Development of a microarray for two rice subspecies: characterization and validation of gene expression in rice tissues

Background

Rice is one of the major crop species in the world helping to sustain approximately half of the global population’s diet especially in Asia. However, due to the impact of extreme climate change and global warming, rice crop production and yields may be adversely affected resulting in a world food crisis. Researchers have been keen to understand the effects of drought, temperature and other environmental stress factors on rice plant growth and development. Gene expression microarray technology represents a key strategy for the identification of genes and their associated expression patterns in response to stress. Here, we report on the development of the rice OneArray® microarray platform which is suitable for two major rice subspecies, japonica and indica.

Results

The rice OneArray® 60-mer, oligonucleotide microarray consists of a total of 21,179 probes covering 20,806 genes of japonica and 13,683 genes of indica. Through a validation study, total RNA isolated from rice shoots and roots were used for comparison of gene expression profiles via microarray examination. The results were submitted to NCBI’s Gene Expression Omnibus (GEO). Data can be found under the GEO accession number GSE50844 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50844). A list of significantly differentially expressed genes was generated; 438 shoot-specific genes were identified among 3,138 up-regulated genes, and 463 root-specific genes were found among 3,845 down-regulated genes. GO enrichment analysis demonstrates these results are in agreement with the known physiological processes of the different organs/tissues. Furthermore, qRT-PCR validation was performed on 66 genes, and found to significantly correlate with the microarray results (R?=?0.95, p?<?0.001***).

Conclusion

The rice OneArray® 22 K microarray, the first rice microarray, covering both japonica and indica subspecies was designed and validated in a comprehensive study of gene expression in rice tissues. The rice OneArray® microarray platform revealed high specificity and sensitivity. Additional information for the rice OneArray® microarray can be found at http://www.phalanx.com.tw/index.php.     

Integrating Constitutive Gene Expression and Chemoactivity: Mining the NCI60 Anticancer Screen

Studies into the genetic origins of tumor cell chemoactivity pose significant challenges to bioinformatic mining efforts. Connections between measures of gene expression and chemoactivity have the potential to identify clinical biomarkers of compound response, cellular pathways important to efficacy and potential toxicities; all vital to anticancer drug development. An investigation has been conducted that jointly explores tumor-cell constitutive NCI60 gene expression profiles and small-molecule NCI60 growth inhibition chemoactivity profiles, viewed from novel applications of self-organizing maps (SOMs) and pathway-centric analyses of gene expressions, to identify subsets of over- and under-expressed pathway genes that discriminate chemo-sensitive and chemo-insensitive tumor cell types. Linear Discriminant Analysis (LDA) is used to quantify the accuracy of discriminating genes to predict tumor cell chemoactivity. LDA results find 15% higher prediction accuracies, using ∼30% fewer genes, for pathway-derived discriminating genes when compared to genes derived using conventional gene expression-chemoactivity correlations. The proposed pathway-centric data mining procedure was used to derive discriminating genes for ten well-known compounds. Discriminating genes were further evaluated using gene set enrichment analysis (GSEA) to reveal a cellular genetic landscape, comprised of small numbers of key over and under expressed on- and off-target pathway genes, as important for a compound’s tumor cell chemoactivity. Literature-based validations are provided as support for chemo-important pathways derived from this procedure. Qualitatively similar results are found when using gene expression measurements derived from different microarray platforms. The data used in this analysis is available at http://pubchem.ncbi.nlm.nih.gov/and http://www.ncbi.nlm.nih.gov/projects/geo ({"type":"entrez-geo","attrs":{"text":"GPL96","term_id":"96"}}GPL96, {"type":"entrez-geo","attrs":{"text":"GSE32474","term_id":"32474"}}GSE32474).     

The effect of interaction between EtOH dosage and exposure time on gene expression in DPSC

Alcohol (EtOH) dosage and exposure time can affect gene expression. However, whether there exists synergistic effect is unknown. Here, we analyzed the hDPSC gene microarray dataset GSE57255 downloaded from Gene Expression Omnibus and found that the interaction between EtOH dosage and exposure time on gene expression are statistically significant for two probes: 201917_s_at near gene SLC25A36 and 217649_at near gene ZFAND5. GeneMania showed that SLC25A36 and ZFAND5 were related to 20 genes, three of which had alcohol-related functions. WebGestalt revealed that the 22 genes were enriched in 10 KEGG pathways, four of which are related to alcoholic diseases. We explored the possible nonlinear interaction effect and got 172 gene probes with significant p-values. However, no significantly enriched pathways based on the 172 probes were detected. Our analyses indicated a possible molecular mechanism that could help explain why alcohol consumption has both deleterious and beneficial effects on human health.     

用于基因数据挖掘的基因表达数据库GEO

使用高通量方法学来检测基因表达情况在最近几年已非常普遍。微集芯片技术可同时定量成千上万的基因转录本。基因表达综合数据库（Gene Expression Omnibus 简称GEO）是目前最大的而且完全公开的高通量分子丰度数据库,主要储存基因表达数据。该数据库以一个灵活开放的设计理念,允许用户或科研人员来递呈,保存和检索多种不同类型的数据。本文综合描述一下近年来该数据库在基因表达数据挖掘中的应用,同时介绍一些通过使用用户友好网络界面能有效探索、查询和再现数百个实验和数百万个基因表达谱的工具,以方便数据进行挖掘和可视化。登录GEO公用数据库的网址为：http://www.ncbi.nlm.nih.gov/geo.     

dbSNP: a database of single nucleotide polymorphisms

In response to a need for a general catalog of genome variation to address the large-scale sampling designs required by association studies, gene mapping and evolutionary biology, the National Cancer for Biotechnology Information (NCBI) has established the dbSNP database. Submissions to dbSNP will be integrated with other sources of information at NCBI such as GenBank, PubMed, LocusLink and the Human Genome Project data. The complete contents of dbSNP are available to the public at website: http://www.ncbi.nlm.nih.gov/SNP. Submitted SNPs can also be downloaded via anonymous FTP at ftp://ncbi.nlm.nih.gov/snp/     

dbSNP: the NCBI database of genetic variation

In response to a need for a general catalog of genome variation to address the large-scale sampling designs required by association studies, gene mapping and evolutionary biology, the National Center for Biotechnology Information (NCBI) has established the dbSNP database [S.T.Sherry, M.Ward and K. Sirotkin (1999) Genome Res., 9, 677-679]. Submissions to dbSNP will be integrated with other sources of information at NCBI such as GenBank, PubMed, LocusLink and the Human Genome Project data. The complete contents of dbSNP are available to the public at website: http://www.ncbi.nlm.nih.gov/SNP. The complete contents of dbSNP can also be downloaded in multiple formats via anonymous FTP at ftp://ncbi.nlm.nih.gov/snp/.     

CDD: a curated Entrez database of conserved domain alignments

The Conserved Domain Database (CDD) is now indexed as a separate database within the Entrez system and linked to other Entrez databases such as MEDLINE(R). This allows users to search for domain types by name, for example, or to view the domain architecture of any protein in Entrez's sequence database. CDD can be accessed on the WorldWideWeb at http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=cdd. Users may also employ the CD-Search service to identify conserved domains in new sequences, at http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi. CD-Search results, and pre-computed links from Entrez's protein database, are calculated using the RPS-BLAST algorithm and Position Specific Score Matrices (PSSMs) derived from CDD alignments. CD-Searches are also run by default for protein-protein queries submitted to BLAST(R) at http://www.ncbi.nlm.nih.gov/BLAST. CDD mirrors the publicly available domain alignment collections SMART and PFAM, and now also contains alignment models curated at NCBI. Structure information is used to identify the core substructure likely to be present in all family members, and to produce sequence alignments consistent with structure conservation. This alignment model allows NCBI curators to annotate 'columns' corresponding to functional sites conserved among family members.     

Bioinformatics Approach to Evaluate Differential Gene Expression of M1/M2 Macrophage Phenotypes and Antioxidant Genes in Atherosclerosis

Atherosclerosis is a pro-inflammatory process intrinsically related to systemic redox impairments. Macrophages play a major role on disease development. The specific involvement of classically activated, M1 (pro-inflammatory), or the alternatively activated, M2 (anti-inflammatory), on plaque formation and disease progression are still not established. Thus, based on meta-data analysis of public micro-array datasets, we compared differential gene expression levels of the human antioxidant genes (HAG) and M1/M2 genes between early and advanced human atherosclerotic plaques, and among peripheric macrophages (with or without foam cells induction by oxidized low density lipoprotein, oxLDL) from healthy and atherosclerotic subjects. Two independent datasets, GSE28829 and GSE9874, were selected from gene expression omnibus (http://www.ncbi.nlm.nih.gov/geo/) repository. Functional interactions were obtained with STRING (http://string-db.org/) and Medusa (http://coot.embl.de/medusa/). Statistical analysis was performed with ViaComplex^® (http://lief.if.ufrgs.br/pub/biosoftwares/viacomplex/) and gene score enrichment analysis (http://www.broadinstitute.org/gsea/index.jsp). Bootstrap analysis demonstrated that the activity (expression) of HAG and M1 gene sets were significantly increased in advance compared to early atherosclerotic plaque. Increased expressions of HAG, M1, and M2 gene sets were found in peripheric macrophages from atherosclerotic subjects compared to peripheric macrophages from healthy subjects, while only M1 gene set was increased in foam cells from atherosclerotic subjects compared to foam cells from healthy subjects. However, M1 gene set was decreased in foam cells from healthy subjects compared to peripheric macrophages from healthy subjects, while no differences were found in foam cells from atherosclerotic subjects compared to peripheric macrophages from atherosclerotic subjects. Our data suggest that, different to cancer, in atherosclerosis there is no M1 or M2 polarization of macrophages. Actually, M1 and M2 phenotype are equally induced, what is an important aspect to better understand the disease progression, and can help to develop new therapeutic approaches.     

Peptidylprolyl isomerase A (PPIA) as a preferred internal control over GAPDH and beta-actin in quantitative RNA analyses

A good internal control is critical in all quantitative analyses of gene expression. Levels of bet-actin, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and peptidylprolyl isomerase A (PPIA) were analyzed in 78 samples (data obtained from our laboratory and from a publicly available database at http://www.ncbi.nlm.nih.gov/SAGE/). These libraries included cell lines and tissues from brain, breast, colon, kidney, ovary, pancreas, prostate, skin, and vascular origin. The level of PPIA mRNA is the most constant among the three genes. Hence, our study suggests that PPIA is a better internal control than beta-actin or GAPDH, the two most commonly used internal controls.     

XREFdb: cross-referencing the genetics and genes of mammals and model organisms

XREFdb supports the investigation of protein function in the context of information available through work in multiple organisms. In addition to facilitating the association of functional data among known genes from multiple organisms, XREFdb has developed strategies that provide access to information associated with as-yet unstudied genes. The database organizes protein similarity and genetic map positional information from diverse sources in the public domain to facilitate investigator evaluation of potential functional significance. XREFdb is found at URL www.ncbi.nlm.nih.gov/XREFdb     

珍珠鸟(Taeniopygia guttata)小染色体基因表达的密码子偏性

从NCBI数据库（http：／／www．ncbi．nlm．nih．gov／projects／mapview／map）下载珍珠鸟全部小染色体基因的cDNA序列,最终共有1586个基因的CDS序列纳入统计分析。密码子的偏性分析使用CodonW（1．4．2）完成,初步确定了UUC、UCC、UCG等27个密码子为珍珠鸟小染色体基因表达的“最优”密码子。对应分析表明,影响珍珠鸟小染色体基因密码子使用的主要因素分别为GC3s、CDS的GC含量基以及因的表达丰度。珍珠鸟小染色体基因的密码子用法受到了基因碱基组成的显著影响,其密码子的偏性是碱基组成及选择等因素综合作用的结果。本研究的目的是系统探究珍珠乌小染色体基因的密码子用法,探究鸟类基因表达的分子调控机制。