Application of microarray technology in primate behavioral neuroscience research

Gene expression profiling of brain tissue samples applied to DNA microarrays promises to provide novel insights into the neurobiological bases of primate behavior. The strength of the microarray technology lies in the ability to simultaneously measure the expression levels of all genes in defined brain regions that are known to mediate behavior. The application of microarrays presents, however, various limitations and challenges for primate neuroscience research. Low RNA abundance, modest changes in gene expression, heterogeneous distribution of mRNA among cell subpopulations, and individual differences in behavior all mandate great care in the collection, processing, and analysis of brain tissue. A unique problem for nonhuman primate research is the limited availability of species-specific arrays. Arrays designed for humans are often used, but expression level differences are inevitably confounded by gene sequence differences in all cross-species array applications. Tools to deal with this problem are currently being developed. Here we review these methodological issues, and provide examples from our experiences using human arrays to examine brain tissue samples from squirrel monkeys. Until species-specific microarrays become more widely available, great caution must be taken in the assessment and interpretation of microarray data from nonhuman primates. Nevertheless, the application of human microarrays in nonhuman primate neuroscience research recovers useful information from thousands of genes, and represents an important new strategy for understanding the molecular complexity of behavior and mental health.     

Lineage-specific loss of function of bitter taste receptor genes in humans and nonhuman primates

Since the process of becoming dead genes or pseudogenes (pseudogenization) is irreversible and can occur rather rapidly under certain environmental circumstances, it is one plausible determinant for characterizing species specificity. To test this evolutionary hypothesis, we analyzed the tempo and mode of duplication and pseudogenization of bitter taste receptor (T2R) genes in humans as well as in 12 nonhuman primates. The results show that primates have accumulated more pseudogenes than mice after their separation from the common ancestor and that lineage-specific pseudogenization becomes more conspicuous in humans than in nonhuman primates. Although positive selection has operated on some amino acids in extracellular domains, functional constraints against T2R genes are more relaxed in primates than in mice and this trend has culminated in the rapid deterioration of the bitter-tasting capability in humans. Since T2R molecules play an important role in avoiding generally bitter toxic and harmful substances, substantial modification of the T2R gene repertoire is likely to reflect different responses to changes in the environment and to result from species-specific food preference during primate evolution.     

Ecological consequences of scaling of chew cycle duration and daily feeding time in Primates

Feeding systems and behaviors must evolve to satisfy the metabolic needs of organisms. This includes modifications to feeding systems as body size and metabolic needs change. Using our own data and data from the literature, we examine how size-related changes in metabolic needs are met by size-related changes in daily feeding time, chew cycle duration, volume of food processed per chew, and daily food volume intake in primates. Increases in chew cycle duration with body mass in haplorhine primates are described by a simple power function (cycle time α body mass^0.181). Daily feeding time increases with body mass when analyzed using raw data from the “tips” of the primate phylogenetic tree, but not when using phylogenetically independent contrasts. Whether or not daily feeding time remains constant or increases with body mass, isometry of ingested bite size and the slow rate of increase in chew cycle time with body size combine to allow daily ingested food volume to scale faster than predicted by metabolic rate. This positive allometry of daily ingested food volume may compensate for negative allometry of nutrient concentration in primate foods. Food material properties such as toughness and hardness have little impact on scaling of chew cycle durations, sequence durations, or numbers of chews in a sequence. Size-related changes in food processing abilities appear to accommodate size-related changes in food material properties, and primates may alter ingested bite sizes in order to minimize the impacts of food material properties on temporal variables such as chew cycle duration and chew sequence duration.     

A global survey of gene regulation during cold acclimation in Arabidopsis thaliana

Many temperate plant species such as Arabidopsis thaliana are able to increase their freezing tolerance when exposed to low, nonfreezing temperatures in a process called cold acclimation. This process is accompanied by complex changes in gene expression. Previous studies have investigated these changes but have mainly focused on individual or small groups of genes. We present a comprehensive statistical analysis of the genome-wide changes of gene expression in response to 14 d of cold acclimation in Arabidopsis, and provide a large-scale validation of these data by comparing datasets obtained for the Affymetrix ATH1 Genechip and MWG 50-mer oligonucleotide whole-genome microarrays. We combine these datasets with existing published and publicly available data investigating Arabidopsis gene expression in response to low temperature. All data are integrated into a database detailing the cold responsiveness of 22,043 genes as a function of time of exposure at low temperature. We concentrate our functional analysis on global changes marking relevant pathways or functional groups of genes. These analyses provide a statistical basis for many previously reported changes, identify so far unreported changes, and show which processes predominate during different times of cold acclimation. This approach offers the fullest characterization of global changes in gene expression in response to low temperature available to date.     

Statistical significance analysis of longitudinal gene expression data

MOTIVATION: Time-course microarray experiments are designed to study biological processes in a temporal fashion. Longitudinal gene expression data arise when biological samples taken from the same subject at different time points are used to measure the gene expression levels. It has been observed that the gene expression patterns of samples of a given tumor measured at different time points are likely to be much more similar to each other than are the expression patterns of tumor samples of the same type taken from different subjects. In statistics, this phenomenon is called the within-subject correlation of repeated measurements on the same subject, and the resulting data are called longitudinal data. It is well known in other applications that valid statistical analyses have to appropriately take account of the possible within-subject correlation in longitudinal data. RESULTS: We apply estimating equation techniques to construct a robust statistic, which is a variant of the robust Wald statistic and accounts for the potential within-subject correlation of longitudinal gene expression data, to detect genes with temporal changes in expression. We associate significance levels to the proposed statistic by either incorporating the idea of the significance analysis of microarrays method or using the mixture model method to identify significant genes. The utility of the statistic is demonstrated by applying it to an important study of osteoblast lineage-specific differentiation. Using simulated data, we also show pitfalls in drawing statistical inference when the within-subject correlation in longitudinal gene expression data is ignored.     

Application of the S-score algorithm for analysis of oligonucleotide microarrays

In the past several years, oligonucleotide microarrays have emerged as a widely used tool for the simultaneous, non-biased measurement of expression levels for thousands of genes. Several challenges exist in successfully utilizing this biotechnology; principal among these is analysis of microarray data. An experiment to measure differential gene expression can consist of a dozen microarrays, each consisting of over a hundred thousand data points. Previously, we have described the use of a novel algorithm for analyzing oligonucleotide microarrays and assessing changes in gene expression [J. Mol. Biol. 317 (2002) 225]. This algorithm describes changes in expression in terms of the statistical significance (S-score) of change, which combines signals detected by multiple probe pairs according to an error model characteristic of oligonucleotide arrays. Software is available that simplifies the use of the application of this algorithm so that it may be applied to improving the analysis of oligonucleotide microarray data. The application of this method to problems of the central nervous system is discussed.     

NOR1基因转染对肝癌细胞HepG2基因表达谱的影响

NOR1基因是一在正常组织中广泛表达且在肿瘤组织中表达下调的新基因.为进一步研究NOR1基因的功能和寻找其下游基因,利用脂质体技术将NOR1基因转染进HepG2细胞,采用cDNA微阵列技术分析其基因表达谱的改变.试验表明NOR1基因的转染能使Grb2,HBP17,TNFRSF11B等59个基因上调,同时也下调Bik,MAp2K6,ZFP95等103个基因.随后用实时荧光定量PCR对cDNA 微阵列结果中上述3个上调表达基因进行验证,结果表明,基因表达差异具有统计学意义(P<0.05),荧光定量PCR结果与微阵列结果相符.这些结果提示,NOR1基因对肝癌HepG2细胞的生物学行为的影响可能与它对细胞信号转导,细胞周期调控,转录、翻译调控相关基因的表达影响有关.     

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.     

Evolutionary conservation of expression profiles between human and mouse orthologous genes

Mouse models are often used to study human genes because it is believed that the expression and function are similar for the majority of orthologous genes between the two species. However, recent comparisons of microarray data from thousands of orthologous human and mouse genes suggested rapid evolution of gene expression profiles under minimal or no selective constraint. These findings appear to contradict non-array-based observations from many individual genes and imply the uselessness of mouse models for studying human genes. Because absolute levels of gene expression are not comparable between species when the data are generated by species-specific microarrays, use of relative mRNA abundance among tissues (RA) is preferred to that of absolute expression signals. We thus reanalyze human and mouse genome-wide gene expression data generated by oligonucleotide microarrays. We show that the mean correlation coefficient among expression profiles detected by different probe sets of the same gene is only 0.38 for humans and 0.28 for mice, indicating that current measures of expression divergence are flawed because the large estimation error (discrepancy in expression signal detected by different probe sets of the same gene) is mistakenly included in the between-species divergence. When this error is subtracted, 84% of human-mouse orthologous gene pairs show significantly lower expression divergence than that of random gene pairs. In contrast to a previous finding, but consistent with the common sense, expression profiles of orthologous tissues between species are more similar to each other than to those of nonorthologous tissues. Furthermore, the evolutionary rate of expression divergence and that of coding sequence divergence are found to be weakly, but significantly positively correlated, when RA and the Euclidean distance are used to measure expression-profile divergence. These results highlight the importance of proper consideration of various estimation errors in comparing the microarray data between species.     

功能基因组学的研究方法

基因组学的研究已从结构基因组学转向功能基因组学,功能基因组学时代对于基因功能的研究也由单一基因转向大规模,批量分析,本综述了功能基因组学的研究内容与方法,主要包括：差异显示反转录PCR,基因表达序列分析（SAGE）,微点阵,遗传足迹法,反求遗传学,蛋白质组学和生物信息学等新方法。     

Graph-based analysis and visualization of experimental results with ONDEX

MOTIVATION: Assembling the relevant information needed to interpret the output from high-throughput, genome scale, experiments such as gene expression microarrays is challenging. Analysis reveals genes that show statistically significant changes in expression levels, but more information is needed to determine their biological relevance. The challenge is to bring these genes together with biological information distributed across hundreds of databases or buried in the scientific literature (millions of articles). Software tools are needed to automate this task which at present is labor-intensive and requires considerable informatics and biological expertise. RESULTS: This article describes ONDEX and how it can be applied to the task of interpreting gene expression results. ONDEX is a database system that combines the features of semantic database integration and text mining with methods for graph-based analysis. An overview of the ONDEX system is presented, concentrating on recently developed features for graph-based analysis and visualization. A case study is used to show how ONDEX can help to identify causal relationships between stress response genes and metabolic pathways from gene expression data. ONDEX also discovered functional annotations for most of the genes that emerged as significant in the microarray experiment, but were previously of unknown function.