Identification and functional analysis of light-responsive unique genes and gene family members in rice

Functional redundancy limits detailed analysis of genes in many organisms. Here, we report a method to efficiently overcome this obstacle by combining gene expression data with analysis of gene-indexed mutants. Using a rice NSF45K oligo-microarray to compare 2-week-old light- and dark-grown rice leaf tissue, we identified 365 genes that showed significant 8-fold or greater induction in the light relative to dark conditions. We then screened collections of rice T-DNA insertional mutants to identify rice lines with mutations in the strongly light-induced genes. From this analysis, we identified 74 different lines comprising two independent mutant lines for each of 37 light-induced genes. This list was further refined by mining gene expression data to exclude genes that had potential functional redundancy due to co-expressed family members (12 genes) and genes that had inconsistent light responses across other publicly available microarray datasets (five genes). We next characterized the phenotypes of rice lines carrying mutations in ten of the remaining candidate genes and then carried out co-expression analysis associated with these genes. This analysis effectively provided candidate functions for two genes of previously unknown function and for one gene not directly linked to the tested biochemical pathways. These data demonstrate the efficiency of combining gene family-based expression profiles with analyses of insertional mutants to identify novel genes and their functions, even among members of multi-gene families.     

Web Tools for Rice Transcriptome Analyses

Gene expression databases provide profiling data for the expression of thousands of genes to researchers worldwide. Oligonucleotide microarray technology is a useful tool that has been employed to produce gene expression profiles in most species. In rice, there are five genome-wide DNA microarray platforms: NSF 45K, BGI/Yale 60K, Affymetrix, Agilent Rice 44K, and NimbleGen 390K. Presently, more than 1,700 hybridizations of microarray gene expression data are available from public microarray depositing databases such as NCBI gene expression omnibus and Arrayexpress at EBI. More processing or reformatting of public gene expression data is required for further applications or analyses. Web-based databases for expression meta-analyses are useful for guiding researchers in designing relevant research schemes. In this review, we summarize various databases for expression meta-analyses of rice genes and web tools for further applications, such as the development of co-expression network or functional gene network.     

A new resource for cereal genomics: 22K barley GeneChip comes of age

In recent years, access to complete genomic sequences, coupled with rapidly accumulating data related to RNA and protein expression patterns, has made it possible to determine comprehensively how genes contribute to complex phenotypes. However, for major crop plants, publicly available, standard platforms for parallel expression analysis have been limited. We report the conception and design of the new publicly available, 22K Barley1 GeneChip probe array, a model for plants without a fully sequenced genome. Array content was derived from worldwide contribution of 350,000 high-quality ESTs from 84 cDNA libraries, in addition to 1,145 barley (Hordeum vulgare) gene sequences from the National Center for Biotechnology Information nonredundant database. Conserved sequences expressed in seedlings of wheat (Triticum aestivum), oat (Avena strigosa), rice (Oryza sativa), sorghum (Sorghum bicolor), and maize (Zea mays) were identified that will be valuable in the design of arrays across grasses. To enhance the usability of the data, BarleyBase, a MIAME-compliant, MySQL relational database, serves as a public repository for raw and normalized expression data from the Barley1 GeneChip probe array. Interconnecting links with PlantGDB and Gramene allow BarleyBase users to perform gene predictions using the 21,439 non-redundant Barley1 exemplar sequences or cross-species comparison at the genome level, respectively. We expect that this first generation array will accelerate hypothesis generation and gene discovery in disease defense pathways, responses to abiotic stresses, development, and evolutionary diversity in monocot plants.     

最短路径法在水稻ABA 和环境胁迫条件下基因应答网络研究中的应用

目前微阵列数据分析方法都基于具有相似表达模式的基因可能具有相近的生物学功能这一假设, 而实际上参与同一生物学功能的基因, 在表达时间和空间上是有关联的, 而并非表现为相似模式。利用水稻cDNA微阵列, 对水稻在ABA及干旱、寒冷和高盐胁迫条件下的基因表达进行了研究。选取环境胁迫和ABA应答的相关基因, 采用最短路径法(shortest path), 利用自行编制的计算软件, 在表达模式不直接相关的基因之间构建最短路径。研究表明, 通过分析这些基因的表达数据, 可以发现它们在功能上的关联性, 并对未知基因的功能预测进行了探索, 为构建水稻在ABA和环境胁迫条件下的分子应答网络奠定了基础。     

Microarray expression profiling resources for plant genomics

Large volumes of genomic data have been generated for several plant species over the past decade, including structural sequence data and functional annotation at the genome level. Various technologies such as expressed sequence tags (ESTs), massively parallel signature sequencing (MPSS) and microarrays have been used to study gene expression and to provide functional data for many genes simultaneously. This review focuses on recent advances in the application of microarrays in plant genomic research and in gene expression databases available for plants. Large sets of Arabidopsis microarray data are publicly available. Recently developed array platforms are currently being used to generate genome-wide expression profiles for several crop species. Coupled to these platforms are public databases that provide access to these large-scale expression data, which can be used to aid the functional discovery of gene function.     

Post-GWAS functional characterization of susceptibility variants for chronic lymphocytic leukemia

Recent genome-wide association studies (GWAS) have identified several gene variants associated with sporadic chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL). Many of these CLL/SLL susceptibility loci are located in non-coding or intergenic regions, posing a significant challenge to determine their potential functional relevance. Here, we review the literature of all CLL/SLL GWAS and validation studies, and apply eQTL analysis to identify putatively functional SNPs that affect gene expression that may be causal in the pathogenesis of CLL/SLL. We tested 12 independent risk loci for their potential to alter gene expression through cis-acting mechanisms, using publicly available gene expression profiles with matching genotype information. Sixteen SNPs were identified that are linked to differential expression of SP140, a putative tumor suppressor gene previously associated with CLL/SLL. Three additional SNPs were associated with differential expression of DACT3 and GNG8, which are involved in the WNT/β-catenin- and G protein-coupled receptor signaling pathways, respectively, that have been previously implicated in CLL/SLL pathogenesis. Using in silico functional prediction tools, we found that 14 of the 19 significant eQTL SNPs lie in multiple putative regulatory elements, several of which have prior implications in CLL/SLL or other hematological malignancies. Although experimental validation is needed, our study shows that the use of existing GWAS data in combination with eQTL analysis and in silico methods represents a useful starting point to screen for putatively causal SNPs that may be involved in the etiology of CLL/SLL.     

A densely interconnected genome-wide network of microRNAs and oncogenic pathways revealed using gene expression signatures

MicroRNAs (miRNAs) are important components of cellular signaling pathways, acting either as pathway regulators or pathway targets. Currently, only a limited number of miRNAs have been functionally linked to specific signaling pathways. Here, we explored if gene expression signatures could be used to represent miRNA activities and integrated with genomic signatures of oncogenic pathway activity to identify connections between miRNAs and oncogenic pathways on a high-throughput, genome-wide scale. Mapping >300 gene expression signatures to >700 primary tumor profiles, we constructed a genome-wide miRNA-pathway network predicting the associations of 276 human miRNAs to 26 oncogenic pathways. The miRNA-pathway network confirmed a host of previously reported miRNA/pathway associations and uncovered several novel associations that were subsequently experimentally validated. Globally, the miRNA-pathway network demonstrates a small-world, but not scale-free, organization characterized by multiple distinct, tightly knit modules each exhibiting a high density of connections. However, unlike genetic or metabolic networks typified by only a few highly connected nodes ("hubs"), most nodes in the miRNA-pathway network are highly connected. Sequence-based computational analysis confirmed that highly-interconnected miRNAs are likely to be regulated by common pathways to target similar sets of downstream genes, suggesting a pervasive and high level of functional redundancy among coexpressed miRNAs. We conclude that gene expression signatures can be used as surrogates of miRNA activity. Our strategy facilitates the task of discovering novel miRNA-pathway connections, since gene expression data for multiple normal and disease conditions are abundantly available.     

Toward the blood-borne miRNome of human diseases

In a multicenter study, we determined the expression profiles of 863 microRNAs by array analysis of 454 blood samples from human individuals with different cancers or noncancer diseases, and validated this 'miRNome' by quantitative real-time PCR. We detected consistently deregulated profiles for all tested diseases; pathway analysis confirmed disease association of the respective microRNAs. We observed significant correlations (P = 0.004) between the genomic location of disease-associated genetic variants and deregulated microRNAs.     

Multievidence microarray mining

Microarray mining is a challenging task because of the superposition of several processes in the data. We believe that the combination of microarray data-based analyses (statistical significance analysis of gene expression) with array-independent analyses (literature-mining and promoter analysis) enables some of the problems of traditional array analysis to be overcome. As a proof-of-principle, we revisited publicly available microarray data derived from an experiment with platelet-derived growth factor (PDGF)-stimulated fibroblasts. Our strategy revealed results beyond the detection of the major metabolic pathway known to be linked to the PDGF response: we were able to identify the crosstalking regulatory networks underlying the metabolic pathway without using a priori knowledge about the experiment.     

Gene identification and expression analysis of 86,136 Expressed Sequence Tags (EST) from the rice genome

Expressed Sequence Tag (EST) analysis has pioneered genome-wide gene discovery and expression profiling. In order to establish a gene expression index in the rice cultivar indica, we sequenced and analyzed 86,136 ESTs from nine rice cDNA libraries from the super hybrid cultivar LYP9 and its parental cultivars. We assembled these ESTs into 13,232 contigs and leave 8,976 singletons. Overall, 7,497 sequences were found similar to the existing sequences in GenBank and 14,711 are novel. These sequences are classified by molecular function, biological process and pathways according to the Gene Ontology. We compared our sequenced ESTs with the publicly available 95,000 ESTs from japonica, and found little sequence variation, despite the large difference between genome sequences. We then assembled the combined 173,000 rice ESTs for further analysis. Using the pooled ESTs, we compared gene expression in metabolism pathway between rice and Arabidopsis according to KEGG. We further profiled gene expression pattern     

Computational methods and evaluation of RNA stabilization reagents for genome-wide expression studies

Gene expression studies require high quality messenger RNA (mRNA) in addition to other factors such as efficient primers and labeling reagents. To prevent RNA degradation and to improve the quality of gene array expression data, several commercial reagents have become available. We examined a conventional hot-phenol lysis method and RNA stabilization reagents, and generated comparative gene expression profiles from Escherichia coli cells grown on minimal medium. Our data indicate that certain RNA stabilization reagents induce stress responses and proper caution must be exercised during their use. We observed that the laboratory reagent (phenol/EtOH, 5:95, v/v) worked efficiently in isolating high quality mRNA and reproducibility was such that reliable gene expression profiles were generated. To assist in the analysis of gene expression data, we wrote a number of macros that use the most recent gene annotation and process data in accordance with gene function. Scripts were also written to examine the occurrence of artifacts, based on GC content, length of the individual open reading frame (ORF), its distribution on plus and minus DNA strands, and the distance from the replication origin.