TY - JOUR
AU - Li,, Ming-Qing
AB - Abstract During gestation, excess palmitate (PA) is enriched in decidua. Both excess PA and decidual dysfunctions are associated with numerous adverse pregnancy outcomes such as gestational diabetes, preeclampsia and preterm birth and intrauterine growth restriction. Here, mRNA data about the effects of PA were collected from multiple databases and analyzed. Human decidual tissues were obtained from clinically normal pregnancies, terminated for non-medical reasons, during the first trimester, and decidual stromal cells (DSCs) were isolated and exposed to PA, alone or together with the inhibitors of Toll-like receptor 4 (TLR4), Jun N-terminal kinase (JNK), nuclear factor-kappa-gene binding (NF-kB) or glutamine (GLN) oxidation. Furthermore, DSCs were transfected with lentiviral particles overexpressing human TLR4. We demonstrate that excess PA interacting with its receptor TLR4 disturbs DSC hemostasis during the first trimester. Specifically, high PA signal induced DSC apoptosis and formed an inflammatory program (elevated interleukin-1 beta and decreased interleukin-10) via the activation of TLR4/JNK/NF-kB pathways. A complexed cross-talk was found between TLR4/JNK/NF-kB signals and PA deposition in DSCs. Besides, under an excess PA environment, GLN oxidation was significantly enhanced in DSCs and the suppression of GLN oxidation further augmented PA-mediated DSC apoptosis and inflammatory responses. In conclusion, excess PA induces apoptosis and inflammation in DSCs via the TLR4/JNK/NF-kB pathways, which can be augmented by the suppression of GLN oxidation. decidual stromal cells, palmitate, glutamine oxidation, TLR4, apoptosis, inflammatory response Introduction Maternal lipid profile exhibits dynamics corresponding to gestational stages such that, at maternal–fetal interface, free fatty acids (FFAs) enrichment occurs during the first and second trimester of pregnancy to facilitate fetal transfer in the third trimester (Haggarty, 2010; Rani et al., 2016; Chavan-Gautam et al., 2018). Among all long-chain saturated fatty acids (LCSFAs), palmitate (PA; C16:0) is the most abundant dietary and plasma LCSFA, accounting for 27% of total plasma FFAs (Staiger et al., 2004; Fatima et al., 2019). It seems that the plasma PA levels cover a wide range among healthy people, making itcomplicated to define the standard of physiological concentrations of PA (Jensen et al., 1989; Fraser et al., 1999; El-Ansary et al., 2011; Cunnane et al., 2012; Trombetta et al., 2013). Mounting evidence has recognized the significant role of PA in plasma, adipose cells and trophoblasts during pregnancy, whereas PA metabolism disorders may be associated with numerous adverse pregnancy outcomes such as gestational diabetes (Stirm et al., 2018; Zhu et al., 2018), preterm birth (Catov et al., 2014), intrauterine growth restriction (Crume et al., 2015; Kishi et al., 2015) and preeclampsia (PE) (Rani et al., 2016). Nevertheless, the PA metabolic status and its biological functions in decidual stromal cells (DSCs), an important type of cells at maternal–fetal interface, remain obscure. During implantation and early pregnancy, the developing decidua undergoes dramatic changes in response to the invading embryo. DSCs are the primary cellular component of decidua, nurturing and supporting fetal intrauterine growth and placentation (Gellersen and Brosens, 2014). In addition to nutritive values, DSCs are believed to be involved in the implantation and gestation maintenance, by which creating a delicate balance between initiating innate immune reactions against external pathogens and tightly confining overactivation of inflammatory responses in case of the immune rejection of the semi-allogeneic conceptus (Arck and Hecher, 2013). Excess deposition of LCSFAs such as PA in our body has been reckoned as a potent apoptotic and inflammatory signal (Huang et al., 2012; Wang et al., 2017; He et al., 2018; Palomer et al., 2018), while several lines of evidence argue against its inflammatory features (Motton et al., 2007; Ono-Moore et al., 2016, 2018). Therefore, it is important to figure out the PA-induced metabolic reprogramming in DSCs since pregnancy can be a pro-inflammatory or anti-inflammatory condition depending on the gestational stages. Previous studies identify PA as a ligand of Toll-like receptor 4 (TLR4) (Jia et al., 2014; Caesar et al., 2015; Fatima et al., 2019). TLR4 is reported to be abundantly expressed in DSCs, and overshooting of the TLR4 signal may lead to undesirable stress responses in DSCs (Schatz et al., 2012; Wang et al., 2015; Lou et al., 2017). TLR4 belongs to innate pattern recognition receptors that can recognize pathogen-associated molecules and activate an innate immune response (Miller et al., 2005). Jun N-terminal kinase (JNK) and nuclear factor-kappa-gene binding (NF-kB) are common downstream transcriptional factors of TLR4 signaling and also the targets of PA (Win et al., 2015; Ji et al., 2018; Kochumon et al., 2018), and the overactivation of JNK/NF-kB pathways may result in DSC dysfunctions (Wang et al., 2015; Lou et al., 2017). However, the relationship and interaction between PA and TLR4 signaling in DSC hemostasis in early pregnancy are largely unknown. PA serves as a type of major energy sources for mitochondrial respiration, i.e. fatty acid oxidation (FAO), while the other two mitochondria energy metabolic pathways refer to glucose (GLC) oxidation and glutamine (GLN) oxidation. In all cells, fuel utilization displays high flexibility, accommodating fluctuating energy requirements (Mogilenko et al., 2019). For example, cells make special metabolic adaption in response to starvation, high-PA and other undesirable environmental stresses by shifting toward GLN metabolism (Chen et al., 2018; Miller et al., 2018; Egnatchik et al., 2019). Thus, this study also emphasizes a metabolic cross-talk between PA and other metabolites. Generally, PA engages in the following metabolic pathways: lipogenesis (formation of lipids) and lipolysis (breakdown of lipids), storing in lipid droplets (LDs) and extracellular transportation and FAO and fatty acid (FA) synthesis (Benador et al., 2019). Cellular lipid metabolism, to some degree, modulates phenotypes and biological functions of the cells. For instances, FAO and cell-intrinsic lysosomal lipolysis may exert influences on macrophage polarization (Huang et al., 2014; O’Neill et al., 2016; Van den Bossche et al., 2017). However, basal energy metabolism in DSCs is unclear, let alone under high-PA stress. Therefore, the aim of this study was to investigate the role and potential mechanisms of PA metabolism in the viability, apoptosis, cytokine secretion and energy metabolism of DSCs in early pregnancy. Materials and Methods Ethical approval The Human Research Ethics Committee of the Obstetrics and Gynecology Hospital of Fudan University has approved the collection and use of the samples. Written informed consent was obtained from all patients before sampling, and decidual tissues were taken solely for research purposes. Samples Human decidual tissues were obtained from clinically normal pregnancies, terminated for non-medical reasons, during the first trimester (gestational age, 6–12 weeks; n = 68). The gestational ages, pregnancy with ultrasound, test of hCG and progesterone levels from peripheral blood were checked. All the methods were carried out in accordance with the approved guidelines. Isolation, culture and treatment of DSCs DSCs were isolated by collagenase IV/DNase-I digestion and discontinuous Percoll gradient centrifugation as described previously (Wang et al., 2015; Lou et al., 2017). This method provides a 98% purity of DSCs. Vimentin was used as a DSC marker in the flow cytometric analysis. Freshly isolated DSCs were cultured overnight in a complete medium and further incubated in a serum-free medium for 12 h, followed by the treatment with sodium PA (SP; 0 μM, 100 μM, 200 μM, 400 μM; Kuanchuang Biotechnology, China) for 0 h, 4 h and 24 h. SP was solubilized in FA-free and low-endotoxin bovine serum albumin (BSA) (1 mM SP/2% BSA) (Kuanchuang Biotechnology). Equal volume of 2% BSA was added in control. Then, some wells were added with inhibitors or an equal volume dimethylsulphoxide (DMSO) for 24 h, including 10-μM TLR4 inhibitor (TLR4i, TAK-242, HY-1109), 2-μM JNK inhibitor (JNKi, CC-930, CNS17363), 2.5-μM NF-kB inhibitor (NF-kBi, BAY 11-7082, HY-13454) and 10-μM glutaminase inhibitor (GLSi, (Bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl)ethyl sulfide (BPTES), HY-12683). These inhibitors were purchased from MedChem Express (MCE, USA or CSNpharm, USA) and stored in DMSO. Lentiviral transfection The coding region sequence of human TLR4 (NM_138554) was cloned into the GV358 vector to produce a recombinant TLR4 construct (Genechem, China). A total of 1.0 × 106 cells were seeded on 6-well plates and transfected with lentiviral particles according to the manufacturer’s instructions. Transfected cells were incubated at a multiplicity of infection for 5 days. The transfection efficiency was evaluated by RT-PCR analyses 5 days after lentiviral injection. Table I Primers information. Gene . ID . Forward primer . Reverse primer . ACTB 60 GCCGACAGGATGCAGAAGGAGATCA AAGCATTTGCGGTGGACGATGGA GLS1 2744 TTCCAGAAGGCACAGACATGGTTG GCCAGTGTCGCAGCCATCAC EIF2AK2 5610 TTGGCTCAGGTGGATTTGGC TACTTCACGCTCCGCCTTCT HTRA2 27 429 CATCGCAGATGTGGTGGAGAAGAC CGTGTCGCCGCTTAGCAGTC SMAC 56 616 GCCTCTATAACCGCCAGGAATCAC GGCCTCCTGCTCCGACTCAG FAS 355 TGGACCCTCCTACCTCTGGT CACCTGGAGGACAGGGCTTA CIAP1 329 GGCCATCTAGTGTTCCAGTTCAGC ACACCTCAAGCCACCATCACAAC XIAP 331 AGGTATTGGTGACCAAGTGCAGTG CTGACCAGGCACGATCACAAGG HO2 3163 GAGGAGCGAGAGCAGCAAGAAC CGGTCGTGTGCTTCCTTGGTC HSP70 3308 TGAAGAGCAACAGCAGCAGACAC GATTCTCGATTGGCAGGTCCACAG FOXO1 2308 TGTCCTACGCCGACCTCATCAC GCACGCTCTTGACCATCCACTC FOXO3 2309 TGGCAAGCACAGAGTTGGATGAAG CATATCAGTCAGCCGTGGCAGTTC AKT1 207 GCAGGATGTGGACCAACGTGAG GCAGGCAGCGGATGATGAAGG AKT2 208 GGTCGCCAACAGCCTCAAGC ACCGCCACTTCCATCTCCTCAG AKT3 10 000 ATCACAGATGCAGCCACCATGAAG ACCAGTCTACTGCTCGGCCATAG ERK1 5594 ACCAGACCTACTGCCAGAGAACC TGGTCATTGCTGAGGTGTTGTGTC ERK2 5595 CATTGTGCAGGACCTGATGGAGAC GTTGGCGGAGTGGATGTACTTGAG JNK 5599 TGAGCAGAAGCAAGCGTGACAAC TGGTCGGCTTAGCTTCTTGATTGC JUN 3725 CCCATCGACATGGAGTCCCA TTTTTCGGCACTTGGAGGCA BAD 572 TGAGCCGAGTGAGCAGGAAGAC GATGGCTGCTGCTGGTTGGC BAK 578 GGACGACATCAACCGACGCTATG AACAGGCTGGTGGCAATCTTGG BAX 581 GATGCGTCCACCAAGAAGCTGAG CACGGCGGCAATCATCCTCTG BID 637 GTCAACAACGGTTCCAGCCTCAG GCTGCGGTTGCCATCAGTCTG BCL2 596 GACTTCGCCGAGATGTCCAG GAACTCAAAGAAGGCCACAATC BCLXL 598 GCAGGTATTGGTGAGTCGGATCG AGCCGCCGTTCTCCTGGATC P65 5970 AGGCTCCTGTGCGTGTCTCC TCGTCTGTATCTGGCAGGTACTGG IΚBΑ 4792 TCCACTCCATCCTGAAGGCTACC AGGTCCACTGCGAGGTGAAGG ILB 3553 GCGGCATCCAGCTACGAATCTC AACCAGCATCTTCCTCAGCTTGTC IL10 3586 GCCAAGCCTTGTCTGAGATGATCC GCTCCACGGCCTTGCTCTTG TLR4 7099 GAGGCAGCTCTTGGTGGAAGTTG CAAGCACACTGAGGACCGACAC CASP3 836 TGGAAGCGAATCAATGGACTCTGG CCAGACCGAGATGTCATTCCAGTG CASP8 841 GACTTTCTGCTGGGGATGGC ATCGCTCTCTCAGGCTCTGG CASP9 842 CTGCTGCGTGGTGGTCATTCTC CACAATCTTCTCGACCGACACAGG KI67 4288 GCCAGCCAGCAAGAAGCAGAG TCAGCTCTTCCGCAGGTTCAATTC PCNA 5111 TAATTTCCTGTGCAAAAGACGG AAGAAGTTCAGGTACCTCAGTG Gene . ID . Forward primer . Reverse primer . ACTB 60 GCCGACAGGATGCAGAAGGAGATCA AAGCATTTGCGGTGGACGATGGA GLS1 2744 TTCCAGAAGGCACAGACATGGTTG GCCAGTGTCGCAGCCATCAC EIF2AK2 5610 TTGGCTCAGGTGGATTTGGC TACTTCACGCTCCGCCTTCT HTRA2 27 429 CATCGCAGATGTGGTGGAGAAGAC CGTGTCGCCGCTTAGCAGTC SMAC 56 616 GCCTCTATAACCGCCAGGAATCAC GGCCTCCTGCTCCGACTCAG FAS 355 TGGACCCTCCTACCTCTGGT CACCTGGAGGACAGGGCTTA CIAP1 329 GGCCATCTAGTGTTCCAGTTCAGC ACACCTCAAGCCACCATCACAAC XIAP 331 AGGTATTGGTGACCAAGTGCAGTG CTGACCAGGCACGATCACAAGG HO2 3163 GAGGAGCGAGAGCAGCAAGAAC CGGTCGTGTGCTTCCTTGGTC HSP70 3308 TGAAGAGCAACAGCAGCAGACAC GATTCTCGATTGGCAGGTCCACAG FOXO1 2308 TGTCCTACGCCGACCTCATCAC GCACGCTCTTGACCATCCACTC FOXO3 2309 TGGCAAGCACAGAGTTGGATGAAG CATATCAGTCAGCCGTGGCAGTTC AKT1 207 GCAGGATGTGGACCAACGTGAG GCAGGCAGCGGATGATGAAGG AKT2 208 GGTCGCCAACAGCCTCAAGC ACCGCCACTTCCATCTCCTCAG AKT3 10 000 ATCACAGATGCAGCCACCATGAAG ACCAGTCTACTGCTCGGCCATAG ERK1 5594 ACCAGACCTACTGCCAGAGAACC TGGTCATTGCTGAGGTGTTGTGTC ERK2 5595 CATTGTGCAGGACCTGATGGAGAC GTTGGCGGAGTGGATGTACTTGAG JNK 5599 TGAGCAGAAGCAAGCGTGACAAC TGGTCGGCTTAGCTTCTTGATTGC JUN 3725 CCCATCGACATGGAGTCCCA TTTTTCGGCACTTGGAGGCA BAD 572 TGAGCCGAGTGAGCAGGAAGAC GATGGCTGCTGCTGGTTGGC BAK 578 GGACGACATCAACCGACGCTATG AACAGGCTGGTGGCAATCTTGG BAX 581 GATGCGTCCACCAAGAAGCTGAG CACGGCGGCAATCATCCTCTG BID 637 GTCAACAACGGTTCCAGCCTCAG GCTGCGGTTGCCATCAGTCTG BCL2 596 GACTTCGCCGAGATGTCCAG GAACTCAAAGAAGGCCACAATC BCLXL 598 GCAGGTATTGGTGAGTCGGATCG AGCCGCCGTTCTCCTGGATC P65 5970 AGGCTCCTGTGCGTGTCTCC TCGTCTGTATCTGGCAGGTACTGG IΚBΑ 4792 TCCACTCCATCCTGAAGGCTACC AGGTCCACTGCGAGGTGAAGG ILB 3553 GCGGCATCCAGCTACGAATCTC AACCAGCATCTTCCTCAGCTTGTC IL10 3586 GCCAAGCCTTGTCTGAGATGATCC GCTCCACGGCCTTGCTCTTG TLR4 7099 GAGGCAGCTCTTGGTGGAAGTTG CAAGCACACTGAGGACCGACAC CASP3 836 TGGAAGCGAATCAATGGACTCTGG CCAGACCGAGATGTCATTCCAGTG CASP8 841 GACTTTCTGCTGGGGATGGC ATCGCTCTCTCAGGCTCTGG CASP9 842 CTGCTGCGTGGTGGTCATTCTC CACAATCTTCTCGACCGACACAGG KI67 4288 GCCAGCCAGCAAGAAGCAGAG TCAGCTCTTCCGCAGGTTCAATTC PCNA 5111 TAATTTCCTGTGCAAAAGACGG AAGAAGTTCAGGTACCTCAGTG Abbreviations ACTB means beta-actin; GLS1, glutaminase 1; EIF2AK2, eukaryotic translation initiation factor 2 alpha kinase 2; HTRA2, HtrA serine peptidase 2; SMAC, diablo IAP-binding mitochondrial protein; FAS, Fas cell surface death receptor; CIAP1, baculoviral IAP repeat containing 2; XIAP, X-linked inhibitor of apoptosis; HO2, heme oxygenase 2; HSP70, heat shock protein family A; FOXO1, forkhead box O1; FOXO3, forkhead box O3; AKT1, AKT serine/threonine kinase 1; AKT2, AKT serine/threonine kinase 2; AKT3, AKT serine/threonine kinase 3; ERK1, mitogen-activated protein kinase 3; ERK2, mitogen-activated protein kinase 1; JNK, mitogen-activated protein kinase 8; JUN, JUN proto-oncogene; BCL2, BCL2 apoptosis regulator; BCL-XL, BCL2 like 1; BAX, BCL2 associated X; BAD, BCL2 associated agonist of cell death; BID, BH3 interacting domain death agonist; BAK, BCL2 killer 1; p65, RELA proto-oncogene; IKBA, NFKB inhibitor alpha; IL1B, interleukin 1 beta; IL10, interleukin 10; TLR4, Toll-like receptor 4 (TLR4); CASP3, caspase 3; CASP8, caspase 8; CASP9, caspase 9; KI67, marker of proliferation Ki-67; PCNA, proliferating cell nuclear antigen. Open in new tab Table I Primers information. Gene . ID . Forward primer . Reverse primer . ACTB 60 GCCGACAGGATGCAGAAGGAGATCA AAGCATTTGCGGTGGACGATGGA GLS1 2744 TTCCAGAAGGCACAGACATGGTTG GCCAGTGTCGCAGCCATCAC EIF2AK2 5610 TTGGCTCAGGTGGATTTGGC TACTTCACGCTCCGCCTTCT HTRA2 27 429 CATCGCAGATGTGGTGGAGAAGAC CGTGTCGCCGCTTAGCAGTC SMAC 56 616 GCCTCTATAACCGCCAGGAATCAC GGCCTCCTGCTCCGACTCAG FAS 355 TGGACCCTCCTACCTCTGGT CACCTGGAGGACAGGGCTTA CIAP1 329 GGCCATCTAGTGTTCCAGTTCAGC ACACCTCAAGCCACCATCACAAC XIAP 331 AGGTATTGGTGACCAAGTGCAGTG CTGACCAGGCACGATCACAAGG HO2 3163 GAGGAGCGAGAGCAGCAAGAAC CGGTCGTGTGCTTCCTTGGTC HSP70 3308 TGAAGAGCAACAGCAGCAGACAC GATTCTCGATTGGCAGGTCCACAG FOXO1 2308 TGTCCTACGCCGACCTCATCAC GCACGCTCTTGACCATCCACTC FOXO3 2309 TGGCAAGCACAGAGTTGGATGAAG CATATCAGTCAGCCGTGGCAGTTC AKT1 207 GCAGGATGTGGACCAACGTGAG GCAGGCAGCGGATGATGAAGG AKT2 208 GGTCGCCAACAGCCTCAAGC ACCGCCACTTCCATCTCCTCAG AKT3 10 000 ATCACAGATGCAGCCACCATGAAG ACCAGTCTACTGCTCGGCCATAG ERK1 5594 ACCAGACCTACTGCCAGAGAACC TGGTCATTGCTGAGGTGTTGTGTC ERK2 5595 CATTGTGCAGGACCTGATGGAGAC GTTGGCGGAGTGGATGTACTTGAG JNK 5599 TGAGCAGAAGCAAGCGTGACAAC TGGTCGGCTTAGCTTCTTGATTGC JUN 3725 CCCATCGACATGGAGTCCCA TTTTTCGGCACTTGGAGGCA BAD 572 TGAGCCGAGTGAGCAGGAAGAC GATGGCTGCTGCTGGTTGGC BAK 578 GGACGACATCAACCGACGCTATG AACAGGCTGGTGGCAATCTTGG BAX 581 GATGCGTCCACCAAGAAGCTGAG CACGGCGGCAATCATCCTCTG BID 637 GTCAACAACGGTTCCAGCCTCAG GCTGCGGTTGCCATCAGTCTG BCL2 596 GACTTCGCCGAGATGTCCAG GAACTCAAAGAAGGCCACAATC BCLXL 598 GCAGGTATTGGTGAGTCGGATCG AGCCGCCGTTCTCCTGGATC P65 5970 AGGCTCCTGTGCGTGTCTCC TCGTCTGTATCTGGCAGGTACTGG IΚBΑ 4792 TCCACTCCATCCTGAAGGCTACC AGGTCCACTGCGAGGTGAAGG ILB 3553 GCGGCATCCAGCTACGAATCTC AACCAGCATCTTCCTCAGCTTGTC IL10 3586 GCCAAGCCTTGTCTGAGATGATCC GCTCCACGGCCTTGCTCTTG TLR4 7099 GAGGCAGCTCTTGGTGGAAGTTG CAAGCACACTGAGGACCGACAC CASP3 836 TGGAAGCGAATCAATGGACTCTGG CCAGACCGAGATGTCATTCCAGTG CASP8 841 GACTTTCTGCTGGGGATGGC ATCGCTCTCTCAGGCTCTGG CASP9 842 CTGCTGCGTGGTGGTCATTCTC CACAATCTTCTCGACCGACACAGG KI67 4288 GCCAGCCAGCAAGAAGCAGAG TCAGCTCTTCCGCAGGTTCAATTC PCNA 5111 TAATTTCCTGTGCAAAAGACGG AAGAAGTTCAGGTACCTCAGTG Gene . ID . Forward primer . Reverse primer . ACTB 60 GCCGACAGGATGCAGAAGGAGATCA AAGCATTTGCGGTGGACGATGGA GLS1 2744 TTCCAGAAGGCACAGACATGGTTG GCCAGTGTCGCAGCCATCAC EIF2AK2 5610 TTGGCTCAGGTGGATTTGGC TACTTCACGCTCCGCCTTCT HTRA2 27 429 CATCGCAGATGTGGTGGAGAAGAC CGTGTCGCCGCTTAGCAGTC SMAC 56 616 GCCTCTATAACCGCCAGGAATCAC GGCCTCCTGCTCCGACTCAG FAS 355 TGGACCCTCCTACCTCTGGT CACCTGGAGGACAGGGCTTA CIAP1 329 GGCCATCTAGTGTTCCAGTTCAGC ACACCTCAAGCCACCATCACAAC XIAP 331 AGGTATTGGTGACCAAGTGCAGTG CTGACCAGGCACGATCACAAGG HO2 3163 GAGGAGCGAGAGCAGCAAGAAC CGGTCGTGTGCTTCCTTGGTC HSP70 3308 TGAAGAGCAACAGCAGCAGACAC GATTCTCGATTGGCAGGTCCACAG FOXO1 2308 TGTCCTACGCCGACCTCATCAC GCACGCTCTTGACCATCCACTC FOXO3 2309 TGGCAAGCACAGAGTTGGATGAAG CATATCAGTCAGCCGTGGCAGTTC AKT1 207 GCAGGATGTGGACCAACGTGAG GCAGGCAGCGGATGATGAAGG AKT2 208 GGTCGCCAACAGCCTCAAGC ACCGCCACTTCCATCTCCTCAG AKT3 10 000 ATCACAGATGCAGCCACCATGAAG ACCAGTCTACTGCTCGGCCATAG ERK1 5594 ACCAGACCTACTGCCAGAGAACC TGGTCATTGCTGAGGTGTTGTGTC ERK2 5595 CATTGTGCAGGACCTGATGGAGAC GTTGGCGGAGTGGATGTACTTGAG JNK 5599 TGAGCAGAAGCAAGCGTGACAAC TGGTCGGCTTAGCTTCTTGATTGC JUN 3725 CCCATCGACATGGAGTCCCA TTTTTCGGCACTTGGAGGCA BAD 572 TGAGCCGAGTGAGCAGGAAGAC GATGGCTGCTGCTGGTTGGC BAK 578 GGACGACATCAACCGACGCTATG AACAGGCTGGTGGCAATCTTGG BAX 581 GATGCGTCCACCAAGAAGCTGAG CACGGCGGCAATCATCCTCTG BID 637 GTCAACAACGGTTCCAGCCTCAG GCTGCGGTTGCCATCAGTCTG BCL2 596 GACTTCGCCGAGATGTCCAG GAACTCAAAGAAGGCCACAATC BCLXL 598 GCAGGTATTGGTGAGTCGGATCG AGCCGCCGTTCTCCTGGATC P65 5970 AGGCTCCTGTGCGTGTCTCC TCGTCTGTATCTGGCAGGTACTGG IΚBΑ 4792 TCCACTCCATCCTGAAGGCTACC AGGTCCACTGCGAGGTGAAGG ILB 3553 GCGGCATCCAGCTACGAATCTC AACCAGCATCTTCCTCAGCTTGTC IL10 3586 GCCAAGCCTTGTCTGAGATGATCC GCTCCACGGCCTTGCTCTTG TLR4 7099 GAGGCAGCTCTTGGTGGAAGTTG CAAGCACACTGAGGACCGACAC CASP3 836 TGGAAGCGAATCAATGGACTCTGG CCAGACCGAGATGTCATTCCAGTG CASP8 841 GACTTTCTGCTGGGGATGGC ATCGCTCTCTCAGGCTCTGG CASP9 842 CTGCTGCGTGGTGGTCATTCTC CACAATCTTCTCGACCGACACAGG KI67 4288 GCCAGCCAGCAAGAAGCAGAG TCAGCTCTTCCGCAGGTTCAATTC PCNA 5111 TAATTTCCTGTGCAAAAGACGG AAGAAGTTCAGGTACCTCAGTG Abbreviations ACTB means beta-actin; GLS1, glutaminase 1; EIF2AK2, eukaryotic translation initiation factor 2 alpha kinase 2; HTRA2, HtrA serine peptidase 2; SMAC, diablo IAP-binding mitochondrial protein; FAS, Fas cell surface death receptor; CIAP1, baculoviral IAP repeat containing 2; XIAP, X-linked inhibitor of apoptosis; HO2, heme oxygenase 2; HSP70, heat shock protein family A; FOXO1, forkhead box O1; FOXO3, forkhead box O3; AKT1, AKT serine/threonine kinase 1; AKT2, AKT serine/threonine kinase 2; AKT3, AKT serine/threonine kinase 3; ERK1, mitogen-activated protein kinase 3; ERK2, mitogen-activated protein kinase 1; JNK, mitogen-activated protein kinase 8; JUN, JUN proto-oncogene; BCL2, BCL2 apoptosis regulator; BCL-XL, BCL2 like 1; BAX, BCL2 associated X; BAD, BCL2 associated agonist of cell death; BID, BH3 interacting domain death agonist; BAK, BCL2 killer 1; p65, RELA proto-oncogene; IKBA, NFKB inhibitor alpha; IL1B, interleukin 1 beta; IL10, interleukin 10; TLR4, Toll-like receptor 4 (TLR4); CASP3, caspase 3; CASP8, caspase 8; CASP9, caspase 9; KI67, marker of proliferation Ki-67; PCNA, proliferating cell nuclear antigen. Open in new tab Quantitative real-time polymerase chain reaction Total RNA in DSCs was extracted (Trizol, Invitrogen, USA) and reverse transcribed with PrimeScript™RT Master Mix (RR036A, TaKaRa, Japan). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed using the TB Green™Premix Ex Taq™II (RR820A, TaKaRa). Briefly, RT-PCR was conducted in a 10-μL reaction system including 0.8-μL primers (10 μL), 5-μL TB GreenPremix Ex TaqII (Tli RNaseH Plus) (2×), 0.2-μL ROX Reference Dye II (50×), 3-μL distilled water and 2-μL cDNA sample (<100 ng). These measurements were performed in triplicate using an ABI Prism™ 7000 Sequence Detector (Applied Biosystems, Carlsbad, CA, USA). The primers (Shenggong Corp., Shanghai, China) are listed in Table I. Cell vitality assay Cell vitality was determined with the Cell Counting Kit-8 (CCK-8) assay (Dojindo, Japan). The isolated DSCs were cultured in a 96-well plate (1 × 106/well) and exposed to PA, alone or together with TLR4i, JNKi, NF-kBi and GLSi. Thereafter, the cells were treated with 10 μL/well CCK-8 solution. Cells were then cultured for 1 h at 37°C before measurement of absorbance at 490 nm with a microplate reader (Bio-Rad, USA). Hoechst staining The isolated DSCs were cultured in a 96-well plate (2 × 104/well) and exposed to PA, alone or together with TLR4i, JNKi, NF-kBi and GLSi. Thereafter, the culture media were removed and washed with PBS. The cells were fixed with 4% paraformaldehyde and stained with a Hoechst Staining Kit (Beyotime, Beijing, China), observed under an Olympus BX51 fluorescence microscope (Tokyo, Japan) and recorded with a high-resolution DP70 Olympus digital camera. The percentage of apoptosis was determined by nuclear morphology using the software ImageJ Version 1.52n. At least 500 cells were counted in each group with the counter ‘blinded’ to sample identity in case of experimental bias. Oil red O staining The isolated DSCs were harvested in a 96-well plate (2 × 104/well). Then, they were washed with PBS twice and fixed in 4% polyoxymethylene for 30 min at room temperature. Oil red O (0.5 g; Amresco, Solon, OH, USA) was dissolved in 60% isopropanol diluted in water at 37°C and filtered. The fixed cells were rinsed with 60% isopropanol, following by being stained with the filtered Oil red O solution for 20 min at room temperature. The stained cells were observed using the Olympus BX51 fluorescence microscope and photographed with the high-resolution DP70 Olympus digital camera. Finally, 50-uL 100% isopropanol was added into each well and the optical density values (570 nm) were recorded. Each experiment was carried out in triplicate. Flow cytometry Expression of cell surface molecules and intracellular cytokine was evaluated using flow cytometry. A minimum of 10 000 events was acquired using a Beckman Coulter (Brea, CA, USA) CyAn ADP flow cytometer and then analyzed with FlowJo software (Tree Star, Ashland, OR, USA). The following antigens (Biolegend, USA) were used in the experiments: PE anti-human Vimentin, APC anti-human IL-1B, BV421 anti-human IL-10 and isotype antibodies. Cell fluorescence was measured using a Beckman Coulter CyAN ADP flow cytometer and analyzed with FlowJo software. Enzyme-linked immunosorbent assay DSCs (1 × 106/well) were grown in 6-well plates in the presence of PA (0 μM, 100 μM, 200 μM, 400 μM). Thereafter, the levels of secreted IL-1B and IL-10 in the supernatant from each experiment were quantified by using the commercially available ELISA kit (R&D System, USA) following the manufacturer’s instructions. Mitochondrial fuel usage assay The mitochondrial fuel usage assay was conducted following the instructions of Agilent Seahorse XF Mito Fuel Flex Test Kit (Agilent Technologies, USA). The kit contains three pathway inhibitors: GLC oxidation (UK5099), GLN oxidation (BPTES) and FAO (etomoxir). The freshly isolated DSCs were cultured in Agilent Seahorse XF96 Cell Culture Microplate (1 × 105/well) overnight and treated with 400-μM PA for 0 h, 4 h or 24 h. Then, Agilent Seahorse XFe/XF Analyzer (XF96, Agilent Technologies) measured the mitochondrial respiration [the oxygen consumption rate, (OCR)] of the treated DSCs in the presence or absence of fuel pathway inhibitors. Sequentially inhibiting the pathway of interest followed by the two alternative pathways enables the calculation of cells’ reliance on a particular fuel pathway to maintain baseline respiration with the formula $$\begin{equation*} \mathrm{Dependency}\ \%=\frac{\mathrm{baseline}\ \mathrm{OCR}-\mathrm{target}\ \mathrm{inhibitor}\ \mathrm{OCR}}{\mathrm{baseline}\ \mathrm{OCR}-\mathrm{all}\ \mathrm{inhibitors}\ \mathrm{OCR}}\times 100\,\%. \end{equation*}$$ Inhibiting the two alternative pathways followed by the pathway of interest allows the calculation of the maximal capacity of fuel usage with the formula $$\begin{equation*} \mathrm{Capacity}\, \%=\!\left(1\!-\!\frac{\mathrm{baseline}\ \mathrm{OCR}-\mathrm{other}\ 2\ \mathrm{inhibitors}\ \mathrm{OCR}}{\mathrm{baseline}\ \mathrm{OCR}-\mathrm{all}\ \mathrm{inhibitors}\ \mathrm{OCR}}\!\right)\times 100\,\%. \end{equation*}$$ Fuel flexibility (Flexibility % = Dependency % − Capacity %) indicates the cells’ mitochondria have the ability of compensating for the inhibited pathway by using other pathways. Baseline respiration rate of oxygen consumption was recorded under initial assay conditions. Data and analysis For human islet samples (GSE53949, n = 5) and human gingival fibroblast samples (GSE62761, n = 2) with or without PA exposure, processed transcript abundance data were downloaded and used directly. For GSE118230 (n = 5), only data from human islets treated with or without 1- or 2-day PA administration were used: data from samples treated for 4 h, 12 h and 7 days were excluded to minimize culturing effects. For GSE53116 (n = 4), data from human myoblast cell line (LHCN-M2) treated with oleate were excluded to exclude irrelevant culturing effects. In case of risking a high false positive rate, differentially expressed genes (DEGs) were identified conservatively: the thresholds were set at |logFC| ≥ 2 (where FC is the fold change) and an adjusted P < 0.01. To further enrich the PA relative gene profile, EVEX database (http://www.evexdb.org/), a text mining resource built on top of PubMed, was implemented with the search term TLR4—the most common PA receptor—only downstream genes of high confidence were included (confidence score > 0.7). In addition, a recent review identified the potential PA target genes, which were also supplemented in subsequent analysis. An article recently published in CELL implicates PA metabolism in dendritic cells, of which microarray datasets are concealed until 2021 (Mogilenko et al., 2019). Nevertheless, the open DEGs in that article are extensively overlapped with the potential PA target genes summarized in this paper, further confirming the reliability of these data. Combining the above source, functions and pathways, enrichment analyses were carried out using Metascape (http://metascape.org; Tripathl et al., 2015), an automated meta-analysis tool based on the following databases: Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes Pathways. Significant terms were then hierarchically clustered into a tree based on Kappa similarities among their gene memberships, and Kappa > 0.3 was considered statistically significant. Among these, statistically significant terms regarding apoptosis and inflammation were sorted out for protein–protein interaction (PPI) enrichment analysis by String (https://string-db.org/). The parameter of PPI (combined score > 0.9.) was set as the cut-off criterion. The functions and pathways enrichment figure, gene hierarchically clustering tree and PPI network were visualized using the R (version 3.6.0) and cytoscape (version 3.6.0). Statistics Significance of differences between two groups was determined by Student’s t-test with or without Welch’s correction, or Mann–Whitney test. Multiple groups were analyzed by one-way ANOVA with Dunnett’s multiple comparisons test, Tukey’s multiple comparisons test or Dunn’s multiple comparisons test. Prism Version 7 software (GraphPad, San Diego, CA, USA) was used for all statistical tests with P < 0.05 considered statistically significant. Results Excess PA disturbs DSC homeostasis To explore the potential effects of PA on DSCs, we collected mRNA sequence data in Gene Expression Omnibus and EVEX database and 1395 PA-induced DEGs were identified (see in Materials andMethods. GO analysis on DEGs using Metascape indicated that PA may be involved in apoptosis and inflammatory response (Fig. 1A). Figure 1 Open in new tabDownload slide PA induces apoptosis and inflammatory responses in decidual stromal cells (DSCs). (A) Metascape enrichment network visualization shows the potential role of palmitate (PA) in various tissues. Significant terms were then hierarchically clustered into a tree based on Kappa similarities among their gene memberships, and Kappa > 0.3 was considered statistically significant. (B–E) DSCs were treated with PA (0 μM, 100 μM, 200 μM or 400 μM; 24 h) and PA 0 μM was set as control group: (B) number of DCSs (left) and Cell Counting Kit-8 (CCK8) analysis of the cell viability of DSCs (right) (n = 6); (C) quantitation of the DSC apoptosis based on Hoechst staining of nuclear morphology (scale bar, 200 μm; n = 6); (D) oil red O staining quantitation of the PA accumulation in DSCs (scale bar, 20 μm; n = 6); (E) flow cytometric analysis of the percentage of interleukin-1 beta (IL-1B; upper left, upper right) and interleukin-10 (IL-10; lower left, lower right) levels in Vimentin+ DSCs (n = 12). (F) The basal mitochondria respiration of DSCs was measured based on oxygen consumption rate (OCR) after 400-μM PA exposure at 0 h, 4 h and 24 h with PA 0 h set as control (n = 3). Data are presented as mean ± SEM. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001 by one-way ANOVA with Dunnett’s multiple comparisons test. Figure 1 Open in new tabDownload slide PA induces apoptosis and inflammatory responses in decidual stromal cells (DSCs). (A) Metascape enrichment network visualization shows the potential role of palmitate (PA) in various tissues. Significant terms were then hierarchically clustered into a tree based on Kappa similarities among their gene memberships, and Kappa > 0.3 was considered statistically significant. (B–E) DSCs were treated with PA (0 μM, 100 μM, 200 μM or 400 μM; 24 h) and PA 0 μM was set as control group: (B) number of DCSs (left) and Cell Counting Kit-8 (CCK8) analysis of the cell viability of DSCs (right) (n = 6); (C) quantitation of the DSC apoptosis based on Hoechst staining of nuclear morphology (scale bar, 200 μm; n = 6); (D) oil red O staining quantitation of the PA accumulation in DSCs (scale bar, 20 μm; n = 6); (E) flow cytometric analysis of the percentage of interleukin-1 beta (IL-1B; upper left, upper right) and interleukin-10 (IL-10; lower left, lower right) levels in Vimentin+ DSCs (n = 12). (F) The basal mitochondria respiration of DSCs was measured based on oxygen consumption rate (OCR) after 400-μM PA exposure at 0 h, 4 h and 24 h with PA 0 h set as control (n = 3). Data are presented as mean ± SEM. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001 by one-way ANOVA with Dunnett’s multiple comparisons test. A tightly controlled apoptosis and an inflammatory response in DSCs are two critical biological processes during implantation and early pregnancy (Arck and Hecher, 2013; Gellersen and Brosens, 2014). Consistent with the GO results, PA inhibited the number and viability of DSCs and induced cell apoptosis in a dose-dependent manner (Fig. 1B and C), which was accompanied by intracellular PA accumulation (Fig. 1D). Additionally, PA promoted the pro-inflammatory cytokine IL-1B production, while decreasing the production of anti-inflammatory cytokine IL-10 in DSCs in a dose-dependent manner (Fig. 1E and Supplementary Fig. S1A). All these effects were significant when DSCs were treated with PA at the high concentration of 400 μM; therefore, this concentration was used in the follow-up experiments. Interestingly, we observed that PA significantly enhanced the mitochondrial aerobic respiration in DSCs (Fig. 1F). Thus, excess PA might disrupt DSC homeostasis, alter the energy metabolism and form an inflammatory microenvironment at the maternal–fetal interface. Excess PA induces apoptosis and inflammation in DSCs via interacting with TLR4 TLR4, abundantly expressed in DSCs, is identified as PA receptor (Jia et al., 2014; Caesar et al., 2015; Fatima et al., 2019), and overshooting of the TLR4 signal may lead to undesirable stress reactions in DSCs (Schatz et al., 2012; Wang et al., 2015; Lou et al., 2017). Indeed, PA upregulated the transcriptional levels of TLR4 (Fig. 2A). Meanwhile, TLR4i minimized PA intracellular accumulation (Fig. 2B), indicating that there is a positive feedback regulation of PA enrichment and TLR4 signaling in DSCs. Thus, we hypothesized that TLR4 might be involved in regulating PA-induced DSC apoptosis and inflammatory responses. To test this hypothesis, we first treated DSCs with PA, in the presence or absence of TLR4 inhibitor (TLR4i, TAK-242) and observed that blockage of the TLR4 signaling pathway abolished the PA-triggered apoptosis and inflammatory responses in DSCs (Fig. 2C and D). Figure 2 Open in new tabDownload slide Excess PA induces DSC dysfunctions via interacting with Toll-like receptor 4 (TLR4). (A–D) DSCs were treated with 400-μM PA for 24 h, alone or together with 10-μM TLR4 inhibitor: (A) real-time PCR analysis of TLR4 mRNA in DSCs, compared with reference gene beta-actin (ACTB) (n = 9); (B) oil red O staining quantitation of the PA accumulation, expressed relative to control (ctrl) in DSCs (n = 6); (C) CCK8 analysis of the cell viability of DSCs (left, n = 6) and quantitation of DSC apoptosis based on Hoechst staining of nuclear morphology (right, n = 6); (D) flow cytometric analysis of the production of IL-1B (left) and IL-10 (right) by DSCs (n = 9). (E–F) DSCs were transfected with pGV358-TLR4 for 5 days, followed by treatment with 400-μM PA for 24 h: (E) CCK8 analysis of the cell viability of DSCs (left, n = 9), and quantitation of DSC apoptosis based on Hoechst staining of nuclear morphology (right, n = 9); (F) real-time PCR analysis of IL1B (left) and IL10 (right) mRNA in DSCs (n = 12). Data are presented as mean ± SEM (B–E, IL10 in F) or median ± 95%CI (IL1B in F). *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001 and ns, non-significant by Student’s t-test with Welch’s correction (A), one-way ANOVA with Tukey’s multiple comparisons test (B-E, IL10 in F) or Dunn’s multiple comparisons test (IL1B in F). Figure 2 Open in new tabDownload slide Excess PA induces DSC dysfunctions via interacting with Toll-like receptor 4 (TLR4). (A–D) DSCs were treated with 400-μM PA for 24 h, alone or together with 10-μM TLR4 inhibitor: (A) real-time PCR analysis of TLR4 mRNA in DSCs, compared with reference gene beta-actin (ACTB) (n = 9); (B) oil red O staining quantitation of the PA accumulation, expressed relative to control (ctrl) in DSCs (n = 6); (C) CCK8 analysis of the cell viability of DSCs (left, n = 6) and quantitation of DSC apoptosis based on Hoechst staining of nuclear morphology (right, n = 6); (D) flow cytometric analysis of the production of IL-1B (left) and IL-10 (right) by DSCs (n = 9). (E–F) DSCs were transfected with pGV358-TLR4 for 5 days, followed by treatment with 400-μM PA for 24 h: (E) CCK8 analysis of the cell viability of DSCs (left, n = 9), and quantitation of DSC apoptosis based on Hoechst staining of nuclear morphology (right, n = 9); (F) real-time PCR analysis of IL1B (left) and IL10 (right) mRNA in DSCs (n = 12). Data are presented as mean ± SEM (B–E, IL10 in F) or median ± 95%CI (IL1B in F). *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001 and ns, non-significant by Student’s t-test with Welch’s correction (A), one-way ANOVA with Tukey’s multiple comparisons test (B-E, IL10 in F) or Dunn’s multiple comparisons test (IL1B in F). Furthermore, we attempted to determine whether the upregulation of TLR4 aggravated such excess PA-induced DSC dysfunctions. DSCs were transfected with lentiviral pGV358-TLR4 for 5 days. The TLR4 expression was measured by RT-PCR, which showed an ~8.2-fold increase in DSC TLR4 expression compared with the control (Supplementary Fig. S1B). Afterwards, the DSCs were treated with PA for 24 h. The overexpression of TLR4 in DSCs caused opposite effects in comparison to TLR4i, enhancing the PA-triggered apoptosis and inflammatory responses (Fig. 2E and F). Therefore, the PA–TLR4 interaction contributes to mediate the apoptosis and inflammatory responses in DSCs. Excess PA mediates DSC apoptosis and inflammatory responses by activation of JNK/NF-kB signaling pathways To further explore the underlying mechanisms, genes regarding apoptosis and inflammation were selected from the GO enrichment results for PPI analysis. The data implied some potential downstream molecules (Fig. 3A), which were verified by RT-PCR (Fig. 3B–D and Supplementary Fig. S2A). Among them, most DEGs were enriched in JNK pathway (JNK, JUN, BID, BAD, BCL2 and BCL-XL) (Fig. 3B and C) and NF-kB pathway (p65, IKBA, IL1β and IL10) (Fig. 3B and D). Consistently, TLR4i reversed the transcriptional levels of PA-induced apoptosis and inflammation relative genes (e.g. JNK, p65 and IKBA) (Fig. 3E) while the overexpression of TLR4 enhanced these genes (e.g. JNK, JUN, p65 and IκBa) (Fig. 3F), indicating that JNK and NF-kB pathways are likely to be the targets of PA and TLR4 signals. Meanwhile, RT-PCR results excluded some possible pathways suggested by PPI analysis like ERK1/2, AKT, EIF2AK2, etc. (Supplementary Fig. S2A). Figure 3 Open in new tabDownload slide PA-TLR4 interaction activates Jun N-terminal kinase (JNK) and nuclear factor-kappa-gene binding (NF-kB) signaling pathways. (A) Protein–protein interaction (PPI) enrichment analysis shows the potential downstream molecules of PA (combined score > 0.9). (B) Heatmap shows real-time PCR analysis of differential expression genes (DEGs) in DSCs after 400-μM PA for 24 h and in controls (ctrl), colored according to their relative mRNA expression (n = 9): (C) and (D) show DEGs enriched in JNK pathway (C) and NF-kB pathway (D), mitogen-activated protein kinase 8 (JNK), JUN proto-oncogene (JUN), BCL2 apoptosis regulator (BCL2), BCL2 like 1 (BCL-XL), BCL2-associated X (BAX), BCL2-associated agonist of cell death (BAD), BH3-interacting domain death agonist (BID), BCL2 killer 1 (BAK), RELA proto-oncogene (p65), NFKB inhibitor alpha (IKBA). (E) DSCs were treated with 400-μM PA for 24 h, alone or together with 10-μM TLR4 inhibitor: real-time PCR analysis of JNK, p65 and IKBA mRNA in DSCs (n = 9). (F) DSCs were transfected with pGV358-TLR4 for 5 days, followed by treatment with 400-μM PA for 24 h: real-time PCR analysis of JNK, JUN, IKBA, p65, BCL2, BAD, BCL-XL and BID mRNA in DSCs (n = 12). Data are presented as mean ± SEM (C–E and JNK, JUN, IKBA, p65, BID and BAD in F) or median ± 95%CI (BCL2 and BCL-XL in F). *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, and ns, non-significant by Student’s t-test with Welch’s correction (C–D), one-way ANOVA with Tukey’s multiple comparisons test (E and JNK, JUN, IKBA, p65 and BID in F), Holm–Sidak’s multiple comparisons test (BAD in F) or Dunn’s multiple comparisons test (BCL2 and BCL-XL in F). Figure 3 Open in new tabDownload slide PA-TLR4 interaction activates Jun N-terminal kinase (JNK) and nuclear factor-kappa-gene binding (NF-kB) signaling pathways. (A) Protein–protein interaction (PPI) enrichment analysis shows the potential downstream molecules of PA (combined score > 0.9). (B) Heatmap shows real-time PCR analysis of differential expression genes (DEGs) in DSCs after 400-μM PA for 24 h and in controls (ctrl), colored according to their relative mRNA expression (n = 9): (C) and (D) show DEGs enriched in JNK pathway (C) and NF-kB pathway (D), mitogen-activated protein kinase 8 (JNK), JUN proto-oncogene (JUN), BCL2 apoptosis regulator (BCL2), BCL2 like 1 (BCL-XL), BCL2-associated X (BAX), BCL2-associated agonist of cell death (BAD), BH3-interacting domain death agonist (BID), BCL2 killer 1 (BAK), RELA proto-oncogene (p65), NFKB inhibitor alpha (IKBA). (E) DSCs were treated with 400-μM PA for 24 h, alone or together with 10-μM TLR4 inhibitor: real-time PCR analysis of JNK, p65 and IKBA mRNA in DSCs (n = 9). (F) DSCs were transfected with pGV358-TLR4 for 5 days, followed by treatment with 400-μM PA for 24 h: real-time PCR analysis of JNK, JUN, IKBA, p65, BCL2, BAD, BCL-XL and BID mRNA in DSCs (n = 12). Data are presented as mean ± SEM (C–E and JNK, JUN, IKBA, p65, BID and BAD in F) or median ± 95%CI (BCL2 and BCL-XL in F). *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, and ns, non-significant by Student’s t-test with Welch’s correction (C–D), one-way ANOVA with Tukey’s multiple comparisons test (E and JNK, JUN, IKBA, p65 and BID in F), Holm–Sidak’s multiple comparisons test (BAD in F) or Dunn’s multiple comparisons test (BCL2 and BCL-XL in F). Notably, JNKi aggravated the PA cellular accumulation, contrary to the negative effects of TLR4i and NF-kBi (Figs 2B and4A), indicating the complicated feedbacks among TLR4/JNK/NF-kB signals and PA depositions. Next, DSCs were treated with PA, alone or together with inhibitors of JNK (CC-930) or NF-kB (BAY 11–7082), respectively. The results showed that JNKi suppressed the PA-triggered apoptosis (Fig. 4B and C), whereas NF-kBi failed to do so (Supplementary Fig. S2B). From the perspective of inflammation, NF-kBi reversed the PA-induced elevated production of IL-1B as well as decreased expression of IL-10 (Fig. 4D and E). Therefore, an excess PA stress enables DSC apoptosis via the TLR4/JNK pathways rather than the NF-kB pathway and elicits an inflammatory program via the TLR4/NF-kB pathways. Figure 4 Open in new tabDownload slide PA-triggered DSC dysfunctions are dependent on the activation of JNK/NF-kB signaling pathways. (A–E) DSCs were treated with 400-μM PA for 24 h, alone or together with 2-μM JNK inhibitor (JNKi; CC-930) or 2.5 μM NF-kB inhibitor (NF-kBi; BAY 11–7082): (A) quantitation of PA accumulation in DSCs based on oil red O staining (n = 6); (B) CCK8 analysis of the DSC viability and DSC apoptosis (%) based on Hoechst staining of nuclear morphology (n = 6); (C) real-time PCR analysis of the downstream molecules of JNK in DSCs (n = 9); (D) flow cytometric analysis and quantitation of the secretion of IL-1B and IL-10 by DSCs (n = 9); (E) real-time PCR analysis of the downstream molecules of NF-kB in DSCs (n = 9). Data are presented as mean ± SEM (A, B, D; JUN and BCL-XL in C; and ILB in E) or median ± 95% CI (BAD and BCL2 in C and IL10 in E). *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, and ns, non-significant by one-way ANOVA with Tukey’s multiple comparisons test (A, B, D; JUN and BCL-XL in C; and IL1B in E) or Dunn’s multiple comparisons test (BAD and BCL2 in C and IL10 in E). Figure 4 Open in new tabDownload slide PA-triggered DSC dysfunctions are dependent on the activation of JNK/NF-kB signaling pathways. (A–E) DSCs were treated with 400-μM PA for 24 h, alone or together with 2-μM JNK inhibitor (JNKi; CC-930) or 2.5 μM NF-kB inhibitor (NF-kBi; BAY 11–7082): (A) quantitation of PA accumulation in DSCs based on oil red O staining (n = 6); (B) CCK8 analysis of the DSC viability and DSC apoptosis (%) based on Hoechst staining of nuclear morphology (n = 6); (C) real-time PCR analysis of the downstream molecules of JNK in DSCs (n = 9); (D) flow cytometric analysis and quantitation of the secretion of IL-1B and IL-10 by DSCs (n = 9); (E) real-time PCR analysis of the downstream molecules of NF-kB in DSCs (n = 9). Data are presented as mean ± SEM (A, B, D; JUN and BCL-XL in C; and ILB in E) or median ± 95% CI (BAD and BCL2 in C and IL10 in E). *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, and ns, non-significant by one-way ANOVA with Tukey’s multiple comparisons test (A, B, D; JUN and BCL-XL in C; and IL1B in E) or Dunn’s multiple comparisons test (BAD and BCL2 in C and IL10 in E). PA/TLR4 signaling pathway induces decidual apoptosis and inflammatory responses via GLN metabolism reprogramming The increase of basal respiration rate in the data of Fig. 1F suggests a role of PA in regulation of DSC aerobic respiration. Generally, the fuels of mitochondrial aerobic respiration contain GLC, FA and GLN (Fig. 5A). To clarify the energy metabolism reprogramming, we determined the rate of oxidation of each fuel by measuring the mitochondrial respiration (OCR) of DSCs after the PA administration for 0 h, 4 h and 24 h (Fig. 5B–E, and Supplementary Fig. S3). During that period, three fuel pathway inhibitors (UK5099-GLC, etomoxir-FA, BPTES-GLN) were added in different orders, and then dependency and capacity of the three pathways were calculated according to the formula. As shown, PA enhanced the dependency of GLN oxidation and GLC oxidation (Fig. 5B and E and Supplementary Fig. S3A) in DSCs while compromising the capability of FAO (Fig. 5D and Supplementary Fig. S3B). Among these, the increased GLN oxidation dependency and decreased FAO capacity were notable. Moreover, the ratio of dependency among three mitochondrial oxidation pathways altered from the combination of GLC pathway and FA pathway to all three pathways (Fig. 5F), suggesting the GLN metabolism reprogramming may be involved in the process of PA-mediated apoptosis and inflammation in DSCs. Figure 5 Open in new tabDownload slide PA enhances GLN oxidation in DSCs. (A) Schematic diagram of mitochondrial respiration fueled by glucose (GLC), fatty acid (FA) and glutamine (GLN). Three fuel pathway inhibitors are UK5099-GLC, etomoxir-FA and BPTES-GLN, respectively. (B–F) DSCs were treated with 400-μM PA for 0 h, 4 h and 24 h (n = 3): (B) assessment of mitochondrial GLN usage assay based on oxygen consumption rate (OCR). (C–E) show dependency (%), flexibility (%) and capacity (%) of GLN (C), FA (D) and GLC (E) in DSCs. (F) shows dependency (%) of GLN, FA and GLC. Data are presented as mean ± SEM (capacity (%) in B–D, dependency (%) in D–E) or median ± 95% CI (dependency (%) in B, capacity (%) in E). #or*P ≤ 0.05, ## or **P ≤ 0.01, ### or ***P ≤ 0.001 by one-way ANOVA with Dunnett’s multiple comparisons test (capacity% in C–D, dependency% in D–E) or Dunn’s multiple comparisons test (dependency% in C, capacity% in E).* and #signify the comparison of dependency% and capacity%, respectively. Figure 5 Open in new tabDownload slide PA enhances GLN oxidation in DSCs. (A) Schematic diagram of mitochondrial respiration fueled by glucose (GLC), fatty acid (FA) and glutamine (GLN). Three fuel pathway inhibitors are UK5099-GLC, etomoxir-FA and BPTES-GLN, respectively. (B–F) DSCs were treated with 400-μM PA for 0 h, 4 h and 24 h (n = 3): (B) assessment of mitochondrial GLN usage assay based on oxygen consumption rate (OCR). (C–E) show dependency (%), flexibility (%) and capacity (%) of GLN (C), FA (D) and GLC (E) in DSCs. (F) shows dependency (%) of GLN, FA and GLC. Data are presented as mean ± SEM (capacity (%) in B–D, dependency (%) in D–E) or median ± 95% CI (dependency (%) in B, capacity (%) in E). #or*P ≤ 0.05, ## or **P ≤ 0.01, ### or ***P ≤ 0.001 by one-way ANOVA with Dunnett’s multiple comparisons test (capacity% in C–D, dependency% in D–E) or Dunn’s multiple comparisons test (dependency% in C, capacity% in E).* and #signify the comparison of dependency% and capacity%, respectively. PA upregulated the transcriptional level of key enzyme, GLS1 of GLN oxidation, which could be downregulated by TLR4i (Fig. 6A), suggesting that TLR4 pathway should be involved in PA-mediated DSC GLN metabolism reprogramming. Inhibition of GLN oxidation via the GLS1 inhibitor (GLSi) had no effects on the PA deposition in DSCs (Fig. 6B). However, the GLSi amplified the PA-triggered apoptosis and inflammation in DSCs (Fig. 6C). PCR results further confirmed the protective role of GLN oxidation in DSC apoptosis and inflammation in response to an excess-PA signal (Fig. 6D). These data suggest that GLN metabolism reprogramming improved excess PA-induced decidual apoptosis and inflammatory responses. Figure 6 Open in new tabDownload slide Suppression of GLN oxidation exacerbates PA-triggered decidual dysfunctions. (A–D) DSCs were treated with 400-μM PA for 24 h, alone or together with 10-μM TLR4 inhibitor (TAK-242) or 10-μM glutaminase (GLS) inhibitor (BPTES): (A) real-time PCR analysis of GLS1 mRNA; (B) oil red O staining quantitation of PA accumulation in DSCs (n = 6); (C) CCK8 analysis of the cell viability of DSCs and quantitation of DSC apoptosis based on Hoechst staining of nuclear morphology (n = 6); flow cytometric analysis of the production of IL-1B and IL-10 by DSCs (n = 9); (D) TLR4 mRNA, JNK mRNA, JUN mRNA, BAD mRNA, BID mRNA, p65 mRNA, IKBA mRNA, IL1B and IL10 in DSCs (n = 9). Data are presented as mean ± SEM (A–C, JNK, JUN, BAD, IKBA and IL1B in D) or median ± 95%CI (TLR4, BID and p65 in D). *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, and ns, non-significant by one-way ANOVA with Tukey’s multiple comparisons test (A–C, F, JNK, JUN, BAD, IKBA and IL1B in D) or Dunn’s multiple comparisons test (TLR4, BID and p65 in D). Figure 6 Open in new tabDownload slide Suppression of GLN oxidation exacerbates PA-triggered decidual dysfunctions. (A–D) DSCs were treated with 400-μM PA for 24 h, alone or together with 10-μM TLR4 inhibitor (TAK-242) or 10-μM glutaminase (GLS) inhibitor (BPTES): (A) real-time PCR analysis of GLS1 mRNA; (B) oil red O staining quantitation of PA accumulation in DSCs (n = 6); (C) CCK8 analysis of the cell viability of DSCs and quantitation of DSC apoptosis based on Hoechst staining of nuclear morphology (n = 6); flow cytometric analysis of the production of IL-1B and IL-10 by DSCs (n = 9); (D) TLR4 mRNA, JNK mRNA, JUN mRNA, BAD mRNA, BID mRNA, p65 mRNA, IKBA mRNA, IL1B and IL10 in DSCs (n = 9). Data are presented as mean ± SEM (A–C, JNK, JUN, BAD, IKBA and IL1B in D) or median ± 95%CI (TLR4, BID and p65 in D). *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, and ns, non-significant by one-way ANOVA with Tukey’s multiple comparisons test (A–C, F, JNK, JUN, BAD, IKBA and IL1B in D) or Dunn’s multiple comparisons test (TLR4, BID and p65 in D). Figure 7 Open in new tabDownload slide Schematic diagram shows PA-triggering DSC dysfunction. PA promotes apoptosis and inflammatory responses (elevated IL-1B and decreased IL-10) in DSCs via the TLR4/JNK/NF-kB pathway. Meanwhile, PA enhances GLN oxidation in DSCs by increasing GLS expression, which in turn ameliorates PA-triggered DSC dysfunctions. Additionally, there is a complex cross-talk between the TLR4/JNK/NF-kB signals and PA accumulation in DSCs. Figure 7 Open in new tabDownload slide Schematic diagram shows PA-triggering DSC dysfunction. PA promotes apoptosis and inflammatory responses (elevated IL-1B and decreased IL-10) in DSCs via the TLR4/JNK/NF-kB pathway. Meanwhile, PA enhances GLN oxidation in DSCs by increasing GLS expression, which in turn ameliorates PA-triggered DSC dysfunctions. Additionally, there is a complex cross-talk between the TLR4/JNK/NF-kB signals and PA accumulation in DSCs. Discussion Throughout gestation, there is an increase in FFAs in blood and at the maternal–fetal interface, of which PA accounts for a large proportion (Haggarty, 2010; Rani et al., 2016; Chavan-Gautam et al., 2018). Gestational dyslipidaemia like excess PA has been associated with a number of adverse pregnancy outcomes (Barrett et al., 2014; Zhu et al., 2018). DSCs are the predominant cellular component of decidua, nurturing and supporting fetal intrauterine growth. Additionally, DSCs are essential for the gestation maintenance through creating a delicate balance between initiating inflammatory reactions against external pathogens and confining overactivation of inflammatory responses in avoidance of the immune rejection of the semi-allogeneic conceptus (Arck and Hecher, 2013). Nevertheless, studies with respect to PA metabolism in DSCs remain largely unknown. Therefore, we collected the transcriptional data regarding the roles of PA in various tissues, indicating that apoptosis and inflammatory responses of DSCs might be the targets of PA signals. It has been previously demonstrated that these two key biological processes in DSCs under strict control are the prerequisite of reproductive success (Vinketova et al., 2016). In accordance with bioinformatics and previous studies, we observed that PA induced DSC apoptosis in a dose-dependent manner, along with incremental intracellular accumulation of LDs. In addition, PA promoted pro-inflammatory cytokine IL-1B production while reducing the expression of anti-inflammatory cytokine IL-10 in DSCs. Notably, the minimal effective concentration of PA, whether in apoptotic experiments or in inflammatory reaction experiments, varies dramatically from person to person. Such individuality agrees with previous investigations that plasma PA levels cover a wide range in human beings (Fatima et al., 2019). The PA receptor, TLR4, belongs to innate pattern recognition receptors that can recognize pathogen-associated molecules and activate innate immune response (Miller et al., 2005). It has been reported that TLR4 is abundantly expressed in DSCs and overshooting of the TLR4 signal may lead to undesirable stress responses in DSCs (Wang et al., 2015; Lou et al., 2017). We observed that blockage of TLR4 signaling partially abolishes PA-mediated apoptosis and inflammatory response in DSCs. Meanwhile, TLR4i minimizes intracellular accumulation of LDs, suggesting a positive action of TLR4 signaling on intracellular accumulation of PA in DSCs. Thus, PA–TLR4 interaction enhances DSC apoptosis and the formation of a pro-inflammatory microenvironment at the maternal–fetal interface. However, the potential mechanism for the regulation between PA enrichment and TLR4 expression in DSCs needs further research. Consistent with previous reports (Wang et al., 2015; Lou et al., 2017), the PPI analysis confirmed by PCR results implies that both the JNK and NF-kB pathways are likely to participate in PA-triggering DSC apoptosis. However, this study shows that suppression of JNK pathway reverses the PA-inducing apoptosis while the inhibition of NF-kB pathways fails to do so. In terms of inflammatory response, pro-inflammatory cytokine IL-1B and anti-inflammatory IL-10 are known targets of NF-kB signaling upon TLR4 activation (Miller et al., 2005; Bradley et al., 2008), consistent with our data. Thus, the NF-kB signal plays critical roles in disrupting a predominantly anti-inflammatory microenvironment at the maternal–fetal interface. Deposition of surplus FAs as PA in nonadipose tissues (lipotoxicity) is toxic and impairs tissue functions by disrupting the mitochondrial membrane integrity and predisposing cells to apoptosis and inflammatory stress (Unger, 2003; Nguyen et al., 2017). Interestingly, JNKi facilitates PA accumulation in DSCs, indicating a negative feedback, contrary to the positive effects on PA enrichment by TLR4 and NF-kB pathways. It is the first time to link the activation of TLR4, JNK and NF-kB signaling pathways with PA intracellular accumulation. Meanwhile, PCR results exclude some other common apoptotic or inflammatory pathways in DSCs, such as the ERK1/ERK2 pathway, Foxo1 pathway, EIF2AK2 pathway, ATF1 pathways, etc. Therefore, the JNK pathway instead of NF-kB pathway is involved in PA-mediated DSC apoptosis and the NF-kB pathway mainly contributes to PA-elicited inflammatory decidual microenvironment. Mitochondria respiration mainly utilizes three types of fuels: GLC, long-chain FAs and GLNs. Ubiquitously, fuel utilization displays high plasticity with mitochondria compensating the exhaustion of a fuel by shifting to another type of fuel in order to meet fluctuating energy demand. In decidua, data show that DSCs without exogenous interference largely depend on GLC and FAs (GLC: FA = 2:1) and that DSCs have a high capacity of utilizing all three fuels. Generally, intracellular excess FAs such as PA engage in diverse metabolic pathways in avoidance of lipotoxicity (Benador et al., 2019). A recent article reported that PA-treated hepatocytes initiated anaplerotic flux from GLN to alpha-ketoglutarate, subsequently entering the mitochondria respiration (Egnatchik et al., 2019). At the maternal–fetal interface, we observed an incremental LD storage in DSCs corresponds to PA treatment and an enhancement of mitochondria respiration, in which the proportions of three pathways alter (GLC: FA: GLN = 2:1:1). However, little dose dependency of FAO metabolism enhanced in DSCs but the capacity of FAO was significantly repressed under a high-PA environment, indicating that adding exogenous PA might not facilitate to mitochondria respiration via the FAO. Instead, mitochondrial respiration remarkably shifted to GLN oxidation following PA treatment. Mechanistically, this metabolic adaptation to excess PA exposure is due to the activation of GLS activity, ultimately increasing GLN oxidation after exhausting the flexibility of GLC oxidation as well as FAO. Moreover, blockage of TLR4 signaling suppresses the expression of GLS, consistent with previous studies (Pais et al., 2008; Egnatchik et al., 2019). Therefore, the PA–TLR4 interaction may be one of regulatory mechanisms of PA-triggering GLN oxidation metabolism. Next, we asked whether inhibition of GLN oxidation would lead to PA-mediated DCS dysfunctions, since it has been previously demonstrated that aberrant GLN metabolism disrupts cell homeostasis (Cai et al., 2018; Gregory et al., 2019; Lu et al., 2019). Indeed, GLSi aggravates PA-mediated apoptosis and inflammatory responses in DSCs, indicating that the enhanced GLN oxidation should prevent DSCs from high-PA metabolic stress. Such protective mechanism may be attributed to markedly impairing antioxidant glutathione production through the inhibition of GLS, ultimately resulting in oxidative stress and apoptotic cell death (Cai et al., 2018; Gregory et al., 2019). In this study, we observed similar results. Further studies are needed to better understand the delicate GLN metabolism reprogramming in PA-induced DSC apoptosis and inflammatory responses. In conclusion, as shown in Figure 7, our data demonstrate that an excess PA environment, during early pregnancy, disturbs DSC homeostasis and elicits apoptosis and inflammatory program in DSCs via the TLR4/JNK/NF-kB pathway. In addition, we demonstrate that a high-PA stress alters energy metabolism profiles in DSCs, which demands higher levels of GLN oxidation for compensatory protection of DSC function. However, this protection should be limited under excess PA. Therefore, extremely excessive decidual PA may be identified as a potential biomarker for decidual apoptosis and inflammatory responses, especially in patients with such decidual dysfunction-related infertility and adverse pregnancy outcomes. Monitoring and controlling PA level or regulating GLN oxidation reprogramming may be valuable in clinical prevention and treatment of these patients. These possibilities need to be clarified by further research. Authors’ roles S.Y.H. conducted all experiments and prepared the figures and the manuscript. X.M.Q., Z.Z.L., H.L.Y., Y.W., L.Y.R. and J.W.S. examined the patients, obtained specimens, generated clinical data and assisted with sample collection. X.Y.Z. and D.J.L. assisted with language editing. M.Q.L. initiated and supervised the project and edited the manuscript. All the authors were involved in writing the manuscript. Funding National Key Research and Development Program of China (2017YFC1001404); National Natural Science Foundation of China (no. 31970798, 31671200, 91542108, 81471513); Oriented Project of Science and Technology Innovation from Key Lab of Reproduction Regulation of National Population and Family Planning Commission (NPFPC) (CX2017-2); Program for Zhuoxue of Fudan University. Conflict of interest The authors declare no financial or commercial conflict of financial interests. References Arck PC , Hecher K . Fetomaternal immune cross-talk and its consequences for maternal and offspring’s health . Nat Med 2013 ; 19 : 548 – 556 . Google Scholar Crossref Search ADS PubMed WorldCat Barrett HL , Dekker NM , McIntyre HD , Callaway LK . Normalizing metabolism in diabetic pregnancy: is it time to target lipids? Diabetes Care 2014 ; 37 : 1484 – 1493 . Google Scholar Crossref Search ADS PubMed WorldCat Benador IY , Veliova M , Liesa M , Shirihai OS . 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TI - Excess palmitate induces decidual stromal cell apoptosis via the TLR4/JNK/NF-kB pathways and possibly through glutamine oxidation
JF - Molecular Human Reproduction
DO - 10.1093/molehr/gaaa004
DA - 2020-02-29
UR - https://www.deepdyve.com/lp/oxford-university-press/excess-palmitate-induces-decidual-stromal-cell-apoptosis-via-the-tlr4-LUa5JZcTcd
SP - 88
VL - 26
IS - 2
DP - DeepDyve
ER -