Ranking of gene set analysis methods.

<p>Surrogate sensitivity, prioritization ability and specificity are combined after transformation into Z-scores. A ranking is produced separately for methods in category I and methods in category II. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079217#s4" target="_blank">Methods</a> in category II produce substantially higher false positives than methods in category I under phenotype permutation.</p><p>We have evaluated the methods ranking stability as a function of several factors that could potentially impact the gene set analysis in different ways, such as the sample size of the microarray datasets, the gene set size, the type of experiment design, and the effect size of the condition under the study. The resulting rankings in the 8 scenarios shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079217#pone-0079217-t002" target="_blank">Table 2</a> were correlated with the original ranking of the methods (based on all datasets), with the Spearman correlation ranging between 0.78 for large gene sets scenario to 0.98 (all p<0.0001) for unpaired design scenario. The exception to the rule was the paired design scenario for which a 0.34 correlation coefficient was observed with the original ranking. Among the possible factors considered in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079217#pone-0079217-t002" target="_blank">Table 2</a>, the sample size had the least effect on the methods ranking, with the correlation between the original ranking (based on 42 datasets) and the one based on the smallest and largest 21 datasets being 0.97 and 0.92 respectively (p<0.0001).</p

Procedure used to compare 16 gene set analysis methods.

<p>42 microarray datasets were used, each studying a phenotype that has a corresponding KEGG or Metacore disease pathway, that we call target pathway. Each method was applied on each datasets and the p-value and rank of the target pathway in each dataset was used to compare the methods.</p

A comparison of sensitivity and prioritization ability of 16 gene set analysis methods.

<p>Each box contains 42 data points representing the p-value (left) and the rank (%) (right) that the target pathway received from a given method when using as input an independent dataset and a collection of gene sets (either KEGG or Metacore). Since the target pathways were designed by KEGG and Metacore for those diseases we expected that, in average, they will be found relevant by the different methods. Methods are ranked from best to worst according to the median p-value (left) and median rank (right).</p

Effects of miR-143 on PTGS2 expression in AECs and AMCs.

<p>A, qRT-PCR analysis shows that 50 nM of miR-143 mimic transfection significantly increases miR-143 expression, while transfection with 200 nM of miR-143 inhibitor has no effect in AECs. In AMCs, miR-143 mimic (50 nM) and hairpin inhibitor (200 nM) transfections significantly increased and decreased the expression of miR-143, respectively. miR-143 and miR-145 expression levels were normalized with 5S rRNA expression. B, Immunoblot analysis of PTGS2 expression in AECs and AMCs transfected with mimic negative control (lane 1), mimic-miR-143 (lane 2), hairpin inhibitor negative control (lane 3), and inhibitor-miR-143 (lane 4). In AECs, PTGS2 protein expression was 1.5-fold decreased in transfection with miR-143 mimic (p = 0.014), but no changes in transfection with miR-143 inhibitor. In AMC, miR-143 mimic and hairpin inhibitor transfections 1.8-fold increased and 1.3-fold decreased the expression of PTGS2 protein (p = 0.014 for each), respectively. PTGS2 protein level was normalized to that of β-actin. C, qRT-PCR analysis of PTGS2 mRNA in transfected AEC and AMC shows there are no significant differences. The PTGS2 mRNA expression was normalized on the content of RPLPO. n = 4 for each experiments; the graphs show means and SE. *, <i>p</i><0.05.</p

Patient demographics and clinical information of cases used for microarray and confirmation analyses.

<p>*, median (range).</p><p>**, The number of term in labor cases is 11 because 6 additional cases were used for qRT-PCR analysis.</p><p>NS, not significant.</p

miR-143 binding to the 3′ UTR of PTGS2 mRNA.

<p>A, Putative miR-143-binding site in 333 bp of PTGS2 3′ UTR construct cloned into pMIR-REPORT™ for luciferase reporter assay. B, Luciferase reporter assay of PTGS2 mRNA 3′ UTR in AECs and AMCs. Luciferase activities from AECs and AMCs transfected with 200 ng (AEC) or 500 ng (AMC) of luciferase reporter plasmid containing PTGS2 3′ UTR (pMIR-REPORT_PTGS2), 10 ng of Renilla luciferase reporter pSV40-RL, and miRIDIAN miR-143 mimic (100 nM) or miR-143 hairpin inhibitor (50 nM) or equal amounts of negative controls were measured using Dual-Lucifrerase Reporter Assay System (Promega). pMIR-REPORT was used as a control. In AECs, there is a 58.5% of decrease in luciferase activity following transfection with miR-143 mimic but transfection of miR-143 hairpin inhibitor does not alter luciferase activity. In AMCs, miR-143 mimic transfection decreased luciferase activity by 25.9% compared to the control, while inhibition of miR-143 increased luciferase activity by 46.2% compared to control. Renilla luciferase activity was used for normalization of firefly luciferase activity (n = 5). The graphs show means and SE. *, <i>p</i><0.05.</p

Characterization of isolated amnion epithelial cells (AECs) and amnion mesenchymal cells (AMCs).

<p>A, Morphological and immunophenotypic characteristics of AECs and AMCs on hematoxylin & eosin staining (H&E) and immunofluorescent staining of cytokeratin-7 (red), type I procollagen (green). AECs are positive for cytokeratin-7, while AMCs are positive for type I procollagen. B, qRT-PCR analysis of miR-143 expression which was normalized to 5S rRNA shows significantly higher expression in AMCs than AECs. C, Densitometric analysis of PTGS2 expression level was normalized to β-actin. PTGS2 protein is less abundant in AMCs than in AECs. D, PTGS2 mRNA expression is not different between AECs and AMCs. AECs and AMCs obtained from five women at term not in labor (TNL) were used for all experiments (B–D). *, <i>p</i><0.05.</p

Transcriptomics of Maternal and Fetal Membranes Can Discriminate between Gestational-Age Matched Preterm Neonates with and without Cognitive Impairment Diagnosed at 18–24 Months

<div><p>Background</p><p>Neurocognitive impairment among children born preterm may arise from complex interactions between genes and the intra-uterine environment.</p><p>Objectives</p><p>(<b>1</b>) To characterize the transcriptomic profiles of chorioamniotic membranes in preterm neonates with and without neurocognitive impairment via microarrays and (<b>2</b>) to determine if neonates with neurocognitive impairment can be identified at birth.</p><p>Materials/Methods</p><p>A retrospective case-control study was conducted to examine the chorioamniotic transcriptome of gestational-age matched very preterm neonates with and without neurocognitive impairment at 18–24 months’ corrected-age defined by a Bayley-III Cognitive Composite Score <80 (n = 14 each). Pathway analysis with down-weighting of overlapping genes (<b>PADOG</b>) was performed to identify KEGG pathways relevant to the phenotype. Select differentially expressed genes were profiled using qRT-PCR and a multi-gene disease prediction model was developed using linear discriminant analysis. The model’s predictive performance was tested on a new set of cases and controls (n = 19 each).</p><p>Results</p><p><b>1</b>) 117 genes were differentially expressed among neonates with and without subsequent neurocognitive impairment (p<0.05 and fold change >1.5); <b>2</b>) Gene ontology analysis indicated enrichment of 19 biological processes and 3 molecular functions; <b>3</b>)<b>PADOG</b> identified 4 significantly perturbed KEGG pathways: oxidative phosphorylation, Parkinson’s disease, Alzheimer’s disease and Huntington’s disease (q-value <0.1); <b>4</b>) 48 of 90 selected differentially expressed genes were confirmed by qRT-PCR, including genes implicated in energy metabolism, neuronal signaling, vascular permeability and response to injury (e.g., up-regulation of <i>SEPP1</i>, <i>APOE</i>, <i>DAB2</i>, <i>CD163</i>, <i>CXCL12</i>, <i>VWF;</i> down-regulation of <i>HAND1</i>, <i>OSR1</i>)(p<0.05); and <b>5</b>) a multi-gene model predicted 18–24 month neurocognitive impairment (using the ratios of <i>OSR1/VWF</i> and <i>HAND1/VWF</i> at birth) in a larger, independent set (sensitivity = 74%, at specificity = 83%).</p><p>Conclusions</p><p>Gene expression patterns in the chorioamniotic membranes link neurocognitive impairment in preterm infants to neurodegenerative disease pathways and might be used to predict neurocognitive impairment. Further prospective studies are needed.</p></div

Biological processes enriched for genes differentially expressed between the neurocognitive impairment and no impairment groups.

<p>a GO: Gene ontology;</p><p>b FDR: False Discovery Rate.</p><p>Biological processes enriched for genes differentially expressed between the neurocognitive impairment and no impairment groups.</p

Molecular functions enriched for genes differentially expressed between the neurocognitive impairment and no impairment groups.

<p>a GO: Gene ontology</p><p>b FDR: False Discovery Rate.</p><p>Molecular functions enriched for genes differentially expressed between the neurocognitive impairment and no impairment groups.</p