Screening for coping style increases the power of gene expression studies

Background: Individuals of many vertebrate species show different stress coping styles and these have a striking influence on how gene expression shifts in response to a variety of challenges. Principal Findings: This is clearly illustrated by a study in which common carp displaying behavioural predictors of different coping styles (characterised by a proactive, adrenaline-based or a reactive, cortisol-based response) were subjected to inflammatory challenge and specific gene transcripts measured in individual brains. Proactive and reactive fish differed in baseline gene expression and also showed diametrically opposite responses to the challenge for 80% of the genes investigated. Significance: Incorporating coping style as an explanatory variable can account for some the unexplained variation that is common in gene expression studies, can uncover important effects that would otherwise have passed unnoticed and greatly enhances the interpretive value of gene expression data

Technical Analysis of cDNA Microarrays

Background: There is extensive variation in gene expression among individuals within and between populations. Accurate measures of the variation in mRNA expression using microarrays can be confounded by technical variation, which includes variation in RNA isolation procedures, day of hybridization and methods used to amplify and dye label RNA for hybridization. Methodology/Principal Findings: In this manuscript we analyze the relationship between the amount of mRNA and the fluorescent signal from the microarray hybridizations demonstrating that for a wide-range of mRNA concentrations the fluorescent signal is a linear function of the amount of mRNA. Additionally, the separate isolation, labeling or hybridization of RNA does not add significant amounts of variation in microarray measures of gene expression. However, single or double rounds of amplification for labeling do have small but significant affects on 10 % of genes, but this source of technical variation is easy to avoid. To examine both technical and stochastic biological variation, mRNA expression was measured from the same five individuals over a six-week time course. Conclusion: There were few, if any, meaningful differences in gene expression among time points. Thus, microarray measures using standard laboratory procedures can be precise and quantitative and are not subject to significant rando

Housekeeping genes for quantitative expression studies in the three-spined stickleback Gasterosteus aculeatus

Background During the last years the quantification of immune response under immunological challenges, e.g. parasitation, has been a major focus of research. In this context, the expression of immune response genes in teleost fish has been surveyed for scientific and commercial purposes. Despite the fact that it was shown in teleostei and other taxa that the gene for beta-actin is not the most stably expressed housekeeping gene (HKG), depending on the tissue and experimental treatment, the gene has been us Results To establish a reliable method for the measurement of immune gene expression in Gasterosteus aculeatus, sequences from the now available genome database and an EST library of the same species were used to select oligonucleotide primers for HKG, in order to perform quantitative reverse-transcription (RT) PCR. The expression stability of ten candidate reference genes was evaluated in three different tissues, and in five parasite treatment groups, using the three algorithms BestKeeper, geNorm and N Conclusion As they were the most stably expressed genes in all tissues examined, we suggest using the genes for the L13a ribosomal binding protein and ubiquitin as alternative or additional reference genes in expression analysis in Gasterosteus aculeatus.

Gene Expression Variability within and between Human Populations and Implications toward Disease Susceptibility

Variations in gene expression level might lead to phenotypic diversity across individuals or populations. Although many human genes are found to have differential mRNA levels between populations, the extent of gene expression that could vary within and between populations largely remains elusive. To investigate the dynamic range of gene expression, we analyzed the expression variability of ∼18, 000 human genes across individuals within HapMap populations. Although ∼20% of human genes show differentiated mRNA levels between populations, our results show that expression variability of most human genes in one population is not significantly deviant from another population, except for a small fraction that do show substantially higher expression variability in a particular population. By associating expression variability with sequence polymorphism, intriguingly, we found SNPs in the untranslated regions (5′ and 3′UTRs) of these variable genes show consistently elevated population heterozygosity. We performed differential expression analysis on a genome-wide scale, and found substantially reduced expression variability for a large number of genes, prohibiting them from being differentially expressed between populations. Functional analysis revealed that genes with the greatest within-population expression variability are significantly enriched for chemokine signaling in HIV-1 infection, and for HIV-interacting proteins that control viral entry, replication, and propagation. This observation combined with the finding that known human HIV host factors show substantially elevated expression variability, collectively suggest that gene expression variability might explain differential HIV susceptibility across individuals

Allele-Specific Gene Expression Is Widespread Across the Genome and Biological Processes

Allelic specific gene expression (ASGE) appears to be an important factor in human phenotypic variability and as a consequence, for the development of complex traits and diseases. In order to study ASGE across the human genome, we have performed a study in which genotyping was coupled with an analysis of ASGE by screening 11,500 SNPs using the Mapping 10 K Array to identify differential allelic expression. We found that from the 5,133 SNPs that were suitable for analysis (heterozygous in our sample and expressed in peripheral blood mononuclear cells), 2,934 (57%) SNPs had differential allelic expression. Such SNPs were equally distributed along human chromosomes and biological processes. We validated the presence or absence of ASGE in 18 out 20 SNPs (90%) randomly selected by real time PCR in 48 human subjects. In addition, we observed that SNPs close to -but not included in- segmental duplications had increased levels of ASGE. Finally, we found that transcripts of unknown function or non-coding RNAs, also display ASGE: from a total of 2,308 intronic SNPs, 1510 (65%) SNPs underwent differential allelic expression. In summary, ASGE is a widespread mechanism in the human genome whose regulation seems to be far more complex than expected

Characterization and Comparison of the Leukocyte Transcriptomes of Three Cattle Breeds

In this study, mRNA-Seq was used to characterize and compare the leukocyte transcriptomes from two taurine breeds (Holstein and Jersey), and one indicine breed (Cholistani). At the genomic level, we identified breed-specific base changes in protein coding regions. Among 7,793,425 coding bases, only 165 differed between Holstein and Jersey, and 3,383 (0.04%) differed between Holstein and Cholistani, 817 (25%) of which resulted in amino acid changes in 627 genes. At the transcriptional level, we assembled transcripts and estimated their abundances including those from more than 3,000 unannotated intergeneic regions. Differential gene expression analysis showed a high similarity between Holstein and Jersey, and a much greater difference between the taurine breeds and the indicine breed. We identified gene ontology pathways that were systematically altered, including the electron transport chain and immune response pathways that may contribute to different levels of heat tolerance and disease resistance in taurine and indicine breeds. At the post-transcriptional level, sequencing mRNA allowed us to identify a number of genes undergoing differential alternative splicing among different breeds. This study provided a high-resolution survey of the variation between bovine transcriptomes at different levels and may provide important biological insights into the phenotypic differentiation among cattle breeds

Morpholino Gene Knockdown in Adult Fundulus heteroclitus: Role of SGK1 in Seawater Acclimation

The Atlantic killifish (Fundulus heteroclitus) is an environmental sentinel organism used extensively for studies on environmental toxicants and salt (NaCl) homeostasis. Previous research in our laboratory has shown that rapid acclimation of killifish to seawater is mediated by trafficking of CFTR chloride channels from intracellular vesicles to the plasma membrane in the opercular membrane within the first hour in seawater, which enhances chloride secretion into seawater, thereby contributing to salt homeostasis. Acute transition to seawater is also marked by an increase in both mRNA and protein levels of serum glucocorticoid kinase 1 (SGK1) within 15 minutes of transfer. Although the rise in SGK1 in gill and its functional analog, the opercular membrane, after seawater transfer precedes the increase in membrane CFTR, a direct role of SGK1 in elevating membrane CFTR has not been established in vivo. To test the hypothesis that SGK1 mediates the increase in plasma membrane CFTR we designed two functionally different vivo-morpholinos to knock down SGK1 in gill, and developed and validated a vivo-morpholino knock down technique for adult killifish. Injection (intraperitoneal, IP) of the splice blocking SGK1 vivo-morpholino reduced SGK1 mRNA in the gill after transition from fresh to seawater by 66%. The IP injection of the translational blocking and splice blocking vivo-morpholinos reduced gill SGK1 protein abundance in fish transferred from fresh to seawater by 64% and 53%, respectively. Moreover, knock down of SGK1 completely eliminated the seawater induced rise in plasma membrane CFTR, demonstrating that the increase in SGK1 protein is required for the trafficking of CFTR from intracellular vesicles in mitochondrion rich cells to the plasma membrane in the gill during acclimation to seawater. This is the first report of the use of vivo-morpholinos in adult killifish and demonstrates that vivo-morpholinos are a valuable genetic tool for this environmentally relevant model organism

Location-Specific Responses to Thermal Stress in Larvae of the Reef-Building Coral Montastraea faveolata

The potential to adapt to a changing climate depends in part upon the standing genetic variation present in wild populations. In corals, the dispersive larval phase is particularly vulnerable to the effects of environmental stress. Larval survival and response to stress during dispersal and settlement will play a key role in the persistence of coral populations.To test the hypothesis that larval transcription profiles reflect location-specific responses to thermal stress, symbiont-free gametes from three to four colonies of the scleractinian coral Montastraea faveolata were collected from Florida and Mexico, fertilized, and raised under mean and elevated (up 1 to 2 degrees C above summer mean) temperatures. These locations have been shown to exchange larvae frequently enough to prevent significant differentiation of neutral loci. Differences among 1,310 unigenes were simultaneously characterized using custom cDNA microarrays, allowing investigation of gene expression patterns among larvae generated from wild populations under stress. Results show both conserved and location-specific variation in key processes including apoptosis, cell structuring, adhesion and development, energy and protein metabolism, and response to stress, in embryos of a reef-building coral.These results provide first insights into location-specific variation in gene expression in the face of gene flow, and support the hypothesis that coral host genomes may house adaptive potential needed to deal with changing environmental conditions

A Population Proportion approach for ranking differentially expressed genes

<p>Abstract</p> <p>Background</p> <p>DNA microarrays are used to investigate differences in gene expression between two or more classes of samples. Most currently used approaches compare mean expression levels between classes and are not geared to find genes whose expression is significantly different in only a subset of samples in a class. However, biological variability can lead to situations where key genes are differentially expressed in only a subset of samples. To facilitate the identification of such genes, a new method is reported.</p> <p>Methods</p> <p>The key difference between the Population Proportion Ranking Method (PPRM) presented here and almost all other methods currently used is in the quantification of variability. PPRM quantifies variability in terms of inter-sample ratios and can be used to calculate the relative merit of differentially expressed genes with a specified difference in expression level between at least some samples in the two classes, which at the same time have lower than a specified variability within each class.</p> <p>Results</p> <p>PPRM is tested on simulated data and on three publicly available cancer data sets. It is compared to the t test, PPST, COPA, OS, ORT and MOST using the simulated data. Under the conditions tested, it performs as well or better than the other methods tested under low intra-class variability and better than t test, PPST, COPA and OS when a gene is differentially expressed in only a subset of samples. It performs better than ORT and MOST in recognizing non differentially expressed genes with high variability in expression levels across all samples. For biological data, the success of predictor genes identified in appropriately classifying an independent sample is reported.</p

Moving toward a system genetics view of disease

Testing hundreds of thousands of DNA markers in human, mouse, and other species for association to complex traits like disease is now a reality. However, information on how variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. In other words, DNA variations impact complex diseases through the perturbations they cause to transcriptional and other biological networks, and these molecular phenotypes are intermediate to clinically defined disease. Because it is also now possible to monitor transcript levels in a comprehensive fashion, integrating DNA variation, transcription, and phenotypic data has the potential to enhance identification of the associations between DNA variation and diseases like obesity and diabetes, as well as characterize those parts of the molecular networks that drive these diseases. Toward that end, we review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data to infer causal relationships among gene expression traits and between expression and clinical traits. We further describe methods to integrate these data in a more comprehensive manner by constructing coexpression gene networks that leverage pairwise gene interaction data to represent more general relationships. To infer gene networks that capture causal information, we describe a Bayesian algorithm that further integrates eQTLs, expression, and clinical phenotype data to reconstruct whole-gene networks capable of representing causal relationships among genes and traits in the network. These emerging network approaches, aimed at processing high-dimensional biological data by integrating data from multiple sources, represent some of the first steps in statistical genetics to identify multiple genetic perturbations that alter the states of molecular networks and that in turn push systems into disease states. Evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease, as research goes beyond a single-gene focus. The early successes achieved with the methods described herein suggest that these more integrative genomics approaches to dissecting disease traits will significantly enhance the identification of key drivers of disease beyond what could be achieved by genetic association studies alone