Project description:Background Housekeeping genes (HKG) are constitutively expressed in all tissues while tissue-enriched genes (TEG) are expressed at a much higher level in a single tissue type than in others. HKGs serve as valuable experimental controls in gene and protein expression experiments, while TEGs tend to represent distinct physiological processes and are frequently candidates for biomarkers or drug targets. The genomic features of these two groups of genes expressed in opposing patterns may shed light on the mechanisms by which cells maintain basic and tissue-specific functions. Results Here, we generate gene expression profiles of 42 normal human tissues on custom high-density microarrays to systematically identify 1,522 HKGs and 975 TEGs and compile a small subset of 20 housekeeping genes that can be used as experimental reference and are superior to many commonly used HKGs. Cross-species comparison shows that both the functions and expression patterns of HKGs are conserved. TEGs are enriched with respect to both segmental duplication and copy number variation, while no such enrichment is observed for HKGs, suggesting the high expression of HKGs are not due to high copy numbers. Analysis of genomic and epigenetic features of HKGs and TEGs reveals that the high expression of HKGs across different tissues is associated with decreased nucleosome occupancy at the transcription start site as indicated by enhanced DNase hypersensitivity. Additionally, we systematically and quantitatively demonstrated that the CpG islands' enrichment in HKGs transcription start sites (TSS) and their depletion in TEGs TSS. Histone methylation patterns differ significantly between HKGs and TEGs, suggesting that methylation contributes to the differential expression patterns as well. Conclusions We have compiled a set of high quality HKGs that should provide higher and more consistent expression when used as references in laboratory experiments than currently used HKGs. The comparison of genomic features between HKGs and TEGs shows that HKGs are more conserved than TEGs in terms of functions, expression pattern and polymorphisms. In addition, our results identify chromatin structure and epigenetic features of HKGs and TEGs that are likely to play an important role in regulating their strikingly different expression patterns. PolyA+ purified RNA pooled from multiple donors of a single human tissue type (e.g. cerebellum) were amplified with random primers and hybridized on a two-color ink-jet oligonucletodie microarray against a common reference pool, comprising ~20 normal adult tissue pools, on custom microarray patterns. Total 42 normal human tissues were testes against the pools. This dataset is part of the TransQST collection.

Project description:Peumonia is the most common cause of death due to infectious diseases in the western hemisphere. The molecular events associated with pulmonary infections caused by Streptococcus pneumoniae and Influenza A virus are incompletely understood. Pathophysiological and protective processes are initiated by immune receptors specifically recognizing pathogenic structures serving to elicit a qualitatively and quantitatively adequate immune response. To provide a molecular framework towards a better understanding of the processes relevant in severe infectious pneumonia we performed a transcriptome analysis of lungs from mice infected with S. pneumoniae or Influenza A virus. Overall, we detected 1300 genes that exhibit significant differential expression after infection with either pathogen. Of those were approximately 36 % specific for pneumococcal and 30 % specific for the viral infection, yielding pathogen-specific as well as common inflammatory transcriptional signatures. Characteristically, these results resolve not only a differential response on the cytokine and chemokine level, although common induction of type I and type II interferons, TNFa, and IL-6 underlies both infections, but emphasize the potentially important role exerted by many genes implicated in the regulation and fine tuning of the inflammatory response. Furthermore, we noted a specific decrease in B cells after S. pneumoniae infection, which is not solely confined to the lung. Massive induction of apoptosis in pulmonary B cells could reflect a pneumococcal virulence strategy aiming against the lymphocyte population that is of utmost importance for the defence against capsulated bacteria. The pathophysiological consequences of Influenza A virus infection become obvious through differential induction of genes implicated in tissue regeneration and proliferation associated with detection of the emerging protective T cell response. These results may provide new insights into the pathogenesis and protective mechanisms important for the development of improved diagnostic and therapeutic strategies. Microarray analyses were performed as two-colour hybridizations. RNA was labelled with a Fluorescent Linear Amplification Kit (Agilent Technologies). Briefly, 4 Âµg total RNA was reverse transcribed with an oligo-dT-T7 promoter primer and MMLV-RT. Second strand synthesis was carried out with random hexamers. Fluorescent antisense cRNA was synthesized with either cyanine 3-CTP (Cy3-CTP) or cyanine 5-CTP (Cy5-CTP) and T7 polymerase. Purified products were quantified at A552nm for Cy3-CTP and A650nm for Cy5-CTP. Prior hybridization, 1.25 Âµg labelled cRNA each were fragmented, mixed with control targets and hybridization buffer according to the supplier's protocol (Agilent Technologies). Hybridizations were performed for 17 h at 60Â°C. The slides were washed according to the manufacturer's protocol and scanning of the arrays was performed with 5 Âµm resolution using a DNA microarray laser scanner (Agilent Technologies). To compensate for dye specific effects, and to ensure validity of the data, a colour swap analysis (fluorescence reversal) was performed. Features were extracted with the image analysis tool Version A6.1.1.1 from Agilent Technologies. Subsequent data analyses were carried out on the Rosetta Inpharmatics platform Resolver Built 3.2.2. Differential expression patterns were identified by applying 2-fold expression level cut-offs and an anti-correlation of the dye reversal experiments with an error weighted p-value < 0.05.

Project description:A series of two color gene expression profiles obtained using Agilent 44K expression microarrays was used to examine sex-dependent and growth hormone-dependent differences in gene expression in rat liver. This series is comprised of pools of RNA prepared from untreated male and female rat liver, hypophysectomized (‘Hypox’) male and female rat liver, and from livers of Hypox male rats treated with either a single injection of growth hormone and then killed 30, 60, or 90 min later, or from livers of Hypox male rats treated with two growth hormone injections spaced 3 or 4 hr apart and killed 30 min after the second injection. The pools were paired to generate the following 6 direct microarray comparisons: 1) untreated male liver vs. untreated female liver; 2) Hypox male liver vs. untreated male liver; 3) Hypox female liver vs. untreated female liver; 4) Hypox male liver vs. Hypox female liver; 5) Hypox male liver + 1 growth hormone injection vs. Hypox male liver; and 6) Hypox male liver + 2 growth hormone injections vs. Hypox male liver. A comparison of untreated male liver and untreated female liver liver gene expression profiles showed that of the genes that showed significant expression differences in at least one of the 6 data sets, 25% were sex-specific. Moreover, sex specificity was lost for 88% of the male-specific genes and 94% of the female-specific genes following hypophysectomy. 25-31% of the sex-specific genes whose expression is altered by hypophysectomy responded to short-term growth hormone treatment in hypox male liver. 18-19% of the sex-specific genes whose expression decreased following hypophysectomy were up-regulated after either one or two growth hormone injections. Finally, growth hormone suppressed 24-36% of the sex-specific genes whose expression was up-regulated following hypophysectomy, indicating that growth hormone acts via both positive and negative regulatory mechanisms to establish and maintain the sex specificity of liver gene expression. For full details, see V. Wauthier and D.J. Waxman, Molecular Endocrinology (2008) This series is comprised of pools of liver RNA prepared from untreated male, hypophysectomized (‘Hypox’) male, untreated female and Hypox female rats (3-4 livers/pool), as well as liver RNA prepared from Hypox male rats treated with a single growth hormone injection and killed either 30, 60, or 90 minutes later (pool of n = 4 livers) or from Hypox male rats treated with two growth hormone injections spaced 3 or 4 hr apart (pool of n = 5 livers). The pools were paired to generate the following 6 direct microarray comparisons: 1) untreated male liver vs. untreated female liver; 2) Hypox male liver vs. untreated male liver; 3) Hypox female liver vs. untreated female liver; 4) Hypox male liver vs. Hypox female liver; 5) Hypox male liver + 1 growth hormone injection vs. Hypox male liver; and 6) Hypox male liver + 2 growth hormone injections vs. Hypox male liver. Dye swapping experiments were carried out for each of the six hybridization experiments, as follows. The Alexa 555-labeled cDNA from one of the two untreated male pools was mixed with the Alexa 647-labeled cDNA from one of the two untreated female pools. Similarly, Alexa 647-labeled cDNA from the second untreated male pool was mixed with the Alexa 555-labeled cDNA from the second untreated female pool. Together, these two mixed cDNA samples comprise a fluorescent reverse pair (dye swap). Dye swaps were similarly carried out for each of the five other competitive hybridization experiments, except that for experiments 5 and 6, a single pool of M-Hypox + GH liver cDNA, or a single pool of M-Hypox + 2GH liver cDNA, was used in each half of the fluorescent reverse pair. Two microarrays, one for each mixed cDNA sample, were hybridized for each of the six fluorescent reverse pairs, giving a total of 12 microarrays.

Project description:Insulin resistance is necessary but not sufficient for the development of type 2 diabetes. Diabetes results when pancreatic beta-cells fail to compensate for insulin resistance by increasing insulin production through an expansion of beta-cell mass or increased insulin secretion. Communication between insulin target tissues and beta-cells may initiate this compensatory response. Correlated changes in gene expression between tissues can provide evidence for such intercellular communication. We profiled gene expression in six tissues of mice from an obesity-induced diabetes-resistant and a diabetes-susceptible strain before and after the onset of diabetes. We studied the correlation structure of mRNA abundance and identified 105 co-expression gene modules. We provide an interactive gene network model showing the correlation structure between the expression modules within and among the six tissues. This resource also provides a searchable database of gene expression profiles for all genes in six tissues in lean and obese diabetes-resistant and diabetes-susceptible mice, at 4 and 10 weeks of age. A cell cycle regulatory module in islets predicts diabetes susceptibility. The module predicts islet replication; we found a strong correlation between ^2 H_2 O incorporation into islet DNA /in vivo/ and the expression pattern of the cell cycle module. This pattern is highly correlated with that of several individual genes in insulin target tissues, including IGF2, which has been shown to promote beta-cell proliferation, suggesting that these genes may provide a link between insulin resistance and beta-cell proliferation. Keywords: time course, mouse strain comparison, effect of obesity, Type 2 diabetes is a disorder that involves an increased demand for insulin brought about by insulin resistance, together with a failure to compensate with sufficient insulin production. Although Insulin resistance occurs in most obese individuals, diabetes is generally forestalled through compensation with increased insulin. This increase in insulin occurs through an expansion of beta-cell mass and/or increased insulin secretion by individual beta-cells. Failure to compensate for insulin resistance leads to type 2 diabetes. One way to understand the pathophysiology of diabetes is to examine the coordinate changes in gene expression that occur in insulin-responsive tissues and pancreatic islets in obese animals that either compensate for insulin resistance or progress to type 2 diabetes. In each case, there are groups of genes that undergo changes in expression in a highly correlated fashion. By identifying groups of correlated transcripts (gene expression modules) during the compensation and development of diabetes, we can gain insight into potential pathways and regulatory networks in obesity-induced diabetes. We study two strains of mice that differ in obesity-induced diabetes susceptibility. In this study, we surveyed gene expression in six tissues of lean and obese C57BL/6 (B6) and BTBR mice aged 4 wks and 10 wks. B6 mice remain essentially non-diabetic at all ages, irrespective of obesity. When obese, BTBR mice become severely diabetic by 10 weeks of age. By analyzing the correlation structure of the genes under three contrast conditions, obesity, strain, and age, we identified gene expression modules associated with the onset of diabetes and provide an interactive co-expression network model of type 2 diabetes. We found a key module that is comprised of cell cycle regulatory genes. In the islet, the expression profile of these transcripts accurately predicts diabetes and is highly correlated with islet cell proliferation.

Project description:The metabolic impact of the common peroxisome proliferator-activated receptor gamma isoform 2 (PPARÎ³2) variant Pro12Ala in human populations has been widely debated. We demonstrate using a Pro12Ala knock-in model that on chow Ala/Ala mice are leaner, have improved insulin sensitivity and plasma lipid profiles, and longer lifespan. Gene-environment interactions played a key role as high-fat feeding eliminated the beneficial effects of the Pro12Ala variant on adiposity, plasma lipids, and insulin sensitivity. The underlying molecular mechanisms involve changes in cofactor interaction and adiponectin signaling. Altogether, our results establish the Pro12Ala variant of PparÎ³2 as an important modulator in metabolic control that strongly depends on the metabolic context. PparÎ³ Pro12Ala and wild-type littermates were fed regular rodent chow or high-fat diet (D12330, 5560 kcal/kg, Research Diets, New Brunswick, NJ), as indicated. Only males were used to minimize possible effects of variation in estrus status of females. Since the body weight development of Pro/Ala heterozygote mice was intermediate to that of Pro/Pro and Ala/Ala mice, we focused on the two homozygote genotypes in further experimentation. Mice were housed with a 12h light-dark cycle and had free access to water and food. Eight week old male Pro/Pro and Ala/Ala littermate mice were fed either regular rodent chow or high-fat diet (as above) for 15 weeks (n = 8-10 per group), at which time blood and tissue samples were collected. Total RNA extraction, sample amplification, labeling, and microarray (Genechip Mouse Genome 430 2.0 Array, Affymetrix Inc., Santa Clara, CA) processing steps were performed by the Rosetta Inpharmatics Gene Expression Laboratory (Seattle, WA) using custom automated procedures in compliance with manufacturer protocols. Microarray data was processed using Rosetta Resolver (Rosetta Inpharmatics, Seattle, WA) and expressed as relative to the virtual pool of HFD â Pro/Pro group (mlratio). Gene expression signatures were generated from these mlratios for expressed genes (25% of RMA processed intensities above 60% quantile) by T-testing (Pro/Pro vs. Ala/Ala) and correcting for multiple testing (20% cut-off for False Discovery Rate). GSEA was performed according to provider suggestions, Pro/Pro vs. Ala/Ala. Statistical significance was declared if P < 0.05.

Project description:Housekeeping genes (HKG) are constitutively expressed in all tissues while tissue-enriched genes (TEG) are expressed at a much higher level in a single tissue type than in others. HKGs serve as valuable experimental controls in gene and protein expression experiments, while TEGs tend to represent distinct physiological processes and are frequently candidates for biomarkers or drug targets. The genomic features of these two groups of genes expressed in opposing patterns may shed light on the mechanisms by which cells maintain basic and tissue-specific functions. Here, we generate gene expression profiles of 42 normal human tissues on custom high-density microarrays to systematically identify 1,522 HKGs and 975 TEGs and compile a small subset of 20 housekeeping genes which are highly expressed in all tissues with lower variance than many commonly used HKGs. Cross-species comparison shows that both the functions and expression patterns of HKGs are conserved. TEGs are enriched with respect to both segmental duplication and copy number variation, while no such enrichment is observed for HKGs, suggesting the high expression of HKGs are not due to high copy numbers. Analysis of genomic and epigenetic features of HKGs and TEGs reveals that the high expression of HKGs across different tissues is associated with decreased nucleosome occupancy at the transcription start site as indicated by enhanced DNase hypersensitivity. Additionally, we systematically and quantitatively demonstrated that the CpG islands' enrichment in HKGs transcription start sites (TSS) and their depletion in TEGs TSS. Histone methylation patterns differ significantly between HKGs and TEGs, suggesting that methylation contributes to the differential expression patterns as well.We have compiled a set of high quality HKGs that should provide higher and more consistent expression when used as references in laboratory experiments than currently used HKGs. The comparison of genomic features between HKGs and TEGs shows that HKGs are more conserved than TEGs in terms of functions, expression pattern and polymorphisms. In addition, our results identify chromatin structure and epigenetic features of HKGs and TEGs that are likely to play an important role in regulating their strikingly different expression patterns. We performed microarray experiment on more tissues and probesets in additional to the previous GEO submission (Series GSE11863). In brief, PolyA+ purified RNA pooled from multiple donors of a single human tissue type (e.g. cerebellum) were amplified with random primers and hybridized on a two-color ink-jet oligonucletodie microarray against a common reference pool, comprising ~20 normal adult tissue pools, on custom microarray patterns containing probes to monitor every exon and exon-exon junction in transcript databases, patent databases, and predicted from mouse transcripts. Data were analyzed for gene expression (the average of multiple probes), exon and junction expression, and splice form proportionality.

Project description:The remarkable success observed in using genome-wide association (GWA) mapping in human cohorts to identify multiple genes linked to a wide number of traits related to complex diseases has renewed interest in applying genome-wide association mapping techniques to model organisms such as inbred laboratory mice. However, unlike humans, the limited genetic diversity present in the ancestry of laboratory mice combined with intense selection pressure over the past decades have yielded an intricate population structure within the genomes of laboratory mouse that could potentially complicate the results obtained from such a study. We sought to empirically assess the viability of genome-wide association studies in inbred mice using hundreds of expression traits where the true location of the eQTL is known a priori. Using data from a previously published experimental mouse cross (C57BL/6J x C3H/HeJ), we selected over a thousand of the strongest cis-acting expression QTLs and measured transcript abundance levels of the associated expression traits in 16 classical and 3 wild-derived inbred strains. We next perform a genome-wide association scan demonstrating the low statistical power of such studies and show empirically the large extent to which high allelic association gives rise to spurious associations. Moreover, we provide evidence illustrating that in a large fraction of cases, the marker with the most significant p-values fails to map to the location of the true eQTL; hence, as a result, selecting the most significant marker may lead to spurious findings. Finally, we demonstrate that combining linkage analysis with association mapping provides significant increases in statistical power over a stand-alone GWA study as well as significantly higher mapping resolution than either study alone. RNA preparation and array hybridizations were performed at Rosetta Inpharmatics. The custom ink-jet microarrays were manufactured by Agilent Technologies (Palo Alto, CA). A custom array was designed for this study and consisted of 39,280 non-control oligonuceotides extracted from the mouse Unigene clusters and combined with RefSeq sequences and RIKEN full-length cDNA clones. Mouse liver tissues were homogenized and total RNA extracted using Trizol reagent (Invitrogen, CA) according to manufacturer’s protocol. Three µg of total RNA was reverse transcribed and labeled with either Cy3 or Cy5 fluorochrome. Labeled complementary RNA (cRNA) from each animal was hybridized against a cross-specific pool of labeled cRNAs constructed from equal aliquots of RNA from representative animals for each strain. The hybridizations were performed in fluor reversal for 24 hours in a hybridization chamber, washed, and scanned using a confocal laser scanner. Arrays were quantified on the basis of spot intensity relative to background, adjusted for experimental variation between arrays using average intensity over multiple channels, and fitted to a previously described error model to determine significance23 (type I error).

Dataset Information

Genome-wide analyses of Loc1, Puf6, and Khd1

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure