Telomeric NAP1L4 and OSBPL5 of the KCNQ1 cluster, and the DECORIN gene are not imprinted in human trophoblast stem cells

Background: Genomic imprinting of the largest known cluster, the Kcnq1/KCNQ1 domain on mChr7/hChr11, displays significant differences between mouse and man. Of the fourteen transcripts in this cluster, imprinting of six is ubiquitous in mice and humans, however, imprinted expression of the other eight transcripts is only found in the mouse placenta. The human orthologues of the latter eight transcripts are biallelically expressed, at least from the first trimester onwards. However, as early development is less divergent between species, placental specific imprinting may be present in very early gestation in both mice and humans. Methodology/Principal Findings: Human embryonic stem (hES) cells can be differentiated to embryoid bodies and then to trophoblast stem (EB-TS) cells. Using EB-TS cells as a model of post-implantation invading cytotrophoblast, we analysed allelic expression of two telomeric transcripts whose imprinting is placental specific in the mouse, as well as the ncRNA KCNQ1OT1, whose imprinted expression is ubiquitous in early human and mouse development. KCNQ1OT1 expression was monoallelic in all samples but OSBPL5 and NAP1L4 expression was biallelic in EB-TS cells, as well as undifferentiated hES cells and first trimester human fetal placenta. DCN on hChr12, another gene imprinted in the mouse placenta only, was also biallelically expressed in EB-TS cells. The germline maternal methylation imprint at the KvDMR was maintained in both undifferentiated hES cells and EB-TS cells. Conclusions/Significance: The question of placental specific imprinting in the human has not been answered fully. Using a model of human trophoblast very early in gestation we show a lack of imprinting of two telomeric genes in the KCNQ1 region and of DCN, whose imprinted expression is placental specific in mice, providing further evidence to suggest that humans do not exhibit placental specific imprinting. The maintenance of both differential methylation of the KvDMR and monoallelic expression of KCNQ1OT1 indicates that the region is appropriately regulated epigenetically in vitro. Human gestational load is less than in the mouse, resulting in reduced need for maternal resource competition, and therefore maybe also a lack of placental specific imprinting. If genomic imprinting exists to control fetal acquisition of maternal resources driven by the placenta, placenta-specific imprinting may be less important in the human than the mouse

Spectral gene set enrichment (SGSE)

Motivation: Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. Results: We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes the statistical association between gene sets and principal components (PCs) using our principal component gene set enrichment (PCGSE) method. The overall statistical association between each gene set and the spectral structure of the data is then computed by combining the PC-level p-values using the weighted Z-method with weights set to the PC variance scaled by Tracey-Widom test p-values. Using simulated data, we show that the SGSE algorithm can accurately recover spectral features from noisy data. To illustrate the utility of our method on real data, we demonstrate the superior performance of the SGSE method relative to standard cluster-based techniques for testing the association between MSigDB gene sets and the variance structure of microarray gene expression data. Availability: http://cran.r-project.org/web/packages/PCGSE/index.html Contact: [email protected] or [email protected]

Principal component gene set enrichment (PCGSE)

Motivation: Although principal component analysis (PCA) is widely used for the dimensional reduction of biomedical data, interpretation of PCA results remains daunting. Most existing methods attempt to explain each principal component (PC) in terms of a small number of variables by generating approximate PCs with few non-zero loadings. Although useful when just a few variables dominate the population PCs, these methods are often inadequate for characterizing the PCs of high-dimensional genomic data. For genomic data, reproducible and biologically meaningful PC interpretation requires methods based on the combined signal of functionally related sets of genes. While gene set testing methods have been widely used in supervised settings to quantify the association of groups of genes with clinical outcomes, these methods have seen only limited application for testing the enrichment of gene sets relative to sample PCs. Results: We describe a novel approach, principal component gene set enrichment (PCGSE), for computing the statistical association between gene sets and the PCs of genomic data. The PCGSE method performs a two-stage competitive gene set test using the correlation between each gene and each PC as the gene-level test statistic with flexible choice of both the gene set test statistic and the method used to compute the null distribution of the gene set statistic. Using simulated data with simulated gene sets and real gene expression data with curated gene sets, we demonstrate that biologically meaningful and computationally efficient results can be obtained from a simple parametric version of the PCGSE method that performs a correlation-adjusted two-sample t-test between the gene-level test statistics for gene set members and genes not in the set. Availability: http://cran.r-project.org/web/packages/PCGSE/index.html Contact: [email protected] or [email protected]

Predicting polaron mobility in organic semiconductors with the Feynman variational approach

We extend the Feynman variational approach to the polaron problem \cite{Feynman1955} to the Holstein (lattice) polaron. This new theory shows a discrete transition to small-polarons is observed in the Holstein model. The method can directly used in the FHIP \cite{Feynman1962} mobility theory to calculate dc mobility and complex impedance. We show that we can take matrix elements from electronic structure calculations on real materials, by modelling charge-carrier mobility in crystalline rubrene. Good agreement is found to measurement, in particular the continuous thermal transition in mobility from band-like to thermally-activated, with a minimum in mobility predicted at 140 K.Comment: 8 pages, 6 figure

Principal Component Gene Set Enrichment (Pcgse)

Background: Although principal component analysis (PCA) is widely used for the dimensional reduction of biomedical data, interpretation of PCA results remains daunting. Most existing interpretation methods attempt to explain each principal component (PC) in terms of a small number of variables by generating approximate PCs with mainly zero loadings. Although useful when just a few variables dominate the population PCs, these methods can perform poorly on genomic data, where interesting biological features are frequently represented by the combined signal of functionally related sets of genes. While gene set testing methods have been widely used in supervised settings to quantify the association of groups of genes with clinical outcomes, these methods have seen only limited application for testing the enrichment of gene sets relative to sample PCs. Results: We describe a novel approach, principal component gene set enrichment (PCGSE), for unsupervised gene set testing relative to the sample PCs of genomic data. The PCGSE method computes the statistical association between gene sets and individual PCs using a two-stage competitive gene set test. To demonstrate the efficacy of the PCGSE method, we use simulated and real gene expression data to evaluate the performance of various gene set test statistics and significance tests. Conclusions: Gene set testing is an effective approach for interpreting the PCs of high-dimensional genomic data. As shown using both simulated and real datasets, the PCGSE method can generate biologically meaningful and computationally efficient results via a two-stage, competitive parametric test that correctly accounts for inter-gene correlation

A Dietary-Wide Association Study (DWAS) of Environmental Metal Exposure in US Children and Adults

Background: A growing body of evidence suggests that exposure to toxic metals occurs through diet but few studies have comprehensively examined dietary sources of exposure in US populations. Purpose: Our goal was to perform a novel dietary-wide association study (DWAS) to identify specific dietary sources of lead, cadmium, mercury, and arsenic exposure in US children and adults. Methods: We combined data from the National Health and Nutrition Examination Survey with data from the US Department of Agriculture’s Food Intakes Converted to Retail Commodities Database to examine associations between 49 different foods and environmental metal exposure. Using blood and urinary biomarkers for lead, cadmium, mercury, and arsenic, we compared sources of dietary exposure among children to that of adults. Results: Diet accounted for more of the variation in mercury and arsenic than lead and cadmium. For instance we estimate 4.5% of the variation of mercury among children and 10.5% among adults is explained by diet. We identified a previously unrecognized association between rice consumption and mercury in a US study population – adjusted for other dietary sources such as seafood, an increase of 10 g/day of rice consumption was associated with a 4.8% (95% CI: 3.6, 5.2) increase in blood mercury concentration. Associations between diet and metal exposure were similar among children and adults, and we recapitulated other known dietary sources of exposure. Conclusion: Utilizing this combination of data sources, this approach has the potential to identify and monitor dietary sources of metal exposure in the US population