Metabolic gene profile in early human fetal heart development.

The primitive cardiac tube starts beating 6-8 weeks post fertilization in the developing embryo. In order to describe normal cardiac development during late first and early second trimester in human fetuses this study used microarray and pathways analysis and created a corresponding 'normal' database. Fourteen fetal hearts from human fetuses between 10 and 18 weeks of gestational age (GA) were prospectively collected at the time of elective termination of pregnancy. RNA from recovered tissues was used for transcriptome analysis with Affymetrix 1.0 ST microarray chip. From the amassed data we investigated differences in cardiac development within the 10-18 GA period dividing the sample by GA in three groups: 10-12 (H1), 13-15 (H2) and 16-18 (H3) weeks. A fold change of 2 or above adjusted for a false discovery rate of 5% was used as initial cutoff to determine differential gene expression for individual genes. Test for enrichment to identify functional groups was carried out using the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Array analysis correctly identified the cardiac specific genes, and transcripts reported to be differentially expressed were confirmed by qRT-PCR. Single transcript and Ontology analysis showed first trimester heart expression of myosin-related genes to be up-regulated >5-fold compared with second trimester heart. In contrast the second trimester hearts showed further gestation-related increases in many genes involved in energy production and cardiac remodeling. In conclusion, fetal heart development during the first trimester was dominated by heart-specific genes coding for myocardial development and differentiation. During the second trimester, transcripts related to energy generation and cardiomyocyte communication for contractile coordination/proliferation were more dominant. Transcripts related to fatty acid metabolism can be seen as early as 10 weeks and clearly increase as the heart matures. Retinol receptor and gamma-aminobutyric acid (GABA) receptor transcripts were detected, and have not been described previously in human fetal heart during this period. For the first time global gene expression of heart has been described in human samples to create a database of normal development to understand and compare with known abnormal fetal heart development.

Differential changes in gene expression in human brain during late first trimester and early second trimester of pregnancy.

OBJECTIVE: This study aimed to describe brain development during the first (B1) and second trimester (B3) in human fetuses. DESIGN: Ten brains from 10 to 18 weeks of gestational age (GA) were collected, and the RNA was used for transcriptome analysis (Affymetrix 1.0 ST microarray chip). Differences in brain development within 10 to 18 GA were investigated by dividing the sample into 10 to 12 (B1), 13 to 15(B2) and 16 to 18(B3) weeks. A fold change of 2 or above, with a false discovery rate of 5%, was used as cut-off to determine differential gene expression for individual genes. Quantitative real-time PCR was used to confirm differences. Tests for enrichment procedures (using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) were then used to identify functional groups of mRNA. RESULTS: At 10 to 12 weeks, brains showed neuronal migration to be upregulated. From 10 to 18 weeks, brains showed genes coding for neuronal migration, differentiation and connectivity upregulated. ALDH1A1 and NPY genes, marker of spinal cord and striatum, were upregulated in B1 and B3 brains, respectively. Also, SLITRK6-HAS2 and CRYAB-PCDH18 genes for ear and eye sensory input were upregulated in B1. CONCLUSIONS: For the first time, brain global gene expression was described in human samples. Period B1 was dominated by genes coding for neuronal migration, differentiation, programmed cell death and sensory organs. B3 was dominated by neuronal proliferation, branching and myelination. Creating such a database will allow comparison with abnormals in future studies.

Microarray analysis of BeWo and JEG3 trophoblast cell lines: identification of differentially expressed transcripts.

Trophoblast cell lines are important research tools used as a surrogate for primary trophoblast cells in the study of placental function. Because the cellular origins of transformed trophoblasts are likely to be diverse, it would be of value to understand the unique and shared phenotypes of the cells on a global scale. We have compared two widely used cell lines, BeWo and JEG3, by microarray analysis in order to identify differentially expressed genes. Results indicated that approximately 2700 genes were differentially expressed between the cell lines, with principal differences observed in the biological processes of response to stress, cell adhesion, signal transduction, and protein and nucleobase metabolisms. These data suggest that BeWo and JEG3 cell lines, and perhaps other trophoblast cell lines, are sufficiently dissimilar from each other such that they will be differentially suited for specific experimental paradigms.

IPI59: An Actionable Biomarker to Improve Treatment Response in Serous Ovarian Carcinoma Patients.

Despite improvements in operative management and therapies, overall survival rates in advanced ovarian cancer have remained largely unchanged over the past three decades. Although it is possible to identify high-risk patients following surgery, the knowledge does not provide information about the genomic aberrations conferring risk, or the implications for treatment. To address these challenges, we developed an integrative pathway-index model and applied it to messenger RNA expression from 458 patients with serous ovarian carcinoma from the Cancer Genome Atlas project. The biomarker derived from this approach, IPI59, contains 59 genes from six pathways. As we demonstrate using independent datasets from six studies, IPI59 is strongly associated with overall and progression-free survival, and also identifies high-risk patients who may benefit from enhanced adjuvant therapy.

Genetic loci controlling breast cancer susceptibility in the Wistar-Kyoto rat.

In this study, the Wistar-Kyoto (WKy) rat was genetically characterized for loci that modify susceptibility to mammary carcinogenesis. We used a genetic backcross between resistant WKy and susceptible Wistar-Furth (WF) rats as a panel for linkage mapping to genetically identify mammary carcinoma susceptibility (Mcs) loci underlying the resistance of the WKy rat. Rats were phenotyped for DMBA-induced mammary carcinomas and genotyped using microsatellite markers. To detect quantitative trait loci (QTL), we analyzed the genome scan data under both parametric and nonparametric distributional assumptions and used permutation tests to calculate significance thresholds. A generalized linear model analysis was also performed to test for interactions between significant QTL. This methodology was extended to identify interactions between the significant QTL and other genome locations. Chromosomes 5, 7, 10, and 14 were found to contain significant QTL, termed Mcs5, Mcs6, Mcs7, and Mcs8, respectively. The WKy alleles of Mcs5, -6, and -8 are associated with mammary carcinoma resistance; the WKy allele of Mcs7 is associated with an increased incidence of mammary cancer. In addition, we identified an interaction between Mcs8 and a region on chromosome 6 termed Mcsm1 (modifier of Mcs), which had no significant main effect on mammary cancer susceptibility in this genetic analysis.

Mapping baroreceptor function to genome: a mathematical modeling approach.

To gain information about the genetic basis of a complex disease such as hypertension, blood pressure averages are often obtained and used as phenotypes in genetic mapping studies. In contrast, direct measurements of physiological regulatory mechanisms are not often obtained, due in large part to the time and expense required. As a result, little information about the genetic basis of physiological controlling mechanisms is available. Such information is important for disease diagnosis and treatment. In this article, we use a mathematical model of blood pressure to derive phenotypes related to the baroreceptor reflex, a short-term controller of blood pressure. The phenotypes are then used in a quantitative trait loci (QTL) mapping study to identify a potential genetic basis of this controller.

On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data.

We consider the problem of inferring fold changes in gene expression from cDNA microarray data. Standard procedures focus on the ratio of measured fluorescent intensities at each spot on the microarray, but to do so is to ignore the fact that the variation of such ratios is not constant. Estimates of gene expression changes are derived within a simple hierarchical model that accounts for measurement error and fluctuations in absolute gene expression levels. Significant gene expression changes are identified by deriving the posterior odds of change within a similar model. The methods are tested via simulation and are applied to a panel of Escherichia coli microarrays.

Evaluating maximum likelihood estimation methods to determine the Hurst coeficient.

A maximum likelihood estimation method implemented in S-PLUS (S-MLE) to estimate the Hurst coefficient (H) is evaluated. The Hurst coefficient, with 0.5 < H <1, characterizes long memory time series by quantifying the rate of decay of the autocorrelation function. S-MLE was developed to estimate H for fractionally differenced (fd) processes. However, in practice it is difficult to distinguish between fd processes and fractional Gaussian noise (fGn) processes. Thus, the method is evaluated for estimating H for both fd and fGn processes. S-MLE gave biased results of H for fGn processes of any length and for fd processes of lengths less than 2(10). A modified method is proposed to correct for this bias. It gives reliable estimates of H for both fd and fGn processes of length greater than or equal to 2(11).

A model selection approach for expression quantitative trait loci (eQTL) mapping.

Identifying the genetic basis of complex traits remains an important and challenging problem with the potential to affect a broad range of biological endeavors. A number of statistical methods are available for mapping quantitative trait loci (QTL), but their application to high-throughput phenotypes has been limited as most require user input and interaction. Recently, methods have been developed specifically for expression QTL (eQTL) mapping, but they too are limited in that they do not allow for interactions and QTL of moderate effect. We here propose an automated model-selection-based approach that identifies multiple eQTL in experimental populations, allowing for eQTL of moderate effect and interactions. Output can be used to identify groups of transcripts that are likely coregulated, as demonstrated in a study of diabetes in mouse.

Statistical methods for expression quantitative trait loci (eQTL) mapping.

Traditional genetic mapping has largely focused on the identification of loci affecting one, or at most a few, complex traits. Microarrays allow for measurement of thousands of gene expression abundances, themselves complex traits, and a number of recent investigations have considered these measurements as phenotypes in mapping studies. Combining traditional quantitative trait loci (QTL) mapping methods with microarray data is a powerful approach with demonstrated utility in a number of recent biological investigations. These expression quantitative trait loci (eQTL) studies are similar to traditional QTL studies, as a main goal is to identify the genomic locations to which the expression traits are linked. However, eQTL studies probe thousands of expression transcripts; and as a result, standard multi-trait QTL mapping methods, designed to handle at most tens of traits, do not directly apply. One possible approach is to use single-trait QTL mapping methods to analyze each transcript separately. This leads to an increased number of false discoveries, as corrections for multiple tests across transcripts are not made. Similarly, the repeated application, at each marker, of methods for identifying differentially expressed transcripts suffers from multiple tests across markers. Here, we demonstrate the deficiencies of these approaches and propose a mixture over markers (MOM) model that shares information across both markers and transcripts. The utility of all methods is evaluated using simulated data as well as data from an F(2) mouse cross in a study of diabetes. Results from simulation studies indicate that the MOM model is best at controlling false discoveries, without sacrificing power. The MOM model is also the only one capable of finding two genome regions previously shown to be involved in diabetes.

On the utility of pooling biological samples in microarray experiments.

Over 15% of the data sets catalogued in the Gene Expression Omnibus Database involve RNA samples that have been pooled before hybridization. Pooling affects data quality and inference, but the exact effects are not yet known because pooling has not been systematically studied in the context of microarray experiments. Here we report on the results of an experiment designed to evaluate the utility of pooling and the impact on identifying differentially expressed genes. We find that inference for most genes is not adversely affected by pooling, and we recommend that pooling be done when fewer than three arrays are used in each condition. For larger designs, pooling does not significantly improve inferences if few subjects are pooled. The realized benefits in this case do not outweigh the price paid for loss of individual specific information. Pooling is beneficial when many subjects are pooled, provided that independent samples contribute to multiple pools.

On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles.

DNA microarrays provide for unprecedented large-scale views of gene expression and, as a result, have emerged as a fundamental measurement tool in the study of diverse biological systems. Statistical questions abound, but many traditional data analytic approaches do not apply, in large part because thousands of individual genes are measured with relatively little replication. Empirical Bayes methods provide a natural approach to microarray data analysis because they can significantly reduce the dimensionality of an inference problem while compensating for relatively few replicates by using information across the array. We propose a general empirical Bayes modelling approach which allows for replicate expression profiles in multiple conditions. The hierarchical mixture model accounts for differences among genes in their average expression levels, differential expression for a given gene among cell types, and measurement fluctuations. Two distinct parameterizations are considered: a model based on Gamma distributed measurements and one based on log-normally distributed measurements. False discovery rate and related operating characteristics of the methodology are assessed in a simulation study. We also show how the posterior odds of differential expression in one version of the model is related to the ratio of the arithmetic mean to the geometric mean of the two sample means. The methodology is used in a study of mammary cancer in the rat, where four distinct patterns of expression are possible.

The efficiency of pooling mRNA in microarray experiments.

In a microarray experiment, messenger RNA samples are oftentimes pooled across subjects out of necessity, or in an effort to reduce the effect of biological variation. A basic problem in such experiments is to estimate the nominal expression levels of a large number of genes. Pooling samples will affect expression estimation, but the exact effects are not yet known as the approach has not been systematically studied in this context. We consider how mRNA pooling affects expression estimates by assessing the finite-sample performance of different estimators for designs with and without pooling. Conditions under which it is advantageous to pool mRNA are defined; and general properties of estimates from both pooled and non-pooled designs are derived under these conditions. A formula is given for the total number of subjects and arrays required in a pooled experiment to obtain gene expression estimates and confidence intervals comparable to those obtained from the no-pooling case. The formula demonstrates that by pooling a perhaps increased number of subjects, one can decrease the number of arrays required in an experiment without a loss of precision. The assumptions that facilitate derivation of this formula are considered using data from a quantitative real-time PCR experiment. The calculations are not specific to one particular method of quantifying gene expression as they assume only that a single, normalized, estimate of expression is obtained for each gene. As such, the results should be generally applicable to a number of technologies provided sufficient pre-processing and normalization methods are available and applied.