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Published in final edited form as: BJOG. 2006 Dec;113(Suppl 3):118–135. doi: 10.1111/j.1471-0528.2006.01150.x

The use of high-dimensional biology (genomics, transcriptomics, proteomics, and metabolomics) to understand the preterm parturition syndrome

Roberto Romero 1,*, Jimmy Espinoza 1,3, Francesca Gotsch 1, Juan Pedro Kusanovic 1, Lara Friel 3, Offer Erez 1, Shali Mazaki-Tovi 3, Gabor Than 1, Sonia Hassan 1,3, Gerard Tromp 2
PMCID: PMC7062297  NIHMSID: NIHMS1048998  PMID: 17206980

Abstract

High-dimensional biology (HDB) refers to the simultaneous study of the genetic variants (DNA variation), transcription (messenger RNA [mRNA]), peptides and proteins, and metabolites of an organ, tissue, or an organism in health and disease. The fundamental premise is that the evolutionary complexity of biological systems renders them difficult to comprehensively understand using only a reductionist approach. Such complexity can become tractable with the use of ‘omics’ research. This term refers to the study of entities in aggregate. The current nomenclature of ‘omics’ sciences includes genomics for DNA variants, transcriptomics for mRNA, proteomics for proteins, and metabolomics for intermediate products of metabolism. Another discipline relevant to medicine is pharmacogenomics. The two major advances that have made HDB possible are technological breakthroughs that allow simultaneous examination of thousands of genes, transcripts, and proteins, etc., with high-throughput techniques and analytical tools to extract information. What is conventionally considered hypothesis-driven research and discovery-driven research (through ‘omic’ methodologies) are complementary and synergistic. Here we review data which have been derived from: 1) genomics to examine predisposing factors for preterm birth; 2) transcriptomics to determine changes in mRNA in reproductive tissues associated with preterm labor and preterm prelabor rupture of membranes; 3) proteomics to identify differentially expressed proteins in amniotic fluid of women with preterm labor; and 4) metabolomics to identify the metabolic footprints of women with preterm labor likely to deliver preterm and those who will deliver at term. The complementary nature of discovery science and HDB is emphasized.

Keywords: analytical tools, genetics, high-throughput, omics, predisposing factors, preterm birth, preterm labor, systems biology

INTRODUCTION

The traditional scientific approach to solving problems has been based upon reductionism, for which there are many definitions, but in essence, it can be described as “divide and conquer.”1 In other words, a complex system can be understood by studying smaller, simpler and therefore more tractable units of the whole. The success of this approach in physics, biology, and medicine is unquestionable. Indeed, reductionism will continue to be an important part of biomedical research, but Strange2 has proposed that “naive reductionism” defined as the belief that reductionism alone will lead to a complete understanding of living organisms is not tenable. High-dimensional biology (HDB) and systems biology have emerged as an alternative/complementary scientific paradigm. The fundamental tenet of these disciplines is that a complex system can be understood more completely by considering it in its entirety, including dimensions such as time, space, and context.

High dimensional biology, the “ome”, omics sciences, and systems biology

The term “HDB” refers to the use of high-throughput techniques which allow simultaneous examination of changes in the genome (DNA), transcriptome (messenger RNA [mRNA]), proteome (proteins), or metabolome (metabolites) in a biological sample, with the goal of understanding the physiology or mechanisms of disease.35 Insights derived from these are expected to assist with the development of new diagnostic, prognostic, and therapeutic tools in medicine. The term “ome” refers to an abstract entity, group, or mass.6 “Omics” sciences refer to the study of entities in aggregate,7 and hence the term “genomics” to study the genome, “proteomics” for the proteome, etc. HDB encompasses the omics techniques.8,9

The integration of “omic” techniques is called “systems biology.” This discipline aims to define the inter-relationships of several or, ideally, all the elements in a system, rather than study each element independently. Thus, systems biology will capture information from genomics, transcriptomics, proteomics, metabolomics, etc. (experimentally derived data) and combine it with theoretical models to predict the behavior of a cell or organism.1014

An important feature of HDB is its application as a “discovery tool” because it allows for a global description of changes in biological samples. Hence, it does not require a specific hypothesis, and it is presumably unbiased in nature, although there are constraints in existing technologies. In contrast, the traditional approach to study biological processes is considered to be reductionist1,2,15 because conclusions are drawn based on one or few hypotheses. For example, the early diagnosis of pregnancy is based on the detection of a single hormone, human chorionic gonadotropin. Reductionist approaches have been successful in identifying diagnostic and prognostic markers of disease. However, these approaches cannot provide a comprehensive description of the biological processes involved in complex disorders such as the preterm parturition syndrome.

Genomics

Genomics is the systematic study of an organism’s genome. The elucidation of the role of human biology and environmental factors in health and disease requires the understanding of the genotype-phenotype relationship. The role of genomics in the study of preterm labor is aimed at determining if there is a genetic predisposition to spontaneous preterm labor and delivery. The clinical importance of this is predicated on the paradigm known as “personalized medicine,” which proposes that it may be possible to ascertain the genetic predisposition of a disease or syndrome and then to implement behavioral and/or pharmacological interventions to prevent adverse outcomes or to use genetic information to tailor individualized treatment that maximizes the benefits and minimizes the risk of adverse reactions.1620

Table 1 describes the duration of pregnancy of some mammalian species. Duration of gestation ranges from 19–20 days in mice to 630 days in elephants. The mean duration of the human gestation is 280 days (post-menstrual age). Is duration of gestation determined genetically? Allen et al.26 have addressed this question in equine breeds. Artificial reproduction technologies were used to study the effects of genetic versus environmental factors in the thoroughbred and pony. In vitro fertilization followed by embryo transfer was conducted in the following circumstances: 1) a thoroughbred fetus implanted in a thoroughbred mare; 2) a pony fetus implanted in a pony mare; 3) a thoroughbred fetus implanted in a pony mare (deprived in utero environment); and 4) a pony fetus implanted in a thoroughbred mare (luxurious in utero environment). The mean gestational age at birth and birthweight of a thoroughbred offspring of a thoroughbred mare were significantly greater than those of a pony offspring of a pony mare. Although both interbred combinations lead to intermediate mean gestational ages at birth and birthweights (Table 2), thoroughbred-in-pony offspring demonstrated classical signs of intrauterine growth restriction, including muscle wastage of the limbs, overextended joints, and ill-formed hooves, while pony-in-thoroughbred offspring had superior muscle tone and well-formed, hardened hooves.26 The results indicate that both genetic and environmental factors influence the duration of pregnancy and fetal weight in equine breeds.

Table 1.

Mean Duration of Gestation in Mammals

Species Days
Mouse21 19–20
Rabbit22 31
Sheep23 148
Human 280
Killer whale24 510
Elephant25 630

Table 2.

Mean gestational age at birth and mean foal birthweight in equines in pregnancies after artificial reproductive technologies.

Fetus in mother
Thoroughbred
in
thoroughbred
Pony
in
pony
Pony
in thoroughbred
(luxurious in utero
environment)
Thoroughbred
in pony
(deprived in utero
environment)
Mean gestational age at birth
(days)
339 ± 3 325 ± 3 331 ± 3 332 ± 3
Mean foal birthweight
(kg)
53 ± 3 24 ± 1 38 ± 2 33 ± 2

Modified from Allen W.R. et al. Reproduction 2002;123:445–453.26

The arguments to consider preterm parturition as a syndrome have been developed elsewhere in this issue of the journal.27 For each mechanism of disease responsible for the syndrome, there could be an environmental and/or a genetic component. The contribution of each varies according to the specific mechanism of disease. For example, a woman who has preterm labor caused by placental abruption following a motor vehicle accident has primarily environmentally induced preterm parturition. In contrast, a woman with Ehlers-Danlos syndrome and an affected fetus has a substantial genetic predisposition to preterm delivery usually caused by spontaneous rupture of the membranes.28 Indeed, the risk of preterm delivery is 12.5% if the mother is affected but has a healthy fetus. In contrast, the risk is 40% if a healthy mother has an affected fetus.28

Epidemiological evidence suggests that genetic factors play a role in the pathogenesis of preterm birth.2931 In the Unites States, African-American women have a significantly higher rate of preterm birth.29 This difference remained significant after adjusting for potential confounding factors including medical insurance type (a surrogate indicator of socioeconomic status).32 Ethnicity was an independent risk factor for spontaneous preterm delivery at <32 and <35 weeks of gestation after controlling for confounding variables, including cervical length, history of preterm birth, and others.33 Collectively, these data support a role for ethnicity as a risk factor for preterm birth.

A genetic predisposition for a particular disorder can be suspected if the following criteria are met: 1) demonstration of familial aggregation; 2) substantiation with segregation studies; and, finally, 3) identification of disease-susceptibility genes.34 Familial aggregation, defined as the occurrence of a trait in members of a family that cannot be readily accounted for by chance, has been shown for preterm birth.3539 However, all studies have focused thus far on preterm birth, rather than spontaneous preterm labor. Similarly, phenotypic differentiation between spontaneous preterm labor with intact membranes and preterm premature rupture of membranes (PPROM) has not been considered in these studies.

Women with a sister who gave birth to a preterm neonate have an 80% higher risk of delivering preterm.36 Moreover, Porter et al.35 reported that women born preterm had a significantly higher risk of delivering preterm (odds ratio [OR] 1.18; 95% confidence interval [CI] 1.02–4.16). Interestingly, these risks of preterm birth was inversely proportional to the gestational age of the mother at birth.35

Another study supporting a genetic predisposition for preterm birth is based on an extensive genealogy database in the state of Utah (USA). Twenty-eight families were identified in which a woman who delivered a preterm singleton neonate had at least five first- or second-degree relatives who had had a preterm delivery. The coefficient of kinship for familial preterm delivery grandparents was more than 50 standard deviations higher than the control group, supporting the familial nature of preterm birth (P < 0.001).39 Finally, twin studies in Sweden and Australia have suggested heritability estimates of 25–40% and 17%, respectively.37,38

Segregation analysis is the main statistical tool for analyzing the inheritance of non-Mendelian traits or diseases. It can provide evidence for a susceptibility locus and suggest whether a complex disease is monogenic, oligogenic, or multifactorial.40 This type of analysis requires large datasets of people affected by a familial but non-Mendelian disease and is sensitive to biases in data collection. Segregation analysis has not been reported for spontaneous preterm parturition.

The final and most persuasive evidence of a genetic predisposition is the identification of disease-susceptibility genes. This evidence is generally derived from genetic association studies. The design, execution, analysis, and interpretation of this type of study is a specialized subject, and the reader is referred to the recent reviews for details.34,41 Genetic association studies in obstetrics have the added complexity of dealing with two genotypes: maternal and fetal. Either of these genotypes may alter the risk for obstetrical syndromes and, therefore, warrant investigation. Moreover, the interaction of the genotypes or conflicting genotypes between mother and fetus may also modify the risk for a specific syndrome.

Polymorphisms (variations in DNA at a specific site) in several genes have been studied in preterm birth4251 (Table 3) and PPROM5263 (Table 4). The results of these genetic association studies have been reviewed elsewhere;64 however, some examples will be described for illustration. The fetal genotype, as well as the maternal genotype, have been found to contribute to obstetrical disease. Matrix metalloproteinases (MMPs) have been implicated in PPROM because: 1) this family of enzymes degrades components of the fetal membranes such as collagens;65,66 2) they are produced within the fetal membranes;6567 3) their production is increased in women with PPROM, as evidenced by higher amniotic fluid concentrations of MMPs in these patients;6870 and 4) pro-inflammatory cytokines induce the production of MMPs.71

Table 3.

Genetic polymorphisms that confer the greatest increased/decreased risk to preterm birth.

Gene Polymorphism P-value Odds ratio First author
Genes that increase the risk for preterm delivery
TNFA1 G/A −308 (N) (NR) 0.002 7.3(2.85, 18.9) RR Aidoo et al42
IL6 C/G −237 (M) (with bacterial vaginosis) (S) 4.4 (1.2, 16.4) Engel et al43
PON2 S311C (N) Homozygosity (NR) 4.6 (1.5, 14) RR Chen et al44
PON1 Q192R (N) Homozygosity (NR) 3.6 (1.3, 11) RR Chen et al44
TNF/IL6/IL6R −3448/−7227/33314 (M) (S) 0.001 3.5 (2.52, 4.87) Menon et al45
IL4 −509 C/C genotype (M) (S) 0.02 with multi-variate analysis 3.4 (1.2, 9.6) Annells et al46
IL4 −509 C/C genotype (M) (S) 0.02 with univariate analysis 3.0 (1.1, 10.3) Annells et al46
IL6 G/C −174 (M) (S) < 0.01 2.32 (1.23, 4.30) Orsi et al47
IL10 A/G −1082 (M) (S) < 0.05 1.95 (1.04, 3.64) Orsi et al48
IL1B C/T −511 (M) (NR) < 0.015 Greenfield et al49
Genes that decrease the risk for preterm delivery
TGFB1 G/C −800, G/C −509 haplotype (M) (S) 0.01 0.7 (0.5, 0.9) Annells et al46
MBL2 LYA (N) (NR) 0.02 0.61 (0.40, 0.93) Bodamer et al50
IL10 A/G −1082 (M) (S) 0.01 0.6 (0.4, 0.9) Annells et al46
IL4 −509 C/T genotype (M) (S) 0.01 with univariate analysis 0.3 (0.1, 0.8) Annells et al46
TNFRSF6 AG/GA −1377, −670 haplotype (M) (S) 0.02 0.1 (0.0, 0.8) Annells et al46
ADRB2 Arg16Gly (M) (S) 0.01 0.08 (0.01, 0.58) Landau et al51

M, maternal; N, neonatal; NR, mode of preterm birth not reported; RR, relative risk; S, spontaneous preterm birth.

Table 4.

Genetic polymorphisms that confer an increased risk to PPROM.

Gene Polymorphism OR
(95% CI)
P-value Reference
MMP1 G/GG −1607 (N) 2.29 (1.1–4.8) 0.028 Fujimoto et al52
MMP8 C/T −799, A/G −381, C/G +17 (N) 4.63 (2.0–11.9) <0.0001 Wang et al53
MMP9 14 CA repeat (N) 3.06 (1.8–5.3) <0.0001 Ferrand et al54
Caspase-recruitment-domain-containing protein15 2936insC (N)  — 0.017 Ferrand et al55
TNFα G/A −308 (M) 3.18 (1.3–7.8) 0.008 Roberts et al56
G/A −308 (N1) 5.98 (1.7–22.1) 0.002 Kalish et al57
G +488, G −238, G −308 (M) 0.7 (0.5–1.0) 0.03 Annells et al46
IL1 receptor antagonist 240-bp tandem repeat (N1 + 2) 8.0 (1.7–50.3) 0.005 Kalish et al58
86-bp tandem repeat (N) 6.5 (1.3–37.7) 0.021 Genc et al59
240-bp tandem repeat & CD14 T/T −159 genotype (M) 4.9 (1.4–15.9) 0.009 Kalish et al60
Heat-shock protein 70 A/G +1267 (N1) <0.05 Kalish et al57
IL10 G −1082, C −819, C −592 (M) 1.9 (1.1–3.3) 0.01 Annells et al46
FAS A/G −670 (N) 3.05 (1.25–7.43) 0.01 Kalish et al61
A/G −670 (N1) 0.003 Fuks et al62
CD14 −159 T/T genotype (M) 2.94 (1.12–7.73) 0.036 Kalish et al60
Heat-shock protein 47 C/T −656 (N) 3.22 (1.5–7.22) 0.001 Wang et al63

M, maternal; N, neonatal; N1, neonate 1 in a multi-fetal pregnancy.

MMP-8 degrades fibrillar collagens (types I and III), which confer tensile strength to the fetal membranes. Promoter fragments containing the minor alleles of three single-nucleotide polymorphisms (SNPs), −799 (T), −381 (G), and +17 (G), were found to have 3-fold greater activity in chorion-like trophoblast cells, compared with the major allele promoter construct. Fetal carriage of the three-allele minor haplotype of MMP-8 confers a significantly increased risk for PPROM (OR 4.63; 95% CI 2.01–11.94; P < 0.0001).53 Conversely, homozygosity for the three-allele major haplotype of MMP-8 confers protection from PPROM (OR 0.52; 95% CI 0.36–0.75; P < 0.0002).

Another example of the identification of fetal disease-susceptibility genes for PPROM involves heat-shock protein 47, encoded by the SERPINH1 gene.63 This heat-shock protein plays an essential role in collagen metabolism by serving as a chaperone to stabilize the collagen triple helix in the endoplasmic reticulum. The minor allele of a functional SNP in the promoter of the SERPINH1 gene, −656 (T), was found to be more frequent in individuals of African ancestry. Wang et al63 investigated the potential contribution of this polymorphism to the risk of PPROM in an African-American population. The authors reported that the minor allele was significantly more frequent in African-American neonates of pregnancies that were complicated by PPROM (OR 3.22; 95% CI 1.5–7.2; P < 0.0009). Statistical significance remained after adjustment for admixture in the population and was confirmed in a second case-control study.63

The maternal and fetal genotypes interact with one another through their complement of gene products at the maternal-fetal interface, as well as during disruptions of this interface. This interaction may lead to either reproductive success or diseases unique to the pregnancy state.72,73 One example of a detrimental interaction of maternal and fetal genotypes is alloimmune hemolytic disease of the fetus such as rhesus (Rh) incompatibility.74 This disorder results from antigenic exposure of a Rh-negative mother to antigens that are expressed on fetal red blood cells [Rh(D) positive]. This adverse outcome is mediated through the humoral response and is dependent upon a precondition of “conflicting genotypes” between mother and fetus, as well as environmental exposure.7476

This well-known example of “conflicting genotypes” which can lead to severe disease was the first of its kind to be described. However, conflicting genotypes may generate disease through a variety of other mechanisms. Conflicting maternal and fetal genotypes may be detrimental because they are, in fact, too similar. The cheetah, Acinonyx jubatus, is considered a paradigm for disease vulnerability because of a loss of genetic diversity caused by a population bottleneck about 12,000 years ago.77 The loss of genetic diversity predisposes the cheetah to neurodegenerative and multisystemic diseases in both juveniles and adults.78

A large candidate gene association study of mothers and their offspring has been performed to examine genes that may predispose to PPROM.79 Candidate genes were selected for biological plausibility for a role in the pathogenesis of PPROM and included genes involved in the immune response, extracellular matrix regulation, angiogenesis, and coagulation, among others. Separate, but distinct, maternal and fetal genes were associated with PPROM. Maternal genes associated with increased risk of PPROM included interleukin (IL)-6, IL-6R, and COX-1. Fetal genes associated with PPROM included the antimicrobial peptide beta-defensin 1 and MMP-19. A novel model for genetic predisposition of disease, “genotype conflict,” was also examined. “Conflicting” maternal-fetal genotypes for four genes were associated with PPROM. The study79 highlights the importance of investigating both maternal and fetal genotypes, as well as their interaction and “genotype conflict,” when examining genetic factors for obstetrical diseases.

For each mechanism of disease responsible for the preterm parturition syndrome, there could be an environmental and/or a genetic component which could also interact. Moreover, there could be gene-to-gene interaction (epistasis). The first evidence of a gene-environment interaction was demonstrated with maternal carriage of an allele of the tumor necrosis factor (TNF) alpha gene and bacterial vaginosis.80 A single nucleotide polymorphism (SNP) within the promoter of the TNF gene at position −308 has been correlated with differences in both constitutive and inducible expression. Carriage of the TNF-2 allele, in particular, yields increased gene expression. Results of the multivariable analysis showed that neither the carriage of the TNF-2 allele (OR 1.6; 95% CI 0.9–2.8) nor bacterial vaginosis (OR 1.3; 95% CI 0.5–2.9) were associated with an increased risk for spontaneous preterm birth. However, the combination of maternal carriage of the TNF-2 allele with bacterial vaginosis resulted in significantly increased risk for preterm birth (OR 6.0; 95% CI 1.6–22.7).80 Similarly, Engel et al43 found that maternal carriage of a polymorphism in the IL-6 gene (C/G −237) did not result in increased risk of spontaneous preterm birth for Caucasian (OR 1.8; 95% CI 0.8–3.6) or African-American women (OR 1.6; 95% CI 0.7–3.8). However, African-American carriers of this allele with concomitant bacterial vaginosis had a 2-fold greater risk of spontaneous preterm birth compared with those who carried the variant but did not have bacterial vaginosis (OR 4.4; 95% CI 1.2–16.4). Together, these data provide evidence for a gene-environment interaction in spontaneous preterm birth. The study of gene-environment interactions may lead to the discovery of modifications of the environmental exposure which might lead to effective therapeutic interventions in the population at risk.

Recent technological advances and completion of the HapMap project (which identified a large number of DNA variants in some specific ethnic groups) have made possible the conduction of whole-genome association studies.8186 Thus, genetics is evolving from a “candidate gene approach,” in which the DNA variants of biologically interesting genes are studied, to a true genomic approach that aims to examine the entire genome. High-density arrays now allow simultaneous examination of 500,000 or more DNA variants in the same individual. It has been proposed that the sample size required for an initial whole-genome association study should include at lease 1000 cases and 1000 controls. Accurate phenotype characterization, DNA quality, population stratification, and availability of a large number of samples for replication are crucial elements for this strategy to be successful in identification of genetic factors predisposing to complex disorders. For example, a whole-genome association study has been successful in identifying DNA variants in complement H which predispose to age-related macular degeneration.87,88

Proteomics

The proteome is the entire set of proteins encoded by the genome, and proteomics is the discipline which studies the global set of proteins and their expression, function, and structure.89,90

In early years of proteomics, protein composition studies relied on two-dimensional gel electrophoresis91 in which proteins were separated in one dimension by molecular weight and in the second dimension by isoelectric point. Spots in the polyacrylamide gel were then cut and protein identification performed by using trypsin digestion and mass spectrometry (MS). The MS tracing yields information about the mass/charge ratio (m/z ratio) of ions,89 which is used to search protein databases such as SwissProt.

A major impetus for proteomics in medicine was the report that serum analysis with a combination of solid chromatography and MS could identify women with ovarian cancer.89 The technique employed in this study was surface-enhanced laser desorption-ionization time-of-flight (SELDI-TOF). Since that report, many other studies have employed proteomic techniques to identify biomarkers for a wide range of disorders including preterm labor and PPROM.

Analysis of amniotic fluid and serum from women with either PPROM or term prelabor rupture of membranes (PROM) and control patients was undertaken to identify biomarkers characteristic of membrane rupture.92 Samples were analyzed using two-dimensional high-resolution electrophoresis followed by MS. Five amniotic fluid spots were considered possibly discriminatory. Of the five proteins, two were identified as new potential markers of PROM, meaning they were present only in amniotic fluid and absent in maternal plasma. These were agrin and perlecan.92 Clinical studies about the utility of these markers have not been published. Subsequently, SELDI-TOF MS was used for the identification of biomarkers of intra-amniotic inflammation in patients with preterm labor and intact membranes and PPROM. Four markers, including neutrophil defensins 1 and 2 and calgranulins A and C, identified patients with intra-amniotic inflammation (defined as a white blood cell count of 100 nucleated cells/mm3) with a sensitivity of 92.9 % and specificity of 91.8 %.93

Studies in monkeys which had experimental intra-amniotic infection and in women with proven intra-amniotic infection showed that a protein profile of amniotic fluid could identify such patients. The informative peptides included calgranulin B and a fragment of insulin-like growth factor-binding protein-1 (molecular weight of 11kDa). Moreover, some of the markers were also identified in maternal serum.94

Subsequently, proteomic analysis of amniotic fluid with SELDI-TOF MS was reported in women with preterm labor and PPROM separately. The protein profiles of women with preterm labor and intra-amniotic infection were different between women with preterm labor and those with PPROM with intra-amniotic inflammation. Intra-amniotic inflammation was defined as a concentration of IL-6 of ≥1.5 ng/mL in amniotic fluid in women with preterm labor and ≥0.80 ng/mL in women with PPROM. The authors reported that 17 proteins were significantly overexpressed in amniotic fluid from intra-amniotic inflammation cases, including human neutrophil protein 1–3, calgranulins A and B.95 This study included examination of cervical fluid; however, no differences could be demonstrated among the groups.

Other studies have attempted to identify biomarkers in maternal blood or vaginal fluid96 using different proteomic techniques such as matrix-assisted laser desorption ionization, SELDI, and electrospray ionization time-of-flight MS. One study used two-dimensional chromatography (the first dimension separated proteins by isoelectric points and the second by protein hydrophobicity) followed by MS. Two-dimensional protein maps allowed the identification of bands differentially expressed in women with preterm labor with intact membranes who delivered preterm (with and without intra-amniotic inflammation) from women with preterm labor who delivered at term. MS was used to identify the proteins in the bands (Figure 1).97

Figure 1.

Figure 1.

Mass spectrometry profiles of amniotic fluid from patients with preterm labor who delivered at term (A) and preterm labor with infection/inflammation (B). Reproduced with permission from Romero R,97 Bujold E, Andrews P. Michigan Proteome Center SU/Perinatology Research Branch.

Collectively, these studies suggest that proteomic techniques can be used to identify biomarkers in women with preterm labor with intra-amniotic inflammation. A crucial question is whether these observations could be translated into a clinically applicable test. Clearly, the approaches used for discovery may not be optimal for a point-of-care test. Specifically, the expense of the equipment, expertise required to interpret the results, and issues of cost and time should be considered.

If the purpose is to identify intra-amniotic inflammation, this can be accomplished with a simple amniotic fluid white blood cell count determination, which requires a hemocytometer chamber and a microscope, available in every hospital around the world. There is no evidence that an MS-based test should be used to obtain the same information as that available rapidly and inexpensively worldwide with the use of a hemocytometer chamber. Moreover, if the desire is to render the diagnosis of intra-amniotic inflammation more objective, this can be accomplished with an enzyme-linked immunosorbent assay for IL-6.98100 There is evidence that an elevated concentration of IL-6 in amniotic fluid is associated with intra-amniotic inflammation, and also short- and long-term neonatal morbidity.101104

A relevant challenge is to develop a point-of-care test for the detection of intra-amniotic inflammation. Such a test has been developed and consists of a rapid method for detecting an elevated concentration of MMP-8 in amniotic fluid.105 Extensive studies70,106110 support an association between an elevation in MMP8 in amniotic fluid and intra-amniotic infection, histologic chorioamnionitis, impending preterm delivery, and adverse neonatal outcome.

Yoon et al developed a test which is configured as a rapid pregnancy test (Figure 2).105 It requires 20 μL of amniotic fluid; laboratory equipment is not required, and results are available within 15 minutes. Initial studies indicated that the test had a 95% sensitivity and a 93% specificity in the detection of intra-amniotic inflammation in women in preterm labor with intact membranes.111 A subsequent study by Nien et al105 showed that the test has a high sensitivity and specificity as well as a likelihood ratio for a positive result in the identification of intra-amniotic inflammation (Table 5) and high positive predictive value for delivery within 14 days (Table 6). This test fulfills most of the criteria for a point-of-care test, namely: 1) simple testing method; 2) rapid availability of the results; 3) easy interpretation of the results; 4) low maintenance because the kit can be stored at room temperature; 5) strong correlation with standard laboratory procedures; and 6) low cost because there is no need for capital equipment and because the market price can be driven by need.112

Figure 2.

Figure 2.

MMP-8 rapid test. Reproduced with permission from Nien JK, et al. Am J Obstet Gynecol. 2006;195:1025–30.105

Table 5.

Diagnostic indices, predictive values, and likelihood ratios of MMP-8 PTD Check™ (In2Gen Co., Ltd., Seoul, Korea) for the detection of intra-amniotic infection and intra-amniotic inflammation.

Prevalence
% (n)
Sensitivity
% (n)
Specificity
% (n)
PPV
% (n)
NPV
% (n)
LR (+)
(95% CI)
LR (−)
(95% CI)
Intra-amniotic
Infection
7.3%
(24/331)
83%
(20/24)
95%
(291/307)
56%
(20/36)
99%
(291/295)
15.9
(9.6–26.6)
0.2
(0.1–0.3)
Intra-amniotic
inflammation
11.5%
(38/331)
84%
(32/38)
99%
(289/293)
89%
(32/36)
98%
(289/295)
61.7
(23.1–164.8)
0.2
(0.1–0.4)

PPV: positive predictive value, NPV: negative predictive value, LR: likelihood ratio.

Accuracy for intra-amniotic infection: 94%

Accuracy for intra-amniotic inflammation: 93%

Reproduced with permission from Nien JK et al Am J Obstet Gynecol. 2006;195:1025–30.105

Table 6.

Diagnostic indices, predictive values and likelihood ratios of MMP-8 PTD Check™ (In2Gen Co., Ltd., Seoul, Korea) for the identification of women with spontaneous preterm delivery within 48 hrs, 7 days, 14 days and <32 and <34 weeks.

Prevalence Sensitivity Specificity PPV NPV LR (+)
(95% CI)
LR (−)
(95% CI)
Delivery within 48 hours 11.6%
(38/327)
61%
(23/38)
97%
(279/290)
70%
(23/33)
95%
(279/294)
17.5
(9–33.9)
0.4
(0.2–0.8)
Delivery within 7 days 20.2
(66/327)
47%
(31/66)
99%
(259/261)
94%
(31/33)
88%
(259/295)
61.3
(15.1–250)
0.5
(0.1–2.2)
Delivery within 14 days 24.5%
(80/327)
39%
(31/80)
99%
(245/247)
94%
(34/36)
83%
(245/295)
50
(12–196)
0.6
(0.2–2.5)

PPV: positive predictive value, NPV: negative predictive value, LR: likelihood ratio.

Reproduced with permission from Nien JK et al Am J Obstet Gynecol. 2006;195:1025–30.105

The availability of this rapid test opens avenues to evaluate interventions in women with intra-amniotic inflammation, such as antibiotic administration and modulators of the inflammatory response. This is pertinent because of beneficial effects of the administration of anti-inflammatory agents such as IL-10,113,114 steroids,114 and antioxidants.115

The end result of the use of a discovery technique or an “omic” methodology is the development of such point-of-care tests. An important challenge of proteomics in premature labor is whether analysis of vaginal fluid, maternal serum, or amniotic fluid can identify women destined to deliver preterm in the absence of intra-amniotic inflammation and also of women who will deliver at term without requiring tocolysis.

Transcriptomics

The transcriptome is the full complement of mRNA in a cell or tissue at any given moment.116 A transcriptome forms the template for protein synthesis, resulting in a corresponding protein complement or proteome.116 Transcriptomics has been used to describe the global mRNA expression of a particular tissue, yielding information about the transcriptional differences between two or more states.

Microarrays for analysis of the mRNA expression profile were first reported in 1995117,118 and have since been applied to a wide range of processes, notably, cancer. Microarrays have been used to: 1) classify tumors; 2) assess cancer prognosis; 3) predict tumors’ response to therapy; and 4) identify new sub-types of cancer that could not be discerned with conventional techniques such as histopathologic examination.119121 The case of breast cancer has been particularly noteworthy. Conventional methods, including staging of the disease, histologic grade, etc., have not been adequate to predict which woman will respond to treatment (chemotherapy or hormonal therapy). A recent advance was made by a group of investigators in the Netherlands who used microarrays in women with breast cancer who only had surgical treatment and found that the expression levels of 70 genes identified women who subsequently developed metastases and that this profile was better than histology and conventional clinical staging.119,120

In the case of parturition, gene expression has been studied using targeted and non-targeted approaches such Northern blot analysis and quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), arrays of complementary DNAs (cDNA) spotted on membranes (macroarrays), and microarrays.122136 It is possible to identify differentially expressed mRNA species in the myometrium, cervix, and chorioamniotic membranes from women not in labor and those in labor. These studies have shown that genes encoding proteins involved in prostaglandin synthesis and in the control of the inflammatory response are differentially expressed in labor. The reader is referred to a review of the subject which describes previous studies, techniques, and findings.137

Studies of the transcriptome can be used to develop a molecular taxonomy of preterm labor and improve the understanding of the physiology and pathology of term and preterm parturition. The analysis of microarray experiments requires considerable expertise, and the reader is referred to recent reviews of the subject for details.138142 Gene expression profiling can be used for three main purposes: 1) class comparison, 2) class prediction, and 3) class discovery. Class comparison studies are undertaken in order to characterize the gene expression profiles of two or more groups of women. For example, it is possible to compare the transcriptome of myometrium in labor versus those not in labor. In class prediction applications, the classes are predefined (e.g. women with and without preterm labor), and the goal is to build a “classifier” able to distinguish between these classes based on the gene expression profiles of the samples. In class discovery studies, a given set of gene expression profiles is analyzed with the goal of discovering subgroups that share common features. The biological interest of this approach is to understanding the mechanisms of disease underlying the syndrome of preterm parturition. This can be realized by examining the functional classifications and pathways of genes differentially expressed.140,143

Aguan et al144 were the first to use functional genomics for the study of parturition. Using arrays for 588 genes and RNA isolated from the myometrium of three women at term in spontaneous labor and from three women not in labor, they noted up- and down-regulation (defined as a change of 2-fold or greater) of 21 genes involved in a wide range of physiological processes, including smooth muscle contraction and relaxation, regulation of DNA metabolism, as well as transcriptional and cell cycle regulation. Chan et al145 used an unrestricted approach to identify differentially regulated genes during spontaneous labor at term. Suppression subtractive hybridization (SSH) was used with cDNA libraries constructed from the myometrium of one woman not in labor and a woman who underwent a cesarean section because of failure to progress in labor. Dot-blot screening of 400 positive clones indicated that 14 genes were up-regulated and 16 were down-regulated. Up-regulated genes included those encoding proteins implicated in the mechanism of parturition in the pregenomic era (oxytocin receptor, MMP-9, fibronectin, and IL-8), those not previously implicated, and four genes with no matching sequences in available databases. Northern blot analysis was performed for six genes and qRT-PCR for three (IL-8, Mn superoxidase dismutase [MnSOD], and cyclophilin) in a set of samples from a larger cohort. The major findings were that: 1) these three genes were up-regulated during spontaneous labor; 2) there were topographic differences in the expression of MnSOD in the lower uterine segment and fundal myometrium, but not for IL-8; and 3) IL-8 expression was higher in spontaneous labor than in induced labor.

Transcriptomics has been used to examine the differences in gene expression profiles in the fetal membranes in women with PPROM and preterm labor with intact membranes with and without histologic chorioamnionitis. The gene with the most differential expression between preterm labor and PPROM was proteinase inhibitor 3 (PI3), also known as elastase-specific inhibitor. This serine proteinase inhibitor is capable of inhibiting neutrophil elastase and proteinase-3.146 The former had been implicated in the mechanisms of membrane rupture both at term and preterm gestation.147 Decreased expression of PI3 in PPROM was found by microarray experiments and was confirmed by qRT-PCR. Immunohistochemistry showed decreased PI3 protein expression. This study showss that a genome-wide approach can identify deficient expression of PI3 in PPROM, a gene that was not suspected previously to play a role in parturition. Moreover, the authors proposed that patients who are not capable of producing adequate amounts of PI3 in the fetal membranes may be predisposed to PPROM.146 For this pregnancy complication, Tashima et al148 was the first to use SSH to investigate differentially expressed genes.

Haddad et al149 performed a prospective cohort study to examine differential gene expression of the chorioamniotic membranes of women not in labor and those in spontaneous labor at term (in the absence of histologic chorioamnionitis). The authors reported that multiple transcripts controlling each of the defined steps of acute inflammation increased during labor and that an “acute inflammation gene expression signature” appeared to be coordinately expressed and was not associated with the duration of labor. For example IL-8, IL-6, PBEF, TLR2, and SOD2 were overexpressed in samples of women in labor compared with those not in labor (Figure 3).149 These observations are consistent with the analysis reported by Bisits et al,150 indicating that genes involved in the control of inflammation participate in the activation of a tissue central to parturition: myometrium.

Figure 3.

Figure 3.

Hierarchical clustering of probe sets that discriminate the chorioamniotic membrane samples of term in labor (TIL) patients from their term no labor (TNL) counterparts. The top 224 probe sets (P% .02) with a minimum average expression difference of 1.4-fold are shown. Reproduced with permission from Haddad et al. Am J Obstet Gynecol 2006;195:394. e1–24.149

Hassan et al151 characterized the transcriptome of cervical tissue in women at term not in labor (n=7) and in those after spontaneous labor (n=9), and reported that the cervical transcriptome of women without labor was dramatically different from those who underwent labor. Indeed, unique genes (n = 1192) were differentially expressed in the cervical tissue from women after spontaneous labor, compared to women in term without labor (false discovery rate less than 0.05, absolute fold change greater than 2). Gene ontology analysis indicated that multiple “biological process” categories were enriched, including “response to biotic stimulus,” “apoptosis,” “epidermis development,” and “steroid metabolism.” Moreover, genes involved in neutrophil chemotaxis were dramatically up-regulated in specimens from women after spontaneous labor. The authors confirmed the increased expression of IL-8, IL-6, and vascular endothelial growth factor in women after spontaneous labor using real-time qRT-PCR. Of interest, toll-like receptor-3 and toll-like receptor-5 showed decreased gene expression in women after spontaneous labor.

One of the most comprehensive studies reported to date is that of Bukowski et al,152 who compared the transcriptome of the uterine fundus, lower uterine segment, and cervix before and during labor. The authors concluded that labor results in a change in the transcriptome in each component of the uterus. Moreover, they have provided a list of differentially regulated genes and performed confirmatory studies with real-time qRT-PCR for two genes: repressor of estrogen receptor activity and retinoid X receptor alpha, both of which were down-regulated in the uterine fundus in women in labor. Genes with similar expression profiles were identified, and networks of co-regulated and co-expressed genes during parturition were discovered.152,153

Transcriptomics has been used to study preterm labor in mice. Muhle et al154 used an experimental paradigm to identify genes differentially expressed in pregnant mice subjected to inoculation with heat-killed bacteria (a model for infection-induced preterm labor)155 and ovariectomy (a model of progesterone-withdrawal-induced preterm delivery). Each model of preterm labor was associated with a different set of differentially expressed genes. We have confirmed these findings using a similar experimental approach.156 Specifically, bacteria-induced preterm labor substantially increased the expression of genes involved in prostaglandin synthesis. In contrast, ovariectomy-induced preterm labor increased the expression of genes involved in lipoxin, leukotriene, and hydroxyeicosatetraenoic acid synthesis. Thus, bacteria-induced and ovariectomy-induced preterm labor express a different profile of genes involved in the synthesis of prostaglandins, lipoxins, leukotrienes, and hydroxyeicosatetraenoic acids.156 These observations suggest that trascriptomics may provide novel insights into the mechanisms involved in different forms of preterm parturition.

The results of the studies reviewed here shows that transcriptome analysis is feasible to study spontaneous preterm and term parturition and that the results can yield novel insights. Future challenges include: 1) the development of a taxonomy of the preterm parturition syndrome; 2) definition of the molecular pathways involved in each taxon; and 3) determination of whether or not the study of the peripheral blood transcriptome can be applied to identify women at risk for preterm delivery and those with intra-amniotic inflammation/intra-amniotic infection.157

Metabolomics

Metabolomics is a discipline that aims to identify and quantify the global composition of “metabolites” of a biological fluid, tissue, or organism.158160 The “metabolome” (analogous to the genome or transcriptome) would refer to the comprehensive catalogue of metabolites in a specific organ or compartment under a set of conditions (e.g. the plasma metabolome or the amniotic fluid metabolome).161 A working definition of a “metabolite” is a native small molecule (non-polymeric compound) that participates in general metabolic reactions and is required for the maintenance, growth, and normal function of cells.161 Metabolomics has theoretical advantages over genomics, transcriptomics, and proteomics because the metabolic network is downstream from gene expression and protein synthesis, and, thus, may reflect more closely the cell activity at a functional level.158,162 In addition, the concentration of a given metabolite is the result of the activity of all enzymes involved in the synthesis and catabolism of that compound and, thus, metabolic profiling has the potential to provide integrative163 information. Moreover, the coupling of reactions in the metabolic network allows that even small perturbations in the proteome (concentrations of a set of enzymes) could cause major changes in the concentrations of several metabolites. Importantly, gene deletion may not result in a visible phenotype change, suggesting that the organism can compensate for the absence of a specific gene, mRNA and protein. This is presumably accomplished by using alternative pathways.159 Metabolic profiling has been used to identify such silent phenotypes.164,165

The experimental approach for global metabolic analysis has evolved over time. Nuclear magnetic resonance spectroscopy (NMR) has been used for the study of the chemical composition of biofluids for decades.166 The current approach consists of using a combination of separation techniques and MS. The typical separation techniques are gas chromatography (GC) (for non-polar compounds) or liquid chromatography (LC) (for polar compounds), followed by MS to identify the specific compound. However, other techniques have been proposed, including pyrolysis MS,167 Fourier transform infrared spectroscopy,167 Raman spectroscopy,168 and direct infusion electrospray-MS.169 NMR has the disadvantage of lower sensitivity than LC-MS or GC/MS but is non-destructive, covers a wide range of metabolites, and requires minimal sample preparation. The reader should be aware that there is no current method capable to detect and quantitate the entire159 metabolome on its own. Indeed, subdivisions of metabolomics are now emerging, such as glycomics, lipidomics, etc.

Metabolic profiling of amniotic fluid has been used to identify women at risk for preterm delivery and also women with intra-amniotic infection/intra-amniotic inflammation.111 Amniotic fluid obtained by amniocentesis from women with premature labor and intact membranes was subjected to separation with GC and LC, followed by MS. Identification of the compounds was performed by using authentic standards. Metabolic profiling was able to identify women as belonging to the correct clinical group with a 96.3% precision (53/55). Indeed, 15 out of 16 women with premature contractions who delivered at term were correctly classified, and all patients with preterm labor without infection and inflammation who delivered preterm neonates were correctly clustered (19/19). Moreover, among women with infection/inflammation, 19 out of 20 were correctly classified. Thus, metabolic profiling can be of value to asses the risk of preterm delivery in the presence or absence of infection/inflammation. Metabolic profiling has also been used in pre-eclampsia.160,170,171

Epigenetics and Epigenomics

“Epigenetics” (etymologically “outside conventional genetics”) refers to heritable changes in gene expression that occur without modification of DNA sequence.172 These changes have been implicated in the integration of environmental and intrinsic signals into the genome. Specifically, epigenetics is concerned with how changes in gene function led to different phenotypes without a change in genotype.173 Epigenomics is the global study of the epigenetic marks of chromatin, such as DNA methylation sites and post-translational modifications.174

Epigenetic processes are essential for development and differentiation. However, they can also arise or be deleted after the embryonic period. Epigenetic changes provide a molecular link between the environment and the expression of the genome, and they have been implicated in the genesis of diseases such as cancer and the process of aging.

The mechanisms of epigenetic regulation are chromatin remodeling and modification (e.g. DNA methylation and histone modifications, such as acetylation, phosphorylation, methylation, ibiquitination, etc.) and RNA interference.175,176 There are hundreds of potentially methylated cytosines in a gene and dozens of known post-translational modifications of chromatin. Thus, there is a need for high-throughput technology to map the sites susceptible to such processes. Several epigenome projects have already been launched to characterize such sites on a genome-wide scale (epigenomics).

DNA methylation is the most widely studied mechanism for underlying epigenetic changes. Methylation can result in gene “repression” by attaching a methyl group to the cytosine nucleotide in CpG islands,177 which are short sequence domains that generally remain unmethylated at all times, regardless of gene expression.178 In most cases, increased DNA methylation is associated with gene “silencing” and decreased methylation is related to gene activation.179

DNA methylation is dependent on folate, vitamin B12, and vitamin B6, which are co-factors in the enzymatic reaction.180 Thus, nutrition can induce epigenetic changes.181 Indeed, maternal supplementation of methyl donors and co-factors (folic acid, vitamin B12, choline, and betaine) in a specific strain of mice has been shown to permanently alter the fur color of the offspring. The mechanism by which this occurs is CpG methylation at the Avy locus of agouti mice.182

Could there be a link between epigenetic changes and spontaneous preterm delivery? Preterm birth has been associated with folate deficiency.183 Furthermore, mutations in genes involved in DNA methylation (such as MTHFR C677T and polymorphic deletion of 19 base pairs within intron I of DHFR) have been associated with preterm birth (the relative rate of spontaneous preterm birth and indicated preterm birth has not been reported in this cohort).184,185 Thus, both environmental and genetic susceptibility to impaired methylation were associated with preterm birth. Interestingly, preterm birth has been associated with low concentrations of zinc, another co-factor in the methylation pathway.186,187 Taken together, these findings suggest that epigenetic mechanisms may play a role in the pathogenesis of preterm birth. Indeed, epigenetics may provide a molecular mechanism for the impact of maternal nutrition, environmental factors, and genetic susceptibility on preterm labor.

Infection is a major environmental factor, which has been causally linked to spontaneous preterm labor and delivery. The role for pro-inflammatory cytokines, which act through the transcription factor NF-κB, is well established in the molecular mechanisms responsible for infection-associated preterm parturition.188195

IL-6 plays a central role in the fetal host response to microbial invasion of the amniotic cavity. Fetuses with systemic inflammation (defined as an elevated fetal plasma IL-6) have impending onset of labor and multi-systemic organ involvement: the fetal inflammatory response syndrome.196,197 The possibility that IL-6 can induce re-programming of several fetal physiologic responses, including the immune response through an epigenetic mechanism, must now be considered in light of recent findings that IL-6 upregulates the expression of DNA methyltransferases198200 and histone methyltransferases.201 Furthermore, impaired DNA methylation can increase IL-6 expression.202 Hence, DNA-methylation-related events may affect the magnitude of the inflammatory response in the mother and fetus and predispose to preterm labor and fetal injury.

Experimental evidence that epigenetics may be important in the onset of labor was reported in 2003.203 The administration of a histone deacetylase inhibitor (trichostatin A) to pregnant mice on a daily basis from day 15 to 19 was associated with delayed onset of parturition (24 to 48 hours).203 The authors postulated that the mechanism of action was impairment of the function of the progesterone-progesterone receptor complex. Of interest is that women in labor had lower ratio of acetylated histone to total H3 in the nuclei of human myometrial cells obtained from uterine fundus than women not in labor.

An epigenetic mechanism has been implicated in the control of prostaglandin production by human amnion. Mitchell204 has proposed that the production of prostaglandins during pregnancy is downregulated by enhanced DNA methylation and increased histone deacetylation of the gene for cycloxygenase-2 (also known as prostaglandin H synthetase-2). The author demonstrated that prostaglandin E2 production by human amnion in response to IL-1 beta was reduced by inhibition of DNA methylation and histone deacetylation (with 5-aza-2’ deoxycytidine and trichostatin A, respectively).

Recently, our group has demonstrated changes in the expression of specific microRNAs in the myometrium and chorioamniotic membranes of women with spontaneous labor at term, as well as in preterm labor associated with inflammation. MicroRNAs operate through RNA interference, which is an important mechanism for epigenetic change. These findings suggest that several mechanisms may operate to accomplish epigenetic regulation of parturition (R Romero, personal communication).

In summary, studies regarding the role of epigenetic regulation in spontaneous term and preterm labor have begun, and the evidence suggests that these mechanisms may be involved in the control of the duration of pregnancy. The possibility that exposure to environmental factors may re-program the fetal and maternal immune systems must be considered. A systematic study of the role of epigenetic mechanisms in term and preterm parturition is needed.

Conclusion

The beginning of the 21st century is likely to be considered a pivotal period in the comprehension of biology, as dramatic advances allow freedom from the constraints of reductionism and the birth of HDB and systems biology. A new theoretical framework assisted by computational biology may render tractable the study of many of the problems presented by the major obstetrical syndromes. It is hoped that these disciplines will improve the understanding of the taxonomy and pathobiology of obstetrical disorders and lead to the improved identification of the woman at risk, diagnosis, treatment, and prevention of these conditions.

Acknowledgement:

This work was supported by the Division of Intramural Research of the National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH), US Department of Health and Human Services (DHHS).

Footnotes

No reprints are available from the authors.

Reference List

  • 1.Ahn AC, Tewari M, Poon CS, Phillips RS. The Limits of Reductionism in Medicine: Could Systems Biology Offer an Alternative? PLoS.Med 2006;3:e208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Strange K The end of “naive reductionism”: rise of systems biology or renaissance of physiology? Am.J.Physiol Cell Physiol 2005;288:C968–C974. [DOI] [PubMed] [Google Scholar]
  • 3.Evans GA. Designer science and the “omic” revolution. Nat.Biotechnol 2000;18:127. [DOI] [PubMed] [Google Scholar]
  • 4.Gracey AY, Cossins AR. Application of microarray technology in environmental and comparative physiology. Annu.Rev.Physiol 2003;65:231–59. [DOI] [PubMed] [Google Scholar]
  • 5.Mehta T, Tanik M, Allison DB. Towards sound epistemological foundations of statistical methods for high-dimensional biology. Nat.Genet 2004;36:943–47. [DOI] [PubMed] [Google Scholar]
  • 6.Webster’s Third New International Dictionary. G&L Merrinian, Co., Springfield, MA, 1981. [Google Scholar]
  • 7.Weinstein JN. Fishing expeditions. Science 1998;282:628–9. [DOI] [PubMed] [Google Scholar]
  • 8.Weinstein JN. ‘Omic’ and hypothesis-driven research in the molecular pharmacology of cancer. Curr Opin Pharmacol. 2002. August;2:361–5. [DOI] [PubMed] [Google Scholar]
  • 9.Coulton G Are histochemistry and cytochemistry ‘Omics’? J Mol Histol. 2004. August;35:603–13. [DOI] [PubMed] [Google Scholar]
  • 10.Kitano H Looking beyond the details: a rise in system-oriented approaches in genetics and molecular biology. Curr.Genet 2002;41:1–10. [DOI] [PubMed] [Google Scholar]
  • 11.Kitano H Systems biology: a brief overview. Science 2002;295:1662–64. [DOI] [PubMed] [Google Scholar]
  • 12.Hood L Leroy Hood expounds the principles, practice and future of systems biology. Drug Discov.Today 2003;8:436–38. [DOI] [PubMed] [Google Scholar]
  • 13.Hood L Systems biology: integrating technology, biology, and computation. Mech.Ageing Dev 2003;124:9–16. [DOI] [PubMed] [Google Scholar]
  • 14.Hood L, Heath JR, Phelps ME, Lin B. Systems biology and new technologies enable predictive and preventative medicine. Science 2004;306:640–43. [DOI] [PubMed] [Google Scholar]
  • 15.Ahn AC, Tewari M, Poon CS, Phillips RS. The clinical applications of a systems approach. PLoS.Med 2006;3:e209. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Collins FS, Guttmacher AE. Genetics moves into the medical mainstream. JAMA 2001;286:2322–24. [DOI] [PubMed] [Google Scholar]
  • 17.Collins FS, Mansoura MK. The Human Genome Project. Revealing the shared inheritance of all humankind. Cancer 2001;91:221–25. [DOI] [PubMed] [Google Scholar]
  • 18.Ginsburg GS, McCarthy JJ. Personalized medicine: revolutionizing drug discovery and patient care. Trends Biotechnol. 2001;19:491–96. [DOI] [PubMed] [Google Scholar]
  • 19.McKusick VA. The anatomy of the human genome: a neo-Vesalian basis for medicine in the 21st century. JAMA 2001;286:2289–95. [DOI] [PubMed] [Google Scholar]
  • 20.Subramanian G, Adams MD, Venter JC, Broder S. Implications of the human genome for understanding human biology and medicine. JAMA 2001;286:2296–307. [DOI] [PubMed] [Google Scholar]
  • 21.Nolte DL, Mason JR. Maternal ingestion of ortho-aminoacetophenone during gestation affects intake by offspring. Physiol Behav. 1995;58:925–28. [DOI] [PubMed] [Google Scholar]
  • 22.Hudson R, Cruz Y, Lucio A, Ninomiya J, Martinez-Gomez M. Temporal and behavioral patterning of parturition in rabbits and rats. Physiol Behav. 1999;66:599–604. [DOI] [PubMed] [Google Scholar]
  • 23.Nathanielsz PW. Comparative studies on the initiation of labor. Eur.J.Obstet.Gynecol.Reprod.Biol 1998;78:127–32. [DOI] [PubMed] [Google Scholar]
  • 24.Walker LA, Cornell L, Dahl KD, Czekala NM, Dargen CM, Joseph B et al. Urinary concentrations of ovarian steroid hormone metabolites and bioactive follicle-stimulating hormone in killer whales (Orcinus orchus) during ovarian cycles and pregnancy. Biol.Reprod 1988;39:1013–20. [DOI] [PubMed] [Google Scholar]
  • 25.Allen WR, Mathias S, Ford M. Placentation in the African elephant, Loxodonta africana. IV. Growth and function of the fetal gonads. Reproduction. 2005;130:713–20. [DOI] [PubMed] [Google Scholar]
  • 26.Allen WR, Wilsher S, Turnbull C, Stewart F, Ousey J, Rossdale PD et al. Influence of maternal size on placental, fetal and postnatal growth in the horse. I. Development in utero. Reproduction. 2002;123:445–53. [PubMed] [Google Scholar]
  • 27.Romero R, Espinoza J, Kusanovic JP, Gotsch F, Hassan S, Erez O, Chaiworapongsa T, Mazor M. The preterm parturition syndrome. BJOG. 2006. December;113 Suppl 3:17–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Lind J, Wallenburg HC. Pregnancy and the Ehlers-Danlos syndrome: a retrospective study in a Dutch population. Acta Obstet.Gynecol.Scand 2002;81:293–300. [DOI] [PubMed] [Google Scholar]
  • 29.Martin JA, Hamilton BE, Sutton PD, Ventura SJ, Menacker F, Munson ML. Births: final data for 2002. Natl.Vital Stat.Rep 2003;52:1–113. [PubMed] [Google Scholar]
  • 30.Adams KM, Eschenbach DA. The genetic contribution towards preterm delivery. Semin Fetal Neonatal Med. 2004. December;9(6):445–52. [DOI] [PubMed] [Google Scholar]
  • 31.Varner MW, Esplin MS. Current understanding of genetic factors in preterm birth. BJOG. 2005. March;112 Suppl 1:28–31. [DOI] [PubMed] [Google Scholar]
  • 32.Schieve LA, Handler A. Preterm delivery and perinatal death among black and white infants in a Chicago-area perinatal registry. Obstet.Gynecol 1996;88:356–63. [DOI] [PubMed] [Google Scholar]
  • 33.Odibo AO, Talucci M, Berghella V. Prediction of preterm premature rupture of membranes by transvaginal ultrasound features and risk factors in a high-risk population. Ultrasound Obstet.Gynecol 2002;20:245–51. [DOI] [PubMed] [Google Scholar]
  • 34.Romero R, Kuivaniemi H, Tromp G, Olson J. The design, execution, and interpretation of genetic association studies to decipher complex diseases. Am.J.Obstet.Gynecol 2002;187:1299–312. [DOI] [PubMed] [Google Scholar]
  • 35.Porter TF, Fraser AM, Hunter CY, Ward RH, Varner MW. The risk of preterm birth across generations. Obstet.Gynecol 1997;90:63–67. [DOI] [PubMed] [Google Scholar]
  • 36.Winkvist A, Mogren I, Hogberg U. Familial patterns in birth characteristics: impact on individual and population risks. Int.J.Epidemiol 1998;27:248–54. [DOI] [PubMed] [Google Scholar]
  • 37.Clausson B, Lichtenstein P, Cnattingius S. Genetic influence on birthweight and gestational length determined by studies in offspring of twins. BJOG. 2000;107:375–81. [DOI] [PubMed] [Google Scholar]
  • 38.Treloar SA, Macones GA, Mitchell LE, Martin NG. Genetic influences on premature parturition in an Australian twin sample. Twin.Res 2000;3:80–82. [DOI] [PubMed] [Google Scholar]
  • 39.Ward K, Argyle V, Meade M, Nelson L. The heritability of preterm delivery. Obstet.Gynecol 2005;106:1235–39. [DOI] [PubMed] [Google Scholar]
  • 40.Strachan T, Read AP. Human Molecular Genetics. New York (NY): John Wiley & Sons, Inc, 1999. [Google Scholar]
  • 41.Pennell CE, Jacobsson B, Williams SM, Buus RM, Muglia LJ, Dolan SM et al. Genetic epidemiological studies of preterm birth: Guidelines for research. Am.J.Obstet.Gynecol 2007. February;196:107–18. [DOI] [PubMed] [Google Scholar]
  • 42.Aidoo M, McElroy PD, Kolczak MS, Terlouw DJ, ter Kuile FO, Nahlen B et al. Tumor necrosis factor-alpha promoter variant 2 (TNF2) is associated with pre-term delivery, infant mortality, and malaria morbidity in western Kenya: Asembo Bay Cohort Project IX. Genet.Epidemiol 2001;21:201–11. [DOI] [PubMed] [Google Scholar]
  • 43.Engel SA, Erichsen HC, Savitz DA, Thorp J, Chanock SJ, Olshan AF. Risk of spontaneous preterm birth is associated with common proinflammatory cytokine polymorphisms. Epidemiology 2005;16:469–77. [DOI] [PubMed] [Google Scholar]
  • 44.Chen D, Hu Y, Chen C, Yang F, Fang Z, Wang L et al. Polymorphisms of the paraoxonase gene and risk of preterm delivery. Epidemiology 2004;15:466–70. [DOI] [PubMed] [Google Scholar]
  • 45.Menon R, Velez DR, Simhan H, Ryckman K, Jiang L, Thorsen P et al. Multilocus interactions at maternal tumor necrosis factor-alpha, tumor necrosis factor receptors, interleukin-6 and interleukin-6 receptor genes predict spontaneous preterm labor in European-American women. Am.J.Obstet.Gynecol 2006;194:1616–24. [DOI] [PubMed] [Google Scholar]
  • 46.Annells MF, Hart PH, Mullighan CG, Heatley SL, Robinson JS, Bardy P et al. Interleukins-1, −4, −6, −10, tumor necrosis factor, transforming growth factor-beta, FAS, and mannose-binding protein C gene polymorphisms in Australian women: Risk of preterm birth. Am.J.Obstet.Gynecol 2004;191:2056–67. [DOI] [PubMed] [Google Scholar]
  • 47.Orsi NM, Logghe HL, Gopichandran N, Bottomley L, Lynch K, Levene MI, et al. Carriage of a low-secretory phenotype C allele of the interleukin (IL)-6–174 (G/C) polymorphism is associated with preterm delivery and idiopathic preterm labour (<32 weeks). J Soc Gynecol Investig 2003;2:320A (abstract). [Google Scholar]
  • 48.Orsi NM, Logghe HL, Gopichandran N, Bottomley L, Lynch K, Levene MI, et al. Carriage of the mutant high-secretory phenotype G allele of the interleukin (IL)-10–1082 (A/G) is associated with preterm delivery and premature rupture of membranes (<32 weeks). J Soc Gynecol Investig 2003;2:318A (abstract). [Google Scholar]
  • 49.Greenfield PJ, Kessling AM, Lamont RF. Variation in the interlukin-1beta-511 polymorphism genotype is associated with gestational length in women from the Indian sub-continent who delivered preterm. J Soc Gynecol Investig 2003;2:320A (abstract). [Google Scholar]
  • 50.Bodamer OA, Mitterer G, Maurer W, Pollak A, Mueller MW, Schmidt WM. Evidence for an association between mannose-binding lectin 2 (MBL2) gene polymorphisms and pre-term birth. Genet.Med 2006;8:518–24. [DOI] [PubMed] [Google Scholar]
  • 51.Landau R, Xie HG, Dishy V, Stein CM, Wood AJ, Emala CW et al. Beta2-adrenergic receptor genotype and preterm delivery. Am J Obstet Gynecol 2002;187:1294–98. [DOI] [PubMed] [Google Scholar]
  • 52.Fujimoto T, Parry S, Urbanek M, Sammel M, Macones G, Kuivaniemi H et al. A single nucleotide polymorphism in the matrix metalloproteinase-1 (MMP-1) promoter influences amnion cell MMP-1 expression and risk for preterm premature rupture of the fetal membranes. J.Biol.Chem 2002;277:6296–302. [DOI] [PubMed] [Google Scholar]
  • 53.Wang H, Parry S, Macones G, Sammel MD, Ferrand PE, Kuivaniemi H et al. Functionally significant SNP MMP8 promoter haplotypes and preterm premature rupture of membranes (PPROM). Hum.Mol.Genet 2004;13:2659–69. [DOI] [PubMed] [Google Scholar]
  • 54.Ferrand PE, Parry S, Sammel M, Macones GA, Kuivaniemi H, Romero R et al. A polymorphism in the matrix metalloproteinase-9 promoter is associated with increased risk of preterm premature rupture of membranes in African Americans. Mol.Hum.Reprod 2002;8:494–501. [DOI] [PubMed] [Google Scholar]
  • 55.Ferrand PE, Fujimoto T, Chennathukuzhi V, Parry S, Macones GA, Sammel M et al. The CARD15 2936insC mutation and TLR4 896 A>G polymorphism in African Americans and risk of preterm premature rupture of membranes (PPROM). Mol.Hum.Reprod 2002;8:1031–34. [DOI] [PubMed] [Google Scholar]
  • 56.Roberts AK, Monzon-Bordonaba F, Van Deerlin PG, Holder J, Macones GA, Morgan MA et al. Association of polymorphism within the promoter of the tumor necrosis factor alpha gene with increased risk of preterm premature rupture of the fetal membranes. Am.J.Obstet.Gynecol 1999;180:1297–302. [DOI] [PubMed] [Google Scholar]
  • 57.Kalish RB, Vardhana S, Gupta M, Perni SC, Chasen ST, Witkin SS. Polymorphisms in the tumor necrosis factor-alpha gene at position −308 and the inducible 70 kd heat shock protein gene at position +1267 in multifetal pregnancies and preterm premature rupture of fetal membranes. Am.J.Obstet.Gynecol 2004;191:1368–74. [DOI] [PubMed] [Google Scholar]
  • 58.Kalish RB, Vardhana S, Gupta M, Chasen ST, Perni SC, Witkin SS. Interleukin-1 receptor antagonist gene polymorphism and multifetal pregnancy outcome. Am.J.Obstet.Gynecol 2003;189:911–14. [DOI] [PubMed] [Google Scholar]
  • 59.Genc MR, Gerber S, Nesin M, Witkin SS. Polymorphism in the interleukin-1 gene complex and spontaneous preterm delivery. Am.J.Obstet.Gynecol 2002;187:157–63. [DOI] [PubMed] [Google Scholar]
  • 60.Kalish RB, Vardhana S, Normand NJ, Gupta M, Witkin SS. Association of a maternal CD14 −159 gene polymorphism with preterm premature rupture of membranes and spontaneous preterm birth in multi-fetal pregnancies. J.Reprod.Immunol 2006;70:109–17. [DOI] [PubMed] [Google Scholar]
  • 61.Kalish RB, Nguyen DP, Vardhana S, Gupta M, Perni SC, Witkin SS. A single nucleotide A>G polymorphism at position −670 in the Fas gene promoter: relationship to preterm premature rupture of fetal membranes in multifetal pregnancies. Am.J.Obstet.Gynecol 2005;192:208–12. [DOI] [PubMed] [Google Scholar]
  • 62.Fuks A, Parton LA, Polavarapu S, Netta D, Strassberg S, Godi I et al. Polymorphism of Fas and Fas ligand in preterm premature rupture of membranes in singleton pregnancies. Am.J.Obstet.Gynecol 2005;193:1132–36. [DOI] [PubMed] [Google Scholar]
  • 63.Wang H, Parry S, Macones G, Sammel MD, Kuivaniemi H, Tromp G et al. A functional SNP in the promoter of the SERPINH1 gene increases risk of preterm premature rupture of membranes in African Americans. Proc.Natl.Acad.Sci.U.S.A 2006;103:13463–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Crider KS, Whitehead N, Buus RM. Genetic variation associated with preterm birth: a HuGE review. Genet.Med 2005;7:593–604. [DOI] [PubMed] [Google Scholar]
  • 65.Fortunato SJ, Menon R, Swan K, Baricos W. Expression of matrix degrading enzymes and tissue inhibitors of metalloproteinases (TIMP) in human fetal membranes. Presented at 15th annual meeting of the Society of Perinatal Obstetricians Georgia, January 23–28, Atlanta, 1995. (abstract). [Google Scholar]
  • 66.Fortunato SJ, Menon R, Lombardi S. Induction of MMP-9 and normal presence of MMP-2, TIMP-1and 2 in human fetal membranes. 17th annual meeting of the Society of Perinatal Obstetricians Anaheim. Am J Obstet Gynecol 1997;176:1.9024081 [Google Scholar]
  • 67.Fortunato SJ, LaFleur B, Menon R. Collagenase-3 (MMP-13) in fetal membranes and amniotic fluid during pregnancy. Am J Reprod Immunol 2003;49:120–5. [DOI] [PubMed] [Google Scholar]
  • 68.Vadillo-Ortega F, Hernandez A, Gonzalez-Avila G, Bermejo L, Iwata K, Strauss JF III. Increased matrix metalloproteinase activity and reduced tissue inhibitor of metalloproteinases-1 levels in amniotic fluids from pregnancies complicated by premature rupture of membranes. Am J Obstet Gynecol 1996;174:1371–6. [DOI] [PubMed] [Google Scholar]
  • 69.Biggio JR, Ramsey PS, Cliver SP, Lyon MD, Goldenberg RL, Wenstrom KD. Midtrimester amniotic fluid matrix metalloproteinase-8 (MMP-8) levels above the 90th percentile area marker for subsequent preterm rupture of membrane. Am J Obstet Gynecol 2005;192:109–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Maymon E, Romero R, Pacora P, Gomez R, Athayde N, Edwin S et al. Human neutrophil collagenase (matrix metalloproteinase 8) in parturition, premature rupture of the membranes, and intrauterine infection. Am.J.Obstet.Gynecol 2000;183:94–99. [DOI] [PubMed] [Google Scholar]
  • 71.Johnatty RN, Taub DD, Reeder SP, Turcovski-Corrales SM, Cottam DW, Stephenson TJ, et al. Cytokine and chemokine regulation of proMMP-9 and TIMP-1 production by human peripheral blood lymphocytes. J Immunol 1997;158:2327–33. [PubMed] [Google Scholar]
  • 72.Hiby SE, Walker JJ, O’shaughnessy KM, Redman CW, Carrington M, Trowsdale J, et al. Combinations of maternal KIR and fetal HLA-C genes influence the risk of preeclampsia and reproductive success. J Exp Med 2004;200:957–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Skjaerven R, Vatten LJ,Wilcox AJ, Ronning T, Irgens LM, Lie RT. Recurrence of pre-eclampsia across generations: exploring fetal and maternal genetic components in a population based cohort. BMJ 2005; 331:877. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Levine P, Katzin EM, Burham L. Isoimmunization in pregnancy: Its possible bearing on etiology of erythroblastosis foetalis. JAMA 1941;116:825. [Google Scholar]
  • 75.Sinsheimer JS, Palmer CG, Woodward JA. Detecting genotype combinations that increase risk for disease: maternal-fetal genotype incompatibility test. Genet.Epidemiol 2003;24:1–13. [DOI] [PubMed] [Google Scholar]
  • 76.Urbaniak SJ. Alloimmunity to RhD in humans. Transfus Clin Biol 2006; 13:19–22. [DOI] [PubMed] [Google Scholar]
  • 77.Driscoll CA, Menotti-Raymond M, Nelson G, Goldstein D, O’Brien SJ. Genomic microsatellites as evolutionary chronometers: a test in wild cats. Genome Res. 2002;12:414–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Burger PA, Steinborn R, Walzer C, Petit T, Mueller M, Schwarzenberger F. Analysis of the mitochondrial genome of cheetahs (Acinonyx jubatus) with neurodegenerative disease. Gene 2004;338:111–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Friel L, Kuivaniemi H, Gomez R, Goddard K, Nien JK, Tromp G, Lu Q, Xu Z, Behnke E, Solari M, Espinoza J, Kim CJ, Chaiworapongsa T, Kim YM, Lenk G, Volkenant K, and Romero R. Genetic predisposition for preterm PROM: Results of a large candidate-gene association study of mothers and their offspring. Am.J.Obstet.Gynecol 193(6), S17 2005. Ref Type: Abstract [Google Scholar]
  • 80.Macones GA, Parry S, Elkousy M, Clothier B, Ural SH, Strauss JF III. A polymorphism in the promoter region of TNF and bacterial vaginosis: preliminary evidence of gene-environment interaction in the etiology of spontaneous preterm birth. Am.J.Obstet.Gynecol 2004;190:1504–08. [DOI] [PubMed] [Google Scholar]
  • 81.[No authors listed] Harvesting the fruits of the human genome. Nat.Genet 2001;27:227–28. [DOI] [PubMed] [Google Scholar]
  • 82.The International HapMap Project. Nature 2003;426:789–96. [DOI] [PubMed] [Google Scholar]
  • 83.Kukita Y, Miyatake K, Stokowski R, Hinds D, Higasa K, Wake N et al. Genome-wide definitive haplotypes determined using a collection of complete hydatidiform moles. Genome Res. 2005;15:1511–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Pollack JR, Iyer VR. Characterizing the physical genome. Nat.Genet 2002;32 Suppl:515–21. [DOI] [PubMed] [Google Scholar]
  • 85.Hennah W, Varilo T, Paunio T, Peltonen L. Haplotype analysis and identification of genes for a complex trait: examples from schizophrenia. Ann Med. 2004;36:322–31. [DOI] [PubMed] [Google Scholar]
  • 86.Carlson CS, Eberle MA, Kruglyak L, Nickerson DA. Mapping complex disease loci in whole-genome association studies. Nature 2004;429:446–52. [DOI] [PubMed] [Google Scholar]
  • 87.Rivera A, Fisher SA, Fritsche LG, Keilhauer CN, Lichtner P, Meitinger T et al. Hypothetical LOC387715 is a second major susceptibility gene for age-related macular degeneration, contributing independently of complement factor H to disease risk. Hum.Mol.Genet 2005;14:3227–36. [DOI] [PubMed] [Google Scholar]
  • 88.Hageman GS, Anderson DH, Johnson LV, Hancox LS, Taiber AJ, Hardisty LI, et al. A common haplotype in the complement regulatory gene factor H (HF1/CFH) predisposes individuals to age-related macular degeneration. Proc Natl Acad Sci U S A 2005;102:7227–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Petricoin EF, Ardekani AM, Hitt BA, Levine PJ, Fusaro VA, Steinberg SM et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 2002;359:572–77. [DOI] [PubMed] [Google Scholar]
  • 90.Phizicky E, Bastiaens PI, Zhu H, Snyder M, Fields S. Protein analysis on a proteomic scale. Nature 2003;422:208–15. [DOI] [PubMed] [Google Scholar]
  • 91.O’Farrell PH. High resolution two-dimensional electrophoresis of proteins. J Biol Chem 1975;250:4007–21. [PMC free article] [PubMed] [Google Scholar]
  • 92.Vuadens F, Benay C, Crettaz D, Gallot D, Sapin V, Schneider P et al. Identification of biologic markers of the premature rupture of fetal membranes: proteomic approach. Proteomics. 2003;3:1521–25. [DOI] [PubMed] [Google Scholar]
  • 93.Buhimschi I, Christner R, Buhimschi C, Chaiworapongsa T, and Romero R Proteomic analysis of preterm parturition: a novel method of identifying the patient at risk for preterm delivery. Am.J.Obstet.Gynecol 187(6), S55 2002. Abstract. [Google Scholar]
  • 94.Gravett MG, Novy MJ, Rosenfeld RG, Reddy AP, Jacob T, Turner M et al. Diagnosis of intra-amniotic infection by proteomic profiling and identification of novel biomarkers. JAMA 2004;292:462–69. [DOI] [PubMed] [Google Scholar]
  • 95.Ruetschi U, Rosen A, Karlsson G, Zetterberg H, Rymo L, Hagberg H, et al. Proteomic analysis using protein chips to detect biomarkers in cervical and amniotic fluid in women with intra-amniotic inflammation. J Proteome Res 2005;4:2236–42. [DOI] [PubMed] [Google Scholar]
  • 96.Klein LL, Reisdorph N, Jonscher KR, Kushner EJ, Gibbs RS, McManaman JL. A novel method for vaginal proteomics in preterm labor. J Soc Gynecol Investig 2006;2:144A (abstract). [Google Scholar]
  • 97.Romero R, Chaiworapongsa T, Gomez R, Kim YM, Edwin S, Bujold E, and Yoon BH Proteomic profiling of premature labor: a method to identify clinical biomarkers and mechanisms of disease. Am.J.Obstet.Gynecol 189(6 (S1)), S63 2003. (abstract) [Google Scholar]
  • 98.Romero R, Yoon BH, Mazor M, Gomez R, Diamond MP, Kenney JS et al. The diagnostic and prognostic value of amniotic fluid white blood cell count, glucose, interleukin-6, and gram stain in patients with preterm labor and intact membranes. Am.J.Obstet.Gynecol 1993;169:805–16. [DOI] [PubMed] [Google Scholar]
  • 99.Romero R, Yoon BH, Kenney JS, Gomez R, Allison AC, Sehgal PB. Amniotic fluid interleukin-6 determinations are of diagnostic and prognostic value in preterm labor. Am.J.Reprod.Immunol 1993;30:167–83. [DOI] [PubMed] [Google Scholar]
  • 100.Yoon BH, Romero R, Kim CJ, Jun JK, Gomez R, Choi JH et al. Amniotic fluid interleukin-6: a sensitive test for antenatal diagnosis of acute inflammatory lesions of preterm placenta and prediction of perinatal morbidity. Am.J.Obstet.Gynecol 1995;172:960–70. [DOI] [PubMed] [Google Scholar]
  • 101.Yoon BH, Jun JK, Romero R, Park KH, Gomez R, Choi JH et al. Amniotic fluid inflammatory cytokines (interleukin-6, interleukin-1beta, and tumor necrosis factor-alpha), neonatal brain white matter lesions, and cerebral palsy. Am.J.Obstet.Gynecol 1997;177:19–26. [DOI] [PubMed] [Google Scholar]
  • 102.Yoon BH, Romero R, Kim CJ, Koo JN, Choe G, Syn HC et al. High expression of tumor necrosis factor-alpha and interleukin-6 in periventricular leukomalacia. Am.J.Obstet.Gynecol 1997;177:406–11. [DOI] [PubMed] [Google Scholar]
  • 103.Yoon BH, Romero R, Jun JK, Park KH, Park JD, Ghezzi F et al. Amniotic fluid cytokines (interleukin-6, tumor necrosis factor-alpha, interleukin-1 beta, and interleukin-8) and the risk for the development of bronchopulmonary dysplasia. Am.J.Obstet.Gynecol 1997;177:825–30. [DOI] [PubMed] [Google Scholar]
  • 104.Yoon BH, Romero R, Park JS, Kim CJ, Kim SH, Choi JH et al. Fetal exposure to an intra-amniotic inflammation and the development of cerebral palsy at the age of three years. Am.J.Obstet.Gynecol 2000;182:675–81. [DOI] [PubMed] [Google Scholar]
  • 105.Nien JK, Yoon BH, Espinoza J, Kusanovic JP, Erez O, Soto E et al. A rapid MMP-8 bedside test for the detection of intra-amniotic inflammation identifies patients at risk for imminent preterm delivery. Am.J.Obstet.Gynecol 2006;195:1025–30. [DOI] [PubMed] [Google Scholar]
  • 106.Maymon E, Romero R, Chaiworapongsa T, Berman S, Conoscenti G, Gomez R et al. Amniotic fluid matrix metalloproteinase-8 in preterm labor with intact membranes. Am.J.Obstet.Gynecol 2001;185:1149–55. [DOI] [PubMed] [Google Scholar]
  • 107.Yoon BH, Oh SY, Romero R, Shim SS, Han SY, Park JS et al. An elevated amniotic fluid matrix metalloproteinase-8 level at the time of mid-trimester genetic amniocentesis is a risk factor for spontaneous preterm delivery. Am.J.Obstet.Gynecol 2001;185:1162–67. [DOI] [PubMed] [Google Scholar]
  • 108.Angus SR, Segel SY, Hsu CD, Locksmith GJ, Clark P, Sammel MD et al. Amniotic fluid matrix metalloproteinase-8 indicates intra-amniotic infection. Am.J.Obstet.Gynecol 2001;185:1232–38. [DOI] [PubMed] [Google Scholar]
  • 109.Park JS, Romero R, Yoon BH, Moon JB, Oh SY, Han SY et al. The relationship between amniotic fluid matrix metalloproteinase-8 and funisitis. Am.J.Obstet.Gynecol 2001;185:1156–61. [DOI] [PubMed] [Google Scholar]
  • 110.Shim SS, Romero R, Hong JS, Park CW, Jun JK, Kim BI et al. Clinical significance of intra-amniotic inflammation in patients with preterm premature rupture of membranes. Am.J.Obstet.Gynecol 2004;191:1339–45. [DOI] [PubMed] [Google Scholar]
  • 111.Romero R, Gomez R, Nien JK, Yoon BH, Luo R, Beecher C, and Mazor M Metabolomics in premature labor: a novel approach to identify patients at risk for preterm delivery. Am.J.Obstet.Gynecol 191(6 (S1)), S2 2004. (abstract) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 112.Gutierres SL, Welty TE. Point-of-care testing: an introduction. Ann.Pharmacother 2004;38:119–25. [DOI] [PubMed] [Google Scholar]
  • 113.Terrone DA, Rinehart BK, Granger JP, Barrilleaux PS, Martin JN Jr, Bennett WA. Interleukin-10 administration and bacterial endotoxin-induced preterm birth in a rat model. Obstet.Gynecol 2001;98:476–80. [DOI] [PubMed] [Google Scholar]
  • 114.Sadowsky DW, Novy MJ, Witkin SS, Gravett MG. Dexamethasone or interleukin-10 blocks interleukin-1beta-induced uterine contractions in pregnant rhesus monkeys. Am.J.Obstet.Gynecol 2003;188:252–63. [DOI] [PubMed] [Google Scholar]
  • 115.Beloosesky R, Gayle DA, Ross MG. Maternal N-acetylcysteine suppresses fetal inflammatory cytokine responses to maternal lipopolysaccharide. Am.J.Obstet.Gynecol 2006;195:1053–57. [DOI] [PubMed] [Google Scholar]
  • 116.Hegde PS, White IR, Debouck C. Interplay of transcriptomics and proteomics. Curr.Opin.Biotechnol 2003;14:647–51. [DOI] [PubMed] [Google Scholar]
  • 117.Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995;270:467–70. [DOI] [PubMed] [Google Scholar]
  • 118.Lipshutz RJ, Morris D, Chee M, Hubbell E, Kozal MJ, Shah N et al. Using oligonucleotide probe arrays to access genetic diversity. Biotechniques 1995;19:442–47. [PubMed] [Google Scholar]
  • 119.van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Bernards R, et al. Expression profiling predicts outcome in breast cancer. Breast Cancer Res 2003;5:57–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 120.van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002;347:1999–2009. [DOI] [PubMed] [Google Scholar]
  • 121.Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000;403:503–11. [DOI] [PubMed] [Google Scholar]
  • 122.Helmer H, Dobianer K, Koukal T, Husslein P. Quantitative polymerase chain reaction for determination of different oxytocin receptor mRNA expression in non-pregnant and pregnant uterine tissue. Adv Exp Med Biol 1995;395:487–8. [PubMed] [Google Scholar]
  • 123.Chien EK, Tokuyama Y, Rouard M, Phillippe M, Bell GI. Identification of gestationally regulated genes in rat myometrium by use of messenger ribonucleic acid differential display. Am J Obstet Gynecol 1997;177:645–52. [DOI] [PubMed] [Google Scholar]
  • 124.Sparey C, Robson SC, Bailey J, Lyall F, Europe-Finner GN. The differential expression of myometrial connexin-43, cyclooxygenase-1 and −2, and Gs alpha proteins in the upper and lower segments of the human uterus during pregnancy and labor. J Clin.Endocrinol.Metab 1999;84:1705–10. [DOI] [PubMed] [Google Scholar]
  • 125.Wu WX, Zhang Q, Ma XH, Unno N, Nathanielsz PW. Suppression subtractive hybridization identified a marked increase in thrombospondin-1 associated with parturition in pregnant sheep myometrium. Endocrinology 1999;140:2364–71. [DOI] [PubMed] [Google Scholar]
  • 126.Challis JRG, Matthews SG, Gibb W, Lye SJ. Endocrine and paracrine regulation of birth at term and preterm. Endocr.Rev 2000;21:514–50. [DOI] [PubMed] [Google Scholar]
  • 127.Wu WX, Zhang Q, Unno N, Derks JB, Nathanielsz PW. Characterization of decorin mRNA in pregnant intrauterine tissues of the ewe and regulation by steroids. Am J Physiol Cell Physiol 2000;278:C199–C206. [DOI] [PubMed] [Google Scholar]
  • 128.Bethin KE, Nagai Y, Sladek R, Asada M, Sadovsky Y, Hudson TJ et al. Microarray analysis of uterine gene expression in mouse and human pregnancy. Mol.Endocrinol 2003;17:1454–69. [DOI] [PubMed] [Google Scholar]
  • 129.Charpigny G, Leroy MJ, Breuiller-Fouche M, Tanfin Z, Mhaouty-Kodja S, Robin P et al. A functional genomic study to identify differential gene expression in the preterm and term human myometrium. Biol.Reprod 2003;68:2289–96. [DOI] [PubMed] [Google Scholar]
  • 130.Keelan JA, Blumenstein M, Helliwell RJ, Sato TA, Marvin KW, Mitchell MD. Cytokines, prostaglandins and parturition--a review. Placenta 2003;24 Suppl A:S33–S46. [DOI] [PubMed] [Google Scholar]
  • 131.Esplin MS, Fausett MB, Peltier MR, Hamblin S, Silver RM, Branch DW et al. The use of cDNA microarray to identify differentially expressed labor-associated genes within the human myometrium during labor. Am J Obstet Gynecol 2005;193:404–13. [DOI] [PubMed] [Google Scholar]
  • 132.Michaelis SA, Okuducu AF, Sarioglu NM, von Deimling A, Dudenhausen JW. The transcription factor Ets-1 is expressed in human amniochorionic membranes and is up-regulated in term and preterm premature rupture of membranes. J Perinat Med 2005;33:314–19. [DOI] [PubMed] [Google Scholar]
  • 133.Lindstrom T, Loudon J, Bennett P. Transcriptional regulation of labour-associated genes In: Norman J, Greer I, editors. Preterm Labour.Managing Risk in a Clinical Practice. Cambridge, UK: Cambridge University Press; 2005. pp. 76–108. [Google Scholar]
  • 134.Havelock JC, Keller P, Muleba N, Mayhew BA, Casey BM, Rainey WE, et al. Human myometrial gene expression before and during parturition. Biol Reprod 2005;72:707–19. [DOI] [PubMed] [Google Scholar]
  • 135.Huber A, Hudelist G, Czerwenka K, Husslein P, Kubista E, Singer CF. Gene expression profiling of cervical tissue during physiological cervical effacement. Obstet Gynecol 2005;105:91–8. [DOI] [PubMed] [Google Scholar]
  • 136.Word RA, Landrum CP, Timmons BC, Young SG, Mahendroo MS. Transgene insertion on mouse chromosome 6 impairs function of the uterine cervix and causes failure of parturition. Biol Reprod 2005;73:1046–56. [DOI] [PubMed] [Google Scholar]
  • 137.Romero R, Kuivaniemi H, Tromp G. Functional genomics and proteomics in term and preterm parturition. J.Clin.Endocrinol.Metab 2002;87:2431–34. [DOI] [PubMed] [Google Scholar]
  • 138.Weston GC, Ponnampalam A, Vollenhoven BJ, Healy DL, Rogers PA. Genomics in obstetrics and gynaecology. Aust N Z J Obstet Gynaecol 2003;43:264–72. [DOI] [PubMed] [Google Scholar]
  • 139.Wilson RD. Genomics: new technology for obstetrics. J Obstet Gynaecol Can 2005;27:63–75. [DOI] [PubMed] [Google Scholar]
  • 140.Tarca AL, Romero R, Draghici S. Analysis of microarray experiments of gene expression profiling. Am.J.Obstet.Gynecol 2006;195:373–88. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 141.Romero R, Tromp G. High-dimensional biology in obstetrics and gynecology: functional genomics in microarray studies. Am J Obstet Gynecol 2006;195:360–3. [DOI] [PubMed] [Google Scholar]
  • 142.Ward K Microarray technology in obstetrics and gynecology: a guide for clinicians. Am J Obstet Gynecol 2006;195:364–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 143.Mason CW, Swaan PW, Weiner CP. Identification of interactive gene networks: a novel approach in gene array profiling of myometrial events during guinea pig pregnancy. Am J Obstet Gynecol 2006;194:1513–23. [DOI] [PubMed] [Google Scholar]
  • 144.Aguan K, Carvajal JA, Thompson LP, Weiner CP. Application of a functional genomics approach to identify differentially expressed genes in human myometrium during pregnancy and labour. Mol.Hum.Reprod 2000;6:1141–45. [DOI] [PubMed] [Google Scholar]
  • 145.Chan EC, Fraser S, Yin S, Yeo G, Kwek K, Fairclough RJ et al. Human myometrial genes are differentially expressed in labor: a suppression subtractive hybridization study. J.Clin.Endocrinol.Metab 2002;87:2435–41. [DOI] [PubMed] [Google Scholar]
  • 146.Tromp G, Kuivaniemi H, Romero R, Chaiworapongsa T, Kim YM, Kim MR et al. Genome-wide expression profiling of fetal membranes reveals a deficient expression of proteinase inhibitor 3 in premature rupture of membranes. Am.J.Obstet.Gynecol 2004;191:1331–38. [DOI] [PubMed] [Google Scholar]
  • 147.Helmig BR, Romero R, Espinoza J, Chaiworapongsa T, Bujold E, Gomez R et al. Neutrophil elastase and secretory leukocyte protease inhibitor in prelabor rupture of membranes, parturition and intra-amniotic infection. J.Matern.Fetal Neonatal Med 2002;12:237–46. [DOI] [PubMed] [Google Scholar]
  • 148.Tashima LS, Millar LK, Bryant-Greenwood GD. Genes upregulated in human fetal membranes by infection or labor. Obstet.Gynecol 1999;94:441–49. [DOI] [PubMed] [Google Scholar]
  • 149.Haddad R, Tromp G, Kuivaniemi H, Chaiworapongsa T, Kim YM, Mazor M et al. Human spontaneous labor without histologic chorioamnionitis is characterized by an acute inflammation gene expression signature. Am.J.Obstet.Gynecol 2006;195:394.e1-e24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 150.Bisits AM, Smith R, Mesiano S, Yeo G, Kwek K, MacIntyre D et al. Inflammatory aetiology of human myometrial activation tested using directed graphs. PLoS.Comput.Biol 2005;1:132–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 151.Hassan SS, Romero R, Haddad R, Hendler I, Khalek N, Tromp G et al. The transcriptome of the uterine cervix before and after spontaneous term parturition. Am.J.Obstet.Gynecol 2006;195:778–86. [DOI] [PubMed] [Google Scholar]
  • 152.Bukowski R, Hankins GD, Saade GR, Anderson GD, Thornton S. Labor-associated gene expression in the human uterine fundus, lower segment, and cervix. PLoS.Med 2006;3:e169. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 153.Romero R, Tarca AL, Tromp G. Insights into the physiology of childbirth using transcriptomics. PLoS.Med 2006;3:e276. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 154.Muhle RA, Pavlidis P, Grundy WN, Hirsch E. A high-throughput study of gene expression in preterm labor with a subtractive microarray approach. Am.J.Obstet.Gynecol 2001;185:716–24. [DOI] [PubMed] [Google Scholar]
  • 155.Hirsch E, Wang H. The molecular pathophysiology of bacterially induced preterm labor: insights from themurine model. J Soc Gynecol Investig 2005;12:145–55. [DOI] [PubMed] [Google Scholar]
  • 156.Haddad R, Gould BR, Romero R, Tromp G, Farookhi R, Edwin SS et al. Uterine transcriptomes of bacteria-induced and ovariectomy-induced preterm labor in mice are characterized by differential expression of arachidonate metabolism genes. Am.J.Obstet.Gynecol 2006;195:822–28. [DOI] [PubMed] [Google Scholar]
  • 157.Pennell CE, Oldenhof AD, Perkins JE, Dunk CE, Keunen J, Tan P, et al. Identification of a gene expression signature in leukocytes that predicts preterm delivery in women with threatened preterm labour. J Soc Gynecol Investig 2006;2(Suppl):175A (abstract). [Google Scholar]
  • 158.Goodacre R, Vaidyanathan S, Dunn WB, Harrigan GG, Kell DB. Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol. 2004;22:245–52. [DOI] [PubMed] [Google Scholar]
  • 159.Nobeli I, Thornton JM. A bioinformatician’s view of the metabolome. Bioessays 2006;28:534–45. [DOI] [PubMed] [Google Scholar]
  • 160.Jain S, Jayasimhulu K, Clark JF. Metabolomic analysis of molecular species of phospholipids from normotensive and preeclamptic human placenta electrospray ionization mass spectrometry. Front Biosci. 2004;9:3167–75. [DOI] [PubMed] [Google Scholar]
  • 161.Rochfort S Metabolomics reviewed: a new “omics” platform technology for systems biology and implications for natural products research. J Nat Prod 2005;68:1813–20. [DOI] [PubMed] [Google Scholar]
  • 162.Allen J, Davey HM, Broadhurst D, Heald JK, Rowland JJ, Oliver SG et al. High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat.Biotechnol 2003;21:692–96. [DOI] [PubMed] [Google Scholar]
  • 163.Nielsen J, Oliver S. The next wave in metabolome analysis. Trends Biotechnol. 2005;23:544–46. [DOI] [PubMed] [Google Scholar]
  • 164.Raamsdonk LM, Teusink B, Broadhurst D, Zhang N, Hayes A, Walsh MC et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat.Biotechnol 2001;19:45–50. [DOI] [PubMed] [Google Scholar]
  • 165.Weckwerth W, Loureiro ME, Wenzel K, Fiehn O. Differential metabolic networks unravel the effects of silent plant phenotypes. Proc.Natl.Acad.Sci.U.S.A 2004;101:7809–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 166.Harrigan GG, Goodacre R. Introduction In: Harrigan GG, Goodacre R, editors. Metabolic Profiling. Its role in biomarker discovery and gene function analysis. Norwell: Kluwer Academic Publishers; 2003. p. 1–8. [Google Scholar]
  • 167.Goodacre R, Timmins EM, Burton R, Kaderbhai N, Woodward AM, Kell DB et al. Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks. Microbiology 1998;144 ( Pt 5):1157–70. [DOI] [PubMed] [Google Scholar]
  • 168.Clarke S, Goodacre R Raman spectroscopy for whole organism and tissue profiling In: Harrigan GG, Goodacre R, editors. Metabolic profiling. Its role in biomarker discovery and gene function analysis. Norwell: Kluwer Academic Publishers; 2003. p. 95–110. [Google Scholar]
  • 169.Zahn JA, Higgs RE, Hilton MD. Use of direct-infusion electrospray mass spectrometry to guide empirical development of improved conditions for expression of secondary metabolites from actinomycetes. Appl.Environ.Microbiol 2001;67:377–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 170.Kenny LC, Dunn WB, Ellis DI, Myers JE, Robinson AE, Kell DB, and Baker PN Novel biomarkers for preeclampsia detected using metabolomics and machine learning. J.Soc.Gynecol.Investig 12(2 (Suppl)), 199A 2005. [Google Scholar]
  • 171.Kenny LC, Harding KEL, Ellis DI, Broadhurst D, Dunn WB, Kell DB, and Baker PN Rapid high-throughput detection of preeclampsia. J.Soc.Gynecol.Investig 13(2 (Suppl)), 124A 2006. [Google Scholar]
  • 172.Wolffe AP, Matzke MA. Epigenetics: regulation through repression. Science 1999;286:481–86. [DOI] [PubMed] [Google Scholar]
  • 173.Jaenisch R, Bird A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat.Genet 2003;33 Suppl:245–54. [DOI] [PubMed] [Google Scholar]
  • 174.Callinan PA, Feinberg AP. The emerging science of epigenomics. Hum.Mol.Genet 2006;15 Spec No 1:R95–101. [DOI] [PubMed] [Google Scholar]
  • 175.Li E Chromatin modification and epigenetic reprogramming in mammalian development. Nat.Rev.Genet 2002;3:662–73. [DOI] [PubMed] [Google Scholar]
  • 176.Vanden BW, Ndlovu MN, Hoya-Arias R, Dijsselbloem N, Gerlo S, Haegeman G. Keeping up NF-kappaB appearances: Epigenetic control of immunity or inflammation-triggered epigenetics. Biochem.Pharmacol 2006. [DOI] [PubMed] [Google Scholar]
  • 177.Razin A, Shemer R. DNA methylation in early development. Hum.Mol.Genet 1995;4 Spec No:1751–55. [DOI] [PubMed] [Google Scholar]
  • 178.Bird AP. CpG-rich islands and the function of DNA methylation. Nature 1986;321:209–13. [DOI] [PubMed] [Google Scholar]
  • 179.Crews D, McLachlan JA. Epigenetics, evolution, endocrine disruption, health, and disease. Endocrinology 2006;147:S4–10. [DOI] [PubMed] [Google Scholar]
  • 180.Waterland RA, Jirtle RL. Early nutrition, epigenetic changes at transposons and imprinted genes, and enhanced susceptibility to adult chronic diseases. Nutrition 2004;20:63–68. [DOI] [PubMed] [Google Scholar]
  • 181.Van den Veyver IB. Genetic effects of methylation diets. Annu.Rev.Nutr 2002;22:255–82. [DOI] [PubMed] [Google Scholar]
  • 182.Wolff GL, Kodell RL, Moore SR, Cooney CA. Maternal epigenetics and methyl supplements affect agouti gene expression in Avy/a mice. FASEB J. 1998;12:949–57. [PubMed] [Google Scholar]
  • 183.Scholl TO, Hediger ML, Schall JI, Khoo CS, Fischer RL. Dietary and serum folate: their influence on the outcome of pregnancy. Am.J.Clin.Nutr 1996;63:520–25. [DOI] [PubMed] [Google Scholar]
  • 184.Valdez LL, Quintero A, Garcia E, Olivares N, Celis A, Rivas F, Jr. et al. Thrombophilic polymorphisms in preterm delivery. Blood Cells Mol.Dis 2004;33:51–56. [DOI] [PubMed] [Google Scholar]
  • 185.Johnson WG, Scholl TO, Spychala JR, Buyske S, Stenroos ES, Chen X. Common dihydrofolate reductase 19-base pair deletion allele: a novel risk factor for preterm delivery. Am.J.Clin.Nutr 2005;81:664–68. [DOI] [PubMed] [Google Scholar]
  • 186.Scholl TO, Hediger ML, Schall JI, Fischer RL, Khoo CS. Low zinc intake during pregnancy: its association with preterm and very preterm delivery. Am.J.Epidemiol 1993;137:1115–24. [DOI] [PubMed] [Google Scholar]
  • 187.Goldenberg RL, Tamura T, Neggers Y, Copper RL, Johnston KE, DuBard MB et al. The effect of zinc supplementation on pregnancy outcome. JAMA 1995;274:463–68. [DOI] [PubMed] [Google Scholar]
  • 188.Belt AR, Baldassare JJ, Molnar M, Romero R, Hertelendy F. The nuclear transcription factor NF-kappaB mediates interleukin-1beta-induced expression of cyclooxygenase-2 in human myometrial cells. Am J Obstet Gynecol 1999;181:359–66. [DOI] [PubMed] [Google Scholar]
  • 189.Wickelgren I Premature labor. Resetting pregnancy’s clock. Science 2004;304:666–8. [DOI] [PubMed] [Google Scholar]
  • 190.Lappas M, Permezel M, Georgiou HM, Rice GE. Regulation of phospholipase isozymes by nuclear factor-kappaB in human gestational tissues in vitro. J Clin Endocrinol Metab 2004;89:2365–72. [DOI] [PubMed] [Google Scholar]
  • 191.Lindstrom TM, Bennett PR. The role of nuclear factor kappa B in human labour. Reproduction 2005;130:569–81. [DOI] [PubMed] [Google Scholar]
  • 192.Mendelson CR, Condon JC. New insights into the molecular endocrinology of parturition. J Steroid Biochem Mol Biol 2005;93:113–19. [DOI] [PubMed] [Google Scholar]
  • 193.Ackerman WE, Zhang XL, Rovin BH, Kniss DA. Modulation of cytokine-induced cyclooxygenase 2 expression by PPARG ligands through NFkappaB signal disruption in human WISH and amnion cells. Biol Reprod 2005;73:527–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 194.Lindstrom TM, Bennett PR. 15-Deoxy-{delta}12,14-prostaglandin j2 inhibits interleukin-1{beta}-induced nuclear factor-{kappa}b in human amnion and myometrial cells: mechanisms and implications. J Clin Endocrinol Metab 2005;90:3534–43. [DOI] [PubMed] [Google Scholar]
  • 195.Condon JC, Hardy DB, Mendelson CR. Upregulation of the progesterone receptor (PR)-C isoform in laboring myometrium by activation of NF-kB may contribute to the onset of labor through inhibition of PR function. J Soc Gynecol Investig 2006;2:65A (abstract). [DOI] [PubMed] [Google Scholar]
  • 196.Romero R, Gomez R, Ghezzi F, Yoon BH, Mazor M, Edwin SS et al. A fetal systemic inflammatory response is followed by the spontaneous onset of preterm parturition. Am.J.Obstet.Gynecol 1998;179:186–93. [DOI] [PubMed] [Google Scholar]
  • 197.Gomez R, Romero R, Ghezzi F, Yoon BH, Mazor M, Berry SM. The fetal inflammatory response syndrome. Am.J.Obstet.Gynecol 1998;179:194–202. [DOI] [PubMed] [Google Scholar]
  • 198.Hodge DR, Peng B, Cherry JC, Hurt EM, Fox SD, Kelley JA et al. Interleukin 6 supports the maintenance of p53 tumor suppressor gene promoter methylation. Cancer Res. 2005;65:4673–82. [DOI] [PubMed] [Google Scholar]
  • 199.Peng B, Hodge DR, Thomas SB, Cherry JM, Munroe DJ, Pompeia C et al. Epigenetic silencing of the human nucleotide excision repair gene, hHR23B, in interleukin-6-responsive multiple myeloma KAS-6/1 cells. J.Biol.Chem 2005;280:4182–87. [DOI] [PubMed] [Google Scholar]
  • 200.Pompeia C, Hodge DR, Plass C, Wu YZ, Marquez VE, Kelley JA et al. Microarray analysis of epigenetic silencing of gene expression in the KAS-6/1 multiple myeloma cell line. Cancer Res. 2004;64:3465–73. [DOI] [PubMed] [Google Scholar]
  • 201.Croonquist PA, Van NB. The polycomb group protein enhancer of zeste homolog 2 (EZH 2) is an oncogene that influences myeloma cell growth and the mutant ras phenotype. Oncogene 2005;24:6269–80. [DOI] [PubMed] [Google Scholar]
  • 202.Armenante F, Merola M, Furia A, Palmieri M. Repression of the IL-6 gene is associated with hypermethylation. Biochem.Biophys.Res.Commun 1999;258:644–47. [DOI] [PubMed] [Google Scholar]
  • 203.Condon JC, Jeyasuria P, Faust JM, Wilson JW, Mendelson CR. A decline in the levels of progesterone receptor coactivators in the pregnant uterus at term may antagonize progesterone receptor function and contribute to the initiation of parturition. Proc.Natl.Acad.Sci.U.S.A 2003;100:9518–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 204.Mitchell MD. Unique suppression of prostaglandin H synthase-2 expression by inhibition of histone deacetylation, specifically in human amnion but not adjacent choriodecidua. Mol.Biol.Cell 2006;17:549–53. [DOI] [PMC free article] [PubMed] [Google Scholar]

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