杂交粳稻花优14及其亲本孕穗期剑叶的基因表达谱芯片分析

本文以孕穗期的杂交粳稻花优14及其母本申9A和父本繁14的剑叶为材料,利用Affymetrix水稻基因组芯片检测了3个样品中的基因表达谱,并用生物信息学方法对差异表达基因进行了分析。结果表明：与其亲本相比,杂交粳稻花优14中共有2057个基因的表达水平出现了2倍(变化倍数≥2或≤0.5)以上的变化。通过对这些差异表达基因进行GO(Gene Ontology)功能分类,发现差异表达基因在光合系统Ⅰ、叶绿体膜和叶绿体被膜等与叶绿体相关的细胞组分中显著富集;同时差异表达基因还在叶绿素合成、叶绿素代谢和类胡萝卜素合成等生物学过程中富集。光合作用效率的改变可能和花优14杂种优势的形成相关。与已报道结果不同,本文在代谢途径分析结果中并没有发现花优14中差异表达基因在碳固定和光合作用等途径中显著的富集,但是发现差异表达基因在光合作用-天线蛋白以及淀粉和蔗糖的代谢途径中显著富集。该结果表明,在不同的杂交组合中,参与杂种优势形成的基因或者代谢途径可能是不同的。     

Gene expression analysis in LLC-PK1 renal tubular cells by atrial natriuretic peptide (ANP): correlation of homologous human genes with renal response

We used human DNA microarray to explore the differential gene expression profiling of atrial natriuretic peptide (ANP)-stimulated renal tubular epithelial kidney cells (LLC-PK1) in order to understand the biological effect of ANP on renal kidney cell's response. Gene expression profiling revealed 807 differentially expressed genes, consisting of 483 up-regulated and 324 down-regulated genes. The bioinformatics tool was used to gain a better understanding of differentially expressed genes in porcine genome homologous with human genome and to search the gene ontology and category classification, such as cellular component, molecular function and biological process. Four up-regulated genes of ATP1B1, H3F3A, ITGB1 and RHO that were typically validated by real-time quantitative PCR (RT-qPCR) analysis serve important roles in the alleviation of renal hypertrophy as well as other related effects. Therefore, the human array can be used for gene expression analysis in pig kidney cells and we believe that our findings of differentially expressed genes served as genetic markers and biological functions can lead to a better understanding of ANP action on the renal protective system and may be used for further therapeutic application.     

AffyTrees: facilitating comparative analysis of Affymetrix plant microarray chips

Microarrays measure the expression of large numbers of genes simultaneously and can be used to delve into interaction networks involving many genes at a time. However, it is often difficult to decide to what extent knowledge about the expression of genes gleaned in one model organism can be transferred to other species. This can be examined either by measuring the expression of genes of interest under comparable experimental conditions in other species, or by gathering the necessary data from comparable microarray experiments. However, it is essential to know which genes to compare between the organisms. To facilitate comparison of expression data across different species, we have implemented a Web-based software tool that provides information about sequence orthologs across a range of Affymetrix microarray chips. AffyTrees provides a quick and easy way of assigning which probe sets on different Affymetrix chips measure the expression of orthologous genes. Even in cases where gene or genome duplications have complicated the assignment, groups of comparable probe sets can be identified. The phylogenetic trees provide a resource that can be used to improve sequence annotation and detect biases in the sequence complement of Affymetrix chips. Being able to identify sequence orthologs and recognize biases in the sequence complement of chips is necessary for reliable cross-species microarray comparison. As the amount of work required to generate a single phylogeny in a nonautomated manner is considerable, AffyTrees can greatly reduce the workload for scientists interested in large-scale cross-species comparisons.     

Systematic order-dependent effect in expression values, variance, detection calls and differential expression in Affymetrix GeneChips

MOTIVATION: Affymetrix GeneChips are common 3' profiling platforms for quantifying gene expression. Using publicly available datasets of expression profiles from human and mouse experiments, we sought to characterize features of GeneChip data to better compare and evaluate analyses for differential expression, regulation and clustering. We uncovered an unexpected order dependence in expression data that holds across a variety of chips in both human and mouse data. RESULTS: Order dependence among GeneChips affected relative expression measures pre-processed and normalized with the Affymetrix MAS5.0 algorithm and the robust multi-array average summarization method. The effect strongly influenced detection calls and tests for differential expression and can potentially significantly bias experimental results based on GeneChip profiling.     

Novel transcriptome data analysis implicates circulating microRNAs in epigenetic inheritance in mammals

Experimental evidence supports a role of mobile small non-coding RNAs in mediating soma to germline hereditary information transfer in epigenetic inheritance in plants and worms. Similar evidence in mammals has not been reported so far. In this bioinformatic analysis, differentially expressed microRNAs (miRNAs) or mRNAs reported previously in genome level expression profiling studies related to or relevant in epigenetic inheritance in mammals were examined for circulating miRNA association. The reported sets of differentially expressed miRNAs or miRNAs that are known to target the reported sets of differentially expressed genes, in that order, showed enrichment of circulating miRNAs across environmental factors, tissues, life cycle stages, generations, genders and species. Circulating miRNAs commonly representing the expression profiles enriched various epigenetic processes. These results provide bioinformatic evidence for a role of circulating miRNAs in epigenetic inheritance in mammals.     

Probe-level measurement error improves accuracy in detecting differential gene expression

MOTIVATION: Finding differentially expressed genes is a fundamental objective of a microarray experiment. Numerous methods have been proposed to perform this task. Existing methods are based on point estimates of gene expression level obtained from each microarray experiment. This approach discards potentially useful information about measurement error that can be obtained from an appropriate probe-level analysis. Probabilistic probe-level models can be used to measure gene expression and also provide a level of uncertainty in this measurement. This probe-level measurement error provides useful information which can help in the identification of differentially expressed genes. RESULTS: We propose a Bayesian method to include probe-level measurement error into the detection of differentially expressed genes from replicated experiments. A variational approximation is used for efficient parameter estimation. We compare this approximation with MAP and MCMC parameter estimation in terms of computational efficiency and accuracy. The method is used to calculate the probability of positive log-ratio (PPLR) of expression levels between conditions. Using the measurements from a recently developed Affymetrix probe-level model, multi-mgMOS, we test PPLR on a spike-in dataset and a mouse time-course dataset. Results show that the inclusion of probe-level measurement error improves accuracy in detecting differential gene expression. AVAILABILITY: The MAP approximation and variational inference described in this paper have been implemented in an R package pplr. The MCMC method is implemented in Matlab. Both software are available from http://umber.sbs.man.ac.uk/resources/puma.     

A microarray analysis for differential gene expression in the soybean genome using Bioconductor and R

This article describes specific procedures for conducting quality assessment of Affymetrix GeneChip(R) soybean genome data and for performing analyses to determine differential gene expression using the open-source R programming environment in conjunction with the open-source Bioconductor software. We describe procedures for extracting those Affymetrix probe set IDs related specifically to the soybean genome on the Affymetrix soybean chip and demonstrate the use of exploratory plots including images of raw probe-level data, boxplots, density plots and M versus A plots. RNA degradation and recommended procedures from Affymetrix for quality control are discussed. An appropriate probe-level model provides an excellent quality assessment tool. To demonstrate this, we discuss and display chip pseudo-images of weights, residuals and signed residuals and additional probe-level modeling plots that may be used to identify aberrant chips. The Robust Multichip Averaging (RMA) procedure was used for background correction, normalization and summarization of the AffyBatch probe-level data to obtain expression level data and to discover differentially expressed genes. Examples of boxplots and MA plots are presented for the expression level data. Volcano plots and heatmaps are used to demonstrate the use of (log) fold changes in conjunction with ordinary and moderated t-statistics for determining interesting genes. We show, with real data, how implementation of functions in R and Bioconductor successfully identified differentially expressed genes that may play a role in soybean resistance to a fungal pathogen, Phakopsora pachyrhizi. Complete source code for performing all quality assessment and statistical procedures may be downloaded from our web source: http://css.ncifcrf.gov/services/download/MicroarraySoybean.zip.     

Corticosteroid-regulated genes in rat kidney: mining time series array data

Kidney is a major target for adverse effects associated with corticosteroids. A microarray dataset was generated to examine changes in gene expression in rat kidney in response to methylprednisolone. Four control and 48 drug-treated animals were killed at 16 times after drug administration. Kidney RNA was used to query 52 individual Affymetrix chips, generating data for 15,967 different probe sets for each chip. Mining techniques applicable to time series data that identify drug-regulated changes in gene expression were applied. Four sequential filters eliminated probe sets that were not expressed in the tissue, not regulated by drug, or did not meet defined quality control standards. These filters eliminated 14,890 probe sets (94%) from further consideration. Application of judiciously chosen filters is an effective tool for data mining of time series datasets. The remaining data can then be further analyzed by clustering and mathematical modeling. Initial analysis of this filtered dataset identified a group of genes whose pattern of regulation was highly correlated with prototype corticosteroid enhanced genes. Twenty genes in this group, as well as selected genes exhibiting either downregulation or no regulation, were analyzed for 5' GRE half-sites conserved across species. In general, the results support the hypothesis that the existence of conserved DNA binding sites can serve as an important adjunct to purely analytic approaches to clustering genes into groups with common mechanisms of regulation. This dataset, as well as similar datasets on liver and muscle, are available online in a format amenable to further analysis by others.     

Using high-density exon arrays to profile gene expression in closely related species

Global comparisons of gene expression profiles between species provide significant insight into gene regulation, evolutionary processes and disease mechanisms. In this work, we describe a flexible and intuitive approach for global expression profiling of closely related species, using high-density exon arrays designed for a single reference genome. The high-density probe coverage of exon arrays allows us to select identical sets of perfect-match probes to measure expression levels of orthologous genes. This eliminates a serious confounding factor in probe affinity effects of species-specific microarray probes, and enables direct comparisons of estimated expression indexes across species. Using a newly designed Affymetrix exon array, with eight probes per exon for approximately 315 000 exons in the human genome, we conducted expression profiling in corresponding tissues from humans, chimpanzees and rhesus macaques. Quantitative real-time PCR analysis of differentially expressed candidate genes is highly concordant with microarray data, yielding a validation rate of 21/22 for human versus chimpanzee differences, and 11/11 for human versus rhesus differences. This method has the potential to greatly facilitate biomedical and evolutionary studies of gene expression in nonhuman primates and can be easily extended to expression array design and comparative analysis of other animals and plants.     

Hotelling's T2 multivariate profiling for detecting differential expression in microarrays

The most widely used statistical methods for finding differentially expressed genes (DEGs) are essentially univariate. In this study, we present a new T(2) statistic for analyzing microarray data. We implemented our method using a multiple forward search (MFS) algorithm that is designed for selecting a subset of feature vectors in high-dimensional microarray datasets. The proposed T2 statistic is a corollary to that originally developed for multivariate analyses and possesses two prominent statistical properties. First, our method takes into account multidimensional structure of microarray data. The utilization of the information hidden in gene interactions allows for finding genes whose differential expressions are not marginally detectable in univariate testing methods. Second, the statistic has a close relationship to discriminant analyses for classification of gene expression patterns. Our search algorithm sequentially maximizes gene expression difference/distance between two groups of genes. Including such a set of DEGs into initial feature variables may increase the power of classification rules. We validated our method by using a spike-in HGU95 dataset from Affymetrix. The utility of the new method was demonstrated by application to the analyses of gene expression patterns in human liver cancers and breast cancers. Extensive bioinformatics analyses and cross-validation of DEGs identified in the application datasets showed the significant advantages of our new algorithm.     

Molecular correlates of emotional learning using genetically selected rat lines

The genetic contributions to active avoidance learning in rodents have been well established, yet the molecular basis for genetically selected line differences remains poorly understood. To identify candidate genes influencing this active avoidance paradigm, we utilized the bidirectionally selected Syracuse high- and low-avoidance (SHA and SLA) rat lines that markedly differ in their two-way active avoidance behavior. Rats were phenotyped, rested to allow recovery from testing stress and then hippocampi were dissected for gene expression profiling (Affymetrix U34A chips; approximately 7000 known genes), comparing SLA to SHA. Next, a subset of differentially expressed genes was confirmed by real-time PCR (RT-PCR) in hippocampi. Additional studies at the protein level were performed for some genes. Using triplicate arrays on pooled hippocampal samples, differentially expressed genes were identified by microarray suite 5.0 and robust multi-array average analyses. By RT-PCR analysis in hippocampi, eight genes were nominated as potential candidate genes consistent with the differential expression from the microarray data. Four genes, Veli1 (mlin-7B), SLC3a1, Ptpro and Ykt6p, showed higher expression in SHA hippocampi than SLA. Four genes, SLC6A4, Aldh1a4, Id3a and Cd74, showed higher expression in SLA hippocampi than SHA. The active avoidance behavioral difference between lines probably emerges from 'many small things'. These potential candidate genes generate hypotheses for future testing in human association and rodent studies. Differences in levels of a pleiotropic gene like Ptpro and SLC6A4 suggest that small differences over a lifespan may contribute to large behavioral differences.     

The RNA expression signature of the HepG2 cell line as determined by the integrated analysis of miRNA and mRNA expression profiles

Understanding miRNAs' regulatory networks and target genes could facilitate the development of therapies for human diseases such as cancer. Although much useful gene expression profiling data for tumor cell lines is available, microarray data for miRNAs and mRNAs in the human HepG2 cell line have only been compared with that of other cell lines separately. The relationship between miRNAs and mRNAs in integrated expression profiles for HepG2 cells is still unknown. To explore the miRNA–mRNA correlations in hepatocellular carcinoma (HCC) cells, we performed miRNA and mRNA expression profiling in HepG2 cells and normal liver HL-7702 cells at the genome scale using next-generation sequencing technology. We identified 193 miRNAs that are differentially expressed in these two cell lines. Of these, 89 miRNAs were down-regulated in HepG2 cells compared with HL-7702 cells, while 104 miRNAs were up-regulated. We also observed 3035 mRNAs that are significantly dys-regulated in HepG2 cells. We then performed an integrated analysis of the expression data for differentially expressed miRNAs and mRNAs and found several miRNA–mRNA pairs that are significantly correlated in HepG2 cells. Further analysis suggested that these differentially expressed genes were enriched in four tumorigenesis-related signaling pathways, namely, ErbB, JAK–STAT, mTOR, and WNT, which until now had not been fully reported. Our results could be helpful in understanding the mechanisms of HCC occurrence and development.     

Microarray analyses of peripheral blood cells identifies unique gene expression signature in psoriatic arthritis

Psoriatic arthritis (PsA) is a chronic and erosive form of arthritis of unknown cause. We aimed to characterize the PsA phenotype using gene expression profiling and comparing it with healthy control subjects and patients rheumatoid arthritis (RA). Peripheral blood cells (PBCs) of 19 patients with active PsA and 19 age- and sex-matched control subjects were used in the analyses of PsA, with blood samples collected in PaxGene tubes. A significant alteration in the pattern of expression of 313 genes was noted in the PBCs of PsA patients on Affymetrix U133A arrays: 257 genes were expressed at reduced levels in PsA, and 56 genes were expressed at increased levels, compared with controls. Downregulated genes tended to cluster to certain chromosomal regions, including those containing the psoriasis susceptibility loci PSORS1 and PSORS2. Among the genes with the most significantly reduced expression were those involved in downregulation or suppression of innate and acquired immune responses, such as SIGIRR, STAT3, SHP1, IKBKB, IL-11RA, and TCF7, suggesting inappropriate control that favors proin-flammatory responses. Several members of the MAPK signaling pathway and tumor suppressor genes showed reduced expression. Three proinflammatory genes--S100A8, S100A12, and thioredoxin--showed increased expression. Logistic regression and recursive partitioning analysis determined that one gene, nucleoporin 62 kDa, could correctly classify all controls and 94.7% of the PsA patients. Using a dataset of 48 RA samples for comparison, the combination of two genes, MAP3K3 followed by CACNA1S, was enough to correctly classify all RA and PsA patients. Thus, PBC gene expression profiling identified a gene expression signature that differentiated PsA from RA, and PsA from controls. Several novel genes were differentially expressed in PsA and may prove to be diagnostic biomarkers or serve as new targets for the development of therapies.     

Integrating probe-level expression changes across generations of Affymetrix arrays

There is an urgent need for bioinformatic methods that allow integrative analysis of multiple microarray data sets. While previous studies have mainly concentrated on reproducibility of gene expression levels within or between different platforms, we propose a novel meta-analytic method that takes into account the vast amount of available probe-level information to combine the expression changes across different studies. We first show that the comparability of relative expression changes and the consistency of differentially expressed genes between different Affymetrix array generations can be considerably improved by determining the expression changes at the probe-level and by considering the latest information on probe-level sequence matching instead of the probe annotations provided by the manufacturer. With the improved probe-level expression change estimates, data from different generations of Affymetrix arrays can be combined more effectively. This will allow for the full exploitation of existing results when designing and analyzing new experiments.