Correlated clustering and virtual display of gene expression patterns in the wheat life cycle by large-scale statistical analyses of expressed sequence tags

Compared to rice, wheat exhibits characteristic growth habits and contains complex genome constituents. To assess global changes in gene expression patterns in the wheat life cycle, we conducted large-scale analysis of expressed sequence tags (ESTs) in common wheat. Ten wheat tissues were used to construct cDNA libraries: crown and root from 14-day-old seedlings; spikelet from early and late flowering stages; spike at the booting stage, heading date and flowering date; pistil at the heading date; and seeds at 10 and 30 days post-anthesis. Several thousand colonies were randomly selected from each of these 10 cDNA libraries and sequenced from both 5' and 3' ends. Consequently, a total of 116 232 sequences were accumulated and classified into 25 971 contigs based on sequence homology. By computing abundantly expressed ESTs, correlated expression patterns of genes across the tissues were identified. Furthermore, relationships of gene expression profiles among the 10 wheat tissues were inferred from global gene expression patterns. Genes with similar functions were grouped with one another by clustering gene expression profiles. This technique might enable estimation of the functions of anonymous genes. Multidimensional analysis of EST data that is analogous to the microarray experiments may offer new approaches to functional genomics of plants.     

EST分析技术在鸡基因组学研究中的应用

表达序列标签（EST）是由大量随机取出的cDNA库克隆经测序得到的组织或细胞基因组的一段cDNA序列,一个EST代表生物体某种组织某一时期的一个表达基因。综述了EST分析技术在鸡基因组研究中的应用。如用于鉴定、发现和预测鸡的新基因,用于基因图谱的绘制,用于筛选基因的单核苷酸多态性（SNP）位点,用于基因表达分析和基因芯片制作等。EST数据库和生物信息学的联合分析技术在推动家鸡后基因组的研究中发挥着重要的作用。     

Microarray analysis of gene expression during early adipocyte differentiation

The molecular mechanisms that regulate cellular differentiation during development and throughout life are complex. It is now recognized that precise patterns of differentially expressed genes ultimately direct a particular cell toward a given lineage and many of these are regulated during the earliest stages of differentiation. Using a microarray-based expression analysis, we have examined gene expression profiles during the first 24 h of 3T3-L1 adipocyte differentiation. RNA was isolated at times 0, 2, 8, 16, and 24 h following stimulation of differentiation and hybridized in duplicate to high density Affymetrix microarray gene chips containing a series of 13,179 cDNA/expressed sequence tag (EST) probe sets. Two hundred and eighty-five cDNA/ESTs were shown to have at least a fivefold change in expression levels during this time course and both hierarchical and self-organizing map clustering analysis was performed to categorize them by expression profiles. Several genes known to be regulated during this time period were confirmed and Western blot analysis of the proteins encoded by some of the identified genes revealed expression profiles similar to their mRNA counterparts. As expected, many of the genes identified have not been examined in such a critical time period during adipogenesis and may well represent novel adipogenic mediators.     

Large-scale gene expression profiles, differentially represented in osteoarthritic synovium of the knee joint using cDNA microarray technology

Osteoarthritis (OA) is one of the most common age-related chronic disorders of articular cartilage, joints and bone tissue. Diagnosis of OA commonly depends on clinical and radiographic findings. However, changes in cartilage associated with the early stage of OA cannot be detected using radiographs, because significant cartilage degeneration must occur before radiographic findings show alterations of the appearance of cartilage. To identify new biomarkers of OA, we analysed gene expression profiles of synovium from 43 patients with OA, ten patients with rheumatoid arthritis (RA), and eight non-OA/non-RA patients using a novel cDNA microarray chip. We identified 21 genes with simultaneous significant differences in expression between OA and non-OA/non-RA groups and between OA and RA groups. Linear discriminant analysis showed that the three groups could be well separated using those 21 genes. Statistical analysis also revealed that several of the 21 genes were associated with disease progression and clinical presentation. The graphical modelling method indicated that some of the 21 genes are significantly associated with a particular clinical presentation, suggesting biological relationships among those genes. This is the first report of the use of cDNA microarray technology to create large-scale gene expression profiles differentially expressed in situ in OA synovium of the knee joint.     

Analysis of gene expression profile during 3T3-L1 preadipocyte differentiation

Cellular differentiation is a process in which a group of differentiation specific genes is programmatically induced. This gene expression program leads to changes in both cellular morphological and physiological phenotypes. Using an 18,376-member cDNA/EST microarray, we analyzed the difference in gene expression profiles between differentiated 3T3-L1 adipocyte and non-differentiated 3T3-L1 preadipocyte. From our study a large number of genes and ESTs were identified as differentially induced or suppressed. In this paper we describe the changes of gene expression profile during 3T3-L1 cell differentiation.     

Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat

In order to assess global changes in gene expression patterns in stress-induced tissues, we conducted large-scale analysis of expressed sequence tags (ESTs) in common wheat. Twenty-one cDNA libraries derived from stress-induced tissues, such as callus, as well as liquid cultures and abiotic stress conditions (temperature treatment, desiccation, photoperiod, moisture and ABA) were constructed. Several thousand colonies were randomly selected from each of these 21 cDNA libraries and sequenced from both the 5′ and 3′ ends. By computing abundantly expressed ESTs, correlated expression patterns of genes across the tissues were monitored. Furthermore, the relationships between gene expression profiles among the stress-induced tissues were inferred from the gene expression patterns. Multi-dimensional analysis of EST data is analogous to microarray experiments. As an example, genes specifically induced and/or suppressed by cold acclimation and heat-shock treatments were selected in silico. Four hundred and ninety genes showing fivefold induction or 218 genes for suppression in comparison to the control expression level were selected. These selected genes were annotated with the BLAST search. Furthermore, gene ontology was conducted for these genes with the InterPro search. Because genes regulated in response to temperature treatment were successfully selected, this method can be applied to other stress-treated tissues. Then, the method was applied to screen genes in response to abiotic stresses such as drought and ABA treatments. In silico selection of screened genes from virtual display should provide a powerful tool for functional plant genomics.Electronic Supplementary Material Supplementary material is available to authorised users in the online version of this article at .     

Large-scale gene expression profiles,differentially represented in osteoarthritic synovium of the knee joint using cDNA microarray technology

Osteoarthritis (OA) is one of the most common age-related chronic disorders of articular cartilage, joints and bone tissue. Diagnosis of OA commonly depends on clinical and radiographic findings. However, changes in cartilage associated with the early stage of OA cannot be detected using radiographs, because significant cartilage degeneration must occur before radiographic findings show alterations of the appearance of cartilage. To identify new biomarkers of OA, we analysed gene expression profiles of synovium from 43 patients with OA, ten patients with rheumatoid arthritis (RA), and eight non-OA/non-RA patients using a novel cDNA microarray chip. We identified 21 genes with simultaneous significant differences in expression between OA and non-OA/non-RA groups and between OA and RA groups. Linear discriminant analysis showed that the three groups could be well separated using those 21 genes. Statistical analysis also revealed that several of the 21 genes were associated with disease progression and clinical presentation. The graphical modelling method indicated that some of the 21 genes are significantly associated with a particular clinical presentation, suggesting biological relationships among those genes. This is the first report of the use of cDNA microarray technology to create large-scale gene expression profiles differentially expressed in situ in OA synovium of the knee joint.     

maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments

MOTIVATION: Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis. RESULTS: In this work, we propose a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments. The method is a two-regression step approach where the experimental groups are identified by dummy variables. The procedure first adjusts a global regression model with all the defined variables to identify differentially expressed genes, and in second a variable selection strategy is applied to study differences between groups and to find statistically significant different profiles. The methodology is illustrated on both a real and a simulated microarray dataset.