Chaiworapongsa T, Chaemsaithong P, Yeo L, Romero R. Pre-eclampsia part 1: current understanding of its pathophysiology. Nat Rev Nephrol. 2014;10:466–80. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25003615
Article
CAS
PubMed
Google Scholar
Fisher SJ. Why is placentation abnormal in preeclampsia? Am J Obstet Gynecol. 2015;213:S115–22. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26428489
Article
PubMed
PubMed Central
Google Scholar
Zhou Y, Damsky CH, Fisher SJ. Preeclampsia is associated with failure of human cytotrophoblasts to mimic a vascular adhesion phenotype. One cause of defective endovascular invasion in this syndrome? J Clin Invest. 1997;99:2152–64. Available from: http://www.ncbi.nlm.nih.gov/pubmed/9151787
Article
CAS
PubMed
PubMed Central
Google Scholar
Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, et al. NCBI GEO: archive for functional genomics data sets--10 years on. Nucleic Acids Res. 2011;39:D1005–10. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21097893
Article
CAS
PubMed
Google Scholar
Parkinson H, Sarkans U, Kolesnikov N, Abeygunawardena N, Burdett T, Dylag M, et al. ArrayExpress update--an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res. 2011;39:D1002–4. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21071405
Article
CAS
PubMed
Google Scholar
van Uitert M, Moerland PD, Enquobahrie DA, Laivuori H, van der Post JAM, Ris-Stalpers C, et al. Meta-analysis of placental Transcriptome data identifies a novel molecular pathway related to preeclampsia. PLoS One. 2015;10:e0132468. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26171964
Article
PubMed
PubMed Central
Google Scholar
Yong HEJ, Melton PE, Johnson MP, Freed KA, Kalionis B, Murthi P, et al. Genome-wide transcriptome directed pathway analysis of maternal pre-eclampsia susceptibility genes. PLoS One. 2015;10:e0128230. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26010865
Article
PubMed
PubMed Central
Google Scholar
Leavey K, Bainbridge SA, Cox BJ. Large scale aggregate microarray analysis reveals three distinct molecular subclasses of human preeclampsia. PLoS One. 2015;10:e0116508. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25679511
Article
PubMed
PubMed Central
Google Scholar
Rabaglino MB, Post Uiterweer ED, Jeyabalan A, Hogge WA, Conrad KP. Bioinformatics approach reveals evidence for impaired endometrial maturation before and during early pregnancy in women who developed preeclampsia. Hypertension. 2015;65:421–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25421975
Article
CAS
PubMed
Google Scholar
Tejera E, Bernardes J, Rebelo I. Co-expression network analysis and genetic algorithms for gene prioritization in preeclampsia. BMC Med Genet. 2013;6:51. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24219996
Google Scholar
Tejera E, Bernardes J, Rebelo I. Preeclampsia: a bioinformatics approach through protein-protein interaction networks analysis. BMC Syst Biol. 2012;6:97. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22873350
Article
CAS
PubMed
PubMed Central
Google Scholar
Song Y, Liu J, Huang S, Zhang L. Analysis of differentially expressed genes in placental tissues of preeclampsia patients using microarray combined with the connectivity map database. Placenta. 2013;34:1190–5. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24125805
Article
CAS
PubMed
Google Scholar
Vaiman D, Calicchio R, Miralles F. Landscape of transcriptional deregulations in the preeclamptic placenta. PLoS One. 2013;8:e65498. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23785430
Article
CAS
PubMed
PubMed Central
Google Scholar
Moslehi R, Mills JL, Signore C, Kumar A, Ambroggio X, Dzutsev A. Integrative transcriptome analysis reveals dysregulation of canonical cancer molecular pathways in placenta leading to preeclampsia. Sci Rep. 2013;3:2407. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23989136
Article
PubMed
PubMed Central
Google Scholar
Börnigen D, Tranchevent L-C, Bonachela-Capdevila F, Devriendt K, De Moor B, De Causmaecker P, et al. An unbiased evaluation of gene prioritization tools. Bioinformatics. 2012;28:3081–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23047555
Article
PubMed
Google Scholar
Tranchevent L-C, Capdevila FB, Nitsch D, De Moor B, De Causmaecker P, Moreau Y. A guide to web tools to prioritize candidate genes. Brief Bioinform. 2011;12:22–32. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21278374
Article
CAS
PubMed
Google Scholar
Liekens AML, De Knijf J, Daelemans W, Goethals B, De Rijk P, Del-Favero J. BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation. Genome Biol. 2011;12:R57. BioMed Central, Available from: http://www.ncbi.nlm.nih.gov/pubmed/21696594
Article
PubMed
PubMed Central
Google Scholar
Hutz JE, Kraja AT, McLeod HL. Province MA. CANDID: a flexible method for prioritizing candidate genes for complex human traits. Genet Epidemiol. 2008;32:779–90. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18613097
Article
PubMed
PubMed Central
Google Scholar
Jourquin J, Duncan D, Shi Z, Zhang B. GLAD4U: deriving and prioritizing gene lists from PubMed literature. BMC Genomics. 2012;13(Suppl 8):S20. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23282288
Article
PubMed
PubMed Central
Google Scholar
Cheng D, Knox C, Young N, Stothard P, Damaraju S, Wishart DS. PolySearch: a web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites. Nucleic Acids Res. 2008;36:W399–405. Oxford University Press, Available from: http://www.ncbi.nlm.nih.gov/pubmed/18487273
Article
CAS
PubMed
PubMed Central
Google Scholar
Wu X, Jiang R, Zhang MQ, Li S. Network-based global inference of human disease genes. Mol Syst Biol. 2008;4:189. European Molecular Biology Organization, Available from: http://www.ncbi.nlm.nih.gov/pubmed/18463613
Article
PubMed
PubMed Central
Google Scholar
Guney E, Garcia-Garcia J, Oliva B. GUILDify: a web server for phenotypic characterization of genes through biological data integration and network-based prioritization algorithms. Bioinformatics. 2014;30:1789–90. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24532728
Article
CAS
PubMed
Google Scholar
Piñero J, Queralt-Rosinach N, Bravo À, Deu-Pons J, Bauer-Mehren A, Baron M, et al. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database (Oxford). 2015;2015:bav028. Oxford University Press, Available from: http://www.ncbi.nlm.nih.gov/pubmed/25877637
Article
Google Scholar
Yu W, Wulf A, Liu T, Khoury MJ, Gwinn M, Rebbeck T, et al. Gene prospector: an evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases. BMC Bioinformatics. 2008;9:528. BioMed Central, Available from: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-528
Article
PubMed
PubMed Central
Google Scholar
Fontaine J-F, Priller F, Barbosa-Silva A, Andrade-Navarro MA. Génie: literature-based gene prioritization at multi genomic scale. Nucleic Acids Res. 2011;39:W455–61. Oxford University Press, Available from: http://www.ncbi.nlm.nih.gov/pubmed/21609954
Article
CAS
PubMed
PubMed Central
Google Scholar
Yue P, Melamud E, Moult J. SNPs3D: candidate gene and SNP selection for association studies. BMC Bioinformatics. 2006;7:166. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16551372
Article
PubMed
PubMed Central
Google Scholar
Seelow D, Schwarz JM, Schuelke M. GeneDistiller--distilling candidate genes from linkage intervals. PLoS One. 2008;3:e3874. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19057649
Article
PubMed
PubMed Central
Google Scholar
Pers TH, Dworzyński P, Thomas CE, Lage K, Brunak S. MetaRanker 2.0: a web server for prioritization of genetic variation data. Nucleic Acids Res. 2013;41:W104–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23703204
Article
PubMed
PubMed Central
Google Scholar
Gonzalez GH, Tahsin T, Goodale BC, Greene AC, Greene CS. Recent advances and emerging applications in text and data Mining for Biomedical Discovery. Brief Bioinform. 2016;17:33–42. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26420781
Article
PubMed
Google Scholar
Helguera AM, Perez-Castillo Y, Cordeiro MN DS, Tejera E, Paz-Y-Miño C, Sánchez-Rodríguez A, et al. Ligand-based virtual screening using tailored ensembles: a prioritization tool for dual A2AAdenosine receptor antagonists / monoamine Oxidase B inhibitors. Curr Pharm Des. 2016;22:3082–96. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26932160
Article
CAS
PubMed
Google Scholar
Perez-Castillo Y, Helguera AM, Cordeiro MNDS, Tejera E, Paz-Y-Miño C, Sánchez-Rodríguez A, et al. Fusing docking scoring functions improves the virtual screening performance for discovering Parkinson’s disease dual target Ligands. Curr Neuropharmacol. 2017 [cited 2017 Mar 29]; Available from: http://www.ncbi.nlm.nih.gov/pubmed/28067172.
Truchon J-F, Bayly CI. Evaluating virtual screening methods: good and bad metrics for the "early recognition" problem. J Chem Inf Model. 47:488–508. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17288412
Cruz-Monteagudo M, Borges F, Paz-y-Miño C, Cordeiro MNDS, Rebelo I, Perez-Castillo Y, et al. Efficient and biologically relevant consensus strategy for Parkinson’s disease gene prioritization. BMC Med Genet. 2016;9:12. BioMed Central, Available from: http://www.biomedcentral.com/1755-8794/9/12
Google Scholar
Mackey MD, Melville JL. Better than random? The chemotype enrichment problem. J Chem Inf Model. 2009;49:1154–62. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19397275
Article
CAS
PubMed
Google Scholar
Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19131956
Article
CAS
Google Scholar
Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37:1–13. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19033363
Article
Google Scholar
Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One. 2011;6:e21800. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21789182
Article
CAS
PubMed
PubMed Central
Google Scholar
Antonov AV, Schmidt EE, Dietmann S, Krestyaninova M, Hermjakob H. R spider: a network-based analysis of gene lists by combining signaling and metabolic pathways from Reactome and KEGG databases. Nucleic Acids Res. 2010;38:W78–83. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20519200
Article
CAS
PubMed
PubMed Central
Google Scholar
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43:D447–52. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25352553
Article
CAS
PubMed
Google Scholar
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504. Available from: http://www.ncbi.nlm.nih.gov/pubmed/14597658
Article
CAS
PubMed
PubMed Central
Google Scholar
Palla G, Derényi I, Farkas I, Vicsek T. Uncovering the overlapping community structure of complex networks in nature and society. Nature. 2005;435:814–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15944704
Article
CAS
PubMed
Google Scholar
Walsh CJ, Hu P, Batt J, Dos SCC. Microarray meta-analysis and cross-platform normalization: integrative genomics for robust biomarker discovery. Microarrays (Basel, Switzerland). 2015;4:389–406. Multidisciplinary Digital Publishing Institute (MDPI), Available from: http://www.ncbi.nlm.nih.gov/pubmed/27600230
Google Scholar
Cox B. Bioinformatic approach to the genetics of preeclampsia. Obstet Gynecol. 2014;124:633. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25162267
Article
PubMed
Google Scholar
Jia R, Li J, Rui C, Ji H, Ding H, Lu Y, et al. Comparative proteomic profile of the human umbilical cord blood Exosomes between normal and preeclampsia pregnancies with high-resolution mass spectrometry. Cell Physiol Biochem. 2015;36:2299–306. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26279434
Article
CAS
PubMed
Google Scholar
Tejera E, Bernardes J, Rebelo I. Preeclampsia: a bioinformatics approach through protein-protein interaction networks analysis. BMC Syst Biol. 2012;2012:97.
Article
Google Scholar
Khangura RK, Khangura CK, Desai A, Goyert G, Sangha R. Metastatic colorectal cancer resembling severe preeclampsia in pregnancy. Case Rep Obstet Gynecol. 2015;2015:487824. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26770850
PubMed
PubMed Central
Google Scholar
Romero R, Grivel J-C, Tarca AL, Chaemsaithong P, Xu Z, Fitzgerald W, et al. Evidence of perturbations of the cytokine network in preterm labor. Am J Obstet Gynecol. 2015;213:836.e1. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26232508
Article
CAS
Google Scholar
Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–27. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16632515
Article
PubMed
Google Scholar
Iriyama T, Wang W, Parchim NF, Song A, Blackwell SC, Sibai BM, et al. Hypoxia-independent upregulation of placental hypoxia inducible factor-1α gene expression contributes to the pathogenesis of preeclampsia. Hypertension. 2015;65:1307–15. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25847948
Article
CAS
PubMed
PubMed Central
Google Scholar
Xia Y, Kellems RE. Angiotensin receptor agonistic autoantibodies and hypertension: preeclampsia and beyond. Circ Res. 2013;113:78–87. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23788505
Article
CAS
PubMed
PubMed Central
Google Scholar
Parrish MR, Murphy SR, Rutland S, Wallace K, Wenzel K, Wallukat G, et al. The effect of immune factors, tumor necrosis factor-alpha, and agonistic autoantibodies to the angiotensin II type I receptor on soluble fms-like tyrosine-1 and soluble endoglin production in response to hypertension during pregnancy. Am J Hypertens. 2010;23:911–6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20431529
Article
CAS
PubMed
PubMed Central
Google Scholar
Maynard SE, Min J-Y, Merchan J, Lim K-H, Li J, Mondal S, et al. Excess placental soluble fms-like tyrosine kinase 1 (sFlt1) may contribute to endothelial dysfunction, hypertension, and proteinuria in preeclampsia. J Clin Invest. 2003;111:649–58. Available from: http://www.ncbi.nlm.nih.gov/pubmed/12618519
Article
CAS
PubMed
PubMed Central
Google Scholar
Venkatesha S, Toporsian M, Lam C, Hanai J, Mammoto T, Kim YM, et al. Soluble endoglin contributes to the pathogenesis of preeclampsia. Nat Med. 2006;12:642–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16751767
Article
CAS
PubMed
Google Scholar
Staines-Urias E, Paez MC, Doyle P, Dudbridge F, Serrano NC, Ioannidis JPA, et al. Genetic association studies in pre-eclampsia: systematic meta-analyses and field synopsis. Int J Epidemiol. 2012;41:1764–75. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23132613
Article
PubMed
Google Scholar
Li X, Shen L, Tan H. Polymorphisms and plasma level of transforming growth factor-Beta 1 and risk for preeclampsia: a systematic review. PLoS One. 2014;9:e97230. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24823830
Article
PubMed
PubMed Central
Google Scholar
Macintire K, Tuohey L, Ye L, Palmer K, Gantier M, Tong S, et al. PAPPA2 is increased in severe early onset pre-eclampsia and upregulated with hypoxia. Reprod Fertil Dev. 2014;26:351–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23484525
Article
CAS
PubMed
Google Scholar
Wagner PK, Otomo A, Christians JK. Regulation of pregnancy-associated plasma protein A2 (PAPPA2) in a human placental trophoblast cell line (BeWo). Reprod Biol Endocrinol. 2011;9:48. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21496272
Article
CAS
PubMed
PubMed Central
Google Scholar
Fong FM, Sahemey MK, Hamedi G, Eyitayo R, Yates D, Kuan V, et al. Maternal genotype and severe preeclampsia: a HuGE review. Am J Epidemiol. 2014;180:335–45. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25028703
Article
PubMed
Google Scholar
Nezi M, Mastorakos G, Mouslech Z. Corticotropin releasing hormone and the immune/inflammatory response [internet]. Endotext. 2000. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25905246.
Song J, Li Y, An RF. Identification of early-onset preeclampsia-related genes and MicroRNAs by bioinformatics approaches. Reprod Sci. 2015;22:954–63. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25717061
Article
CAS
PubMed
Google Scholar
Noris M, Perico N, Remuzzi G. Mechanisms of disease: pre-eclampsia. Nat Clin Pract Nephrol. 2005;1:98–114. Nature Publishing Group, Available from: http://www.nature.com/doifinder/10.1038/ncpneph0035
Article
CAS
PubMed
Google Scholar
Dimmeler S, Fleming I, Fisslthaler B, Hermann C, Busse R, Zeiher AM. Activation of nitric oxide synthase in endothelial cells by Akt-dependent phosphorylation. Nature. 1999;399:601–5. Available from: http://www.ncbi.nlm.nih.gov/pubmed/10376603.
Article
CAS
PubMed
Google Scholar
Cindrova-Davies T, Sanders DA, Burton GJ, Charnock-Jones DS. Soluble FLT1 sensitizes endothelial cells to inflammatory cytokines by antagonizing VEGF receptor-mediated signalling. Cardiovasc Res. 2011;89:671–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21139021.
Article
CAS
PubMed
Google Scholar
Nagai A, Sado T, Naruse K, Noguchi T, Haruta S, Yoshida S, et al. Antiangiogenic-induced hypertension: the molecular basis of signaling network. Gynecol Obstet Investig. 2012;73:89–98. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22222493.
Article
CAS
Google Scholar
Chappell JC, Taylor SM, Ferrara N, Bautch VL. Local guidance of emerging vessel sprouts requires soluble Flt-1. Dev Cell. 2009;17:377–86. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19758562
Article
CAS
PubMed
PubMed Central
Google Scholar
Powe CE, Levine RJ, Karumanchi SA. Preeclampsia, a disease of the maternal endothelium: the role of antiangiogenic factors and implications for later cardiovascular disease. Circulation. 2011;123:2856–69. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21690502
Article
PubMed
Google Scholar
Sundrani DP, Reddy US, Joshi AA, Mehendale SS, Chavan-Gautam PM, Hardikar AA, et al. Differential placental methylation and expression of VEGF, FLT-1 and KDR genes in human term and preterm preeclampsia. Clin. Epigenetics. BioMed Central. 2013;5:6. Available from: http://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/1868-7083-5-6
CAS
Google Scholar
Hromadnikova I, Dvorakova L, Kotlabova K, Kestlerova A, Hympanova L, Novotna V, et al. Assessment of placental and maternal stress responses in patients with pregnancy related complications via monitoring of heat shock protein mRNA levels. Mol Biol Rep. 2015;42:625–37. Available from: http://link.springer.com/10.1007/s11033-014-3808-z
Article
CAS
PubMed
Google Scholar
Shu C, Liu Z, Cui L, Wei C, Wang S, Tang JJ, et al. Protein profiling of preeclampsia placental tissues. Buratti E, editor. PLoS One. 2014;9:e112890. Public Library of Science, Available from: http://dx.plos.org/10.1371/journal.pone.0112890.
Article
PubMed
PubMed Central
Google Scholar
Padmini E, Venkatraman U, Srinivasan L. Mechanism of JNK signal regulation by placental HSP70 and HSP90 in endothelial cell during preeclampsia. Toxicol Mech Methods. 2012;22:367–74. Available from: http://www.tandfonline.com/doi/full/10.3109/15376516.2012.673091.
Article
CAS
PubMed
Google Scholar
Padmini E, Uthra V, Lavanya S. Effect of HSP70 and 90 in modulation of JNK, ERK expression in Preeclamptic placental endothelial cell. Cell Biochem Biophys. 2012;64:187–95. Available from: http://link.springer.com/10.1007/s12013-012-9371-0.
Article
CAS
PubMed
Google Scholar
Khodzhaeva ZS, Kogan YA, Shmakov RG, Klimenchenko NI, Akatyeva AS, Vavina OV, et al. Clinical and pathogenetic features of early- and late-onset pre-eclampsia. J Matern Neonatal Med. 2015;2015:1–7. Available from: http://www.tandfonline.com/doi/full/10.3109/14767058.2015.1111332.
Article
Google Scholar
Siu MKY, Yeung MCW, Zhang H, Kong DSH, Ho JWK, Ngan HYS, et al. p21-activated kinase-1 promotes aggressive phenotype, cell proliferation, and invasion in gestational trophoblastic disease. Am J Pathol. 2010;176:3015–22. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20413688.
Article
CAS
PubMed
PubMed Central
Google Scholar
Barry DM, Xu K, Meadows SM, Zheng Y, Norden PR, Davis GE, et al. Cdc42 is required for cytoskeletal support of endothelial cell adhesion during blood vessel formation in mice. Development. 2015;142:3058–70. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26253403.
Article
CAS
PubMed
PubMed Central
Google Scholar
Dubrac A, Genet G, Ola R, Zhang F, Pibouin-Fragner L, Han J, et al. Targeting NCK-mediated endothelial cell front-rear polarity inhibits NeovascularizationCLINICAL PERSPECTIVE. Circulation. 2016;133:409–21. Available from: http://circ.ahajournals.org/lookup/doi/10.1161/CIRCULATIONAHA.115.017537.
Article
PubMed
Google Scholar
Mistry HD, Kurlak LO, Broughton Pipkin F. The placental renin-angiotensin system and oxidative stress in pre-eclampsia. Placenta. 2013;34:182–6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23246097.
Article
CAS
PubMed
Google Scholar
Kurlak LO, Williams PJ, Bulmer JN, Broughton Pipkin F, Mistry HD. Placental expression of adenosine A2A receptor and hypoxia inducible factor-1 alpha in early pregnancy, term and pre-eclamptic pregnancies: interactions with placental renin-angiotensin system. Placenta. 2015;36:611–3. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0143400415008103.
Article
CAS
PubMed
Google Scholar
Kurlak LO, Mistry HD, Cindrova-Davies T, Burton GJ, Broughton Pipkin F. Human placental renin-angiotensin system in normotensive and pre-eclamptic pregnancies at high altitude and after acute hypoxia-reoxygenation insult. J Physiol. 2016;594:1327–40. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26574162.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ni S, Zhang Y, Deng Y, Gong Y, Huang J, Bai Y, et al. AGT M235T polymorphism contributes to risk of preeclampsia: evidence from a meta-analysis. J Renin-Angiotensin-Aldosterone Syst. 2012;13:379–86. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22513276.
Article
CAS
PubMed
Google Scholar
Zhao L, Dewan AT, Bracken MB. Association of maternal AGTR1 polymorphisms and preeclampsia: a systematic review and meta-analysis. J Matern Fetal Neonatal Med. 2012;25:2676–80. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22758920.
Article
CAS
PubMed
PubMed Central
Google Scholar
Dechend R, Gratze P, Wallukat G, Shagdarsuren E, Plehm R, Bräsen J-H, et al. Agonistic autoantibodies to the AT1 receptor in a transgenic rat model of preeclampsia. Hypertension. 2005;45:742–6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15699466.
Article
CAS
PubMed
Google Scholar
Fettke F, Schumacher A, Costa S-D, Zenclussen AC. B cells: the old new players in reproductive immunology. Front Immunol. 2014;5:285. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25002862.
Article
PubMed
PubMed Central
Google Scholar
Spradley FT, Palei AC, Granger JP. Immune mechanisms linking obesity and preeclampsia. Biomol Ther. 2015;5:3142–76. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26569331.
CAS
Google Scholar
Harmon AC, Cornelius DC, Amaral LM, Faulkner JL, Cunningham MW, Wallace K, et al. The role of inflammation in the pathology of preeclampsia. Clin Sci (Lond). 2016;130:409–19. Portland Press Limited, Available from: http://www.ncbi.nlm.nih.gov/pubmed/26846579.
Article
CAS
Google Scholar
Dhillion P, Wallace K, Herse F, Scott J, Wallukat G, Heath J, et al. IL-17-mediated oxidative stress is an important stimulator of AT1-AA and hypertension during pregnancy. Am J Phys Regul Integr Comp Phys. 2012;303:R353–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22718806.
CAS
Google Scholar
Austdal M, Thomsen LCV, Tangerås LH, Skei B, Mathew S, Bjørge L, et al. Metabolic profiles of placenta in preeclampsia using HR-MAS MRS metabolomics. Placenta. 2015;36:1455–62. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26582504.
Article
CAS
PubMed
Google Scholar
Bahado-Singh RO, Syngelaki A, Akolekar R, Mandal R, Bjondahl TC, Han B, et al. Validation of metabolomic models for prediction of early-onset preeclampsia. Am J Obstet Gynecol. 2015;213:530.e1–530.e10. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26116099.
Article
Google Scholar
Zheng J-J, Wang H-O, Huang M, Zheng F-Y. Assessment of ADMA, estradiol, and progesterone in severe preeclampsia. Clin Exp Hypertens. 2016;38:347–51. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27152507.
Article
CAS
PubMed
Google Scholar
Kanasaki K, Palmsten K, Sugimoto H, Ahmad S, Hamano Y, Xie L, et al. Deficiency in catechol-O-methyltransferase and 2-methoxyoestradiol is associated with pre-eclampsia. Nature. 2008;453:1117–21. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18469803.
Article
CAS
PubMed
Google Scholar
Lee DK. Nevo O. 2-Methoxyestradiol regulates VEGFR-2 and sFlt-1 expression in human placenta. Placenta. 2015;36:125–30. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25499009.
Article
CAS
PubMed
Google Scholar