Translational analysis of mouse and human placental protein and mRNA reveals distinct molecular pathologies in human preeclampsia - PubMed (original) (raw)

. 2011 Dec;10(12):M111.012526.

doi: 10.1074/mcp.M111.012526. Epub 2011 Oct 10.

Parveen Sharma, Andreas I Evangelou, Kathie Whiteley, Vladimir Ignatchenko, Alex Ignatchenko, Dora Baczyk, Marie Czikk, John Kingdom, Janet Rossant, Anthony O Gramolini, S Lee Adamson, Thomas Kislinger

Affiliations

Translational analysis of mouse and human placental protein and mRNA reveals distinct molecular pathologies in human preeclampsia

Brian Cox et al. Mol Cell Proteomics. 2011 Dec.

Abstract

Preeclampsia (PE) adversely impacts ~5% of pregnancies. Despite extensive research, no consistent biomarkers or cures have emerged, suggesting that different molecular mechanisms may cause clinically similar disease. To address this, we undertook a proteomics study with three main goals: (1) to identify a panel of cell surface markers that distinguish the trophoblast and endothelial cells of the placenta in the mouse; (2) to translate this marker set to human via the Human Protein Atlas database; and (3) to utilize the validated human trophoblast markers to identify subgroups of human preeclampsia. To achieve these goals, plasma membrane proteins at the blood tissue interfaces were extracted from placentas using intravascular silica-bead perfusion, and then identified using shotgun proteomics. We identified 1181 plasma membrane proteins, of which 171 were enriched at the maternal blood-trophoblast interface and 192 at the fetal endothelial interface with a 70% conservation of expression in humans. Three distinct molecular subgroups of human preeclampsia were identified in existing human microarray data by using expression patterns of trophoblast-enriched proteins. Analysis of all misexpressed genes revealed divergent dysfunctions including angiogenesis (subgroup 1), MAPK signaling (subgroup 2), and hormone biosynthesis and metabolism (subgroup 3). Subgroup 2 lacked expected changes in known preeclampsia markers (sFLT1, sENG) and uniquely overexpressed GNA12. In an independent set of 40 banked placental specimens, GNA12 was overexpressed during preeclampsia when co-incident with chronic hypertension. In the current study we used a novel translational analysis to integrate mouse and human trophoblast protein expression with human microarray data. This strategy identified distinct molecular pathologies in human preeclampsia. We conclude that clinically similar preeclampsia patients exhibit divergent placental gene expression profiles thus implicating divergent molecular mechanisms in the origins of this disease.

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Figures

Fig. 1.

Fig. 1.

Proteomics strategy to identify novel proteins at the fetal and maternal blood-tissue interfaces in the mouse placenta. A, Scheme of our applied workflow. Cationic silica-beads were perfused via the maternal aorta to reach trophoblast lined maternal blood spaces or via the umbilical cord to reach fetal endothelial-lined vessels in the placental labyrinth. Silica-beads were isolated from labyrinth tissue to obtain in vivo surface-associated proteins. Proteins isolated from beads were analyzed by MudPIT-based proteomics. B, A zoomed-in scatter plot representation of the entire dataset highlighting known markers of endothelial cells (EC; blue text) and trophoblast cells (TC; red text). Green data points were significantly associated with either the EC or TC surfaces (FDR p value ≤0.1), whereas gray data points did not significantly differ (FDR p value >0.1).

Fig. 2.

Fig. 2.

Plasma membrane markers of the blood-tissue interface in the mouse placenta. A, A plot of the log2 transformed ratio of SpC in the EC over the TC versus the mean SpC of the EC and TC of proteins predicted to be plasma membrane associated by BasesNet machine learning. Proteins are color coded to represent those that were significantly enriched to the TC fraction (n = 171; red), EC fraction (n = 191; blue), or not statistically enriched (n = 891; gray). The plot is annotated with the same known markers presented in Fig. 1_B. B_, A chart showing selected significantly enriched annotation terms in the membrane predicted data set. Represented are the percentages of genes linked to an enriched term. C, Box plot showing that the predictive membrane data set was enriched for annotation terms CD antigen and surfaceome (36) significantly better than random sampling of the entire dataset. D–G, Heat map displays of selected, significantly over-represented functional classes in the plasma membrane enriched proteins showing differences in expression between TC and EC fractions. Heat maps are sorted in order of highest to lowest expression in TC. Although there is a high degree of similarity between the trophoblast (TC) and endothelial (EC) compartments there are some key differences in protein expression.

Fig. 3.

Fig. 3.

Immunohistochemistry validation of mass spectrometry-based proteomics data using the Human Protein Atlas database. A, Availability of antibodies in the Human Protein Atlas for 171 trophoblast enriched plasma membrane proteins identified in the mouse placenta. B, Systematic scoring results from all IHC images available at the Human Protein Atlas. Images were scored as “strongest staining in the fetal trophoblast” (TC>EC), “staining equally in the endothelium and trophoblast cells” (EC = TC), “no staining observed” or “staining stronger in the fetal endothelium” (TC<EC). C) Representative images from Human Protein Atlas (ProteinAtlas.org) for proteins enriched to either the trophoblast (ALG5, SLC39A14, TGM1) or endothelial (ACE, ICAM1, GNG2) cells.

Fig. 4.

Fig. 4.

Classification of human PE patient mRNA expression data into molecular-based subgroups. A, Plot of Bayesian Information Criterion (BIC) scores versus models with increasing numbers of subgroups using gene expression data of human preeclamptic placentas (49) based on 143 trophoblast enriched proteins. B, Strip chart of BIC scores for the optimal model solution for the 143 trophoblast enriched proteins (red filled square) and 1150 random draws of 143 probes (black open squares) and the top 143 differentially regulated probes on the arrays (blue filled square). C, 3D plot of first three principal components of patient samples colored based on their subgroup membership (1 is red, 2 is blue and 3 is green). Plot shows strong segregation between subgroups and clustering within subgroups. D, Venn diagram of the overlap of differentially regulated genes in each group. Of note is the higher degree of overlap between groups 1 and 3. E, Strip charts of the microarray probe signal for individual patient samples showing four known markers of PE (FLT1, ENG, PAPPA2, ADAM12). Note that in each case the samples in subgroup 2 have a signal that is in the range of the controls. Subgroups 1 and 3 have a mean expression that is significantly higher (p < 0.01) than controls for all PE markers, whereas for subgroup 2 these markers are not significantly different than controls.

Fig. 5.

Fig. 5.

Differential enrichment of gene functional categories among human PE subgroups. A, Distribution of the 94 genes deregulated in the PE patient mRNA expression dataset annotated with the mouse mutant phenotype term abnormal cardiovascular systems morphology. Fewer than 30% show overlap with at least one other patient group although each group was independently assessed as enriched in this functional category. This suggests different molecular pathways are affected in each subgroup leading to a similar phenotypic outcome. B, Specific functional categories are enriched in each subgroup. Heat maps show similar expression for some members of each category across PE subgroups, but the majorities are uniquely deregulated in a single subgroup. Red intensity is increasing and blue is decreasing fold change of a subgroup versus controls. Numbers in the individual cells of the heat map indicate if the observed change is significant at a FDR corrected p value ≤ 0.05: −1 significantly down-regulated, 0 no significance and 1 significantly up-regulated.

Fig. 6.

Fig. 6.

Correlation of GNA12 expression with human PE and chronic hypertension. A–B, Box plot representation of densitometry readings from Western blot results for two selected trophoblast surface markers identified in the current study versus the patient groups: Control, IUGR-preeclampsia (Mixed) and preeclampsia without IUGR (PE). Statistical differences between groups were calculated using the Wilcoxson test for nonparametric data. Significant differences between controls and either Mixed or PE patients were observed for GNA12. A control protein GPR50, for which we did not observe any significant difference by microarray, had no significant difference by proteins. p values are reported at the 95% confidence level. C, Pair wise correlation plot of GNA12 and GPR50 protein expression and mother/fetus/child clinical data showing significant correlation of GNA12 with chronic hypertension (CHTN; green box bottom row on right). Also of note is the correlation with other signs of PE such as abnormal umbilical Doppler (green box bottom row on left). D–E, strip chart of GNA12 and GPR50 protein expression of individual patients colored by presence of (red triangles) or absence (black boxes) of chronic hypertension. GNA12 expression was statistically higher in the chronic hypertension group (p < 0.05) versus the non-chronic hypertension group in either the mixed or PE groups. None of the controls had chronic hypertension.

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