Subcellular localization of HCV core protein regulates its ability for p53 activation and p21 suppression

An internal RNA standard proved less suitable in bacterial gene expression experiments. We therefore developed a method for simultaneous RNA and gDNA (genomic DNA) isolation from in vitro and in vivo samples containing staphylococci and combined it with quantitative PCR. The reliability of gDNA for bacterial quantification and for standardisation in gene expression experiments was evaluated. Quantitative PCR proves equivalent to quantitative culture for in vitro samples, and superior for in vivo samples. In gene expression experiments, gDNA permits a good standardisation for the initial amount of bacteria. The average interassay variability of the in vitro expression is 20.1%. The in vivo intersample variability was 73.3%. This higher variability can be attributed to the biological variation of gene expression in vivo. This method permits exact quantification of the number of bacteria and the expression of genes in staphylococci in vivo (e.g., in biofilms, evolution in time) and in vitro.     

Development of a toxicological gene array and quantitative assessment of this technology

High-density arrays of DNA bound to solid substrates offer a powerful approach to identifying changes in gene expression in response to toxicants. While DNA arrays have been used to explore qualitative changes in gene regulation, less attention has focused on the quantitative aspects of this technology. Arrays containing expressed sequence tags for xenobiotic metabolizing enzymes, proteins associated with glutathione regulation, DNA repair enzymes, heat shock proteins, and housekeeping genes were used to examine gene expression in response to beta-naphthoflavone (beta-NF). Upregulation of cytochrome P4501a1 (Cyp1a1) and 1a2 in mouse liver was maximal 8 h after beta-NF administration. Significant upregulation of Cyp1a2 was noted at beta-NF doses as low as 0.62 and 1.2 mg/kg when gene expression was measured by microarray or Northern blotting, respectively. Maximal Cyp1a2 induction is 5-fold by Northern analysis and 10-fold by microarray. Induction of Cyp1a1 was 15- and 20-fold by Northern and microarray analysis, respectively. The coefficient of variation for spot to spot and slide to slide comparisons was <15%; this variability was smaller than interanimal variability (18-60%). Comparison of mRNA expression in control animals indicated that there are differences in labeling/detection associated with Cy3/Cy5 dyes; accordingly, experiments must include methods for establishing baseline signals for all genes. We conclude that the dynamic range and sensitivity of DNA microarrays on glass slides is comparable to Northern blotting analysis and that variability of the data introduced during spotting and hybridization is less than the interanimal variability.     

SDED: a novel filter method for cancer-related gene selection

Gene selection is to detect the most significantly expressed genes under different conditions expression data. The current challenge in gene selection is the comparison of a large number of genes with limited patient samples. Thus it is trivial task in simple statistical analysis. Various statistical measurements are adopted by filter methods applied in gene selection studies. Their ability to discriminate phenotypes is crucial in classification and selection. Here we describe the standard deviation error distribution (SDED) method for gene selection. It utilizes variations within-class and among-class in gene expression data. We tested the method using 4 leukemia datasets available in the public domain. The method was compared with the GS2 and CHO methods. The Prediction accuracies by SDED are better than both GS2 and CHO for different datasets. These are 0.8-4.2% and 1.6-8.4% more that in GS2 and CHO. The related OMIM annotations and KEGG pathways analyses verified that SDED can pick out more 4.0% and 6.1% genes with biological significance than GS2 and CHO, respectively.     

Gene Expression Variability in Human Hepatic Drug Metabolizing Enzymes and Transporters

Interindividual variability in the expression of drug-metabolizing enzymes and transporters (DMETs) in human liver may contribute to interindividual differences in drug efficacy and adverse reactions. Published studies that analyzed variability in the expression of DMET genes were limited by sample sizes and the number of genes profiled. We systematically analyzed the expression of 374 DMETs from a microarray data set consisting of gene expression profiles derived from 427 human liver samples. The standard deviation of interindividual expression for DMET genes was much higher than that for non-DMET genes. The 20 DMET genes with the largest variability in the expression provided examples of the interindividual variation. Gene expression data were also analyzed using network analysis methods, which delineates the similarities of biological functionalities and regulation mechanisms for these highly variable DMET genes. Expression variability of human hepatic DMET genes may affect drug-gene interactions and disease susceptibility, with concomitant clinical implications.     

Exploiting aberrant mRNA expression in autism for gene discovery and diagnosis

Autism spectrum disorder (ASD) is characterized by substantial phenotypic and genetic heterogeneity, which greatly complicates the identification of genetic factors that contribute to the disease. Study designs have mainly focused on group differences between cases and controls. The problem is that, by their nature, group difference-based methods (e.g., differential expression analysis) blur or collapse the heterogeneity within groups. By ignoring genes with variable within-group expression, an important axis of genetic heterogeneity contributing to expression variability among affected individuals has been overlooked. To this end, we develop a new gene expression analysis method—aberrant gene expression analysis, based on the multivariate distance commonly used for outlier detection. Our method detects the discrepancies in gene expression dispersion between groups and identifies genes with significantly different expression variability. Using this new method, we re-visited RNA sequencing data generated from post-mortem brain tissues of 47 ASD and 57 control samples. We identified 54 functional gene sets whose expression dispersion in ASD samples is more pronounced than that in controls, as well as 76 co-expression modules present in controls but absent in ASD samples due to ASD-specific aberrant gene expression. We also exploited aberrantly expressed genes as biomarkers for ASD diagnosis. With a whole blood expression data set, we identified three aberrantly expressed gene sets whose expression levels serve as discriminating variables achieving >70 % classification accuracy. In summary, our method represents a novel discovery and diagnostic strategy for ASD. Our findings may help open an expression variability-centered research avenue for other genetically heterogeneous disorders.     

Relative levels of methylation in human growth hormone and chorionic somatomammotropin genes in expressing and non-expressing tissues.

It has been shown that the extent of methylation of cytosine in vertebrate DNA is inversely correlated with gene expression. We studied cytosine methylation in and around the homologous human growth hormone (GH) and chorionic somatomammotropin (CS) genes to determine if these genes are undermethylated in DNA from tissues in which they are expressed (pituitary and placenta, respectively) compared to other tissues. Hpa II and Hha I (which cleave only unmethylated 5' CCGG 3' and 5' GCGC 3' respectively) and Msp I (which cleaves CCGG and CmeCGG) were used to digest DNA samples followed by gel electrophoresis, Southern transfer and hybridization with a GH cDNA probe. The extent of methylation of Hpa II and Hha I sites in the GH and CS genes was leukocyte much greater than pituitary greater than placenta = hydatidiform mole. Taken as a whole, our data support the hypothesis that undermethylation is a necessary but not sufficient condition for gene expression since placental and pituitary DNAs are less methylated than leukocyte DNA in this region. However, the correlation between gene expression and undermethylation is imperfect since (1) hydatiform mole DNA has a very similar methylation pattern compared to placental DNA even though moles make little or no CS and (2) the level of methylation of the GH gene compared to the CS gene does not vary in a tissue-specific manner.     

MicroRNA regulation and the variability of human cortical gene expression

Understanding the driving forces of gene expression variation within human populations will provide important insights into the molecular basis of human phenotypic variation. In the genome, the gene expression variability differs among genes, and at present, most research has focused on identifying the genetic variants responsible for the within population gene expression variation. However, little is known about whether microRNAs (miRNAs), which are small noncoding RNAs modulating expression of their target genes, could have impact on the variability of gene expression. Here we demonstrate that miRNAs likely lead to the difference of expression variability among genes. With the use of the genome-wide expression data in 193 human brain samples, we show that the increased variability of gene expression is concomitant with the increased number of the miRNA seeds interacting with the target genes, suggesting a direct influence of miRNA on gene expression variability. Compared with the non-miRNA-target genes, genes targeted by more than two miRNA seeds have increased expression variability, independent of the miRNA types. In addition, single-nucleotide polymorphisms (SNPs) located in the miRNA binding sites could further increase the gene expression variability of the target genes. We propose that miRNAs are one of the driving forces causing expression variability in the human genome.     

Quantification of human fecal bifidobacterium species by use of quantitative real-time PCR analysis targeting the groEL gene

Quantitative real-time PCR assays targeting the groEL gene for the specific enumeration of 12 human fecal Bifidobacterium species were developed. The housekeeping gene groEL (HSP60 in eukaryotes) was used as a discriminative marker for the differentiation of Bifidobacterium adolescentis, B. angulatum, B. animalis, B. bifidum, B. breve, B. catenulatum, B. dentium, B. gallicum, B. longum, B. pseudocatenulatum, B. pseudolongum, and B. thermophilum. The bifidobacterial chromosome contains a single copy of the groEL gene, allowing the determination of the cell number by quantification of the groEL copy number. Real-time PCR assays were validated by comparing fecal samples spiked with known numbers of a given Bifidobacterium species. Independent of the Bifidobacterium species tested, the proportion of groEL copies recovered from fecal samples spiked with 5 to 9 log(10) cells/g feces was approximately 50%. The quantification limit was 5 to 6 log(10) groEL copies/g feces. The interassay variability was less than 10%, and variability between different DNA extractions was less than 23%. The method developed was applied to fecal samples from healthy adults and full-term breast-fed infants. Bifidobacterial diversity in both adults and infants was low, with mostly ≤3 Bifidobacterium species and B. longum frequently detected. The predominant species in infant and adult fecal samples were B. breve and B. adolescentis, respectively. It was possible to distinguish B. catenulatum and B. pseudocatenulatum. We conclude that the groEL gene is a suitable molecular marker for the specific and accurate quantification of human fecal Bifidobacterium species by real-time PCR.     

Bayesian meta-analysis models for microarray data: a comparative study

Background

With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods.

Results

Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets were pre-scaled.

Conclusion

The Bayesian meta-analysis model that combines probabilities across studies does not aggregate gene expression measures, thus an inter-study variability parameter is not included in the model. This results in a simpler modeling approach than aggregating expression measures, which accounts for variability across studies. The probability integration model identified more true discovered genes and fewer true omitted genes than combining expression measures, for our data sets.     

Extent of over-expression of hepatocyte growth factor receptor in colorectal tumours is dependent on the choice of normaliser

For reliable results from quantitative RT-PCR, the starting quantity of total RNA and other parameters need to be controlled. Most studies do this by normalising their results to a single reference gene. This study quantified the mRNA expression of three putative reference genes (ubiquitin C, cyclophilin E, and porphobilinogen deaminase) and the target gene hepatocyte growth factor receptor (HGFR) in matched colorectal tumour and normal mucosa samples. Each of the putative reference genes was found to be significantly over-expressed in the tumour samples compared to the normal samples. When HGFR expression was normalised to each of these reference genes using the 2 (-DeltaDeltaC(T)) method of relative quantification, the number of tumour samples in which HGFR was found to be over-expressed varied from 30% to 63% depending on the reference gene chosen for normalisation. This shows that normalising to a single reference gene without prior validation is inappropriate.     

Quantitative biomarker analysis of synovial gene expression by real-time PCR

Synovial biomarker analysis in rheumatoid arthritis can be used to evaluate drug effect in clinical trials of novel therapeutic agents. Previous studies of synovial gene expression for these studies have mainly relied on histological methods including immunohistochemistry and in situ hybridization. To increase the reliability of mRNA measurements on small synovial tissue samples, we developed and validated real time quantitative PCR (Q-PCR) methods on biopsy specimens. RNA was isolated from synovial tissue and cDNA was prepared. Cell-based standards were prepared from mitogen-stimulated peripheral blood mononuclear cells. Real time PCR was performed using TaqMan chemistry to quantify gene expression relative to the cell-based standard. Application of the cellular standard curve method markedly reduced intra- and inter-assay variability and corrected amplification efficiency errors compared with the C(t) method. The inter-assay coefficient of variation was less than 25% over time. Q-PCR methods were validated by demonstrating increased expression of IL-1β and IL-6 expression in rheumatoid arthritis synovial samples compared with osteoarthritis synovium. Based on determinations of sampling error and coefficient of variation, twofold differences in gene expression in serial biopsies can be detected by assaying approximately six synovial tissue biopsies from 8 to 10 patients. These data indicate that Q-PCR is a reliable method for determining relative gene expression in small synovial tissue specimens. The technique can potentially be used in serial biopsy studies to provide insights into mechanism of action and therapeutic effect of new anti-inflammatory agents.     

Technical variability is greater than biological variability in a microarray experiment but both are outweighed by changes induced by stimulation

Introduction

A central issue in the design of microarray-based analysis of global gene expression is that variability resulting from experimental processes may obscure changes resulting from the effect being investigated. This study quantified the variability in gene expression at each level of a typical in vitro stimulation experiment using human peripheral blood mononuclear cells (PBMC). The primary objective was to determine the magnitude of biological and technical variability relative to the effect being investigated, namely gene expression changes resulting from stimulation with lipopolysaccharide (LPS).

Methods and Results

Human PBMC were stimulated in vitro with LPS, with replication at 5 levels: 5 subjects each on 2 separate days with technical replication of LPS stimulation, amplification and hybridisation. RNA from samples stimulated with LPS and unstimulated samples were hybridised against common reference RNA on oligonucleotide microarrays. There was a closer correlation in gene expression between replicate hybridisations (0.86–0.93) than between different subjects (0.66–0.78). Deconstruction of the variability at each level of the experimental process showed that technical variability (standard deviation (SD) 0.16) was greater than biological variability (SD 0.06), although both were low (SD<0.1 for all individual components). There was variability in gene expression both at baseline and after stimulation with LPS and proportion of cell subsets in PBMC was likely partly responsible for this. However, gene expression changes after stimulation with LPS were much greater than the variability from any source, either individually or combined.

Conclusions

Variability in gene expression was very low and likely to improve further as technical advances are made. The finding that stimulation with LPS has a markedly greater effect on gene expression than the degree of variability provides confidence that microarray-based studies can be used to detect changes in gene expression of biological interest in infectious diseases.     

Nucleotide variability and linkage disequilibrium patterns at the porcine FABP5 gene

Fatty acid binding protein 5 (FABP5) is a major positional and physiological candidate gene for the porcine FAT1 QTL on SSC4. Here we characterize the nucleotide polymorphism and haplotype variability of FABP5 and we compare it with that of FABP4, given their close physical location and similar metabolic roles. DNA resequencing of the FABP5 gene region in 29 pigs from 14 breeds and in European and Japanese wild boars revealed 36 polymorphisms in 5.2 kb, and a nucleotide diversity of 0.19%, comparable to values reported in other domestic species but sixfold lower than that previously found for FABP4. Remarkably, both the nucleotide variability and the haplotype structure of FABP5 and FABP4 were dramatically different, and the Hudson-Kreitman-Aguadé test was highly significant. Nevertheless, both genes also had similarities. The neighbour-joining trees of their haplotypes did not show a geographical arrangement for any of the genes. Besides, both genes presented a similar extent and pattern of linkage disequilibrium. Haplotype blocks did not extend for large stretches ( approximately 1 kb in both genes), and the number of tag SNPs required to capture all variability was higher than previously expected. Our findings indicate that FABP4 and FABP5 have undergone different selective or evolutive processes. The fact that haplotype blocks were so small may require us to increase the number of SNPs in prospective whole-genome association studies in the pig.     

Concordant gene expression in leukemia cells and normal leukocytes is associated with germline cis-SNPs

The degree to which gene expression covaries between different primary tissues within an individual is not well defined. We hypothesized that expression that is concordant across tissues is more likely influenced by genetic variability than gene expression which is discordant between tissues. We quantified expression of 11,873 genes in paired samples of primary leukemia cells and normal leukocytes from 92 patients with acute lymphoblastic leukemia (ALL). Genetic variation at >500,000 single nucleotide polymorphisms (SNPs) was also assessed. The expression of only 176/11,783 (1.5%) genes was correlated (p<0.008, FDR = 25%) in the two tissue types, but expression of a high proportion (20 of these 176 genes) was significantly related to cis-SNP genotypes (adjusted p<0.05). In an independent set of 134 patients with ALL, 14 of these 20 genes were validated as having expression related to cis-SNPs, as were 9 of 20 genes in a second validation set of HapMap cell lines. Genes whose expression was concordant among tissue types were more likely to be associated with germline cis-SNPs than genes with discordant expression in these tissues; genes affected were involved in housekeeping functions (GSTM2, GAPDH and NCOR1) and purine metabolism.     

Genetic Variants Contribute to Gene Expression Variability in Humans

Expression quantitative trait loci (eQTL) studies have established convincing relationships between genetic variants and gene expression. Most of these studies focused on the mean of gene expression level, but not the variance of gene expression level (i.e., gene expression variability). In the present study, we systematically explore genome-wide association between genetic variants and gene expression variability in humans. We adapt the double generalized linear model (dglm) to simultaneously fit the means and the variances of gene expression among the three possible genotypes of a biallelic SNP. The genomic loci showing significant association between the variances of gene expression and the genotypes are termed expression variability QTL (evQTL). Using a data set of gene expression in lymphoblastoid cell lines (LCLs) derived from 210 HapMap individuals, we identify cis-acting evQTL involving 218 distinct genes, among which 8 genes, ADCY1, CTNNA2, DAAM2, FERMT2, IL6, PLOD2, SNX7, and TNFRSF11B, are cross-validated using an extra expression data set of the same LCLs. We also identify ∼300 trans-acting evQTL between >13,000 common SNPs and 500 randomly selected representative genes. We employ two distinct scenarios, emphasizing single-SNP and multiple-SNP effects on expression variability, to explain the formation of evQTL. We argue that detecting evQTL may represent a novel method for effectively screening for genetic interactions, especially when the multiple-SNP influence on expression variability is implied. The implication of our results for revealing genetic mechanisms of gene expression variability is discussed.     

Gene expression profiling of individual cases reveals consistent transcriptional changes in alcoholic human brain

Chronic alcohol exposure induces lasting behavioral changes, tolerance, and dependence. This results, at least partially, from neural adaptations at a cellular level. Previous genome-wide gene expression studies using pooled human brain samples showed that alcohol abuse causes widespread changes in the pattern of gene expression in the frontal and motor cortices of human brain. Because these studies used pooled samples, they could not determine variability between different individuals. In the present study, we profiled gene expression levels of 14 postmortem human brains (seven controls and seven alcoholic cases) using cDNA microarrays (46,448 clones per array). Both frontal cortex and motor cortex brain regions were studied. The list of genes differentially expressed confirms and extends previous studies of alcohol responsive genes. Genes identified as differentially expressed in two brain regions fell generally into similar functional groups, including metabolism, immune response, cell survival, cell communication, signal transduction and energy production. Importantly, hierarchical clustering of differentially expressed genes accurately distinguished between control and alcoholic cases, particularly in the frontal cortex.     

Characterization of variability in large-scale gene expression data: implications for study design

Large-scale gene expression measurement techniques provide a unique opportunity to gain insight into biological processes under normal and pathological conditions. To interpret the changes in expression profiles for thousands of genes, we face the nontrivial problem of understanding the significance of these changes. In practice, the sources of background variability in expression data can be divided into three categories: technical, physiological, and sampling. To assess the relative importance of these sources of background variation, we generated replicate gene expression profiles on high-density Affymetrix GeneChip oligonucleotide arrays, using either identical RNA samples or RNA samples obtained under similar biological states. We derived a novel measure of dispersion in two-way comparisons, using a linear characteristic function. When comparing expression profiles from replicate tests using the same RNA sample (a test for technical variability), we observed a level of dispersion similar to the pattern obtained with RNA samples from replicate cultures of the same cell line (a test for physiological variability). On the other hand, a higher level of dispersion was observed when tissue samples of different animals were compared (an example of sampling variability). This implies that, in experiments in which samples from different subjects are used, the variation induced by the stimulus may be masked by non-stimuli-related differences in the subjects' biological state. These analyses underscore the need for replica experiments to reliably interpret large-scale expression data sets, even with simple microarray experiments.