Predicting housekeeping genes based on Fourier analysis

Housekeeping genes (HKGs) generally have fundamental functions in basic biochemical processes in organisms, and usually have relatively steady expression levels across various tissues. They play an important role in the normalization of microarray technology. Using Fourier analysis we transformed gene expression time-series from a Hela cell cycle gene expression dataset into Fourier spectra, and designed an effective computational method for discriminating between HKGs and non-HKGs using the support vector machine (SVM) supervised learning algorithm which can extract significant features of the spectra, providing a basis for identifying specific gene expression patterns. Using our method we identified 510 human HKGs, and then validated them by comparison with two independent sets of tissue expression profiles. Results showed that our predicted HKG set is more reliable than three previously identified sets of HKGs.     

Identification of Novel Reference Genes Using Multiplatform Expression Data and Their Validation for Quantitative Gene Expression Analysis

Normalization of mRNA levels using endogenous reference genes (ERGs) is critical for an accurate comparison of gene expression between different samples. Despite the popularity of traditional ERGs (tERGs) such as GAPDH and ACTB, their expression variability in different tissues or disease status has been reported. Here, we first selected candidate housekeeping genes (HKGs) using human gene expression data from different platforms including EST, SAGE, and microarray, and 13 novel ERGs (nERGs) (ARL8B, CTBP1, CUL1, DIMT1L, FBXW2, GPBP1, LUC7L2, OAZ1, PAPOLA, SPG21, TRIM27, UBQLN1, ZNF207) were further identified from these HKGs. The mean coefficient variation (CV) values of nERGs were significantly lower than those of tERGs and the expression level of most nERGs was relatively lower than high expressing tERGs in all dataset. The higher expression stability and lower expression levels of most nERGs were validated in 108 human samples including formalin-fixed paraffin-embedded (FFPE) tissues, frozen tissues and cell lines, through quantitative real-time RT-PCR (qRT-PCR). Furthermore, the optimal number of nERGs required for accurate normalization was as few as two, while four genes were required when using tERGs in FFPE tissues. Most nERGs identified in this study should be better reference genes than tERGs, based on their higher expression stability and fewer numbers needed for normalization when multiple ERGs are required.     

Microarray reality checks in the context of a complex disease

A problem in analyzing microarray-based gene expression data is the separation of genes causally involved in a disease from innocent bystander genes, whose expression levels have been secondarily altered by primary changes elsewhere. To investigate this issue systematically in the context of a class of complex human diseases, we have compared microarray-based gene expression data with non-microarray-based clinical and biological data about the schizophrenias to ask whether these two approaches prioritize the same genes. We find that genes whose expression changes are deemed to be of importance from microarrays are rarely those classified as of importance from clinical, in situ, molecular, single-nucleotide polymorphism (SNP) association, knockout and drug perturbation data. This disparity is not limited to the schizophrenias but characterizes other human disease data sets. It also extends to biological validation of microarray data in model organisms, in which genome-wide phenotypic data have been systematically compared with microarray data. In addition, different bioinformatic protocols applied to the same microarray data yield quite different gene sets and thus make clinical decisions less straightforward. We discuss how progress may be improved in the clinical area by the assignment of high-quality phenotypic values to each member of a microarray-assigned gene set.     

Suitable reference genes for the analysis of direct hyperplasia in mice

The liver is capable of undergoing a proliferative growth, known as direct hyperplasia, in which the naïve liver increases in size due to stimulation with primary mitogens. To produce accurate gene expression data, housekeeping genes (HKGs) that are stably expressed need to be determined. In the present study, liver regeneration was promoted via the direct hyperplasia mode by inducing mice with 1,4-bis[2-(3,5-dichloropyridyloxy)]benzene. Gene expression levels of nine commonly used HKGs were analyzed in the liver of different timing during the regeneration. The stability of gene expression was assessed using two different analysis programs, geNorm and NormFinder. Using these analyses, we identified that PPIA and RPL4 showed the most stable expression regardless of the status of the liver regeneration. In conclusion, the present study demonstrated that the use of PPIA and RPL4 were the most optimal in providing reliable normalization of gene expression when assessing liver regeneration attributed to direct hyperplasia.     

Global gene expression profile of Orientia tsutsugamushi

Orientia tsutsugamushi, an obligate intracellular bacterium, is the causative agent of Scrub typhus. The control mechanisms for bacterial gene expression are largely unknown. Here, the global gene expression of O. tsutsugamushi within eukaryotic cells was examined using a microarray and proteomic approaches for the first time. These approaches identified 643 genes, corresponding to approximately 30% of the genes encoded in the genome. The majority of expressed genes belonged to several functional categories including protein translation, protein processing/secretion, and replication/repair. We also searched the conserved sequence blocks (CSBs) in the O. tsutsugamushi genome which is unique in that up to 40% of its genome consists of dispersed repeated sequences. Although extensive shuffling of genomic sequences was observed between two different strains, 204 CSBs, covering 48% of the genome, were identified. When combining the data of CSBs and global gene expression, the CSBs correlates well with the location of expressed genes, suggesting the functional conservation between gene expression and genomic location. Finally, we compared the gene expression of the bacteria‐infected fibroblasts and macrophages using microarray analysis. Some major changes were the downregulation of genes involved in translation, protein processing and secretion, which correlated with the reduction in bacterial translation rates and growth within macrophages.     

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.     

Mouse FKBP23 mediates conformer-specific functions of BiP by catalyzing Pro117 cis/trans isomerization

Gene expression analysis is frequently used to analyze the response to viral infection, and 18S RNA, SHDA and GAPDH represent popular house keeping genes (HKGs) often used to normalize gene expression. Here we describe the first systematic selection and evaluation of suitable HKGs for gene expression analysis in chicken embryo fibroblasts (CEF) infected with NDV adapted to the guidelines from Gorzelniak and Ferguson. Our results indicate that ACTB, HPRT1 and HMBS were valuable and stable HKGs, while 18S RNA, GAPDH and SHDA are considerably regulated during the course of infection and thus precluded for normalization. Normalizing the infection dependent gene IFN-a and the infection independent gene B2M to inappropriate HKGs consequently misleads to significant errors in estimating their regulations. Our study emphasizes that even the most popular HKGs like 18S RNA and GAPDH can lead to divergent and inaccurate data interpretation of significant magnitude if not carefully analyzed for stability before.     

Analysis of variance for gene expression microarray data.

Spotted cDNA microarrays are emerging as a powerful and cost-effective tool for large-scale analysis of gene expression. Microarrays can be used to measure the relative quantities of specific mRNAs in two or more tissue samples for thousands of genes simultaneously. While the power of this technology has been recognized, many open questions remain about appropriate analysis of microarray data. One question is how to make valid estimates of the relative expression for genes that are not biased by ancillary sources of variation. Recognizing that there is inherent "noise" in microarray data, how does one estimate the error variation associated with an estimated change in expression, i.e., how does one construct the error bars? We demonstrate that ANOVA methods can be used to normalize microarray data and provide estimates of changes in gene expression that are corrected for potential confounding effects. This approach establishes a framework for the general analysis and interpretation of microarray data.     

Selection of Suitable Housekeeping Genes for Real-Time Quantitative PCR in CD4+ Lymphocytes from Asthmatics with or without Depression

Objective

No optimal housekeeping genes (HKGs) have been identified for CD4⁺ T cells from non-depressive asthmatic and depressive asthmatic adults for normalizing quantitative real-time PCR (qPCR) assays. The aim of present study was to select appropriate HKGs for gene expression analysis in purified CD4⁺ T cells from these asthmatics.

Methods

Three groups of subjects (Non-depressive asthmatic, NDA, n = 10, Depressive asthmatic, DA, n = 11, and Healthy control, HC, n = 10 respectively) were studied. qPCR for 9 potential HKGs, namely RNA, 28S ribosomal 1 (RN28S1), ribosomal protein, large, P0 (RPLP0), actin, beta (ACTB), cyclophilin A (PPIA), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), phosphoglycerate kinase 1 (PGK1), beta-2-microglobulin (B2M), glucuronidase, beta (GUSB) and ribosomal protein L13a (RPL13A), was performed. Then the data were analyzed with three different applications namely BestKeeper, geNorm, and NormFinder.

Results

The analysis of gene expression data identified B2M and RPLP0 as the most stable reference genes and showed that the level of PPIA was significantly different among subjects of three groups when the two best HKGs identified were applied. Post-hoc analysis by Student-Newman-Keuls correction shows that depressive asthmatics and non-depressive asthmatics exhibited lower expression level of PPIA than healthy controls (p<0.05).

Conclusions

B2M and RPLP0 were identified as the most optimal HKGs in gene expression studies involving human blood CD4⁺ T cells derived from normal, depressive asthmatics and non-depressive asthmatics. The suitability of using the PPIA gene as the HKG for such studies was questioned due to its low expression in asthmatics.     

Microarray standard data set and figures of merit for comparing data processing methods and experiment designs

MOTIVATION: There is a very large and growing level of effort toward improving the platforms, experiment designs, and data analysis methods for microarray expression profiling. Along with a growing richness in the approaches there is a growing confusion among most scientists as to how to make objective comparisons and choices between them for different applications. There is a need for a standard framework for the microarray community to compare and improve analytical and statistical methods. RESULTS: We report on a microarray data set comprising 204 in-situ synthesized oligonucleotide arrays, each hybridized with two-color cDNA samples derived from 20 different human tissues and cell lines. Design of the approximately 24 000 60mer oligonucleotides that report approximately 2500 known genes on the arrays, and design of the hybridization experiments, were carried out in a way that supports the performance assessment of alternative data processing approaches and of alternative experiment and array designs. We also propose standard figures of merit for success in detecting individual differential expression changes or expression levels, and for detecting similarities and differences in expression patterns across genes and experiments. We expect this data set and the proposed figures of merit will provide a standard framework for much of the microarray community to compare and improve many analytical and statistical methods relevant to microarray data analysis, including image processing, normalization, error modeling, combining of multiple reporters per gene, use of replicate experiments, and sample referencing schemes in measurements based on expression change. AVAILABILITY/SUPPLEMENTARY INFORMATION: Expression data and supplementary information are available at http://www.rii.com/publications/2003/HE_SDS.htm     

Evaluation and validation of internal control genes for studying gene expression in the dinoflagellate Prorocentrum minimum using real-time PCR

Housekeeping genes (HKGs) are required for the normalization of expression levels in real-time PCR, and their selection is critical for gene expression studies. However, stable expressions of candidate HKGs vary among organisms and tissues and even according to environmental conditions. Here, we evaluated the gene expressions of 10 frequently used HKGs, including 18S rRNA, P2, ACT, TUA, TUB, CYC, eIF4E, MDH, UBQ, and GAPDH, with a total of 17 RNA samples extracted from the dinoflagellate Prorocentrum minimum. All the RNAs were prepared from various cells cultured under different conditions, including thermal shocks, toxic chemical exposures, and different life stages. Via C(T) analyses of the 10 HKGs using the geNorm software, TUA was selected as the most stable HKG for gene expression studies of the dinoflagellate, followed by MDH. Pair-wise variation (V) analysis showed that at least 2 genes were required for accurate normalization of gene expression studies depending on the experimental conditions. These HKGs are useful internal controls for the normalization of gene expression in P. minimum.     

Sex genes for genomic analysis in human brain: internal controls for comparison of probe level data extraction.

Background

Genomic studies of complex tissues pose unique analytical challenges for assessment of data quality, performance of statistical methods used for data extraction, and detection of differentially expressed genes. Ideally, to assess the accuracy of gene expression analysis methods, one needs a set of genes which are known to be differentially expressed in the samples and which can be used as a "gold standard". We introduce the idea of using sex-chromosome genes as an alternative to spiked-in control genes or simulations for assessment of microarray data and analysis methods.

Results

Expression of sex-chromosome genes were used as true internal biological controls to compare alternate probe-level data extraction algorithms (Microarray Suite 5.0 [MAS5.0], Model Based Expression Index [MBEI] and Robust Multi-array Average [RMA]), to assess microarray data quality and to establish some statistical guidelines for analyzing large-scale gene expression. These approaches were implemented on a large new dataset of human brain samples. RMA-generated gene expression values were markedly less variable and more reliable than MAS5.0 and MBEI-derived values. A statistical technique controlling the false discovery rate was applied to adjust for multiple testing, as an alternative to the Bonferroni method, and showed no evidence of false negative results. Fourteen probesets, representing nine Y- and two X-chromosome linked genes, displayed significant sex differences in brain prefrontal cortex gene expression.

Conclusion

In this study, we have demonstrated the use of sex genes as true biological internal controls for genomic analysis of complex tissues, and suggested analytical guidelines for testing alternate oligonucleotide microarray data extraction protocols and for adjusting multiple statistical analysis of differentially expressed genes. Our results also provided evidence for sex differences in gene expression in the brain prefrontal cortex, supporting the notion of a putative direct role of sex-chromosome genes in differentiation and maintenance of sexual dimorphism of the central nervous system. Importantly, these analytical approaches are applicable to all microarray studies that include male and female human or animal subjects.