A simplified,robust, and streamlined procedure for the production of <Emphasis Type="Italic">C. elegans</Emphasis>transgenes via recombineering

Background  

The nematode Caenorhabditis elegans has emerged as a powerful system to study biologic questions ranging from development to aging. The generation of transgenic animals is an important experimental tool and allows use of GFP fusion proteins to study the expression of genes of interest or generation of epitope tagged versions of specific genes. Transgenes are often generated by placing a promoter upstream of a reporter gene or cDNA. This often produces a representative expression pattern, but important exceptions have been observed. To better capture the genuine expression pattern and timing, several investigators have modified large pieces of DNA carried by BACs or fosmids for use in the construction of transgenic animals via recombineering. However, these techniques are not in widespread use despite the advantages when compared to traditional approaches. Additionally, some groups have encountered problems with employing these techniques. Hence, we sought identify ways to improve the simplicity and reliability of the procedure.     

Gene expression analysis of the ovary of hybrid females of <Emphasis Type="Italic">Xenopus laevis</Emphasis> and <Emphasis Type="Italic">X. muelleri</Emphasis>

Background  

Interspecific hybrids of frogs of the genus Xenopus result in sterile hybrid males and fertile hybrid females. Previous work has demonstrated a dramatic asymmetrical pattern of misexpression in hybrid males compared to the two parental species with relatively few genes misexpressed in comparisons of hybrids and the maternal species (X. laevis) and dramatically more genes misexpressed in hybrids compared to the paternal species (X. muelleri). In this work, we examine the gene expression pattern in hybrid females of X. laevis × X. muelleri to determine if this asymmetrical pattern of expression also occurs in hybrid females.     

Transcription profiling of fertilization and early seed development events in a solanaceous species using a 7.7 K cDNA microarray from <Emphasis Type="Italic">Solanum chacoense</Emphasis>ovules

Background  

To provide a broad analysis of gene expression changes in developing embryos from a solanaceous species, we produced amplicon-derived microarrays with 7741 ESTs isolated from Solanum chacoense ovules bearing embryos from all developmental stages. Our aims were to: 1) identify genes expressed in a tissue-specific and temporal-specific manner; 2) define clusters of genes showing similar patterns of spatial and temporal expression; and 3) identify stage-specific or transition-specific candidate genes for further functional genomic analyses.     

Microarray profile of differentially expressed genes in a monkey model of allergic asthma

Background

Inhalation of Ascaris suum antigen by allergic monkeys causes an immediate bronchoconstriction and delayed allergic reaction, including a pulmonary inflammatory infiltrate. To identify genes involved in this process, the gene-expression pattern of allergic monkey lungs was profiled by microarrays. Monkeys were challenged by inhalation of A. suum antigen or given interleukin-4 (IL-4) treatment; lung tissue was collected at 4, 18 or 24 h after antigen challenge or 24 h after IL-4. Each challenged monkey lung was compared to a pool of normal, unchallenged monkey lungs.

Results

Of the approximately 40,000 cDNAs represented on the microarray, expression levels of 169 changed by more than 2.5-fold in at least one of the pairwise probe comparisons; these cDNAs encoded 149 genes, of which two thirds are known genes. The largest number of regulated genes was observed 4 h after challenge. Confirmation of differential expression in the original tissue was obtained for 95% of a set of these genes using real-time PCR. Cluster analysis revealed at least five groups of genes with unique expression patterns. One cluster contained genes for several chemokine mediators including eotaxin, PARC, MCP-1 and MCP-3. Genes involved in tissue remodeling and antioxidant responses were also identified as regulated by antigen and IL-4 or by antigen only.

Conclusion

This study provides a large-scale profile of gene expression in the primate lung following allergen or IL-4 challenge. It shows that microarrays, with real-time PCR, are a powerful tool for identifying and validating differentially expressed genes in a disease model.     

Not proper ROC curves as new tool for the analysis of differentially expressed genes in microarray experiments

Background  

Most microarray experiments are carried out with the purpose of identifying genes whose expression varies in relation with specific conditions or in response to environmental stimuli. In such studies, genes showing similar mean expression values between two or more groups are considered as not differentially expressed, even if hidden subclasses with different expression values may exist. In this paper we propose a new method for identifying differentially expressed genes, based on the area between the ROC curve and the rising diagonal (ABCR). ABCR represents a more general approach than the standard area under the ROC curve (AUC), because it can identify both proper (i.e., concave) and not proper ROC curves (NPRC). In particular, NPRC may correspond to those genes that tend to escape standard selection methods.     

MORPHOMETRIC CORRELATION PATTERNS OF ADULT BIRDS (FRINGILLIDAE: PASSERIFORMES AND COLUMBIFORMES) MIRROR THE EXPRESSION OF DEVELOPMENTAL CONTROL GENES

Stimulated by the rapid progress in developmental genetics, recent approaches to evolutionary theory focus on the interface function of developmental processes in the study of genotype-phenotype mapping. From this viewpoint, the main result of the present analysis is that the expression patterns of developmental control genes are reflected in the infraspecific correlation patterns of phenotypic characters in the adult stage. The study is based on 42 logarithmically scaled skeletal measurements of two avian clades, finches (43 species, n = 313) and pigeons (27 species, n = 219). First, for each clade an “observed correlation matrix” was calculated by computing a bias-reduced pooled-species correlation matrix based on the clade-specific pooled within-species variance-covariance matrix between measurements. Second, the expression domains of diverse developmental control genes, that is, Hox, Msx, Pax, Mhox, Shh, Bmp, and Gdf, in characters were represented by “theoretical matrices.” Finally, the observed and the theoretical matrices were compared by Mantel's test to test hypotheses about pattern similarities between phenotypic correlations and the expression of developmental control genes. Seventeen percent of the single matrix comparisons revealed significant (P ≤ 0.05) pattern correspondences in finches, whereas 63% were significant in pigeons. The multiple comparisons revealed correspondences at the highest significance level (P ≤ 0.001) in both clades and disclosed that 15% of the observed matrix patterns are explained in finches versus 22% in pigeons. Presumably, as finches have less pronounced correspondences between gene expression and phenotypic correlation, they are more derived than pigeons. Out of the significant single matrix comparisons, four correspondences are common to both clades: one of them is connected with the Gdf gene expression concerning limb length relations and also harmonizes with the dominant pattern within the infraspecific correlation matrices. The general implication is that the significant correspondences detected here between observed and theoretical matrices are based on a correspondence between phenotypic and genetic modules. Because the phenotypic modules are potential candidates for a direct impact of selection, the important role of genotype-phenotype mapping in molding the body plan becomes apparent.     

Differential expression of ferritin genes in response to abiotic stresses and hormones in pear (<Emphasis Type="Italic">Pyrus pyrifolia</Emphasis>)

In this study, the expression patterns of four ferritin genes (PpFer1, PpFer2, PpFer3, and PpFer4) in pear were investigated using quantitative real-time PCR. Analysis of tissue-specific expression revealed higher expression level of these genes in leaves than in other tested tissues. These ferritin genes were differentially expressed in response to various abiotic stresses and hormones treatments. The expression of ferritin wasn’t affected by Fe(III)-citrate treatment. Abscisic acid significantly enhanced the expression of all four ferritin genes, especially PpFer2, followed by N-benzylyminopurine, gibberellic acid, and indole-3-acetic acid. The expression peaks of PpFer1 and PpFer3 in leaves appeared at 6, 6, and 12 h, respectively, after pear plant was exposed to oxidative stress (5 mM H₂O₂), salt stress (200 mM NaCl), and heat stress (40°C). A significant increase in PpFer4 expression was detected at 6 h after salt stress or heat stress. The expression of ferritin genes was not altered by cold stress. These results suggested that ferritin genes might be functionally important in acclimation of pear to salt and oxidative stresses. Hormone treatments had no significant effect on expression of ferritin genes compared to abiotic stresses. This showed accumulation of ferritin genes could be operated by different transduction pathways under abiotic stresses and hormones treatments.     

CoXpress: differential co-expression in gene expression data

Background  

Traditional methods of analysing gene expression data often include a statistical test to find differentially expressed genes, or use of a clustering algorithm to find groups of genes that behave similarly across a dataset. However, these methods may miss groups of genes which form differential co-expression patterns under different subsets of experimental conditions. Here we describe coXpress, an R package that allows researchers to identify groups of genes that are differentially co-expressed.     

A robust measure of correlation between two genes on a microarray

Background  

The underlying goal of microarray experiments is to identify gene expression patterns across different experimental conditions. Genes that are contained in a particular pathway or that respond similarly to experimental conditions could be co-expressed and show similar patterns of expression on a microarray. Using any of a variety of clustering methods or gene network analyses we can partition genes of interest into groups, clusters, or modules based on measures of similarity. Typically, Pearson correlation is used to measure distance (or similarity) before implementing a clustering algorithm. Pearson correlation is quite susceptible to outliers, however, an unfortunate characteristic when dealing with microarray data (well known to be typically quite noisy.)     

Conserved and non-conserved enhancers direct tissue specific transcription in ancient germ layer specific developmental control genes

Background

In a recent genomic study, Leung et al. used a factorial microarray analysis to identify Smarca4 (Brg1)-regulated genes in micro-dissected zebrafish retinas. Two hundred and fifty nine genes were grouped in three-way ANOVA models which carried the most specific retinal change. To validate the microarray results and to elucidate cellular expression patterns of the significant genes for further characterization, 32 known genes were randomly selected from this group. In situ hybridization of these genes was performed on the same types of samples (wild-type (WT) and smarca4 ^a50/a50 (yng) mutant) at the same stages (36 and 52 hours post-fertilization (hpf)) as in the microarray study.

Results

Thirty out of 32 riboprobes showed a positive in situ staining signal. Twenty seven out of these 30 genes were originally further classified as Smarca4-regulated retinal genes, while the remaining three as retinal-specific expression independent of Smarca4 regulation. It was found that 90.32% of the significant microarray comparisons that were used to identify Smarca4-regulated retinal genes had a corresponding qualitative expression change in the in situ hybridization comparisons. This is highly concordant with the theoretical true discovery rate of 95%. Hierarchical clustering was used to investigate the similarity of the cellular expression patterns of 25 out of the 27 Smarca4-regulated retinal genes that had a sufficiently high expression signal for an unambiguous identification of retinal expression domains. Three broad groups of expression pattern were identified; including 1) photoreceptor layer/outer nuclear layer specific expression at 52 hpf, 2) ganglion cell layer (GCL) and/or inner nuclear layer (INL) specific expression at both 36 & 52 hpf, and 3) GCL and/or INL specific expression at 52 hpf only. Some of these genes have recently been demonstrated to play key roles in retinal cell-type specification, differentiation and lamination. For the remaining three retinal-specific genes that are independent of Smarca4 regulation, they all had a subtle expression difference between WT and smarca4 ^a50/a50 retinas as detected by in situ hybridization. This subtle expression difference was also detected by the original microarray analysis. However, the difference was lower than the fold change cut-off used in that study and hence these genes were not inferred as Smarca4-regulated retinal genes.

Conclusions

This study has successfully investigated the expression pattern of 32 genes identified from the original factorial microarray analysis. The results have demonstrated that the true discovery rate for identifying Smarca4-regulated retinal genes is 90.3%. Hence, the significant genes from the microarray study are good candidates for cell-type specific markers and will aid further investigation of retinal differentiation.     

Dynamic covariation between gene expression and proteome characteristics

Background  

Cells react to changing intra- and extracellular signals by dynamically modulating complex biochemical networks. Cellular responses to extracellular signals lead to changes in gene and protein expression. Since the majority of genes encode proteins, we investigated possible correlations between protein parameters and gene expression patterns to identify proteome-wide characteristics indicative of trends common to expressed proteins.     

Differential selection on gene translation efficiency between the filamentous fungus <Emphasis Type="Italic">Ashbya gossypii</Emphasis> and yeasts

Background  

The filamentous fungus Ashbya gossypii grows into a multicellular mycelium that is distinct from the unicellular morphology of its closely related yeast species. It has been proposed that genes important for cell cycle regulation play central roles for such phenotypic differences. Because A. gossypii shares an almost identical set of cell cycle genes with the typical yeast Saccharomyces cerevisiae, the differences might occur at the level of orthologous gene regulation. Codon usage patterns were compared to identify orthologous genes with different gene regulation between A. gossypii and nine closely related yeast species.     

Exact Multiple Comparisons of Three or More Regression Lines: Pairwise Comparisons and Comparisons with a Control

The problem of finding exact simultaneous confidence bounds for differences in regression models for k groups via the union‐intersection method is considered. The error terms are taken to be iid normal random variables. Under an assumption slightly more general than having identical design matrices for each of the k groups, it is shown that an existing probability point for the multivariate studentized range can be used to find the necessary probability point for pairwise comparisons of regression models. The resulting methods can be used with simple or multiple regression. Under a weaker assumption on the k design matrices that allows more observations to be taken from the control group than from the k‐1 treatment groups, a method is developed for computing exact probability points for comparing the simple linear regression models of the k‐1 groups to that of the control. Within a class of designs, the optimal design for comparisons with a control takes the square root of (k‐1) times as many observations from the control than from each treatment group. The simultaneous confidence bounds for all pairwise differences and for comparisons with a control are much narrower than Spurrier's intervals for all contrasts of k regression lines.     

Two non-homologous brain diseases-related genes, <Emphasis Type="Italic">SERPINI1</Emphasis> and <Emphasis Type="Italic">PDCD10</Emphasis>, are tightly linked by an asymmetric bidirectional promoter in an evolutionarily conserved manner

Background  

Despite of the fact that mammalian genomes are far more spacious than prokaryotic genomes, recent nucleotide sequencing data have revealed that many mammalian genes are arranged in a head-to-head orientation and separated by a small intergenic sequence. Extensive studies on some of these neighboring genes, in particular homologous gene pairs, have shown that these genes are often co-expressed in a symmetric manner and regulated by a shared promoter region. Here we report the identification of two non-homologous brain disease-related genes, with one coding for a serine protease inhibitor (SERPINI1) and the other for a programmed cell death-related gene (PDCD10), being tightly linked together by an asymmetric bidirectional promoter in an evolutionarily conserved fashion. This asymmetric bidirectional promoter, in cooperation with some cis-acting elements, is responsible for the co-regulation of the gene expression pattern as well as the tissue specificity of SERPINI1 and PDCD10.