Delineating differential regulatory signatures of the human transcriptome in the choriodecidua and myometrium at term labor,

Delineating differential regulatory signatures of the human transcriptome in the choriodecidua... Abstract Preterm deliveries remain the leading cause of neonatal morbidity and mortality. Current therapies target only myometrial contractions and are largely ineffective. As labor involves multiple coordinated events across maternal and fetal tissues, identifying fundamental regulatory pathways of normal term labor is vital to understanding successful parturition and consequently labor pathologies. We aimed to identify transcriptomic signatures of human normal term labor of two tissues: in the fetal-facing choriodecidua and the maternal myometrium. Microarray transcriptomic data from choriodecidua and myometrium following term labor were analyzed for functional hierarchical networks, using Cytoscape 2.8.3. Hierarchically high candidates were analyzed for their regulatory casual relationships using Ingenuity Pathway Analysis. Selected master regulators were then chemically inhibited and effects on downstream targets were assessed using real-time quantitative PCR (RT-qPCR). Unbiased network analysis identified upstream molecular components in choriodecidua including vimentin, TLR4, and TNFSF13B. In the myometrium, candidates included metallothionein 2 (MT2A), TLR2, and RELB. These master regulators had significant differential gene expression during labor, hierarchically high centrality in community cluster networks, interactions amongst the labor gene set, and strong causal relationships with multiple downstream effects. In vitro experiments highlighted MT2A as an effective regulator of labor-associated genes. We have identified unique potential regulators of the term labor transcriptome in uterine tissues using a robust sequence of unbiased mathematical and literature-based in silico analyses. These findings encourage further investigation into the efficacy of predicted master regulators in blocking multiple pathways of labor processes across maternal and fetal tissues, and their potential as therapeutic approaches. Introduction Labor involves sensitization of the uterus to contractile signals, with amplification and increased frequency of myometrial contractions. However, labor also involves a multitude of events, including inflammatory signaling, decidual activation, fetal and maternal endocrine coordination, cervical ripening and dilation, fetal membrane dissociation, and placental separation [1–5]. Pathologies such as preterm delivery remain a leading cause of infant morbidity and mortality [6]. Treatments for preterm labor target a single event—myometrial contractions—and these tocolytics have low efficacy, do not improve fetal outcomes, and are associated with maternal side effects [7]. Multiple pathways in the labor cascade are likely to be contributing factors in premature deliveries and focusing on more than a single process and in more than one tissue layer, may be more effective in preventing preterm labor. To date, most studies investigating physiological changes during labor have focused on a single tissue layer, often the myometrium. The decidua is the interface between the fetal tissues and myometrium and is the site of significant labor-associated inflammatory, progesterone, and prostaglandin activity [1, 4, 8–10]. Moreover, recent studies propose cellular senescence in the amniochorion as a key dictator of timing of labor [11]. Our approach to characterizing uterine changes during term labor therefore includes analyses of concurrent events in both the myometrium and choriodecidua. Early events of normal term labor are still being characterized in maternal and fetal tissues and there is a wealth of studies identifying differential expression of genes involved in labor; however, their interactions and regulatory relationships in the different tissue types are poorly understood. Identifying the upstream regulators of a suite of essential labor mediators is essential for understanding the complexities of normal labor and consequently labor pathologies and development of effective therapeutics. Microarray technology has generated extensive lists of genes whose expression changes with labor. In the myometrium, these include contractile-associated genes: prostaglandin synthase 2 (PTGS2), prostaglandin F2A receptor, oxytocin receptor (OXTR), and gap junction 1 [3, 5]. Other significantly altered genes include those involved in cytoskeletal structure, cell metabolism, cell death, signal transduction, and inflammatory cytokines [2, 3, 8, 9]. Likewise, studies have identified a strong inflammatory response not associated with a microbial response in intact fetal membranes and choriodecidua. These include widespread upregulation of chemokines, cytokines, NFKB complex components, and toll-like receptors (TLR), and with genes governing tissue remodeling and cell survival, e.g. TP53, BCL2 family [1, 4]. Preliminary pathway analysis identified putative upstream regulators of choriodecidual labor transcriptome, including NFKB components and microRNAs (miR-21, -46, -141, -200) [4]. Further network analysis is required to identify key components of the labor process, regulators and characterization of causal relationships of downstream effector genes. We hypothesized that a comprehensive global network analysis of transcriptomic data from choriodecidua and myometrium would identify master regulators of labor in these two intimately associated uterine tissues. We aimed to (1) perform hierarchical network analysis to prioritize the most significant interactions between genomic clusters and genes differentially expressed in labor; (2) identify potential regulatory candidate genes in each tissue; and (3) test whether inhibition of the predicted master regulator candidates would alter expression of downstream labor-associated molecules. Materials and methods Microarray data Term labor choriodecidua transcriptome data described by Stephen et al. were used [4]. In brief, all samples were from term deliveries (37–42 weeks gestation) with uncomplicated pregnancies: elective caesareans, no active labor (n = 12; term no labor), and vaginal deliveries (n = 11; term active labor). Isolated RNA samples were hybridized to genechip Human Genome U133 Plus 2.0 Array (Affymetrix, UK), data normalized using robust multiarray analysis from Affymetrix Microarray Suite [10], and statistically processed using PUMA package [11]. This study used the parameters of probability of positive log ratio (PPLR) of at least 0.997 and less than 0.00001. Publicly available myometrial transcriptomic data accession GSE9159 was accessed from NCBI GEO database [5]. The data were generated using the same genechip as above, with uncomplicated pregnancies at term (38–41 weeks gestation) from caesarean sections after onset of labor (n = 6; emergency section after normal active labor but arrest of cervical dilation at ≥6cm), or no labor (n = 6; elective cesarean section after 38 weeks of gestation, with no signs of labor). Data were extracted and normalized using the R statistical package and a statistical restriction of P < 0.02. Network analysis of the labor transcriptome To investigate the relationships between differentially expressed genes, all known protein:protein interactions were referenced from the Biological General Repository for Interaction Datasets (BioGrid Version 3.2.117; http://thebiogrid.org) [12]. To prioritize and identify the hierarchy of interactions and ensure robustness of the analysis, a network analysis protocol was developed using two mathematical algorithms, cross-referencing to the differentially expressed transcript data, followed by analysis of downstream interactions using literature-based causal analysis (Ingenuity Pathway Analysis software; IPA) (Figure 1) [13]. The work flow was as follows: Mathemathical network modeling using Cytoscape (software version 2.8.3 with BioGrid database). This study used the plugin ModuLand to measure centrality of gene clusters with an overlapping community approach, thus forming an interconnected hierarchy where nodes (clusters of genes) form the core for the upper level, designated as a meta-node (cluster of nodes) [14]. Each meta-node is represented by the top-most central gene in the cluster. The highest ranked interconnected regulators (all meta-nodes, nodes in the top 5 meta-nodes, and any duplicate nodes) were cross-referenced to array data for differential gene expression associated with labor. The refined dataset was subjected to secondary mathemathical modeling using ClusterOne plugin [15]. To prioritize the volume of hierarchially ranked data, only regulators identified by both mathematical models were included for casual analysis. Literature-based causal analysis was performed using IPA to identify master regulators with a network of downstream genes with established association with labor [16]. Figure 1. View largeDownload slide Workflow of in silico analyses. The microarray data were extracted and analyzed for candidate master regulators with high centrality and connectivity using (1) ModuLand (2) ClusterOne, and (3) Causal analysis. Figure 1. View largeDownload slide Workflow of in silico analyses. The microarray data were extracted and analyzed for candidate master regulators with high centrality and connectivity using (1) ModuLand (2) ClusterOne, and (3) Causal analysis. In vitro analyses: choriodecidua Placentas were obtained from women with uncomplicated pregnancies undergoing elective caesarean section at term (37–42 weeks, n = 6). Written informed consent was obtained and ethical approval granted by North West REC (#08/H1010/55(+55)). Choriodecidua was sampled from the mid-zone area of fetal membranes as described [17, 18]. Media culture products were purchased from GIBCO (Life Technologies, UK) unless otherwise stated. Choriodecidual cells were isolated using methods adapted from published studies [19, 20]. In brief, tissues were mechanically macerated and digested with collagenase (1.0 mg/ml, Sigma-Aldrich, UK) and DNase (20mg/ml, Sigma-Aldrich) at 37°C for up to 60 min. Ten percent fetal bovine serum (FBS) was added to inhibit enzymatic activity before filtering through a wire mesh and 40 μm filter. Choriodecidual cells were separated from erythrocytes using histopaque (Sigma-Aldrich) and then cultured at 5 × 105 per ml in DMEM/F-12 (supplemented with L-glutamine (1.5 mg/ml), penicillin streptomycin (0.5 mg/ml), 10% (v/v) FBS, B-estradiol (10−8 M, Sigma-Aldrich), and progesterone (5 × 10−7 M, Sigma-Aldrich). To confirm isolated cells were choriodecidual, they were characterized by immunocytochemical staining as described [21], using monoclonal antibodies against: IGFBP-1 and vimentin (VIM) for decidual stromal cells, CD45 (DAKO, UK) for leukocytes, cytokeratin 7 for epithelial cells (Supplementary Figure S1). Primary antibodies are listed in Table 1 and were purchased from DAKO, UK. Negative control was a non-immune mouse IgG (Sigma-Aldrich, UK) at matching concentrations. Table 1. Antibodies used for immunocytochemistry. Primary antibody  Company  Concentration  TLR4 mouse monoclonal (ab22048)  Abcam, UK  0.63 μg/ml  CXCR4 mouse monoclonal (SC53534)  Santa Cruz, DEU  1 μg/ml  CD44 mouse monoclonal (M7082)  DAKO, UK  1.6 μg/mL  Vimentin mouse monoclonal (M0760)  DAKO, UK  0.364 μg/mL  Secondary antibody  Company  Concentration  Goat anti mouse  DAKO, UK  3.86 μg/mL  Primary antibody  Company  Concentration  TLR4 mouse monoclonal (ab22048)  Abcam, UK  0.63 μg/ml  CXCR4 mouse monoclonal (SC53534)  Santa Cruz, DEU  1 μg/ml  CD44 mouse monoclonal (M7082)  DAKO, UK  1.6 μg/mL  Vimentin mouse monoclonal (M0760)  DAKO, UK  0.364 μg/mL  Secondary antibody  Company  Concentration  Goat anti mouse  DAKO, UK  3.86 μg/mL  View Large To inhibit VIM, the protein kinase C (PKC) inhibitor Gö6983 was used at 100 nm (G1918; Sigma Aldrich, UK). This concentration has been shown to reduce VIM protein expression [22], decrease focal attachments between adherent cells and reduce cellular motility [23]. For TLR4, the cyclohexene inhibitor TAK-242 was used at 1 μm (TLRL-CLI95; Invivogen, France) [24]. The vehicle control was DMSO (0.002% v/v). Cells were treated for 24 h with inhibitors or vehicle. In vitro analyses: myometrium An immortalized human myometrial smooth muscle cell line, htert-C3, [25] was used for the function of putative myometrial regulators. Cells were cultured at 5 × 104 per well in DMEM/F12 (as above, with gentomycin (100 μg/ml)). The PKC inhibitor Gö6983 was used at 15 μm; this concentration inhibits metallothionein 2A (MT2A) mRNA expression [26]. A neutralizing antibody Mab hTLR2, Clone TL2.1 (100 ng/ml, Invivogen, France) was used to inhibit TLR2 [27]. Vehicle controls were DMSO (0.002% for MT2A) or media (for TLR2). Cells were treated for 24 h with inhibitors or vehicle. Quantitative real-time quantitative PCR Total RNA was isolated from cultured cells using the Mirvana miRNA Isolation Kit (Ambion, UK), followed by assessment by Nanodrop 2000 UV-Vis Spectrophotometer. RNA was reverse transcribed using Affinity Script Multiple Temperature cDNA Synthesis Kit (Agilent, UK) and RT-qPCR performed using Ultra-Fast SYBR Green qPCR Master Mix III (Agilent), and Stratagene MX3005 machine. Multiple reference genes were screened before selecting GAPDH for choriodecidual cells and YWHAZ for myometrial cell line, as the most stably expressed reference genes for data normalization. Primers are in Table 2. Table 2. Primers used for real-time quantitative PCR. Gene  Forward primer 5΄-3΄  Reverse primer 5΄-3΄  Accession number  TBP  CACGAACCACGGCACTGATT  TTTTCTTGCTGCCAGTCTGGAC  NM_003194  YWHAZ  ACTTTTGGTACATTGTGGCTTCAA  CCGCCAGGACAAACCAGTAT  NM_003406  BACTIN  ATGTGGCCGAGGACTTTGATT  AGTGGGGTGGCTTTTAGGATG  NM_001101  RPL13  CTTCTCGGCCTGTTTCCGTAG  CGAGGTTGGCTGGAAGTACC  NM_012423  GAPDH  GCCAAATATGATGACATCAAGAAGG  GGTGTCGCTGTTGAAGTCAGAG  NM_002046.5  CCL2  AATCAATGCCCCAGTCACCTGC  CGGAGTTTGGGTTTGCTTGTCC  NM_002982  CCL4  CAGCACCAATGGGCTCAGA  CACTGGGATCAGCACAGACT  NM_002984  CCL8  GAGAGCTACACAAGAATCACCAA  TGGTCCAGATGCTTCATGGAA  NM_005623  CD44  CTGCCGCTTTGCAGGTGTA  CATTGTGGGCAAGGTGCTATT  NM_000610.3  IL1RN  CGGGTGCTACTTTATGGGCA  GGTCGGCAGATCGTCTCTAA  NM_000577  IL1A  TGGTAGTAGCAACCAACGGGA  ACTTTGATTGAGGGCGTCATTC  NM_000575.3  IL1B  CTCGCCAGTGAAATGATGGCT  GTCGGAGATTCGTAGCTGGAT  NM_000576.2  IL6  CCTGAACCTTCCAAAGATGGC  TTCACCAGGCAAGTCTCCTCA  NM_000600  IL8  CACCGGAAGGAACCATCTCACT  TGGGGACACCTTTTAGCATC  NM_000584.3  MYLK  CCCGAGGTTGTCTGGTTCAAA  GCAGGTGTACTTGGCATCGT  NM_053025  NFKBIA  CCAACTACAATGGCCACACGTGTCTACA  GAGCATTGACATCAGCACCCAAGG  NM_020529  PTGER4  CATCATCTGCGCCATGAGTGT  GCTTGTCCACGTAGTGGCT  NM_000958  PTGS2  CGATGCTCATGCTCTTCGC  GGGAGACTGCATAGATGACAGG  NM_000956.3  VIM  AGTCCACTGAGTACCGGAGAC  CATTTCACGCATCTGGCGTTC  NM_003380.3  ZEB2  TCTGTAGATGGTCCAGTGAAGA  GTCACTGCGCTGAAGGTACT  NM_001171653  Gene  Forward primer 5΄-3΄  Reverse primer 5΄-3΄  Accession number  TBP  CACGAACCACGGCACTGATT  TTTTCTTGCTGCCAGTCTGGAC  NM_003194  YWHAZ  ACTTTTGGTACATTGTGGCTTCAA  CCGCCAGGACAAACCAGTAT  NM_003406  BACTIN  ATGTGGCCGAGGACTTTGATT  AGTGGGGTGGCTTTTAGGATG  NM_001101  RPL13  CTTCTCGGCCTGTTTCCGTAG  CGAGGTTGGCTGGAAGTACC  NM_012423  GAPDH  GCCAAATATGATGACATCAAGAAGG  GGTGTCGCTGTTGAAGTCAGAG  NM_002046.5  CCL2  AATCAATGCCCCAGTCACCTGC  CGGAGTTTGGGTTTGCTTGTCC  NM_002982  CCL4  CAGCACCAATGGGCTCAGA  CACTGGGATCAGCACAGACT  NM_002984  CCL8  GAGAGCTACACAAGAATCACCAA  TGGTCCAGATGCTTCATGGAA  NM_005623  CD44  CTGCCGCTTTGCAGGTGTA  CATTGTGGGCAAGGTGCTATT  NM_000610.3  IL1RN  CGGGTGCTACTTTATGGGCA  GGTCGGCAGATCGTCTCTAA  NM_000577  IL1A  TGGTAGTAGCAACCAACGGGA  ACTTTGATTGAGGGCGTCATTC  NM_000575.3  IL1B  CTCGCCAGTGAAATGATGGCT  GTCGGAGATTCGTAGCTGGAT  NM_000576.2  IL6  CCTGAACCTTCCAAAGATGGC  TTCACCAGGCAAGTCTCCTCA  NM_000600  IL8  CACCGGAAGGAACCATCTCACT  TGGGGACACCTTTTAGCATC  NM_000584.3  MYLK  CCCGAGGTTGTCTGGTTCAAA  GCAGGTGTACTTGGCATCGT  NM_053025  NFKBIA  CCAACTACAATGGCCACACGTGTCTACA  GAGCATTGACATCAGCACCCAAGG  NM_020529  PTGER4  CATCATCTGCGCCATGAGTGT  GCTTGTCCACGTAGTGGCT  NM_000958  PTGS2  CGATGCTCATGCTCTTCGC  GGGAGACTGCATAGATGACAGG  NM_000956.3  VIM  AGTCCACTGAGTACCGGAGAC  CATTTCACGCATCTGGCGTTC  NM_003380.3  ZEB2  TCTGTAGATGGTCCAGTGAAGA  GTCACTGCGCTGAAGGTACT  NM_001171653  View Large Results Network analysis and identification of regulatory genes Choriodecidua The original data identified 796 significant transcriptional changes following labor [4]; these were used to generate a network model of protein:protein interactions (4980 nodes (genes) with 12914 interactions). Analysis of the network structure via ModuLand identified a hierarchy of 71 meta-nodes (gene modules), with 1610 interactions (Figure 2). The genes representing each meta-node possessed high centrality in the cluster of connected genes (nodes). The top 20 meta-nodes in the choriodecidua labor network and their respective core nodes are hierarchically illustrated in Table 3, together with their biological functions and pathways. The network is dominated by gene clusters governing cellular responses to stress and hypoxia, cell death/survival, cell cycle regulation, and circadian rhythm. Immune regulatory and inflammatory clusters are also highly ranked. The core nodes within the top meta-nodes also have a high hierarchal ranking, e.g., NFKBIA, TP53, and VIM are found within the top meta-node clusters, indicating a strong interactive community within the network. Figure 2. View largeDownload slide Network modeling of meta-node expression in human choriodecidua. The upper level of network clusters assessed using the ModuLand algorithm in Cytoscape 2.8.3. These principle clusters are meta-nodes and their interactions of term labor in choriodecidua. The lines indicate interactions between meta-nodes and lower level nodes. The yellow lines indicate highly ranked major interactions. Figure 2. View largeDownload slide Network modeling of meta-node expression in human choriodecidua. The upper level of network clusters assessed using the ModuLand algorithm in Cytoscape 2.8.3. These principle clusters are meta-nodes and their interactions of term labor in choriodecidua. The lines indicate interactions between meta-nodes and lower level nodes. The yellow lines indicate highly ranked major interactions. Table 3. Hierarchical ranking of the top 20 meta-nodes and their highest ranked nodes in the term labor choriodecidual network map. Metanode  Major functions and pathways  Core nodes in metanode   UBC  Cell responses to stress, apoptosis, circadian clock, DNA repair (P < 1.0 × 10−4)  UBC, NFKBIA, RUVBL2, NOTCH1, VIM, UBE2D3, RPS4X, ARRB1, RPSA, XPO1  CDC34  Responses to hypoxia, apoptosis, circadian clock, DNA repair (P < 1.0 × 10−4)  CDC34, UBE2D3, UBC, RBX1, ARRB1, MDM2, CUL1, RUVBL2, NOTCH1, NRF1  STUB1  Cell responses to hypoxia, apoptosis, circadian clock, DNA repair (P < 1.0 × 10−4)  STUB1, UBC, UBE2D3, ARRB1, HSP90AA1, HSPA4, UBE2D1, CSNK1A1, PARK7, TP53  SHC1  Adaptive and innate immunity (P < 3.8 × 10−6), growth factor signaling (P < 3.9 × 10−7)  SHC1, GRB2, LCP2, EGFR, ERBB3, CBL, PAG1, SOS1, ERBB2, GRAP2  BIRC3  Innate immunity (P < 1.4 × 10−6)  BIRC3, TRAF2, TNFAIP3, USP53, UBE2D2, BCL10, RIPK1, PEG3, TRAF6, DIABLO  BRCA1  DNA repair (P < 2.1 × 10−7)  BRCA1, BRIP1, BACH1, NBN, LMO4, MLH1, RPA1, PMS2, HNRNPA2B1, EZR  NBN  Cellular senescence (P < 1.5 × 10−5), DNA repair (P < 1.8 × 10−9)  NBN, MRE11A, RAD50, H2AFX, MDC1, CALR, ATM, USP53, BACH1, EP300  SMC3  Cell cycle (P < 1.3 × 10−4)  SMC3, RAD21, SMC1A, CASP4, STAG1, ANKRD28, S100A9, PDS5A, STAG2, CLU  CKS1B  G1/S transition (P < 6.5 × 10−9), DNA replication (P < 7.5 × 10−7)  CKS1B, CDK2, SKP2, CDK1, MCM2, SKP1, CDKN1B, COPS6, CCNA2, CCNG2  MBD3  Chromatin organization (P < 5.6 × 10−8)  MBD3, HDAC1, BCL11B, HDAC2, MTA2, MBD2, MXD1, ATF3, RBBP4, ARID4A  BRD4  TGFB signaling (P < 7.8 × 10−5)  BRD4, CDK9, AFF4, CCNT2, ELL2, CCNT1, MLLT3, MLLT1, AFF1, HEXIM1  FAS  Apoptosis (P < 8.2 × 10−10)  FAS, FADD, DAXX, FASLG, KRIT1, FAF1, CASP10, CASP8, TGFB2, CFLAR  SOCS3  Cytokine signaling (P < 1.3 × 10−7)  SOCS3, JAK2, CSF2RB, TCEB1, PRLR, PTPN11, TCEB2, IL2RB, IFNGR1, JAK1  UBE2B  Adaptive immune system (P < 3.5 × 10−2)  UBE2B, RAD18, UBR2, UBR3, UBA1, CTNNB1, UBQLN1, DSTN, PCNA, CPLX1  EIF3EBP1  MTOR signaling (P < 1.0 × 10−8), cellular response to heat stress (P < 1.6 × 10−4)  EIF4EBP1, MTOR, EIF4E, RPTOR, EIF2C2, LRRK2, ATM, SLMAP, PPP2R4, LRPAP1  ACD  Telomere maintenance, glycogen breakdown (P < 8.0 × 10−4)  ACD, POT1, TINF2, CALD1, DBNL, PGM1, ANXA4, SARS, ACOT7, IL1RN  CDK19  Metabolism of lipids (P < 6.0 × 10−9)  CDK19, MED9, MED16, MED28, MED19, MED26, MED29, MED12, MED18, CDK8  TLR4  Cytokine and TLR signaling (P < 5.0 × 10−3)  TLR4, MYD88, TLR2, TIRAP, LY96, TICAM2, TOLLIP, TLR1, SRC, SYK  MXD1  Chromatin modification (P < 6.0 × 10−3)  MXD1, MAX, SIN3A, AKT1, MLX, ARID4A, SAP30, VDR, KDM5A, HDAC2  TBXA2R  Regulation of insulin secretion, WNT signaling (P < 2.7 × 10−5)  TBXA2R, GNAQ, GNA13, RAB11A, PTGIR, PRKCA, PSME3, KCNMA1, PSMA7, GNB1  Metanode  Major functions and pathways  Core nodes in metanode   UBC  Cell responses to stress, apoptosis, circadian clock, DNA repair (P < 1.0 × 10−4)  UBC, NFKBIA, RUVBL2, NOTCH1, VIM, UBE2D3, RPS4X, ARRB1, RPSA, XPO1  CDC34  Responses to hypoxia, apoptosis, circadian clock, DNA repair (P < 1.0 × 10−4)  CDC34, UBE2D3, UBC, RBX1, ARRB1, MDM2, CUL1, RUVBL2, NOTCH1, NRF1  STUB1  Cell responses to hypoxia, apoptosis, circadian clock, DNA repair (P < 1.0 × 10−4)  STUB1, UBC, UBE2D3, ARRB1, HSP90AA1, HSPA4, UBE2D1, CSNK1A1, PARK7, TP53  SHC1  Adaptive and innate immunity (P < 3.8 × 10−6), growth factor signaling (P < 3.9 × 10−7)  SHC1, GRB2, LCP2, EGFR, ERBB3, CBL, PAG1, SOS1, ERBB2, GRAP2  BIRC3  Innate immunity (P < 1.4 × 10−6)  BIRC3, TRAF2, TNFAIP3, USP53, UBE2D2, BCL10, RIPK1, PEG3, TRAF6, DIABLO  BRCA1  DNA repair (P < 2.1 × 10−7)  BRCA1, BRIP1, BACH1, NBN, LMO4, MLH1, RPA1, PMS2, HNRNPA2B1, EZR  NBN  Cellular senescence (P < 1.5 × 10−5), DNA repair (P < 1.8 × 10−9)  NBN, MRE11A, RAD50, H2AFX, MDC1, CALR, ATM, USP53, BACH1, EP300  SMC3  Cell cycle (P < 1.3 × 10−4)  SMC3, RAD21, SMC1A, CASP4, STAG1, ANKRD28, S100A9, PDS5A, STAG2, CLU  CKS1B  G1/S transition (P < 6.5 × 10−9), DNA replication (P < 7.5 × 10−7)  CKS1B, CDK2, SKP2, CDK1, MCM2, SKP1, CDKN1B, COPS6, CCNA2, CCNG2  MBD3  Chromatin organization (P < 5.6 × 10−8)  MBD3, HDAC1, BCL11B, HDAC2, MTA2, MBD2, MXD1, ATF3, RBBP4, ARID4A  BRD4  TGFB signaling (P < 7.8 × 10−5)  BRD4, CDK9, AFF4, CCNT2, ELL2, CCNT1, MLLT3, MLLT1, AFF1, HEXIM1  FAS  Apoptosis (P < 8.2 × 10−10)  FAS, FADD, DAXX, FASLG, KRIT1, FAF1, CASP10, CASP8, TGFB2, CFLAR  SOCS3  Cytokine signaling (P < 1.3 × 10−7)  SOCS3, JAK2, CSF2RB, TCEB1, PRLR, PTPN11, TCEB2, IL2RB, IFNGR1, JAK1  UBE2B  Adaptive immune system (P < 3.5 × 10−2)  UBE2B, RAD18, UBR2, UBR3, UBA1, CTNNB1, UBQLN1, DSTN, PCNA, CPLX1  EIF3EBP1  MTOR signaling (P < 1.0 × 10−8), cellular response to heat stress (P < 1.6 × 10−4)  EIF4EBP1, MTOR, EIF4E, RPTOR, EIF2C2, LRRK2, ATM, SLMAP, PPP2R4, LRPAP1  ACD  Telomere maintenance, glycogen breakdown (P < 8.0 × 10−4)  ACD, POT1, TINF2, CALD1, DBNL, PGM1, ANXA4, SARS, ACOT7, IL1RN  CDK19  Metabolism of lipids (P < 6.0 × 10−9)  CDK19, MED9, MED16, MED28, MED19, MED26, MED29, MED12, MED18, CDK8  TLR4  Cytokine and TLR signaling (P < 5.0 × 10−3)  TLR4, MYD88, TLR2, TIRAP, LY96, TICAM2, TOLLIP, TLR1, SRC, SYK  MXD1  Chromatin modification (P < 6.0 × 10−3)  MXD1, MAX, SIN3A, AKT1, MLX, ARID4A, SAP30, VDR, KDM5A, HDAC2  TBXA2R  Regulation of insulin secretion, WNT signaling (P < 2.7 × 10−5)  TBXA2R, GNAQ, GNA13, RAB11A, PTGIR, PRKCA, PSME3, KCNMA1, PSMA7, GNB1  View Large The meta-nodes, core nodes in top 5 meta-nodes, and duplicated genes (in either meta-nodes or nodes) were independently analyzed using the ClusterOne plugin; this identified 20 candidates with highly significant predictive interactions in the labor transcriptome (data not shown). These were subjected to IPA Causal Analysis to refine the list of regulator candidates to those with the strongest predicted regulatory relationships with established mediators of labor. The final potential regulators of the choriodecidual labor transcriptome were VIM, TLR4, and TNFSF13B (Table 4). All were significantly upregulated by labor, highly ranked in hierarchical networks by both algorithms, and had regulatory effects on multiple genes associated with term labor. Table 4. Summary of the in silico analyses data for the final master regulator candidates of the choriodecidua and myometrial labor transcriptome.   Microarray  Moduland network analysis  Clusterone analysis  Causal network    Fold  PPLR or  Metanode  Nodes in      Downstream  analysis  Gene  change  P value  (node rank)  cluster  P value  Z-score  genes  P value  Choriodecidua  VIM  1.9  0.999  1st (5th)  40  1.3 × 10−9  3.2  41  1.4 × 10−17  TLR4  3.7  0.999  18th (1st)  20  0.009  4.2  25  4.7 × 10−10  TNFSF13B  2.1  0.997  47th (1st)  5  0.042  3.4  105  9.6 × 10−8  Myometrium  MT2A  1.2  0.0058  37th (1st)  11  0.048  3.3  30  3.3 × 10−6  TLR2  1.3  0.0101  39th (1st)  8  0.03  1.3  90  1.13 × 10−6  RELB  1.4  0.0193  15th (1st)  27  0.012  2.4  123  5.9 × 10−9    Microarray  Moduland network analysis  Clusterone analysis  Causal network    Fold  PPLR or  Metanode  Nodes in      Downstream  analysis  Gene  change  P value  (node rank)  cluster  P value  Z-score  genes  P value  Choriodecidua  VIM  1.9  0.999  1st (5th)  40  1.3 × 10−9  3.2  41  1.4 × 10−17  TLR4  3.7  0.999  18th (1st)  20  0.009  4.2  25  4.7 × 10−10  TNFSF13B  2.1  0.997  47th (1st)  5  0.042  3.4  105  9.6 × 10−8  Myometrium  MT2A  1.2  0.0058  37th (1st)  11  0.048  3.3  30  3.3 × 10−6  TLR2  1.3  0.0101  39th (1st)  8  0.03  1.3  90  1.13 × 10−6  RELB  1.4  0.0193  15th (1st)  27  0.012  2.4  123  5.9 × 10−9  View Large The causal analysis for VIM-predicted regulation of five genes, including mitogen-activated protein kinase 1 (MAPK1 or ERK), snail family transcriptional repressor 1 (SNAI1), and phospholipidase A2, which in turn have regulatory actions on 41 genes including matrix metalloproteinases (MMPs), cytokines, chemokines, and PTGS2 (Figure 3). As expected, TLR4 regulated a suite of inflammatory mediators, including cytokines, chemokines, MMPs, and PTGS2 (Supplementary Figure S2). The causal network for TNFSF13B had 14 lower level regulators, including NFKB1 and ERK, with regulatory actions on 105 downstream genes, including interleukins (IL) IL1RN, IL1A, CXCR4, suppressor of cytokine signaling 3 (SOCS3) and multiple chemokines (Supplementary Figure S3). Figure 3. View largeDownload slide Causal network analysis of vimentin identifying relationships with downstream labor-associated choriodecidual genes. Vimentin is the master regulator with five lower level regulators. Bottom level is an edited summary of genes affected (full map includes 41 genes) with at least two network connections or established labor-associated functions. A dashed line (—) indicates one known function, and a solid line (__) indicates multiple functions identified between the regulator and downstream gene. Upregulated regulator = orange; upregulated gene = red. The darker intensity of color indicates a higher degree of change. Figure 3. View largeDownload slide Causal network analysis of vimentin identifying relationships with downstream labor-associated choriodecidual genes. Vimentin is the master regulator with five lower level regulators. Bottom level is an edited summary of genes affected (full map includes 41 genes) with at least two network connections or established labor-associated functions. A dashed line (—) indicates one known function, and a solid line (__) indicates multiple functions identified between the regulator and downstream gene. Upregulated regulator = orange; upregulated gene = red. The darker intensity of color indicates a higher degree of change. Myometrium The myometrial genomic dataset was analyzed using the analysis workflow developed. From the 717 myometrial transcripts significantly altered with labor, the BioGrid network model generated 4886 nodes with 12 112 interactive relationships. At the next hierarchical level, there were 64 meta-nodes and 1277 interactions (Figure 4). There were similarities in the biological and canonical pathways with the choriodecidua network meta-nodes, with prominent involvement of community clusters regulating responses to hypoxia, cell cycle, and survival, together with TGFB, immune and inflammatory signaling (Table 5). ClusterOne analysis reduced the number of candidate clusters to the 30 most interconnected (data not shown). These were refined by causal analysis to three final gene candidates with strong causal relationships, with a significant number of differentially expressed labor-associated genes (Table 4). These were intracellular ion-regulator, MT2A (or MT2), RELB, and TLR2. The downstream causal relationships supporting MT2A as a primary component demonstrated a primary regulation of NFKB, leading to regulation of transcription factors (e.g., estrogen receptor, PPARG, TP53) and inflammatory genes (Figure 5). The candidate regulator TLR2 was predicted to regulate multiple inflammatory and labor-associated genes, including PTGS2, interleukins, NFKB, and signaling molecules (Supplementary Figure S4). The regulator RELB downstream genes exhibited strong overlap with those of MT2A, including estrogen receptor, cytokines, and NFKBIA (Supplementary Figure S5). Figure 4. View largeDownload slide Network modeling of meta-node expression in the human myometrium. The upper level of network clusters assessed using the ModuLand algorithm in Cytoscape 2.8.3. These 64 clusters are meta-nodes, the lines indicate interactions between meta-nodes and lower level nodes. The yellow lines indicate highly-ranked major interactions. Figure 4. View largeDownload slide Network modeling of meta-node expression in the human myometrium. The upper level of network clusters assessed using the ModuLand algorithm in Cytoscape 2.8.3. These 64 clusters are meta-nodes, the lines indicate interactions between meta-nodes and lower level nodes. The yellow lines indicate highly-ranked major interactions. Figure 5. View largeDownload slide Causal network analysis of metallothionein 2. MT2A is the top regulator with 13 lower level regulators. The bottom level is a summary of downstream genes (full map includes 30 genes). A dashed line (——) indicates one known function, and a solid line (__) indicates multiple functions identified between the regulator and downstream gene. Upregulated regulator = orange; downregulated regulator = blue; upregulated gene = red, downregulated gene = green. The darker intensity of color indicates a higher degree of change. Figure 5. View largeDownload slide Causal network analysis of metallothionein 2. MT2A is the top regulator with 13 lower level regulators. The bottom level is a summary of downstream genes (full map includes 30 genes). A dashed line (——) indicates one known function, and a solid line (__) indicates multiple functions identified between the regulator and downstream gene. Upregulated regulator = orange; downregulated regulator = blue; upregulated gene = red, downregulated gene = green. The darker intensity of color indicates a higher degree of change. Table 5. Hierarchical ranking of the top 20 meta-nodes and their highest ranked nodes in the myometrial term labor network map. Metanode  Major functions and pathways  Core nodes in metanode  CDKN1  Cell response to hypoxia, Apoptosis, DNA replication and repair (P < 1.0 × 10−5)  CDKN1A, UBC, YWHAQ, CDK2, PCNA, GAPDH, UBE2D3, HNRNPM, DHX9, SET  UBC  Cell response to hypoxia, apoptosis, DNA replication and repair (P < 1.0 × 10−5)  UBC, VCP, YWHAQ, UBE2D3, HNRNPM, ITCH, CD81, PSMC2, NCL, DHX9  XRCC5  Cell responses to hypoxia, apoptosis, DNA replication and repair (P < 1.0 × 10−5)  XRCC5, XRCC6, BAX, DHX9, FMNL1, UBC, NCL, CEBPA, CBX5, SUMO2  SMAD2  TGFB signaling (P < 1.9 × 10−11)  SMAD2, SMAD4, SMAD6, HDAC9, HDAC1, RUNX1, TGIF1, SMURF2, ENG, TGFBR1  PIK3R1  Adaptive immune system (P < 8.9 × 10−7) Growth factor signalling (P < 2.8 × 10−6)  PIK3R1, CBL, SHC1, BLNK, SLA, IRS1, GRB2, SYN1, CD28, EGFR  SHC1  Adaptive immune system (P < 8.5 × 10−4) Growth factor signaling (P < 1.3 × 10−5)  SHC1, GRB2, EGFR, BLNK, PIK3R1, CAV1, SYN1, ABL2, ERBB2, SOS1  MAPK14  Cellular senescence (P < 4.2 × 10−6. Innate immune system (P < 3.9 × 10−4)  MAPK14, ATF2, RPS6KA5, MBP, MAPKAPK2, OBSL1, MAP2K6, MAP2K3, MKNK1, PHC2  CBX5  Wnt signaling (P < 8.9 × 10−2)  CBX5, CBX3, TRIM28, CHAF1A, HIST3H3, HDAC9, SMARCA4, CBX1, DNMT3B, MIS12  UPF2  Nonsense mediated decay (P < 1.2 × 10−15)  UPF2, RBM8A, UPF1, DCP2, UPF3B, SMG1, MAGOH, EIF4A3, RNPS1, UPF3A  RICTOR  Cell response to heat stress (P < 8.5 × 10−5) Growth factor signalling (P < 1.0 × 10−5)  RICTOR, MTOR, FKBP1A, MAPKAP1, AKT1, MLST8, HSPA4, PREX1, RPTOR, PDIA3  MBP  Cell senescence (P < 3.4 × 10−7). Innate immunity (P < 1.3 × 10−5). Map kinase (6.6 × 10−4)  MBP, ELK1, MAPK1, MAPK3, LRRK2, MAPK8, MAPK14, MAPK9, PRKCA, UBE2I  SMARCA  Chromatin modification (P < 3.3 × 10−13)  SMARCA2 AND 4, SMARCC1AND 2, PHF10, SMARCB1, SMARCE1, SMARCD1, ACTL6A, ARID1A  RUNX1T  Chromatin modification (P < 1.2 × 10−4). Circadian clock (P < 7.9 × 10−3)  RUNX1T1, NCOR1, HDAC9, HDAC1, RUNX1, HEY2, CBFA2T2, SIN3A, NCOR2, HDAC3  BCL3  Innate immune system (P < 1.5 × 10−3). Cytokine signaling (P < 7.4 × 10−4)  BCL3, MAP3K8, NFKB1, RELB, NFKB2, NFKBIZ, RELA, TRAF2, KAT5, MAP2K1  RELB  Cytokine signaling (P < 4.0 × 10−3)  RELB, BCL3, NFKB2, MAP3K8, RELA, DAXX, DPF2, NFKB1, GSK3B, NFKBIZ  VEGFA  Signaling by VEGF (P < 4.4 × 10−7)  VEGFA, NRP1, KDR, PGF, FLT1, CRYAB, TGFBR2, HNRNPD, HNRNPL, CTGF  NOD2  Cytokine and TLR signalling (P < 6.7 × 10−4)  NOD2, NLRC4, RIPK2, MAP3K7, CASP1, RNF31, SHARPIN, XIAP, RBCK1, SUGT1  MLLT3  TGF signaling and RNA Pol II transcription (P < 1.0 × 10−3)  MLLT3, CDK9, DOT1L, AFF1, MLLT1, AFF4, AFF3, ELL, CCNT1, BCOR  GMNN  Cell cycle and DNA replication (P < 1.0 × 10−3)  GMNN, CDT1, CDC20, AURKA, NCF1, KAT7, REPIN1, IMPDH1, CDH1, FZR1  DCP2  Deadenylation-dependent mRNA decay (P < 7.8 × 10−4)  DCP2, EDC4, DCP1B, MMS19, CAPN2, FANCD2, TRMT2A, POLA2, IKBKAP, INTS7  Metanode  Major functions and pathways  Core nodes in metanode  CDKN1  Cell response to hypoxia, Apoptosis, DNA replication and repair (P < 1.0 × 10−5)  CDKN1A, UBC, YWHAQ, CDK2, PCNA, GAPDH, UBE2D3, HNRNPM, DHX9, SET  UBC  Cell response to hypoxia, apoptosis, DNA replication and repair (P < 1.0 × 10−5)  UBC, VCP, YWHAQ, UBE2D3, HNRNPM, ITCH, CD81, PSMC2, NCL, DHX9  XRCC5  Cell responses to hypoxia, apoptosis, DNA replication and repair (P < 1.0 × 10−5)  XRCC5, XRCC6, BAX, DHX9, FMNL1, UBC, NCL, CEBPA, CBX5, SUMO2  SMAD2  TGFB signaling (P < 1.9 × 10−11)  SMAD2, SMAD4, SMAD6, HDAC9, HDAC1, RUNX1, TGIF1, SMURF2, ENG, TGFBR1  PIK3R1  Adaptive immune system (P < 8.9 × 10−7) Growth factor signalling (P < 2.8 × 10−6)  PIK3R1, CBL, SHC1, BLNK, SLA, IRS1, GRB2, SYN1, CD28, EGFR  SHC1  Adaptive immune system (P < 8.5 × 10−4) Growth factor signaling (P < 1.3 × 10−5)  SHC1, GRB2, EGFR, BLNK, PIK3R1, CAV1, SYN1, ABL2, ERBB2, SOS1  MAPK14  Cellular senescence (P < 4.2 × 10−6. Innate immune system (P < 3.9 × 10−4)  MAPK14, ATF2, RPS6KA5, MBP, MAPKAPK2, OBSL1, MAP2K6, MAP2K3, MKNK1, PHC2  CBX5  Wnt signaling (P < 8.9 × 10−2)  CBX5, CBX3, TRIM28, CHAF1A, HIST3H3, HDAC9, SMARCA4, CBX1, DNMT3B, MIS12  UPF2  Nonsense mediated decay (P < 1.2 × 10−15)  UPF2, RBM8A, UPF1, DCP2, UPF3B, SMG1, MAGOH, EIF4A3, RNPS1, UPF3A  RICTOR  Cell response to heat stress (P < 8.5 × 10−5) Growth factor signalling (P < 1.0 × 10−5)  RICTOR, MTOR, FKBP1A, MAPKAP1, AKT1, MLST8, HSPA4, PREX1, RPTOR, PDIA3  MBP  Cell senescence (P < 3.4 × 10−7). Innate immunity (P < 1.3 × 10−5). Map kinase (6.6 × 10−4)  MBP, ELK1, MAPK1, MAPK3, LRRK2, MAPK8, MAPK14, MAPK9, PRKCA, UBE2I  SMARCA  Chromatin modification (P < 3.3 × 10−13)  SMARCA2 AND 4, SMARCC1AND 2, PHF10, SMARCB1, SMARCE1, SMARCD1, ACTL6A, ARID1A  RUNX1T  Chromatin modification (P < 1.2 × 10−4). Circadian clock (P < 7.9 × 10−3)  RUNX1T1, NCOR1, HDAC9, HDAC1, RUNX1, HEY2, CBFA2T2, SIN3A, NCOR2, HDAC3  BCL3  Innate immune system (P < 1.5 × 10−3). Cytokine signaling (P < 7.4 × 10−4)  BCL3, MAP3K8, NFKB1, RELB, NFKB2, NFKBIZ, RELA, TRAF2, KAT5, MAP2K1  RELB  Cytokine signaling (P < 4.0 × 10−3)  RELB, BCL3, NFKB2, MAP3K8, RELA, DAXX, DPF2, NFKB1, GSK3B, NFKBIZ  VEGFA  Signaling by VEGF (P < 4.4 × 10−7)  VEGFA, NRP1, KDR, PGF, FLT1, CRYAB, TGFBR2, HNRNPD, HNRNPL, CTGF  NOD2  Cytokine and TLR signalling (P < 6.7 × 10−4)  NOD2, NLRC4, RIPK2, MAP3K7, CASP1, RNF31, SHARPIN, XIAP, RBCK1, SUGT1  MLLT3  TGF signaling and RNA Pol II transcription (P < 1.0 × 10−3)  MLLT3, CDK9, DOT1L, AFF1, MLLT1, AFF4, AFF3, ELL, CCNT1, BCOR  GMNN  Cell cycle and DNA replication (P < 1.0 × 10−3)  GMNN, CDT1, CDC20, AURKA, NCF1, KAT7, REPIN1, IMPDH1, CDH1, FZR1  DCP2  Deadenylation-dependent mRNA decay (P < 7.8 × 10−4)  DCP2, EDC4, DCP1B, MMS19, CAPN2, FANCD2, TRMT2A, POLA2, IKBKAP, INTS7  View Large Commonality within the each network of master regulators of labor in the choriodecidua and myometrium The network relationships of the candidate regulators to selected downstream labor-associated genes are summarized in Figure 6. Although each master regulator had a distinct repertoire of downstream genes, there was a degree of overlap, particularly for immunoregulatory or inflammatory genes, well-known to be associated with labor. To explore potential intertissue relationships further, the interconnections between the top 15 meta-nodes for each tissue were mapped (Figure 7). The majority of meta-nodes were distinct for myometrium and choriodecidua; however, the highest ranked common core biological functions of active labor were identified and cross referenced across the myometrium and choriodecidua networks generated. In particular, SHC adaptor protein 1 (SHC1) featured as a highly ranked meta-node in both tissues, and there was strong functional overlap with the myometrial meta-node and node network represented by phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1). Figure 6. View largeDownload slide Master regulators of labor in choriodecidua and myometrium. Summary illustrating causal relationships with key established labor-associated mediators that are in common downstream of the master regulators. Figure 6. View largeDownload slide Master regulators of labor in choriodecidua and myometrium. Summary illustrating causal relationships with key established labor-associated mediators that are in common downstream of the master regulators. Figure 7. View largeDownload slide Connective relationships between hierarchically ranked meta-nodes-nodes in human choriodecidua and myometrium. Meta-nodes are arranged hierarchically by color intensity in an anticlockwise direction (marked by rank [see Tables 3 and 5]). Dotted gray line separates the choriodecidua and myometrium network models. Biologically common relationships between the meta-node clusters are marked by two headed arrows, sharing at least three clusters of genes (nodes). The intersection demonstrates two major meta-nodes PIK3R1 and SHC1 sharing a significant number of nodes and genes. Figure 7. View largeDownload slide Connective relationships between hierarchically ranked meta-nodes-nodes in human choriodecidua and myometrium. Meta-nodes are arranged hierarchically by color intensity in an anticlockwise direction (marked by rank [see Tables 3 and 5]). Dotted gray line separates the choriodecidua and myometrium network models. Biologically common relationships between the meta-node clusters are marked by two headed arrows, sharing at least three clusters of genes (nodes). The intersection demonstrates two major meta-nodes PIK3R1 and SHC1 sharing a significant number of nodes and genes. Inhibition of master regulators Inhibition of VIM in choriodecidual cells (n = 7) with Gö6983 resulted in reduced IL8 mRNA expression (P < 0.05; Figure 8A). Inhibition of TLR4 resulted in elevated IL1RN (P < 0.05), reduced PTGS2 (P < 0.05), and a trend for reduced CCL2 and CCL8 (P = 0.07; Figure 8B, n = 7). Inhibition of MT2A in myometrial cells (n = 9) resulted in downregulated IL1A, IL1B, IL1RN, IL6, IL8, CD44, and VIM mRNA expression (P < 0.05–0.01; Figure 9A). Inhibition of TLR2 led to downregulation of IL8 and CD44 (P < 0.05; Figure 9B). Figure 8. View largeDownload slide Choriodecidual cell mRNA expression of downstream labor-associated genes after inhibition of master regulators. (A) Inhibition of VIM with Go6983 and (B) inhibition of TLR4 with CLI-095. Data are 2ΔΔCT (normalization to GAPDH and vehicle control). Medians ± IQ; n = 7. Dotted line represents vehicle control expression level. Wilcoxon-signed rank test, *P ≤ 0.05. Figure 8. View largeDownload slide Choriodecidual cell mRNA expression of downstream labor-associated genes after inhibition of master regulators. (A) Inhibition of VIM with Go6983 and (B) inhibition of TLR4 with CLI-095. Data are 2ΔΔCT (normalization to GAPDH and vehicle control). Medians ± IQ; n = 7. Dotted line represents vehicle control expression level. Wilcoxon-signed rank test, *P ≤ 0.05. Figure 9. View largeDownload slide Myometrial cell expression of downstream labor-associated genes after inhibition of master regulators. (A) Inhibition of MT2A with Go6983 and (B) inhibition of TLR2 with neutralizing antibody (Mab-htlr2). Data are 2ΔΔCT (normalization to YWHAZ and vehicle control). Medians ± IQ; n = 9. Dotted line represents vehicle control expression level. Wilcoxon-signed rank test, *P ≤ 0.05; **P ≤ 0.01. Figure 9. View largeDownload slide Myometrial cell expression of downstream labor-associated genes after inhibition of master regulators. (A) Inhibition of MT2A with Go6983 and (B) inhibition of TLR2 with neutralizing antibody (Mab-htlr2). Data are 2ΔΔCT (normalization to YWHAZ and vehicle control). Medians ± IQ; n = 9. Dotted line represents vehicle control expression level. Wilcoxon-signed rank test, *P ≤ 0.05; **P ≤ 0.01. Discussion Using a robust and unbiased global network analysis workflow, we have defined relationships of transcriptomic communities altered during active term labor in human myometrium and in human choriodecidua. Additionally, we have identified genes with high centrality highlighting the functional importance of gene:gene inter-relationships within each network during labor. We have further identified candidate genes with strong predicted regulatory actions within each of these transcriptomic networks. The top six regulators of active labor (VIM, TLR4, and TNFSF13B in the choriodecidua, and MT2A, TLR2, and RELB in the myometrium) were prioritized based on their regulatory effects on known mediators of labor. The identification of TLRs as master regulators in this study, which have established roles in parturition [28–34], highlights their central regulatory role in the cascade of labor and provides confidence in our use of unbiased predictive in silico methods. The regulator VIM is a cytoskeletal intermediate filament component with roles in a number of cellular processes. It is abundantly expressed by decidual stromal cells, decidual resident macrophages, and in the chorionic trophoblast layer that directly overlies the decidua [4]. It is a marker of epithelial to mesenchymal transition classically involved in maintaining cell structure, polarity, cell-to-cell adhesion, and cellular motility [35]. Duquette et al. proposed that the upregulation of VIM protein may sustain regular muscle contractions in laboring myometrium [36], but this idea has not been further explored to date. The VIM gene promoter contains a conserved NFKB sequence [37], is associated with elevated PTGS2 expression [38], and is also associated with increased cytokine/chemokine expression and macrophage infiltration [4, 9, 17, 18]. These interactions support our analysis of VIM as a major regulator of labor in the choriodecidual network with interactions and downstream actions on key elements of the labor cascade. The other highly ranked regulator, TNF superfamily member TNFSF13B, is involved in infection and innate immune responses [39]. Upregulated in placenta during late pregnancy, it contributes to maternal T-helper cell adaptations to the fetus [40]. Transcript variants or reduced expression are associated with pregnancy pathologies such as pre-eclampsia and miscarriage [41, 42]. The cellular origin of choriodecidual expression of TNFSF13B is unknown; however, it localizes to the endometrial stroma and endometrial glands, and is also expressed in the syncytiotrophoblast, trophoblasts, and some stromal cells of the placenta [43]. It is also abundantly expressed by macrophages [44], which are present in significant numbers in decidua during term labor [17]. Our network prediction supports the regulatory role of TNFSF13B with widespread downstream effects on inflammation, growth factor signaling, and ECM remodeling. The genes TLR2 and 4 were predicted as regulators of the myometrial and choriodecidual labor transcriptome, respectively. They are microbial response receptors that activate NFKB signaling and coordinate inflammatory responses in response pathogens, including in infection-associated preterm labor [4, 45, 46]. TLR2 mRNA and protein localizes to myometrial cells, is strongly expressed in tissue-resident leukocytes, and its expression is increased during active labor [28]. TLR4 mRNA and protein localizes to decidual cells and tissue-resident decidual macrophages; its expression in the decidua exhibits a cyclic pattern across normal pregnancy with its highest expression at term [47]. Abnormally high protein expression of decidual TLR4 is evident in pregnancy pathologies such as early onset pre-eclampsia [48]. These two specific TLRs can be activated by noninfectious danger-associated molecular pattern molecules liberated as a result of tissue trauma, cell death, and cellular stress [49]. Our in vitro results in basal conditions suggest a homeostatic role for these receptors, and preparation for normal term labor. Both TLRs are upregulated in placental and uterine tissues during active labor [1, 4, 31, 50], with roles in amplifying cytokine and MMP expression and prostaglandin synthesis [18, 51–53]. Previous studies reported reduced PTGS2 and cytokine expression in decidual cells following TLR4 inhibition [51]; our data support the TLR4 mediation of PTGS2 and upregulation of anti-inflammatory IL1RN in normal term choriodecidual cells. Neutralization of TLR2 inhibited IL8 and CD44 mRNA expression in myometrial cells, consistent with their involvement in immune responses during labor [3, 5, 8]. A member of the NFKB complex, RELB, was also defined as a predicted master regulator of myometrial labor transcriptome. Studies of labor have classically focused on other NFKB isoforms (RELA, C-REL, P52, and P50) [54], and the role of RELB and its noncanonical pathways of NFKB activation are unclear. RELB mRNA is expressed in term myometrial cells [55]. Although it was not experimentally tested in this study, we identified it as a highly ranked component of the myometrial network with a potential role in mediating major networks of labor. The myometrial regulator MT2A is involved in heavy metal binding and cellular homeostasis [56]. Elevated expression in the myometrium has been reported in term labor [57], and possesses functions that overlap with multiple labor processes, including inflammation [58], and progesterone and estrogen signaling [59, 60]. Our findings from in silico and in vitro analyses support a role for MT2A as a core regulator of myometrial inflammatory genes. These findings expose an alternative inflammatory regulation during labor, via alteration of heavy metal homeostasis, as described in macrophages where low intracellular iron levels affect TLR signaling and cytokine production [61]. Iron chelation contributes to immunomodulation, in determining cellular pro- or anti-inflammatory responses [62]. In this study, widespread suppression of established labor mediators by MT2A inhibition strengthens the hypothesis that MT2A is a master regulator of the labor cascade. To our knowledge, this is the first study to perform detailed network analyses of transcriptomic changes and identify key upstream regulators, in two uterine layers during term labor. The inclusion of both tissues are a significant strength of the study, as extensive labor-associated changes occur in the choriodecidua and myometrium and as such, therapeutic approaches to prevent preterm labor are likely to be more successful if they target both tissue layers. As expected from the differences in their biological functions, there are distinct transcriptomes and regulatory processes in each of the myometrium and decidua. We also identified shared core labor-associated biological functions such as immunoregulatory and growth factor signaling, indicating there are molecular pathways that are fundamental to active labor. However, interpretations of overlaps and interactions between these tissue layers must be made with caution, as there are inevitable differences in the way these two tissue types were sampled. Importantly, the myometrial samples were collected during labor, while the choriodecidua was sampled immediately following active labor, and therefore the transcriptome of the latter likely includes additional changes related to delivery. For this reason, we have restricted the majority of our analyses to each tissue layer separately, but include a tentative comparison of the core regulators to examine potential commonalities and interactions. Future studies could confirm the putative interactions involving SHC1 and PIK3R1 between decidua and myometrial using tissue samples from the same patients. Strengths of this study include the unbiased in-depth analyses of the laboring transcriptome and stringent prioritization of master regulators, and identification of causal relationships within the labor cascade of those regulators. Importantly, the only method relying on literature sources (and therefore susceptible to publication bias) was the final casual analysis, with prior analysis methods utilizing strictly mathematical algorithms to predict gene–gene and protein–protein interactions. The coupling of in silico and in vitro approaches adds an additional perspective, and while the latter did not have alter expression of all predicted mediators, the inhibition of MT2A in particular provides confidence that interfering with a single regulatory gene can impact a broad gene network. Other limitations concern the basal expression of the cells studied—successful inhibition of labor-associated genes is likely to require prior stimulation, i.e., by labor. Optimization of treatment regime and concentrations would better elucidate the pharmacokinetics and effectiveness of inhibitors, and this should be embarked upon before in-depth studies to test their therapeutic potential. Using chemical inhibitors does not preclude off-target effects. We used a PKC inhibitor to deplete MT2A and VIM at different concentrations based on published studies. However, blocking PKC activity can downregulate other pathways including prostaglandin F2A receptor [63], OXTR [64], cell junctions [65], and cytokine production [66]. Direct suppression of the gene targets would be possible using siRNA-mediated depletion; however, the goal of this study was to provide proof of concept evidence for generating regulatory targets for labor by network analysis and to investigate potential therapeutics that could be more easily translated to clinical practice. In summary, we identified unique gene candidates with differential expression during labor, high centrality in the network of laboring signals, and significant causal relationships within the labor cascade. These centrality features indicate important roles in regulating the labor transcriptome and therefore identifies them as putative master regulators of labor. Future work is required to investigate the therapeutic potential of these candidate regulators of labor. Though the regulator candidates were dissimilar between choriodecidua and myometrium, they shared common downstream genes, many of which were inflammatory in nature. Future comparative network analyses may identify more interconnected regulatory meta-nodes between choriodecidua and myometrium, enabling targeting of therapies to both tissues. Supplementary data Supplementary data are available at BIOLRE online. Supplementary Figure S1. Immunohistochemical characterization of isolated human choriodecidual cells. Isolated choriodecidual cells comprised (A) IGFBP1+ and (B) vimentin+ decidual stromal cells, and (C) cytokeratin-7 positive chorion cells, representing the major cell types present in choriodecidua. (D) No CD45+ immune cells were detected. (E) Negative mouse IgG control. Scale bar = 50 μm. Supplementary Figure S2. Causal network analysis of the TLR complex in the choriodecidua. A summary of downstream genes (full map includes 25 genes) with at least two network connections (dashed line). Upregulated expression of regulators = orange; upregulated expression of genes = pink. Supplementary Figure S3. Causal network analysis of TNFSF13B with supporting downstream relationships with choriodecidual genes. A summary network (full map includes 105 genes) with (i) at least two independent network connections (dashed line) or (ii) have multiple associated functions from a single regulator (solid line). Upregulated regulator = orange, upregulated gene = pink; downregulated regulator = blue, downregulated gene = green where the intensity of color indicates the degree of up- or downregulated expression. Lines indicate activation (orange) or inhibition (blue) or the change was not as predicted in literature (yellow). Supplementary Figure S4. Causal network analysis of TLR2 with downstream relationships in the laboring myometrium. A summary network (full map includes 90 genes) with (i) at least two network connections (dashed line) or (ii) have multiple associated functions from an independent regulator (solid line). Upregulated regulator = orange, upregulated gene = pink; downregulated regulator = blue, downregulated gene = green where the darker intensity of color indicates the greater degree of up- or downregulated expression. Lines indicate activation (orange) or inhibition (blue), or the change was not as predicted in literature (yellow). Supplementary Figure S5. Causal network analysis of RELB in the laboring myometrium. Relationships with downstream genes (full map includes 123 genes) with (i) at least two independent network connections (dashed line) or (ii) have many associated functions from a single regulator (solid line). Upregulated regulator = orange, upregulated gene = pink; downregulated regulator = blue, downregulated gene = green where the darker the intensity of color indicates the higher degree of up- or downregulated expression. Lines indicate activation (orange) or inhibition (blue) or the change was not as predicted in literature (yellow). Acknowledgments This work was supported by The James Tudor Foundation and Tommy's the Baby Charity. We thank the research midwives at St. Mary's Hospital, Manchester, for their invaluable help with subject recruitment and tissue collection, and also to Mrs Helen Bishof for her technical assistance in the in vitro studies. Declarations: The authors declare that they have no competing interests. The funding bodies had no involvement in study design, data analysis or manuscript preparation. Footnotes † Grant Support: This work was supported by The James Tudor Foundation and Tommy's the Baby Charity. The Maternal and Fetal Health Research Centre is supported by Tommy's the Baby Charity, an Action Research Endowment Fund, the Manchester BRC and the Greater Manchester CLRN. LKH is supported by a BBSRC David Phillips Research Fellowship. CD and SG are supported by SickKids Foundation and the Canadian Institutes of Health Research Institute of Human Development, Child and Youth Health. ‡ Accession Number: GSE9159 References 1. Haddad R, Tromp G, Kuivaniemi H, Chaiworapongsa T, Kim YM, Mazor M, Romero R. Human spontaneous labor without histologic chorioamnionitis is characterized by an acute inflammation gene expression signature. Am J Obstet Gynecol  2006; 195( 2): 394.e1– 394.e24. Google Scholar CrossRef Search ADS   2. Khanjani S, Kandola MK, Lindstrom TM, Sooranna SR, Melchionda M, Lee YS, Terzidou V, Johnson MR, Bennett PR. NF-κB regulates a cassette of immune/inflammatory genes in human pregnant myometrium at term. J Cell Mol Med  2011; 15( 4): 809– 824. 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Delineating differential regulatory signatures of the human transcriptome in the choriodecidua and myometrium at term labor,

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Abstract

Abstract Preterm deliveries remain the leading cause of neonatal morbidity and mortality. Current therapies target only myometrial contractions and are largely ineffective. As labor involves multiple coordinated events across maternal and fetal tissues, identifying fundamental regulatory pathways of normal term labor is vital to understanding successful parturition and consequently labor pathologies. We aimed to identify transcriptomic signatures of human normal term labor of two tissues: in the fetal-facing choriodecidua and the maternal myometrium. Microarray transcriptomic data from choriodecidua and myometrium following term labor were analyzed for functional hierarchical networks, using Cytoscape 2.8.3. Hierarchically high candidates were analyzed for their regulatory casual relationships using Ingenuity Pathway Analysis. Selected master regulators were then chemically inhibited and effects on downstream targets were assessed using real-time quantitative PCR (RT-qPCR). Unbiased network analysis identified upstream molecular components in choriodecidua including vimentin, TLR4, and TNFSF13B. In the myometrium, candidates included metallothionein 2 (MT2A), TLR2, and RELB. These master regulators had significant differential gene expression during labor, hierarchically high centrality in community cluster networks, interactions amongst the labor gene set, and strong causal relationships with multiple downstream effects. In vitro experiments highlighted MT2A as an effective regulator of labor-associated genes. We have identified unique potential regulators of the term labor transcriptome in uterine tissues using a robust sequence of unbiased mathematical and literature-based in silico analyses. These findings encourage further investigation into the efficacy of predicted master regulators in blocking multiple pathways of labor processes across maternal and fetal tissues, and their potential as therapeutic approaches. Introduction Labor involves sensitization of the uterus to contractile signals, with amplification and increased frequency of myometrial contractions. However, labor also involves a multitude of events, including inflammatory signaling, decidual activation, fetal and maternal endocrine coordination, cervical ripening and dilation, fetal membrane dissociation, and placental separation [1–5]. Pathologies such as preterm delivery remain a leading cause of infant morbidity and mortality [6]. Treatments for preterm labor target a single event—myometrial contractions—and these tocolytics have low efficacy, do not improve fetal outcomes, and are associated with maternal side effects [7]. Multiple pathways in the labor cascade are likely to be contributing factors in premature deliveries and focusing on more than a single process and in more than one tissue layer, may be more effective in preventing preterm labor. To date, most studies investigating physiological changes during labor have focused on a single tissue layer, often the myometrium. The decidua is the interface between the fetal tissues and myometrium and is the site of significant labor-associated inflammatory, progesterone, and prostaglandin activity [1, 4, 8–10]. Moreover, recent studies propose cellular senescence in the amniochorion as a key dictator of timing of labor [11]. Our approach to characterizing uterine changes during term labor therefore includes analyses of concurrent events in both the myometrium and choriodecidua. Early events of normal term labor are still being characterized in maternal and fetal tissues and there is a wealth of studies identifying differential expression of genes involved in labor; however, their interactions and regulatory relationships in the different tissue types are poorly understood. Identifying the upstream regulators of a suite of essential labor mediators is essential for understanding the complexities of normal labor and consequently labor pathologies and development of effective therapeutics. Microarray technology has generated extensive lists of genes whose expression changes with labor. In the myometrium, these include contractile-associated genes: prostaglandin synthase 2 (PTGS2), prostaglandin F2A receptor, oxytocin receptor (OXTR), and gap junction 1 [3, 5]. Other significantly altered genes include those involved in cytoskeletal structure, cell metabolism, cell death, signal transduction, and inflammatory cytokines [2, 3, 8, 9]. Likewise, studies have identified a strong inflammatory response not associated with a microbial response in intact fetal membranes and choriodecidua. These include widespread upregulation of chemokines, cytokines, NFKB complex components, and toll-like receptors (TLR), and with genes governing tissue remodeling and cell survival, e.g. TP53, BCL2 family [1, 4]. Preliminary pathway analysis identified putative upstream regulators of choriodecidual labor transcriptome, including NFKB components and microRNAs (miR-21, -46, -141, -200) [4]. Further network analysis is required to identify key components of the labor process, regulators and characterization of causal relationships of downstream effector genes. We hypothesized that a comprehensive global network analysis of transcriptomic data from choriodecidua and myometrium would identify master regulators of labor in these two intimately associated uterine tissues. We aimed to (1) perform hierarchical network analysis to prioritize the most significant interactions between genomic clusters and genes differentially expressed in labor; (2) identify potential regulatory candidate genes in each tissue; and (3) test whether inhibition of the predicted master regulator candidates would alter expression of downstream labor-associated molecules. Materials and methods Microarray data Term labor choriodecidua transcriptome data described by Stephen et al. were used [4]. In brief, all samples were from term deliveries (37–42 weeks gestation) with uncomplicated pregnancies: elective caesareans, no active labor (n = 12; term no labor), and vaginal deliveries (n = 11; term active labor). Isolated RNA samples were hybridized to genechip Human Genome U133 Plus 2.0 Array (Affymetrix, UK), data normalized using robust multiarray analysis from Affymetrix Microarray Suite [10], and statistically processed using PUMA package [11]. This study used the parameters of probability of positive log ratio (PPLR) of at least 0.997 and less than 0.00001. Publicly available myometrial transcriptomic data accession GSE9159 was accessed from NCBI GEO database [5]. The data were generated using the same genechip as above, with uncomplicated pregnancies at term (38–41 weeks gestation) from caesarean sections after onset of labor (n = 6; emergency section after normal active labor but arrest of cervical dilation at ≥6cm), or no labor (n = 6; elective cesarean section after 38 weeks of gestation, with no signs of labor). Data were extracted and normalized using the R statistical package and a statistical restriction of P < 0.02. Network analysis of the labor transcriptome To investigate the relationships between differentially expressed genes, all known protein:protein interactions were referenced from the Biological General Repository for Interaction Datasets (BioGrid Version 3.2.117; http://thebiogrid.org) [12]. To prioritize and identify the hierarchy of interactions and ensure robustness of the analysis, a network analysis protocol was developed using two mathematical algorithms, cross-referencing to the differentially expressed transcript data, followed by analysis of downstream interactions using literature-based causal analysis (Ingenuity Pathway Analysis software; IPA) (Figure 1) [13]. The work flow was as follows: Mathemathical network modeling using Cytoscape (software version 2.8.3 with BioGrid database). This study used the plugin ModuLand to measure centrality of gene clusters with an overlapping community approach, thus forming an interconnected hierarchy where nodes (clusters of genes) form the core for the upper level, designated as a meta-node (cluster of nodes) [14]. Each meta-node is represented by the top-most central gene in the cluster. The highest ranked interconnected regulators (all meta-nodes, nodes in the top 5 meta-nodes, and any duplicate nodes) were cross-referenced to array data for differential gene expression associated with labor. The refined dataset was subjected to secondary mathemathical modeling using ClusterOne plugin [15]. To prioritize the volume of hierarchially ranked data, only regulators identified by both mathematical models were included for casual analysis. Literature-based causal analysis was performed using IPA to identify master regulators with a network of downstream genes with established association with labor [16]. Figure 1. View largeDownload slide Workflow of in silico analyses. The microarray data were extracted and analyzed for candidate master regulators with high centrality and connectivity using (1) ModuLand (2) ClusterOne, and (3) Causal analysis. Figure 1. View largeDownload slide Workflow of in silico analyses. The microarray data were extracted and analyzed for candidate master regulators with high centrality and connectivity using (1) ModuLand (2) ClusterOne, and (3) Causal analysis. In vitro analyses: choriodecidua Placentas were obtained from women with uncomplicated pregnancies undergoing elective caesarean section at term (37–42 weeks, n = 6). Written informed consent was obtained and ethical approval granted by North West REC (#08/H1010/55(+55)). Choriodecidua was sampled from the mid-zone area of fetal membranes as described [17, 18]. Media culture products were purchased from GIBCO (Life Technologies, UK) unless otherwise stated. Choriodecidual cells were isolated using methods adapted from published studies [19, 20]. In brief, tissues were mechanically macerated and digested with collagenase (1.0 mg/ml, Sigma-Aldrich, UK) and DNase (20mg/ml, Sigma-Aldrich) at 37°C for up to 60 min. Ten percent fetal bovine serum (FBS) was added to inhibit enzymatic activity before filtering through a wire mesh and 40 μm filter. Choriodecidual cells were separated from erythrocytes using histopaque (Sigma-Aldrich) and then cultured at 5 × 105 per ml in DMEM/F-12 (supplemented with L-glutamine (1.5 mg/ml), penicillin streptomycin (0.5 mg/ml), 10% (v/v) FBS, B-estradiol (10−8 M, Sigma-Aldrich), and progesterone (5 × 10−7 M, Sigma-Aldrich). To confirm isolated cells were choriodecidual, they were characterized by immunocytochemical staining as described [21], using monoclonal antibodies against: IGFBP-1 and vimentin (VIM) for decidual stromal cells, CD45 (DAKO, UK) for leukocytes, cytokeratin 7 for epithelial cells (Supplementary Figure S1). Primary antibodies are listed in Table 1 and were purchased from DAKO, UK. Negative control was a non-immune mouse IgG (Sigma-Aldrich, UK) at matching concentrations. Table 1. Antibodies used for immunocytochemistry. Primary antibody  Company  Concentration  TLR4 mouse monoclonal (ab22048)  Abcam, UK  0.63 μg/ml  CXCR4 mouse monoclonal (SC53534)  Santa Cruz, DEU  1 μg/ml  CD44 mouse monoclonal (M7082)  DAKO, UK  1.6 μg/mL  Vimentin mouse monoclonal (M0760)  DAKO, UK  0.364 μg/mL  Secondary antibody  Company  Concentration  Goat anti mouse  DAKO, UK  3.86 μg/mL  Primary antibody  Company  Concentration  TLR4 mouse monoclonal (ab22048)  Abcam, UK  0.63 μg/ml  CXCR4 mouse monoclonal (SC53534)  Santa Cruz, DEU  1 μg/ml  CD44 mouse monoclonal (M7082)  DAKO, UK  1.6 μg/mL  Vimentin mouse monoclonal (M0760)  DAKO, UK  0.364 μg/mL  Secondary antibody  Company  Concentration  Goat anti mouse  DAKO, UK  3.86 μg/mL  View Large To inhibit VIM, the protein kinase C (PKC) inhibitor Gö6983 was used at 100 nm (G1918; Sigma Aldrich, UK). This concentration has been shown to reduce VIM protein expression [22], decrease focal attachments between adherent cells and reduce cellular motility [23]. For TLR4, the cyclohexene inhibitor TAK-242 was used at 1 μm (TLRL-CLI95; Invivogen, France) [24]. The vehicle control was DMSO (0.002% v/v). Cells were treated for 24 h with inhibitors or vehicle. In vitro analyses: myometrium An immortalized human myometrial smooth muscle cell line, htert-C3, [25] was used for the function of putative myometrial regulators. Cells were cultured at 5 × 104 per well in DMEM/F12 (as above, with gentomycin (100 μg/ml)). The PKC inhibitor Gö6983 was used at 15 μm; this concentration inhibits metallothionein 2A (MT2A) mRNA expression [26]. A neutralizing antibody Mab hTLR2, Clone TL2.1 (100 ng/ml, Invivogen, France) was used to inhibit TLR2 [27]. Vehicle controls were DMSO (0.002% for MT2A) or media (for TLR2). Cells were treated for 24 h with inhibitors or vehicle. Quantitative real-time quantitative PCR Total RNA was isolated from cultured cells using the Mirvana miRNA Isolation Kit (Ambion, UK), followed by assessment by Nanodrop 2000 UV-Vis Spectrophotometer. RNA was reverse transcribed using Affinity Script Multiple Temperature cDNA Synthesis Kit (Agilent, UK) and RT-qPCR performed using Ultra-Fast SYBR Green qPCR Master Mix III (Agilent), and Stratagene MX3005 machine. Multiple reference genes were screened before selecting GAPDH for choriodecidual cells and YWHAZ for myometrial cell line, as the most stably expressed reference genes for data normalization. Primers are in Table 2. Table 2. Primers used for real-time quantitative PCR. Gene  Forward primer 5΄-3΄  Reverse primer 5΄-3΄  Accession number  TBP  CACGAACCACGGCACTGATT  TTTTCTTGCTGCCAGTCTGGAC  NM_003194  YWHAZ  ACTTTTGGTACATTGTGGCTTCAA  CCGCCAGGACAAACCAGTAT  NM_003406  BACTIN  ATGTGGCCGAGGACTTTGATT  AGTGGGGTGGCTTTTAGGATG  NM_001101  RPL13  CTTCTCGGCCTGTTTCCGTAG  CGAGGTTGGCTGGAAGTACC  NM_012423  GAPDH  GCCAAATATGATGACATCAAGAAGG  GGTGTCGCTGTTGAAGTCAGAG  NM_002046.5  CCL2  AATCAATGCCCCAGTCACCTGC  CGGAGTTTGGGTTTGCTTGTCC  NM_002982  CCL4  CAGCACCAATGGGCTCAGA  CACTGGGATCAGCACAGACT  NM_002984  CCL8  GAGAGCTACACAAGAATCACCAA  TGGTCCAGATGCTTCATGGAA  NM_005623  CD44  CTGCCGCTTTGCAGGTGTA  CATTGTGGGCAAGGTGCTATT  NM_000610.3  IL1RN  CGGGTGCTACTTTATGGGCA  GGTCGGCAGATCGTCTCTAA  NM_000577  IL1A  TGGTAGTAGCAACCAACGGGA  ACTTTGATTGAGGGCGTCATTC  NM_000575.3  IL1B  CTCGCCAGTGAAATGATGGCT  GTCGGAGATTCGTAGCTGGAT  NM_000576.2  IL6  CCTGAACCTTCCAAAGATGGC  TTCACCAGGCAAGTCTCCTCA  NM_000600  IL8  CACCGGAAGGAACCATCTCACT  TGGGGACACCTTTTAGCATC  NM_000584.3  MYLK  CCCGAGGTTGTCTGGTTCAAA  GCAGGTGTACTTGGCATCGT  NM_053025  NFKBIA  CCAACTACAATGGCCACACGTGTCTACA  GAGCATTGACATCAGCACCCAAGG  NM_020529  PTGER4  CATCATCTGCGCCATGAGTGT  GCTTGTCCACGTAGTGGCT  NM_000958  PTGS2  CGATGCTCATGCTCTTCGC  GGGAGACTGCATAGATGACAGG  NM_000956.3  VIM  AGTCCACTGAGTACCGGAGAC  CATTTCACGCATCTGGCGTTC  NM_003380.3  ZEB2  TCTGTAGATGGTCCAGTGAAGA  GTCACTGCGCTGAAGGTACT  NM_001171653  Gene  Forward primer 5΄-3΄  Reverse primer 5΄-3΄  Accession number  TBP  CACGAACCACGGCACTGATT  TTTTCTTGCTGCCAGTCTGGAC  NM_003194  YWHAZ  ACTTTTGGTACATTGTGGCTTCAA  CCGCCAGGACAAACCAGTAT  NM_003406  BACTIN  ATGTGGCCGAGGACTTTGATT  AGTGGGGTGGCTTTTAGGATG  NM_001101  RPL13  CTTCTCGGCCTGTTTCCGTAG  CGAGGTTGGCTGGAAGTACC  NM_012423  GAPDH  GCCAAATATGATGACATCAAGAAGG  GGTGTCGCTGTTGAAGTCAGAG  NM_002046.5  CCL2  AATCAATGCCCCAGTCACCTGC  CGGAGTTTGGGTTTGCTTGTCC  NM_002982  CCL4  CAGCACCAATGGGCTCAGA  CACTGGGATCAGCACAGACT  NM_002984  CCL8  GAGAGCTACACAAGAATCACCAA  TGGTCCAGATGCTTCATGGAA  NM_005623  CD44  CTGCCGCTTTGCAGGTGTA  CATTGTGGGCAAGGTGCTATT  NM_000610.3  IL1RN  CGGGTGCTACTTTATGGGCA  GGTCGGCAGATCGTCTCTAA  NM_000577  IL1A  TGGTAGTAGCAACCAACGGGA  ACTTTGATTGAGGGCGTCATTC  NM_000575.3  IL1B  CTCGCCAGTGAAATGATGGCT  GTCGGAGATTCGTAGCTGGAT  NM_000576.2  IL6  CCTGAACCTTCCAAAGATGGC  TTCACCAGGCAAGTCTCCTCA  NM_000600  IL8  CACCGGAAGGAACCATCTCACT  TGGGGACACCTTTTAGCATC  NM_000584.3  MYLK  CCCGAGGTTGTCTGGTTCAAA  GCAGGTGTACTTGGCATCGT  NM_053025  NFKBIA  CCAACTACAATGGCCACACGTGTCTACA  GAGCATTGACATCAGCACCCAAGG  NM_020529  PTGER4  CATCATCTGCGCCATGAGTGT  GCTTGTCCACGTAGTGGCT  NM_000958  PTGS2  CGATGCTCATGCTCTTCGC  GGGAGACTGCATAGATGACAGG  NM_000956.3  VIM  AGTCCACTGAGTACCGGAGAC  CATTTCACGCATCTGGCGTTC  NM_003380.3  ZEB2  TCTGTAGATGGTCCAGTGAAGA  GTCACTGCGCTGAAGGTACT  NM_001171653  View Large Results Network analysis and identification of regulatory genes Choriodecidua The original data identified 796 significant transcriptional changes following labor [4]; these were used to generate a network model of protein:protein interactions (4980 nodes (genes) with 12914 interactions). Analysis of the network structure via ModuLand identified a hierarchy of 71 meta-nodes (gene modules), with 1610 interactions (Figure 2). The genes representing each meta-node possessed high centrality in the cluster of connected genes (nodes). The top 20 meta-nodes in the choriodecidua labor network and their respective core nodes are hierarchically illustrated in Table 3, together with their biological functions and pathways. The network is dominated by gene clusters governing cellular responses to stress and hypoxia, cell death/survival, cell cycle regulation, and circadian rhythm. Immune regulatory and inflammatory clusters are also highly ranked. The core nodes within the top meta-nodes also have a high hierarchal ranking, e.g., NFKBIA, TP53, and VIM are found within the top meta-node clusters, indicating a strong interactive community within the network. Figure 2. View largeDownload slide Network modeling of meta-node expression in human choriodecidua. The upper level of network clusters assessed using the ModuLand algorithm in Cytoscape 2.8.3. These principle clusters are meta-nodes and their interactions of term labor in choriodecidua. The lines indicate interactions between meta-nodes and lower level nodes. The yellow lines indicate highly ranked major interactions. Figure 2. View largeDownload slide Network modeling of meta-node expression in human choriodecidua. The upper level of network clusters assessed using the ModuLand algorithm in Cytoscape 2.8.3. These principle clusters are meta-nodes and their interactions of term labor in choriodecidua. The lines indicate interactions between meta-nodes and lower level nodes. The yellow lines indicate highly ranked major interactions. Table 3. Hierarchical ranking of the top 20 meta-nodes and their highest ranked nodes in the term labor choriodecidual network map. Metanode  Major functions and pathways  Core nodes in metanode   UBC  Cell responses to stress, apoptosis, circadian clock, DNA repair (P < 1.0 × 10−4)  UBC, NFKBIA, RUVBL2, NOTCH1, VIM, UBE2D3, RPS4X, ARRB1, RPSA, XPO1  CDC34  Responses to hypoxia, apoptosis, circadian clock, DNA repair (P < 1.0 × 10−4)  CDC34, UBE2D3, UBC, RBX1, ARRB1, MDM2, CUL1, RUVBL2, NOTCH1, NRF1  STUB1  Cell responses to hypoxia, apoptosis, circadian clock, DNA repair (P < 1.0 × 10−4)  STUB1, UBC, UBE2D3, ARRB1, HSP90AA1, HSPA4, UBE2D1, CSNK1A1, PARK7, TP53  SHC1  Adaptive and innate immunity (P < 3.8 × 10−6), growth factor signaling (P < 3.9 × 10−7)  SHC1, GRB2, LCP2, EGFR, ERBB3, CBL, PAG1, SOS1, ERBB2, GRAP2  BIRC3  Innate immunity (P < 1.4 × 10−6)  BIRC3, TRAF2, TNFAIP3, USP53, UBE2D2, BCL10, RIPK1, PEG3, TRAF6, DIABLO  BRCA1  DNA repair (P < 2.1 × 10−7)  BRCA1, BRIP1, BACH1, NBN, LMO4, MLH1, RPA1, PMS2, HNRNPA2B1, EZR  NBN  Cellular senescence (P < 1.5 × 10−5), DNA repair (P < 1.8 × 10−9)  NBN, MRE11A, RAD50, H2AFX, MDC1, CALR, ATM, USP53, BACH1, EP300  SMC3  Cell cycle (P < 1.3 × 10−4)  SMC3, RAD21, SMC1A, CASP4, STAG1, ANKRD28, S100A9, PDS5A, STAG2, CLU  CKS1B  G1/S transition (P < 6.5 × 10−9), DNA replication (P < 7.5 × 10−7)  CKS1B, CDK2, SKP2, CDK1, MCM2, SKP1, CDKN1B, COPS6, CCNA2, CCNG2  MBD3  Chromatin organization (P < 5.6 × 10−8)  MBD3, HDAC1, BCL11B, HDAC2, MTA2, MBD2, MXD1, ATF3, RBBP4, ARID4A  BRD4  TGFB signaling (P < 7.8 × 10−5)  BRD4, CDK9, AFF4, CCNT2, ELL2, CCNT1, MLLT3, MLLT1, AFF1, HEXIM1  FAS  Apoptosis (P < 8.2 × 10−10)  FAS, FADD, DAXX, FASLG, KRIT1, FAF1, CASP10, CASP8, TGFB2, CFLAR  SOCS3  Cytokine signaling (P < 1.3 × 10−7)  SOCS3, JAK2, CSF2RB, TCEB1, PRLR, PTPN11, TCEB2, IL2RB, IFNGR1, JAK1  UBE2B  Adaptive immune system (P < 3.5 × 10−2)  UBE2B, RAD18, UBR2, UBR3, UBA1, CTNNB1, UBQLN1, DSTN, PCNA, CPLX1  EIF3EBP1  MTOR signaling (P < 1.0 × 10−8), cellular response to heat stress (P < 1.6 × 10−4)  EIF4EBP1, MTOR, EIF4E, RPTOR, EIF2C2, LRRK2, ATM, SLMAP, PPP2R4, LRPAP1  ACD  Telomere maintenance, glycogen breakdown (P < 8.0 × 10−4)  ACD, POT1, TINF2, CALD1, DBNL, PGM1, ANXA4, SARS, ACOT7, IL1RN  CDK19  Metabolism of lipids (P < 6.0 × 10−9)  CDK19, MED9, MED16, MED28, MED19, MED26, MED29, MED12, MED18, CDK8  TLR4  Cytokine and TLR signaling (P < 5.0 × 10−3)  TLR4, MYD88, TLR2, TIRAP, LY96, TICAM2, TOLLIP, TLR1, SRC, SYK  MXD1  Chromatin modification (P < 6.0 × 10−3)  MXD1, MAX, SIN3A, AKT1, MLX, ARID4A, SAP30, VDR, KDM5A, HDAC2  TBXA2R  Regulation of insulin secretion, WNT signaling (P < 2.7 × 10−5)  TBXA2R, GNAQ, GNA13, RAB11A, PTGIR, PRKCA, PSME3, KCNMA1, PSMA7, GNB1  Metanode  Major functions and pathways  Core nodes in metanode   UBC  Cell responses to stress, apoptosis, circadian clock, DNA repair (P < 1.0 × 10−4)  UBC, NFKBIA, RUVBL2, NOTCH1, VIM, UBE2D3, RPS4X, ARRB1, RPSA, XPO1  CDC34  Responses to hypoxia, apoptosis, circadian clock, DNA repair (P < 1.0 × 10−4)  CDC34, UBE2D3, UBC, RBX1, ARRB1, MDM2, CUL1, RUVBL2, NOTCH1, NRF1  STUB1  Cell responses to hypoxia, apoptosis, circadian clock, DNA repair (P < 1.0 × 10−4)  STUB1, UBC, UBE2D3, ARRB1, HSP90AA1, HSPA4, UBE2D1, CSNK1A1, PARK7, TP53  SHC1  Adaptive and innate immunity (P < 3.8 × 10−6), growth factor signaling (P < 3.9 × 10−7)  SHC1, GRB2, LCP2, EGFR, ERBB3, CBL, PAG1, SOS1, ERBB2, GRAP2  BIRC3  Innate immunity (P < 1.4 × 10−6)  BIRC3, TRAF2, TNFAIP3, USP53, UBE2D2, BCL10, RIPK1, PEG3, TRAF6, DIABLO  BRCA1  DNA repair (P < 2.1 × 10−7)  BRCA1, BRIP1, BACH1, NBN, LMO4, MLH1, RPA1, PMS2, HNRNPA2B1, EZR  NBN  Cellular senescence (P < 1.5 × 10−5), DNA repair (P < 1.8 × 10−9)  NBN, MRE11A, RAD50, H2AFX, MDC1, CALR, ATM, USP53, BACH1, EP300  SMC3  Cell cycle (P < 1.3 × 10−4)  SMC3, RAD21, SMC1A, CASP4, STAG1, ANKRD28, S100A9, PDS5A, STAG2, CLU  CKS1B  G1/S transition (P < 6.5 × 10−9), DNA replication (P < 7.5 × 10−7)  CKS1B, CDK2, SKP2, CDK1, MCM2, SKP1, CDKN1B, COPS6, CCNA2, CCNG2  MBD3  Chromatin organization (P < 5.6 × 10−8)  MBD3, HDAC1, BCL11B, HDAC2, MTA2, MBD2, MXD1, ATF3, RBBP4, ARID4A  BRD4  TGFB signaling (P < 7.8 × 10−5)  BRD4, CDK9, AFF4, CCNT2, ELL2, CCNT1, MLLT3, MLLT1, AFF1, HEXIM1  FAS  Apoptosis (P < 8.2 × 10−10)  FAS, FADD, DAXX, FASLG, KRIT1, FAF1, CASP10, CASP8, TGFB2, CFLAR  SOCS3  Cytokine signaling (P < 1.3 × 10−7)  SOCS3, JAK2, CSF2RB, TCEB1, PRLR, PTPN11, TCEB2, IL2RB, IFNGR1, JAK1  UBE2B  Adaptive immune system (P < 3.5 × 10−2)  UBE2B, RAD18, UBR2, UBR3, UBA1, CTNNB1, UBQLN1, DSTN, PCNA, CPLX1  EIF3EBP1  MTOR signaling (P < 1.0 × 10−8), cellular response to heat stress (P < 1.6 × 10−4)  EIF4EBP1, MTOR, EIF4E, RPTOR, EIF2C2, LRRK2, ATM, SLMAP, PPP2R4, LRPAP1  ACD  Telomere maintenance, glycogen breakdown (P < 8.0 × 10−4)  ACD, POT1, TINF2, CALD1, DBNL, PGM1, ANXA4, SARS, ACOT7, IL1RN  CDK19  Metabolism of lipids (P < 6.0 × 10−9)  CDK19, MED9, MED16, MED28, MED19, MED26, MED29, MED12, MED18, CDK8  TLR4  Cytokine and TLR signaling (P < 5.0 × 10−3)  TLR4, MYD88, TLR2, TIRAP, LY96, TICAM2, TOLLIP, TLR1, SRC, SYK  MXD1  Chromatin modification (P < 6.0 × 10−3)  MXD1, MAX, SIN3A, AKT1, MLX, ARID4A, SAP30, VDR, KDM5A, HDAC2  TBXA2R  Regulation of insulin secretion, WNT signaling (P < 2.7 × 10−5)  TBXA2R, GNAQ, GNA13, RAB11A, PTGIR, PRKCA, PSME3, KCNMA1, PSMA7, GNB1  View Large The meta-nodes, core nodes in top 5 meta-nodes, and duplicated genes (in either meta-nodes or nodes) were independently analyzed using the ClusterOne plugin; this identified 20 candidates with highly significant predictive interactions in the labor transcriptome (data not shown). These were subjected to IPA Causal Analysis to refine the list of regulator candidates to those with the strongest predicted regulatory relationships with established mediators of labor. The final potential regulators of the choriodecidual labor transcriptome were VIM, TLR4, and TNFSF13B (Table 4). All were significantly upregulated by labor, highly ranked in hierarchical networks by both algorithms, and had regulatory effects on multiple genes associated with term labor. Table 4. Summary of the in silico analyses data for the final master regulator candidates of the choriodecidua and myometrial labor transcriptome.   Microarray  Moduland network analysis  Clusterone analysis  Causal network    Fold  PPLR or  Metanode  Nodes in      Downstream  analysis  Gene  change  P value  (node rank)  cluster  P value  Z-score  genes  P value  Choriodecidua  VIM  1.9  0.999  1st (5th)  40  1.3 × 10−9  3.2  41  1.4 × 10−17  TLR4  3.7  0.999  18th (1st)  20  0.009  4.2  25  4.7 × 10−10  TNFSF13B  2.1  0.997  47th (1st)  5  0.042  3.4  105  9.6 × 10−8  Myometrium  MT2A  1.2  0.0058  37th (1st)  11  0.048  3.3  30  3.3 × 10−6  TLR2  1.3  0.0101  39th (1st)  8  0.03  1.3  90  1.13 × 10−6  RELB  1.4  0.0193  15th (1st)  27  0.012  2.4  123  5.9 × 10−9    Microarray  Moduland network analysis  Clusterone analysis  Causal network    Fold  PPLR or  Metanode  Nodes in      Downstream  analysis  Gene  change  P value  (node rank)  cluster  P value  Z-score  genes  P value  Choriodecidua  VIM  1.9  0.999  1st (5th)  40  1.3 × 10−9  3.2  41  1.4 × 10−17  TLR4  3.7  0.999  18th (1st)  20  0.009  4.2  25  4.7 × 10−10  TNFSF13B  2.1  0.997  47th (1st)  5  0.042  3.4  105  9.6 × 10−8  Myometrium  MT2A  1.2  0.0058  37th (1st)  11  0.048  3.3  30  3.3 × 10−6  TLR2  1.3  0.0101  39th (1st)  8  0.03  1.3  90  1.13 × 10−6  RELB  1.4  0.0193  15th (1st)  27  0.012  2.4  123  5.9 × 10−9  View Large The causal analysis for VIM-predicted regulation of five genes, including mitogen-activated protein kinase 1 (MAPK1 or ERK), snail family transcriptional repressor 1 (SNAI1), and phospholipidase A2, which in turn have regulatory actions on 41 genes including matrix metalloproteinases (MMPs), cytokines, chemokines, and PTGS2 (Figure 3). As expected, TLR4 regulated a suite of inflammatory mediators, including cytokines, chemokines, MMPs, and PTGS2 (Supplementary Figure S2). The causal network for TNFSF13B had 14 lower level regulators, including NFKB1 and ERK, with regulatory actions on 105 downstream genes, including interleukins (IL) IL1RN, IL1A, CXCR4, suppressor of cytokine signaling 3 (SOCS3) and multiple chemokines (Supplementary Figure S3). Figure 3. View largeDownload slide Causal network analysis of vimentin identifying relationships with downstream labor-associated choriodecidual genes. Vimentin is the master regulator with five lower level regulators. Bottom level is an edited summary of genes affected (full map includes 41 genes) with at least two network connections or established labor-associated functions. A dashed line (—) indicates one known function, and a solid line (__) indicates multiple functions identified between the regulator and downstream gene. Upregulated regulator = orange; upregulated gene = red. The darker intensity of color indicates a higher degree of change. Figure 3. View largeDownload slide Causal network analysis of vimentin identifying relationships with downstream labor-associated choriodecidual genes. Vimentin is the master regulator with five lower level regulators. Bottom level is an edited summary of genes affected (full map includes 41 genes) with at least two network connections or established labor-associated functions. A dashed line (—) indicates one known function, and a solid line (__) indicates multiple functions identified between the regulator and downstream gene. Upregulated regulator = orange; upregulated gene = red. The darker intensity of color indicates a higher degree of change. Myometrium The myometrial genomic dataset was analyzed using the analysis workflow developed. From the 717 myometrial transcripts significantly altered with labor, the BioGrid network model generated 4886 nodes with 12 112 interactive relationships. At the next hierarchical level, there were 64 meta-nodes and 1277 interactions (Figure 4). There were similarities in the biological and canonical pathways with the choriodecidua network meta-nodes, with prominent involvement of community clusters regulating responses to hypoxia, cell cycle, and survival, together with TGFB, immune and inflammatory signaling (Table 5). ClusterOne analysis reduced the number of candidate clusters to the 30 most interconnected (data not shown). These were refined by causal analysis to three final gene candidates with strong causal relationships, with a significant number of differentially expressed labor-associated genes (Table 4). These were intracellular ion-regulator, MT2A (or MT2), RELB, and TLR2. The downstream causal relationships supporting MT2A as a primary component demonstrated a primary regulation of NFKB, leading to regulation of transcription factors (e.g., estrogen receptor, PPARG, TP53) and inflammatory genes (Figure 5). The candidate regulator TLR2 was predicted to regulate multiple inflammatory and labor-associated genes, including PTGS2, interleukins, NFKB, and signaling molecules (Supplementary Figure S4). The regulator RELB downstream genes exhibited strong overlap with those of MT2A, including estrogen receptor, cytokines, and NFKBIA (Supplementary Figure S5). Figure 4. View largeDownload slide Network modeling of meta-node expression in the human myometrium. The upper level of network clusters assessed using the ModuLand algorithm in Cytoscape 2.8.3. These 64 clusters are meta-nodes, the lines indicate interactions between meta-nodes and lower level nodes. The yellow lines indicate highly-ranked major interactions. Figure 4. View largeDownload slide Network modeling of meta-node expression in the human myometrium. The upper level of network clusters assessed using the ModuLand algorithm in Cytoscape 2.8.3. These 64 clusters are meta-nodes, the lines indicate interactions between meta-nodes and lower level nodes. The yellow lines indicate highly-ranked major interactions. Figure 5. View largeDownload slide Causal network analysis of metallothionein 2. MT2A is the top regulator with 13 lower level regulators. The bottom level is a summary of downstream genes (full map includes 30 genes). A dashed line (——) indicates one known function, and a solid line (__) indicates multiple functions identified between the regulator and downstream gene. Upregulated regulator = orange; downregulated regulator = blue; upregulated gene = red, downregulated gene = green. The darker intensity of color indicates a higher degree of change. Figure 5. View largeDownload slide Causal network analysis of metallothionein 2. MT2A is the top regulator with 13 lower level regulators. The bottom level is a summary of downstream genes (full map includes 30 genes). A dashed line (——) indicates one known function, and a solid line (__) indicates multiple functions identified between the regulator and downstream gene. Upregulated regulator = orange; downregulated regulator = blue; upregulated gene = red, downregulated gene = green. The darker intensity of color indicates a higher degree of change. Table 5. Hierarchical ranking of the top 20 meta-nodes and their highest ranked nodes in the myometrial term labor network map. Metanode  Major functions and pathways  Core nodes in metanode  CDKN1  Cell response to hypoxia, Apoptosis, DNA replication and repair (P < 1.0 × 10−5)  CDKN1A, UBC, YWHAQ, CDK2, PCNA, GAPDH, UBE2D3, HNRNPM, DHX9, SET  UBC  Cell response to hypoxia, apoptosis, DNA replication and repair (P < 1.0 × 10−5)  UBC, VCP, YWHAQ, UBE2D3, HNRNPM, ITCH, CD81, PSMC2, NCL, DHX9  XRCC5  Cell responses to hypoxia, apoptosis, DNA replication and repair (P < 1.0 × 10−5)  XRCC5, XRCC6, BAX, DHX9, FMNL1, UBC, NCL, CEBPA, CBX5, SUMO2  SMAD2  TGFB signaling (P < 1.9 × 10−11)  SMAD2, SMAD4, SMAD6, HDAC9, HDAC1, RUNX1, TGIF1, SMURF2, ENG, TGFBR1  PIK3R1  Adaptive immune system (P < 8.9 × 10−7) Growth factor signalling (P < 2.8 × 10−6)  PIK3R1, CBL, SHC1, BLNK, SLA, IRS1, GRB2, SYN1, CD28, EGFR  SHC1  Adaptive immune system (P < 8.5 × 10−4) Growth factor signaling (P < 1.3 × 10−5)  SHC1, GRB2, EGFR, BLNK, PIK3R1, CAV1, SYN1, ABL2, ERBB2, SOS1  MAPK14  Cellular senescence (P < 4.2 × 10−6. Innate immune system (P < 3.9 × 10−4)  MAPK14, ATF2, RPS6KA5, MBP, MAPKAPK2, OBSL1, MAP2K6, MAP2K3, MKNK1, PHC2  CBX5  Wnt signaling (P < 8.9 × 10−2)  CBX5, CBX3, TRIM28, CHAF1A, HIST3H3, HDAC9, SMARCA4, CBX1, DNMT3B, MIS12  UPF2  Nonsense mediated decay (P < 1.2 × 10−15)  UPF2, RBM8A, UPF1, DCP2, UPF3B, SMG1, MAGOH, EIF4A3, RNPS1, UPF3A  RICTOR  Cell response to heat stress (P < 8.5 × 10−5) Growth factor signalling (P < 1.0 × 10−5)  RICTOR, MTOR, FKBP1A, MAPKAP1, AKT1, MLST8, HSPA4, PREX1, RPTOR, PDIA3  MBP  Cell senescence (P < 3.4 × 10−7). Innate immunity (P < 1.3 × 10−5). Map kinase (6.6 × 10−4)  MBP, ELK1, MAPK1, MAPK3, LRRK2, MAPK8, MAPK14, MAPK9, PRKCA, UBE2I  SMARCA  Chromatin modification (P < 3.3 × 10−13)  SMARCA2 AND 4, SMARCC1AND 2, PHF10, SMARCB1, SMARCE1, SMARCD1, ACTL6A, ARID1A  RUNX1T  Chromatin modification (P < 1.2 × 10−4). Circadian clock (P < 7.9 × 10−3)  RUNX1T1, NCOR1, HDAC9, HDAC1, RUNX1, HEY2, CBFA2T2, SIN3A, NCOR2, HDAC3  BCL3  Innate immune system (P < 1.5 × 10−3). Cytokine signaling (P < 7.4 × 10−4)  BCL3, MAP3K8, NFKB1, RELB, NFKB2, NFKBIZ, RELA, TRAF2, KAT5, MAP2K1  RELB  Cytokine signaling (P < 4.0 × 10−3)  RELB, BCL3, NFKB2, MAP3K8, RELA, DAXX, DPF2, NFKB1, GSK3B, NFKBIZ  VEGFA  Signaling by VEGF (P < 4.4 × 10−7)  VEGFA, NRP1, KDR, PGF, FLT1, CRYAB, TGFBR2, HNRNPD, HNRNPL, CTGF  NOD2  Cytokine and TLR signalling (P < 6.7 × 10−4)  NOD2, NLRC4, RIPK2, MAP3K7, CASP1, RNF31, SHARPIN, XIAP, RBCK1, SUGT1  MLLT3  TGF signaling and RNA Pol II transcription (P < 1.0 × 10−3)  MLLT3, CDK9, DOT1L, AFF1, MLLT1, AFF4, AFF3, ELL, CCNT1, BCOR  GMNN  Cell cycle and DNA replication (P < 1.0 × 10−3)  GMNN, CDT1, CDC20, AURKA, NCF1, KAT7, REPIN1, IMPDH1, CDH1, FZR1  DCP2  Deadenylation-dependent mRNA decay (P < 7.8 × 10−4)  DCP2, EDC4, DCP1B, MMS19, CAPN2, FANCD2, TRMT2A, POLA2, IKBKAP, INTS7  Metanode  Major functions and pathways  Core nodes in metanode  CDKN1  Cell response to hypoxia, Apoptosis, DNA replication and repair (P < 1.0 × 10−5)  CDKN1A, UBC, YWHAQ, CDK2, PCNA, GAPDH, UBE2D3, HNRNPM, DHX9, SET  UBC  Cell response to hypoxia, apoptosis, DNA replication and repair (P < 1.0 × 10−5)  UBC, VCP, YWHAQ, UBE2D3, HNRNPM, ITCH, CD81, PSMC2, NCL, DHX9  XRCC5  Cell responses to hypoxia, apoptosis, DNA replication and repair (P < 1.0 × 10−5)  XRCC5, XRCC6, BAX, DHX9, FMNL1, UBC, NCL, CEBPA, CBX5, SUMO2  SMAD2  TGFB signaling (P < 1.9 × 10−11)  SMAD2, SMAD4, SMAD6, HDAC9, HDAC1, RUNX1, TGIF1, SMURF2, ENG, TGFBR1  PIK3R1  Adaptive immune system (P < 8.9 × 10−7) Growth factor signalling (P < 2.8 × 10−6)  PIK3R1, CBL, SHC1, BLNK, SLA, IRS1, GRB2, SYN1, CD28, EGFR  SHC1  Adaptive immune system (P < 8.5 × 10−4) Growth factor signaling (P < 1.3 × 10−5)  SHC1, GRB2, EGFR, BLNK, PIK3R1, CAV1, SYN1, ABL2, ERBB2, SOS1  MAPK14  Cellular senescence (P < 4.2 × 10−6. Innate immune system (P < 3.9 × 10−4)  MAPK14, ATF2, RPS6KA5, MBP, MAPKAPK2, OBSL1, MAP2K6, MAP2K3, MKNK1, PHC2  CBX5  Wnt signaling (P < 8.9 × 10−2)  CBX5, CBX3, TRIM28, CHAF1A, HIST3H3, HDAC9, SMARCA4, CBX1, DNMT3B, MIS12  UPF2  Nonsense mediated decay (P < 1.2 × 10−15)  UPF2, RBM8A, UPF1, DCP2, UPF3B, SMG1, MAGOH, EIF4A3, RNPS1, UPF3A  RICTOR  Cell response to heat stress (P < 8.5 × 10−5) Growth factor signalling (P < 1.0 × 10−5)  RICTOR, MTOR, FKBP1A, MAPKAP1, AKT1, MLST8, HSPA4, PREX1, RPTOR, PDIA3  MBP  Cell senescence (P < 3.4 × 10−7). Innate immunity (P < 1.3 × 10−5). Map kinase (6.6 × 10−4)  MBP, ELK1, MAPK1, MAPK3, LRRK2, MAPK8, MAPK14, MAPK9, PRKCA, UBE2I  SMARCA  Chromatin modification (P < 3.3 × 10−13)  SMARCA2 AND 4, SMARCC1AND 2, PHF10, SMARCB1, SMARCE1, SMARCD1, ACTL6A, ARID1A  RUNX1T  Chromatin modification (P < 1.2 × 10−4). Circadian clock (P < 7.9 × 10−3)  RUNX1T1, NCOR1, HDAC9, HDAC1, RUNX1, HEY2, CBFA2T2, SIN3A, NCOR2, HDAC3  BCL3  Innate immune system (P < 1.5 × 10−3). Cytokine signaling (P < 7.4 × 10−4)  BCL3, MAP3K8, NFKB1, RELB, NFKB2, NFKBIZ, RELA, TRAF2, KAT5, MAP2K1  RELB  Cytokine signaling (P < 4.0 × 10−3)  RELB, BCL3, NFKB2, MAP3K8, RELA, DAXX, DPF2, NFKB1, GSK3B, NFKBIZ  VEGFA  Signaling by VEGF (P < 4.4 × 10−7)  VEGFA, NRP1, KDR, PGF, FLT1, CRYAB, TGFBR2, HNRNPD, HNRNPL, CTGF  NOD2  Cytokine and TLR signalling (P < 6.7 × 10−4)  NOD2, NLRC4, RIPK2, MAP3K7, CASP1, RNF31, SHARPIN, XIAP, RBCK1, SUGT1  MLLT3  TGF signaling and RNA Pol II transcription (P < 1.0 × 10−3)  MLLT3, CDK9, DOT1L, AFF1, MLLT1, AFF4, AFF3, ELL, CCNT1, BCOR  GMNN  Cell cycle and DNA replication (P < 1.0 × 10−3)  GMNN, CDT1, CDC20, AURKA, NCF1, KAT7, REPIN1, IMPDH1, CDH1, FZR1  DCP2  Deadenylation-dependent mRNA decay (P < 7.8 × 10−4)  DCP2, EDC4, DCP1B, MMS19, CAPN2, FANCD2, TRMT2A, POLA2, IKBKAP, INTS7  View Large Commonality within the each network of master regulators of labor in the choriodecidua and myometrium The network relationships of the candidate regulators to selected downstream labor-associated genes are summarized in Figure 6. Although each master regulator had a distinct repertoire of downstream genes, there was a degree of overlap, particularly for immunoregulatory or inflammatory genes, well-known to be associated with labor. To explore potential intertissue relationships further, the interconnections between the top 15 meta-nodes for each tissue were mapped (Figure 7). The majority of meta-nodes were distinct for myometrium and choriodecidua; however, the highest ranked common core biological functions of active labor were identified and cross referenced across the myometrium and choriodecidua networks generated. In particular, SHC adaptor protein 1 (SHC1) featured as a highly ranked meta-node in both tissues, and there was strong functional overlap with the myometrial meta-node and node network represented by phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1). Figure 6. View largeDownload slide Master regulators of labor in choriodecidua and myometrium. Summary illustrating causal relationships with key established labor-associated mediators that are in common downstream of the master regulators. Figure 6. View largeDownload slide Master regulators of labor in choriodecidua and myometrium. Summary illustrating causal relationships with key established labor-associated mediators that are in common downstream of the master regulators. Figure 7. View largeDownload slide Connective relationships between hierarchically ranked meta-nodes-nodes in human choriodecidua and myometrium. Meta-nodes are arranged hierarchically by color intensity in an anticlockwise direction (marked by rank [see Tables 3 and 5]). Dotted gray line separates the choriodecidua and myometrium network models. Biologically common relationships between the meta-node clusters are marked by two headed arrows, sharing at least three clusters of genes (nodes). The intersection demonstrates two major meta-nodes PIK3R1 and SHC1 sharing a significant number of nodes and genes. Figure 7. View largeDownload slide Connective relationships between hierarchically ranked meta-nodes-nodes in human choriodecidua and myometrium. Meta-nodes are arranged hierarchically by color intensity in an anticlockwise direction (marked by rank [see Tables 3 and 5]). Dotted gray line separates the choriodecidua and myometrium network models. Biologically common relationships between the meta-node clusters are marked by two headed arrows, sharing at least three clusters of genes (nodes). The intersection demonstrates two major meta-nodes PIK3R1 and SHC1 sharing a significant number of nodes and genes. Inhibition of master regulators Inhibition of VIM in choriodecidual cells (n = 7) with Gö6983 resulted in reduced IL8 mRNA expression (P < 0.05; Figure 8A). Inhibition of TLR4 resulted in elevated IL1RN (P < 0.05), reduced PTGS2 (P < 0.05), and a trend for reduced CCL2 and CCL8 (P = 0.07; Figure 8B, n = 7). Inhibition of MT2A in myometrial cells (n = 9) resulted in downregulated IL1A, IL1B, IL1RN, IL6, IL8, CD44, and VIM mRNA expression (P < 0.05–0.01; Figure 9A). Inhibition of TLR2 led to downregulation of IL8 and CD44 (P < 0.05; Figure 9B). Figure 8. View largeDownload slide Choriodecidual cell mRNA expression of downstream labor-associated genes after inhibition of master regulators. (A) Inhibition of VIM with Go6983 and (B) inhibition of TLR4 with CLI-095. Data are 2ΔΔCT (normalization to GAPDH and vehicle control). Medians ± IQ; n = 7. Dotted line represents vehicle control expression level. Wilcoxon-signed rank test, *P ≤ 0.05. Figure 8. View largeDownload slide Choriodecidual cell mRNA expression of downstream labor-associated genes after inhibition of master regulators. (A) Inhibition of VIM with Go6983 and (B) inhibition of TLR4 with CLI-095. Data are 2ΔΔCT (normalization to GAPDH and vehicle control). Medians ± IQ; n = 7. Dotted line represents vehicle control expression level. Wilcoxon-signed rank test, *P ≤ 0.05. Figure 9. View largeDownload slide Myometrial cell expression of downstream labor-associated genes after inhibition of master regulators. (A) Inhibition of MT2A with Go6983 and (B) inhibition of TLR2 with neutralizing antibody (Mab-htlr2). Data are 2ΔΔCT (normalization to YWHAZ and vehicle control). Medians ± IQ; n = 9. Dotted line represents vehicle control expression level. Wilcoxon-signed rank test, *P ≤ 0.05; **P ≤ 0.01. Figure 9. View largeDownload slide Myometrial cell expression of downstream labor-associated genes after inhibition of master regulators. (A) Inhibition of MT2A with Go6983 and (B) inhibition of TLR2 with neutralizing antibody (Mab-htlr2). Data are 2ΔΔCT (normalization to YWHAZ and vehicle control). Medians ± IQ; n = 9. Dotted line represents vehicle control expression level. Wilcoxon-signed rank test, *P ≤ 0.05; **P ≤ 0.01. Discussion Using a robust and unbiased global network analysis workflow, we have defined relationships of transcriptomic communities altered during active term labor in human myometrium and in human choriodecidua. Additionally, we have identified genes with high centrality highlighting the functional importance of gene:gene inter-relationships within each network during labor. We have further identified candidate genes with strong predicted regulatory actions within each of these transcriptomic networks. The top six regulators of active labor (VIM, TLR4, and TNFSF13B in the choriodecidua, and MT2A, TLR2, and RELB in the myometrium) were prioritized based on their regulatory effects on known mediators of labor. The identification of TLRs as master regulators in this study, which have established roles in parturition [28–34], highlights their central regulatory role in the cascade of labor and provides confidence in our use of unbiased predictive in silico methods. The regulator VIM is a cytoskeletal intermediate filament component with roles in a number of cellular processes. It is abundantly expressed by decidual stromal cells, decidual resident macrophages, and in the chorionic trophoblast layer that directly overlies the decidua [4]. It is a marker of epithelial to mesenchymal transition classically involved in maintaining cell structure, polarity, cell-to-cell adhesion, and cellular motility [35]. Duquette et al. proposed that the upregulation of VIM protein may sustain regular muscle contractions in laboring myometrium [36], but this idea has not been further explored to date. The VIM gene promoter contains a conserved NFKB sequence [37], is associated with elevated PTGS2 expression [38], and is also associated with increased cytokine/chemokine expression and macrophage infiltration [4, 9, 17, 18]. These interactions support our analysis of VIM as a major regulator of labor in the choriodecidual network with interactions and downstream actions on key elements of the labor cascade. The other highly ranked regulator, TNF superfamily member TNFSF13B, is involved in infection and innate immune responses [39]. Upregulated in placenta during late pregnancy, it contributes to maternal T-helper cell adaptations to the fetus [40]. Transcript variants or reduced expression are associated with pregnancy pathologies such as pre-eclampsia and miscarriage [41, 42]. The cellular origin of choriodecidual expression of TNFSF13B is unknown; however, it localizes to the endometrial stroma and endometrial glands, and is also expressed in the syncytiotrophoblast, trophoblasts, and some stromal cells of the placenta [43]. It is also abundantly expressed by macrophages [44], which are present in significant numbers in decidua during term labor [17]. Our network prediction supports the regulatory role of TNFSF13B with widespread downstream effects on inflammation, growth factor signaling, and ECM remodeling. The genes TLR2 and 4 were predicted as regulators of the myometrial and choriodecidual labor transcriptome, respectively. They are microbial response receptors that activate NFKB signaling and coordinate inflammatory responses in response pathogens, including in infection-associated preterm labor [4, 45, 46]. TLR2 mRNA and protein localizes to myometrial cells, is strongly expressed in tissue-resident leukocytes, and its expression is increased during active labor [28]. TLR4 mRNA and protein localizes to decidual cells and tissue-resident decidual macrophages; its expression in the decidua exhibits a cyclic pattern across normal pregnancy with its highest expression at term [47]. Abnormally high protein expression of decidual TLR4 is evident in pregnancy pathologies such as early onset pre-eclampsia [48]. These two specific TLRs can be activated by noninfectious danger-associated molecular pattern molecules liberated as a result of tissue trauma, cell death, and cellular stress [49]. Our in vitro results in basal conditions suggest a homeostatic role for these receptors, and preparation for normal term labor. Both TLRs are upregulated in placental and uterine tissues during active labor [1, 4, 31, 50], with roles in amplifying cytokine and MMP expression and prostaglandin synthesis [18, 51–53]. Previous studies reported reduced PTGS2 and cytokine expression in decidual cells following TLR4 inhibition [51]; our data support the TLR4 mediation of PTGS2 and upregulation of anti-inflammatory IL1RN in normal term choriodecidual cells. Neutralization of TLR2 inhibited IL8 and CD44 mRNA expression in myometrial cells, consistent with their involvement in immune responses during labor [3, 5, 8]. A member of the NFKB complex, RELB, was also defined as a predicted master regulator of myometrial labor transcriptome. Studies of labor have classically focused on other NFKB isoforms (RELA, C-REL, P52, and P50) [54], and the role of RELB and its noncanonical pathways of NFKB activation are unclear. RELB mRNA is expressed in term myometrial cells [55]. Although it was not experimentally tested in this study, we identified it as a highly ranked component of the myometrial network with a potential role in mediating major networks of labor. The myometrial regulator MT2A is involved in heavy metal binding and cellular homeostasis [56]. Elevated expression in the myometrium has been reported in term labor [57], and possesses functions that overlap with multiple labor processes, including inflammation [58], and progesterone and estrogen signaling [59, 60]. Our findings from in silico and in vitro analyses support a role for MT2A as a core regulator of myometrial inflammatory genes. These findings expose an alternative inflammatory regulation during labor, via alteration of heavy metal homeostasis, as described in macrophages where low intracellular iron levels affect TLR signaling and cytokine production [61]. Iron chelation contributes to immunomodulation, in determining cellular pro- or anti-inflammatory responses [62]. In this study, widespread suppression of established labor mediators by MT2A inhibition strengthens the hypothesis that MT2A is a master regulator of the labor cascade. To our knowledge, this is the first study to perform detailed network analyses of transcriptomic changes and identify key upstream regulators, in two uterine layers during term labor. The inclusion of both tissues are a significant strength of the study, as extensive labor-associated changes occur in the choriodecidua and myometrium and as such, therapeutic approaches to prevent preterm labor are likely to be more successful if they target both tissue layers. As expected from the differences in their biological functions, there are distinct transcriptomes and regulatory processes in each of the myometrium and decidua. We also identified shared core labor-associated biological functions such as immunoregulatory and growth factor signaling, indicating there are molecular pathways that are fundamental to active labor. However, interpretations of overlaps and interactions between these tissue layers must be made with caution, as there are inevitable differences in the way these two tissue types were sampled. Importantly, the myometrial samples were collected during labor, while the choriodecidua was sampled immediately following active labor, and therefore the transcriptome of the latter likely includes additional changes related to delivery. For this reason, we have restricted the majority of our analyses to each tissue layer separately, but include a tentative comparison of the core regulators to examine potential commonalities and interactions. Future studies could confirm the putative interactions involving SHC1 and PIK3R1 between decidua and myometrial using tissue samples from the same patients. Strengths of this study include the unbiased in-depth analyses of the laboring transcriptome and stringent prioritization of master regulators, and identification of causal relationships within the labor cascade of those regulators. Importantly, the only method relying on literature sources (and therefore susceptible to publication bias) was the final casual analysis, with prior analysis methods utilizing strictly mathematical algorithms to predict gene–gene and protein–protein interactions. The coupling of in silico and in vitro approaches adds an additional perspective, and while the latter did not have alter expression of all predicted mediators, the inhibition of MT2A in particular provides confidence that interfering with a single regulatory gene can impact a broad gene network. Other limitations concern the basal expression of the cells studied—successful inhibition of labor-associated genes is likely to require prior stimulation, i.e., by labor. Optimization of treatment regime and concentrations would better elucidate the pharmacokinetics and effectiveness of inhibitors, and this should be embarked upon before in-depth studies to test their therapeutic potential. Using chemical inhibitors does not preclude off-target effects. We used a PKC inhibitor to deplete MT2A and VIM at different concentrations based on published studies. However, blocking PKC activity can downregulate other pathways including prostaglandin F2A receptor [63], OXTR [64], cell junctions [65], and cytokine production [66]. Direct suppression of the gene targets would be possible using siRNA-mediated depletion; however, the goal of this study was to provide proof of concept evidence for generating regulatory targets for labor by network analysis and to investigate potential therapeutics that could be more easily translated to clinical practice. In summary, we identified unique gene candidates with differential expression during labor, high centrality in the network of laboring signals, and significant causal relationships within the labor cascade. These centrality features indicate important roles in regulating the labor transcriptome and therefore identifies them as putative master regulators of labor. Future work is required to investigate the therapeutic potential of these candidate regulators of labor. Though the regulator candidates were dissimilar between choriodecidua and myometrium, they shared common downstream genes, many of which were inflammatory in nature. Future comparative network analyses may identify more interconnected regulatory meta-nodes between choriodecidua and myometrium, enabling targeting of therapies to both tissues. Supplementary data Supplementary data are available at BIOLRE online. Supplementary Figure S1. Immunohistochemical characterization of isolated human choriodecidual cells. Isolated choriodecidual cells comprised (A) IGFBP1+ and (B) vimentin+ decidual stromal cells, and (C) cytokeratin-7 positive chorion cells, representing the major cell types present in choriodecidua. (D) No CD45+ immune cells were detected. (E) Negative mouse IgG control. Scale bar = 50 μm. Supplementary Figure S2. Causal network analysis of the TLR complex in the choriodecidua. A summary of downstream genes (full map includes 25 genes) with at least two network connections (dashed line). Upregulated expression of regulators = orange; upregulated expression of genes = pink. Supplementary Figure S3. Causal network analysis of TNFSF13B with supporting downstream relationships with choriodecidual genes. A summary network (full map includes 105 genes) with (i) at least two independent network connections (dashed line) or (ii) have multiple associated functions from a single regulator (solid line). Upregulated regulator = orange, upregulated gene = pink; downregulated regulator = blue, downregulated gene = green where the intensity of color indicates the degree of up- or downregulated expression. Lines indicate activation (orange) or inhibition (blue) or the change was not as predicted in literature (yellow). Supplementary Figure S4. Causal network analysis of TLR2 with downstream relationships in the laboring myometrium. A summary network (full map includes 90 genes) with (i) at least two network connections (dashed line) or (ii) have multiple associated functions from an independent regulator (solid line). Upregulated regulator = orange, upregulated gene = pink; downregulated regulator = blue, downregulated gene = green where the darker intensity of color indicates the greater degree of up- or downregulated expression. Lines indicate activation (orange) or inhibition (blue), or the change was not as predicted in literature (yellow). Supplementary Figure S5. Causal network analysis of RELB in the laboring myometrium. Relationships with downstream genes (full map includes 123 genes) with (i) at least two independent network connections (dashed line) or (ii) have many associated functions from a single regulator (solid line). Upregulated regulator = orange, upregulated gene = pink; downregulated regulator = blue, downregulated gene = green where the darker the intensity of color indicates the higher degree of up- or downregulated expression. Lines indicate activation (orange) or inhibition (blue) or the change was not as predicted in literature (yellow). Acknowledgments This work was supported by The James Tudor Foundation and Tommy's the Baby Charity. We thank the research midwives at St. Mary's Hospital, Manchester, for their invaluable help with subject recruitment and tissue collection, and also to Mrs Helen Bishof for her technical assistance in the in vitro studies. Declarations: The authors declare that they have no competing interests. The funding bodies had no involvement in study design, data analysis or manuscript preparation. Footnotes † Grant Support: This work was supported by The James Tudor Foundation and Tommy's the Baby Charity. The Maternal and Fetal Health Research Centre is supported by Tommy's the Baby Charity, an Action Research Endowment Fund, the Manchester BRC and the Greater Manchester CLRN. LKH is supported by a BBSRC David Phillips Research Fellowship. CD and SG are supported by SickKids Foundation and the Canadian Institutes of Health Research Institute of Human Development, Child and Youth Health. ‡ Accession Number: GSE9159 References 1. Haddad R, Tromp G, Kuivaniemi H, Chaiworapongsa T, Kim YM, Mazor M, Romero R. Human spontaneous labor without histologic chorioamnionitis is characterized by an acute inflammation gene expression signature. Am J Obstet Gynecol  2006; 195( 2): 394.e1– 394.e24. Google Scholar CrossRef Search ADS   2. Khanjani S, Kandola MK, Lindstrom TM, Sooranna SR, Melchionda M, Lee YS, Terzidou V, Johnson MR, Bennett PR. NF-κB regulates a cassette of immune/inflammatory genes in human pregnant myometrium at term. J Cell Mol Med  2011; 15( 4): 809– 824. 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Biology of ReproductionOxford University Press

Published: Mar 1, 2018

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