Axial skeletal patterning in mice lacking all paralogous group 8 Hox genes

We present a detailed study of the genetic basis of mesodermal axial patterning by paralogous group 8 Hox genes in the mouse. The phenotype of Hoxd8 loss-of-function mutants is presented, and compared with that of Hoxb8- and Hoxc8-null mice. Our analysis of single mutants reveals common features for the Hoxc8 and Hoxd8 genes in patterning lower thoracic and lumbar vertebrae. In the Hoxb8 mutant, more anterior axial regions are affected. The three paralogous Hox genes are expressed up to similar rostral boundaries in the mesoderm, but at levels that strongly vary with the axial position. We find that the axial region affected in each of the single mutants mostly corresponds to the area with the highest level of gene expression. However, analysis of double and triple mutants reveals that lower expression of the other two paralogous genes also plays a patterning role when the mainly expressed gene is defective. We therefore conclude that paralogous group 8 Hox genes are involved in patterning quite an extensive anteroposterior (AP) axial region. Phenotypes of double and triple mutants reveal that Hoxb8, Hoxc8 and Hoxd8 have redundant functions at upper thoracic and sacral levels, including positioning of the hindlimbs. Interestingly, loss of functional Hoxb8 alleles partially rescues the phenotype of Hoxc8- and Hoxc8/Hoxd8-null mutants at lower thoracic and lumbar levels. This suggests that Hoxb8 affects patterning at these axial positions differently from the other paralogous gene products. We conclude that paralogous Hox genes can have a unique role in patterning specific axial regions in addition to their redundant function at other AP levels.     

Validation of Zebrafish （Danio redo） Reference Genes for Quantitative Real-time RT-PCR Normalization

The normalization of quantitative real time RT-PCR （qRT-PCR） is important to obtain accurate gene expression data. The most common method for qRT-PCR normalization is to use reference, or housekeeping genes. However, there is emerging evidence that even reference genes can be regulated under different conditions, qRT-PCR has only recently been used in terms of zebrafish gene expression studies and there is no validated set of reference genes. This study characterizes the expression of nine possible reference genes during zebrafish embryonic development and in a zebrafish tissue panel. All nine reference genes exhibited variable expression. The fl-actin, EFlot and Rpll3ot genes comprise a validated reference gene panel for zebrafish developmental time course studies, and the EF1 or, Rpll3α and 18S rRNA genes are more suitable as a reference gene panel for zebrafish tissue analysis. Importantly, the zebrafish GAPDH gene appears unsuitable as reference gene for both types of studies.     

Selection of housekeeping genes for qRT-PCR analysis in potato tubers under cold stress

Quantitative real-time polymerase chain reaction (qRT-PCR) is currently the most sensitive method used for quantitative gene expression studies. However, minimal variation in the amount of material and presence of inhibitors affecting enzyme efficiency can lead to significant quantification errors. Accurate data normalization is vital using reference genes as internal controls. Many so-called housekeeping genes or reference genes with assumed stable expression can exhibit either up- or downregulation depending on the developmental stage or other environmental conditions. We have evaluated six reference genes (actin, APRT, 18S rRNA, ef1α, β-tubulin and ribosomal protein L2) for qRT-PCR profiling experiments in potato tuber tissues of five varieties during cold storage at different temperatures and treatment periods. Genes were ranked according to their expression stability by BestKeeper, geNorm and NormFinder software tools in the same order. This means that any of them can be used for this purpose. The results indicated that ef1α and APRT were the most stably expressed genes in the potato tuber tissues under different cold storage regimes. We therefore recommend use of this pair of genes as internal controls for gene expression studies under the described conditions.     

Exploiting aberrant mRNA expression in autism for gene discovery and diagnosis

Autism spectrum disorder (ASD) is characterized by substantial phenotypic and genetic heterogeneity, which greatly complicates the identification of genetic factors that contribute to the disease. Study designs have mainly focused on group differences between cases and controls. The problem is that, by their nature, group difference-based methods (e.g., differential expression analysis) blur or collapse the heterogeneity within groups. By ignoring genes with variable within-group expression, an important axis of genetic heterogeneity contributing to expression variability among affected individuals has been overlooked. To this end, we develop a new gene expression analysis method—aberrant gene expression analysis, based on the multivariate distance commonly used for outlier detection. Our method detects the discrepancies in gene expression dispersion between groups and identifies genes with significantly different expression variability. Using this new method, we re-visited RNA sequencing data generated from post-mortem brain tissues of 47 ASD and 57 control samples. We identified 54 functional gene sets whose expression dispersion in ASD samples is more pronounced than that in controls, as well as 76 co-expression modules present in controls but absent in ASD samples due to ASD-specific aberrant gene expression. We also exploited aberrantly expressed genes as biomarkers for ASD diagnosis. With a whole blood expression data set, we identified three aberrantly expressed gene sets whose expression levels serve as discriminating variables achieving >70 % classification accuracy. In summary, our method represents a novel discovery and diagnostic strategy for ASD. Our findings may help open an expression variability-centered research avenue for other genetically heterogeneous disorders.     

Cross-Platform Prediction of Gene Expression Signatures

Gene expression signatures can predict the activation of oncogenic pathways and other phenotypes of interest via quantitative models that combine the expression levels of multiple genes. However, as the number of platforms to measure genome-wide gene expression proliferates, there is an increasing need to develop models that can be ported across diverse platforms. Because of the range of technologies that measure gene expression, the resulting signal values can vary greatly. To understand how this variation can affect the prediction of gene expression signatures, we have investigated the ability of gene expression signatures to predict pathway activation across Affymetrix and Illumina microarrays. We hybridized the same RNA samples to both platforms and compared the resultant gene expression readings, as well as the signature predictions. Using a new approach to map probes across platforms, we found that the genes in the signatures from the two platforms were highly similar, and that the predictions they generated were also strongly correlated. This demonstrates that our method can map probes from Affymetrix and Illumina microarrays, and that this mapping can be used to predict gene expression signatures across platforms.     

Universal external RNA controls for microbial gene expression analysis using microarray and qRT-PCR

Gene expression analysis provides significant insight to understand regulatory mechanisms of biology, yet acquisition and reproduction of quality data, as well as data confirmation and verification remain challenging due to a lack of proper quality controls across different assay platforms. We present a set of six universal external RNA quality controls for microbial mRNA expression analysis that can be applied to both DNA oligo microarray and real-time qRT-PCR including using SYBR Green and TaqMan probe-based chemistry. This set of controls was applied for Saccharomyces cerevisiae and Pseudomonas fluorescens Pf-5 microarray assays and qRT-PCR for yeast gene expression analysis. Highly fitted linear relationships between detected signal intensity and mRNA input were described. Valid mRNA detection range, from 10 to 7000 pg and from 100 fg to 1000 pg were defined for microarray and qRT-PCR assay, respectively. Quantitative estimation of mRNA abundance was tested using randomly selected yeast ORF including function unknown genes using the same source of samples by the two assay platforms. Estimates of mRNA abundance by the two methods were similar and highly correlated in an overlapping detection range from 10 to 1000 pg. The universal external RNA controls provide a means to compare microbial gene expression data derived from different experiments and different platforms for verification and confirmation. Such quality controls ensure reliability and reproducibility of gene expression data, and provide unbiased normalization reference for validation, quantification, and estimate of variation of gene expression experiments. Application of these controls also improves efficiency and facilitates high throughput applications of gene expression analysis using the qRT-PCR assay.     

The identification of candidate genes for a reverse genetic analysis of development and function in the Arabidopsis gynoecium

The screening for mutants and their subsequent molecular analysis has permitted the identification of a number of genes of Arabidopsis involved in the development and functions of the gynoecium. However, these processes remain far from completely understood. It is clear that in many cases, genetic redundancy and other factors can limit the efficiency of classical mutant screening. We have taken the alternative approach of a reverse genetic analysis of gene function in the Arabidopsis gynoecium. A high-throughput fluorescent differential display screen performed between two Arabidopsis floral homeotic mutants has permitted the identification of a number of genes that are specifically or preferentially expressed in the gynoecium. Here, we present the results of this screen and a detailed characterization of the expression profiles of the genes identified. Our expression analysis makes novel use of several Arabidopsis floral homeotic mutants to provide floral organ-specific gene expression profiles. The results of these studies permit the efficient targeting of effort into a functional analysis of gynoecium-expressed genes.     

Genetic analysis of early endocrine pancreas formation in zebrafish

Endocrine pancreas of zebrafish consist of at least four different cell types that function similarly to mammalian pancreatic islet. No mutants specifically affecting formation of the endocrine pancreas have been identified during the previous large-scale mutagenesis screens in zebrafish due to invisibility of a pancreatic islet. We combined in situ hybridization method to visualize pancreatic islet with an ethyl-nitroso-urea mutagenesis screen to identify novel genes involved in pancreatic islet formation in zebrafish. We screened 900 genomes and identified 11 mutations belonging to nine different complementation groups. These mutants fall into three major phenotypic classes displaying severely reduced insulin expression, reduced insulin expression with abnormal islet morphology, or abnormal islet morphology with relatively normal number of insulin expressing cells. Seven of these mutants do not have any other visible phenotypes associated. These mutations affect different processes in pancreatic islet development. Additional analysis on glucagon and somatostatin cell specification revealed that somatostatin cells are specified at a separate domain from insulin cells whereas glucagon cells are specified adjacent to insulin cells. Furthermore, glucagon cells and somatostatin cells are always associated with insulin cells in mutants that have scattered insulin expression. These data indicate that there are separate mechanisms regulating endocrine cell migration, proliferation, and differentiation. Further study on these mutants will reveal important information on novel genes involved in pancreatic islet cell specification and morphogenesis.     

Identification of Functionally Related Genes That Stimulate Early Meiotic Gene Expression in Yeast

Meiosis and spore formation in the yeast Saccharomyces cerevisiae are associated with increased expression of sporulation-specific genes. One of these genes, IME2, encodes a putative protein kinase that is a positive regulator of other sporulation-specific genes. We have isolated mutations that cause reduced expression of an ime2-lacZ fusion gene. We found mutations in IME1, a known positive regulator of IME2, and MCK1, a known positive regulator of IME1. We also isolated recessive mutations in 12 other genes, which we designate RIM (Regulator of IME2) genes. Our analysis indicates that the defects in rim1, rim8, rim9 and rim13 mutants are a consequence of diminished IME1 expression and can be suppressed by expression of IME1 from the heterologous ACT1 promoter. These rim mutations also reduced expression of an ime1-HIS3 fusion, in which the HIS3 gene is expressed from the IME1 promoter, and caused reduced levels of IME1 RNA. Although the rim1, rim8, rim9 and rim13 mutant phenotypes are similar to those of mck1 mutants, we found that the defects in ime2-lacZ expression and sporulation of the mck1 rim double mutants were more severe than either single mutant. In contrast, the defects of the rim rim double mutants were similar to either single mutant. The rim1, rim8, rim9 and rim13 mutants also display slow growth at 17° and share a smooth colony morphology that is not evident in mck1 mutants or isogenic wild-type strains. We suggest that RIM1, RIM8, RIM9 and RIM13 encode functionally related products that act in parallel to MCK1 to stimulate IME1 expression.     

Molecular cloning of a cDNA for Chinese hamster ovary asparagine synthetase

In previous reports we have described the isolation and characterization of a number of Chinese hamster ovary (CHO) cell mutants resistant to the amino acid analogue albizziin (Alb). Multistep mutants were derived which showed a high degree of drug resistance and expressed increased levels of asparagine synthetase (AS) levels up to 300-fold over that of the parental cell line. Karyotypic analysis of these mutants revealed homogeneously staining regions (HSRs) usually indicative of gene amplification. In the present work, we provide further proof for gene amplification by showing that the mutants greatly overproduce functional AS mRNA, as evidenced by in vitro translation of purified mRNA and immunoprecipitation of AS.

By using these overproducing mutants as sources of mRNA coupled with velocity centrifugation, we have been able to greatly enrich for AS sequences in our mRNA preparations to the point where they represent 1–5% of the total message. This facilitated cloning and selection of the cDNA sequences complementary to the gene. Utilizing these cloned cDNAs, we have demonstrated a correlation between gene copy number and enzyme expression in the parent and Alb-resistant mutants, thus providing direct evidence that drug resistance is due to gene amplification.     

脉孢菌lca-1基因调控无性产孢及类胡萝卜素的合成

类胡萝卜素是很多生物细胞内重要的抗氧化剂,具有保护细胞免受紫外线伤害的功能。粗糙脉孢菌是少数几个类胡萝卜素合成基因比较清楚的真菌之一,为了深入了解该菌类胡萝卜素合成调控机制,通过对粗糙脉孢菌基因突变体库中6,087株突变体进行筛选,新发现6个基因敲除突变体营养生长正常,但类胡萝卜素的合成降低,其中表型较好的1个突变体,其无性产孢量与类胡萝卜素合成量均明显降低。鉴定发现该突变体所缺失的基因编码一种依赖ATP的染色体重建复合体的ATP酶链ISW1,将该基因命名为lca-1。进一步测定发现lca-1基因的突变导     

iPcc: a novel feature extraction method for accurate disease class discovery and prediction

Gene expression profiling has gradually become a routine procedure for disease diagnosis and classification. In the past decade, many computational methods have been proposed, resulting in great improvements on various levels, including feature selection and algorithms for classification and clustering. In this study, we present iPcc, a novel method from the feature extraction perspective to further propel gene expression profiling technologies from bench to bedside. We define ‘correlation feature space’ for samples based on the gene expression profiles by iterative employment of Pearson’s correlation coefficient. Numerical experiments on both simulated and real gene expression data sets demonstrate that iPcc can greatly highlight the latent patterns underlying noisy gene expression data and thus greatly improve the robustness and accuracy of the algorithms currently available for disease diagnosis and classification based on gene expression profiles.