Placenta-Specific Genes, Their Regulation During Villous Trophoblast Differentiation and Dysregulation in Preterm Preeclampsia.

The human placenta maintains pregnancy and supports the developing fetus by providing nutrition, gas-waste exchange, hormonal regulation, and an immunological barrier from the maternal immune system. The villous syncytiotrophoblast carries most of these functions and provides the interface between the maternal and fetal circulatory systems. The syncytiotrophoblast is generated by the biochemical and morphological differentiation of underlying cytotrophoblast progenitor cells. The dysfunction of the villous trophoblast development is implicated in placenta-mediated pregnancy complications. Herein, we describe gene modules and clusters involved in the dynamic differentiation of villous cytotrophoblasts into the syncytiotrophoblast. During this process, the immune defense functions are first established, followed by structural and metabolic changes, and then by peptide hormone synthesis. We describe key transcription regulatory molecules that regulate gene modules involved in placental functions. Based on transcriptomic evidence, we infer how villous trophoblast differentiation and functions are dysregulated in preterm preeclampsia, a life-threatening placenta-mediated obstetrical syndrome for the mother and fetus. In the conclusion, we uncover the blueprint for villous trophoblast development and its impairment in preterm preeclampsia, which may aid in the future development of non-invasive biomarkers for placental functions and early identification of women at risk for preterm preeclampsia as well as other placenta-mediated pregnancy complications.

Integrated Systems Biology Approach Identifies Novel Maternal and Placental Pathways of Preeclampsia.

Preeclampsia is a disease of the mother, fetus, and placenta, and the gaps in our understanding of the complex interactions among their respective disease pathways preclude successful treatment and prevention. The placenta has a key role in the pathogenesis of the terminal pathway characterized by exaggerated maternal systemic inflammation, generalized endothelial damage, hypertension, and proteinuria. This sine qua non of preeclampsia may be triggered by distinct underlying mechanisms that occur at early stages of pregnancy and induce different phenotypes. To gain insights into these molecular pathways, we employed a systems biology approach and integrated different "omics," clinical, placental, and functional data from patients with distinct phenotypes of preeclampsia. First trimester maternal blood proteomics uncovered an altered abundance of proteins of the renin-angiotensin and immune systems, complement, and coagulation cascades in patients with term or preterm preeclampsia. Moreover, first trimester maternal blood from preterm preeclamptic patients in vitro dysregulated trophoblastic gene expression. Placental transcriptomics of women with preterm preeclampsia identified distinct gene modules associated with maternal or fetal disease. Placental "virtual" liquid biopsy showed that the dysregulation of these disease gene modules originates during the first trimester. In vitro experiments on hub transcription factors of these gene modules demonstrated that DNA hypermethylation in the regulatory region of ZNF554 leads to gene down-regulation and impaired trophoblast invasion, while BCL6 and ARNT2 up-regulation sensitizes the trophoblast to ischemia, hallmarks of preterm preeclampsia. In summary, our data suggest that there are distinct maternal and placental disease pathways, and their interaction influences the clinical presentation of preeclampsia. The activation of maternal disease pathways can be detected in all phenotypes of preeclampsia earlier and upstream of placental dysfunction, not only downstream as described before, and distinct placental disease pathways are superimposed on these maternal pathways. This is a paradigm shift, which, in agreement with epidemiological studies, warrants for the central pathologic role of preexisting maternal diseases or perturbed maternal-fetal-placental immune interactions in preeclampsia. The description of these novel pathways in the "molecular phase" of preeclampsia and the identification of their hub molecules may enable timely molecular characterization of patients with distinct preeclampsia phenotypes.

Longitudinal Changes in Placental Magnetic Resonance Imaging Relaxation Parameter in Murine Pregnancy: Compartmental Analysis.

OBJECTIVE: To quantify gestation-dependent longitudinal changes in the magnetic resonance transverse relaxation time (T2) parameter of the major constituent regions of the mouse placenta and to evaluate their relative contributions to changes in overall placental T2. METHODS: Timed-pregnant CD-1 mice underwent magnetic resonance imaging at 7.0 T field strength, on gestational day 13 (GD13), GD15 and GD17. T2 of the placenta and its constituent high and low blood perfusion regions were quantified. A linear mixed-effects model was used to fit the T2 across gestation, and the significance of coefficients was tested. RESULTS: A decrease in the T2 values of the placenta and its constituent regions was observed across gestation. The temporal change in T2 was estimated to be -1.85 ms/GD (p < 0.0001) for the placenta, -1.00 ms/GD (p < 0.001) for the high-perfusion zones (HPZs) and -1.66 ms/GD (p < 0.0001) for the low-perfusion zones (LPZs). CONCLUSION: T2 of the constituent zones of the murine placenta decreases with advancing gestation. While the T2 of the LPZ is smaller than that of the HPZ, there is no difference in their decrease rate relative to that of the whole placenta (p = 0.24). The results suggest an increased role of constituent volume fractions in affecting overall gestation-dependent placental T2 decrease in mice.

Down-weighting overlapping genes improves gene set analysis.

BACKGROUND: The identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research. Most gene set analysis methods treat genes equally, regardless how specific they are to a given gene set. RESULTS: In this work we propose a new gene set analysis method that computes a gene set score as the mean of absolute values of weighted moderated gene t-scores. The gene weights are designed to emphasize the genes appearing in few gene sets, versus genes that appear in many gene sets. We demonstrate the usefulness of the method when analyzing gene sets that correspond to the KEGG pathways, and hence we called our method Pathway Analysis with Down-weighting of Overlapping Genes (PADOG). Unlike most gene set analysis methods which are validated through the analysis of 2-3 data sets followed by a human interpretation of the results, the validation employed here uses 24 different data sets and a completely objective assessment scheme that makes minimal assumptions and eliminates the need for possibly biased human assessments of the analysis results. CONCLUSIONS: PADOG significantly improves gene set ranking and boosts sensitivity of analysis using information already available in the gene expression profiles and the collection of gene sets to be analyzed. The advantages of PADOG over other existing approaches are shown to be stable to changes in the database of gene sets to be analyzed. PADOG was implemented as an R package available at: http://bioinformaticsprb.med.wayne.edu/PADOG/or http://www.bioconductor.org.

Developing classifiers for the detection of cancer using multi-analytes.

The development of a successful classifier from multiple predictors (analytes) is a multistage process complicated typically by the paucity of the data samples when compared to the number of available predictors. Choosing an adequate validation strategy is key for drawing sound conclusions about the usefulness of the classifier. Other important decisions have to be made regarding the type of prediction model to be used and training algorithm, as well as the way in which the markers are selected. This chapter describes the principles of the classifier development and underlines the most common pitfalls. A simulated dataset is used to illustrate the main concepts involved in supervised classification.

A novel signaling pathway impact analysis.

MOTIVATION: Gene expression class comparison studies may identify hundreds or thousands of genes as differentially expressed (DE) between sample groups. Gaining biological insight from the result of such experiments can be approached, for instance, by identifying the signaling pathways impacted by the observed changes. Most of the existing pathway analysis methods focus on either the number of DE genes observed in a given pathway (enrichment analysis methods), or on the correlation between the pathway genes and the class of the samples (functional class scoring methods). Both approaches treat the pathways as simple sets of genes, disregarding the complex gene interactions that these pathways are built to describe. RESULTS: We describe a novel signaling pathway impact analysis (SPIA) that combines the evidence obtained from the classical enrichment analysis with a novel type of evidence, which measures the actual perturbation on a given pathway under a given condition. A bootstrap procedure is used to assess the significance of the observed total pathway perturbation. Using simulations we show that the evidence derived from perturbations is independent of the pathway enrichment evidence. This allows us to calculate a global pathway significance P-value, which combines the enrichment and perturbation P-values. We illustrate the capabilities of the novel method on four real datasets. The results obtained on these data show that SPIA has better specificity and more sensitivity than several widely used pathway analysis methods. AVAILABILITY: SPIA was implemented as an R package available at http://vortex.cs.wayne.edu/ontoexpress/

Whole-genome microarray and targeted analysis of angiogenesis-regulating gene expression (ENG, FLT1, VEGF, PlGF) in placentas from pre-eclamptic and small-for-gestational-age pregnancies.

OBJECTIVE: To compare the placental pathology associated with pre-eclampsia (PE) and/or fetal growth restriction, the transcriptomes of placental tissues from PE and small-for-gestational-age (SGA) pregnancies were explored. In addition, a targeted analysis of angiogenesis-regulating gene expression was performed. METHODS: Whole-genome microarray analysis was performed on placental tissue from gestational age-matched PE (n = 10), SGA (n = 8) and PE + SGA (n = 10) pregnancies. The expression of genes regulating angiogenesis (endoglin (ENG), fms-related tyrosine kinase 1 (FLT1), vascular endothelial growth factor (VEGF) and placental growth factor (PlGF)) was analyzed by quantitative real time reverse transcriptase polymerase chain reaction (qRT-PCR). RESULTS: Microarray analysis did not reveal any significant differences between groups. However, an increased expression of ENG and FLT1 was detected by qRT-PCR in the PE + SGA group. CONCLUSIONS: The placental transcriptome did not differ between groups, although an increased anti-angiogenic gene expression in PE + SGA was observed with qRT-PCR analysis. Based on this, we conclude that although microarray technology may represent a powerful tool in generating new hypothesis in complex fields, it may not be sensitive enough to detect subtle changes in gene expression.

Gene expression profiling demonstrates a novel role for foetal fibrocytes and the umbilical vessels in human fetoplacental development.

There is a difference in the susceptibility to inflammation between the umbilical vein (UV) and the umbilical arteries (UAs). This led us to hypothesize that there is an intrinsic difference in the pro-inflammatory response between UA and UV. Real-time quantitative RT-PCR and microarray analysis revealed higher expression of interleukin (IL)-1beta and IL-8 mRNA in the UV and differential expression of 567 genes between the UA and UV associated with distinct biological processes, including the immune response. Differential expression of human leukocyte antigen (HLA)-DRA mRNA between the UA and UV was due to unexpected HLA-DR(+) cells migrating via the umbilical vessels into Wharton's jelly, more frequently in the UV. A significant proportion of these cells co-expressed CD45 and type I pro-collagen, and acquired CD163 or alpha-smooth muscle actin immunoreactivity in Wharton's jelly. Migrating cells were also found in the chorionic and stem villous vessels. Furthermore, the extent of migration increased with progression of gestation, but diminished in intrauterine growth restriction (IUGR). The observations herein strongly suggest that circulating foetal fibrocytes, routing via umbilical and placental vessels, are a reservoir for key cellular subsets in the placenta. This study reports fibrocytes in the human umbilical cord and placenta for the first time, and a novel role for both circulating foetal cells and the umbilical vessels in placental development, which is deranged in IUGR.

A systems biology approach for pathway level analysis.

A common challenge in the analysis of genomics data is trying to understand the underlying phenomenon in the context of all complex interactions taking place on various signaling pathways. A statistical approach using various models is universally used to identify the most relevant pathways in a given experiment. Here, we show that the existing pathway analysis methods fail to take into consideration important biological aspects and may provide incorrect results in certain situations. By using a systems biology approach, we developed an impact analysis that includes the classical statistics but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc. The impact analysis is an attempt to a deeper level of statistical analysis, informed by more pathway-specific biology than the existing techniques. On several illustrative data sets, the classical analysis produces both false positives and false negatives, while the impact analysis provides biologically meaningful results. This analysis method has been implemented as a Web-based tool, Pathway-Express, freely available as part of the Onto-Tools (http://vortex.cs.wayne.edu).