doi.org/10.1016/j.ejogrb.2008.09.008

    • PubMed
    • Google Scholar
73. 1. Soncin F
    2. Khater M
    3. To C
    4. Pizzo D
    5. Farah O
    6. Wakeland A
    7. Arul Nambi Rajan K
    8. Nelson KK
    9. Chang C-W
    10. Moretto-Zita M
    11. Natale DR
    12. Laurent LC
    13. Parast MM

    (2018) Comparative analysis of mouse and human placentae across gestation reveals species-specific regulators of placental development

    Development 145:dev156273.

    https://doi.org/10.1242/dev.156273

    • PubMed
    • Google Scholar
74. 1. Soneson C
    2. Love MI
    3. Robinson MD

    (2015) Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

    F1000Research 4:1521.

    https://doi.org/10.12688/f1000research.7563.2

    • PubMed
    • Google Scholar
75. 1. Srikhajon K
    2. Shynlova O
    3. Preechapornprasert A
    4. Chanrachakul B
    5. Lye S

    (2014) A new role for monocytes in modulating myometrial inflammation during human labor

    Biology of Reproduction 91:10.

    https://doi.org/10.1095/biolreprod.113.114975

    • PubMed
    • Google Scholar
76. 1. Steinhauser CB
    2. Lambo CA
    3. Askelson K
    4. Burns GW
    5. Behura SK
    6. Spencer TE
    7. Bazer FW
    8. Satterfield MC

    (2021) Placental transcriptome adaptations to maternal nutrient restriction in sheep

    International Journal of Molecular Sciences 22:7654.

    https://doi.org/10.3390/ijms22147654

    • PubMed
    • Google Scholar
77. 1. Sultana Z
    2. Maiti K
    3. Aitken J
    4. Morris J
    5. Dedman L
    6. Smith R

    (2017) Oxidative stress, placental ageing-related pathologies and adverse pregnancy outcomes

    American Journal of Reproductive Immunology 77:12653.

    https://doi.org/10.1111/aji.12653

    • PubMed
    • Google Scholar
78. 1. Trifunovic A
    2. Wredenberg A
    3. Falkenberg M
    4. Spelbrink JN
    5. Rovio AT
    6. Bruder CE
    7. Bohlooly-Y M
    8. Gidlöf S
    9. Oldfors A
    10. Wibom R
    11. Törnell J
    12. Jacobs HT
    13. Larsson N-G

    (2004) Premature ageing in mice expressing defective mitochondrial DNA polymerase

    Nature 429:417–423.

    https://doi.org/10.1038/nature02517

    • PubMed
    • Google Scholar
79. 1. Wiley CD
    2. Campisi J

    (2016) From ancient pathways to aging cells—connecting metabolism and cellular senescence

    Cell Metabolism 23:1013–1021.

    https://doi.org/10.1016/j.cmet.2016.05.010

    • Google Scholar
80. 1. Wiley CD
    2. Campisi J

    (2021) The metabolic roots of senescence: mechanisms and opportunities for intervention

    Nature Metabolism 3:1290–1301.

    https://doi.org/10.1038/s42255-021-00483-8

    • Google Scholar
81. 1. Xu YY
    2. Liu Y
    3. Cui L
    4. Wu WB
    5. Quinn MJ
    6. Menon R
    7. Zhang HJ

    (2021) Hypoxic effects on the mitochondrial content and functions of the placenta in fetal growth restriction

    Placenta 114:100–107.

    https://doi.org/10.1016/j.placenta.2021.09.003

    • PubMed
    • Google Scholar
82. 1. Zhou QY
    2. Fang MD
    3. Huang TH
    4. Li CC
    5. Yu M
    6. Zhao SH

    (2009) Detection of differentially expressed genes between Erhualian and Large White placentas on day 75 and 90 of gestation

    BMC Genomics 10:337.

    https://doi.org/10.1186/1471-2164-10-337

    • PubMed
    • Google Scholar

Author details

1. Erin J Ciampa

   Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft

   For correspondence

   eciampa@bidmc.harvard.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6153-1994

2. Padraich Flahardy

   Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, United States

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

3. Harini Srinivasan

   Division of Endocrinology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, United States

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

4. Christopher Jacobs

   Division of Endocrinology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, United States

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

5. Linus Tsai

   Division of Endocrinology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, United States

   Contribution

   Data curation, Formal analysis, Supervision

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0134-6949

6. S Ananth Karumanchi

   Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, United States

   Contribution

   Conceptualization, Resources, Supervision

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2281-6831

7. Samir M Parikh

   Division of Nephrology, Departments of Internal Medicine and Pharmacology, University of Texas Southwestern Medical School, Dallas, United States

   Contribution

   Conceptualization, Resources, Supervision

   For correspondence

   Samir.Parikh@utsouthwestern.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1038-981X

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