Project description:Metazoan transcription is controlled through either coordinated recruitment of transcription machinery to the gene promoter, or subsequently, through regulated pausing of RNA polymerase II (Pol II) in early elongation. We report that a key difference between genes that use these distinct regulatory strategies lies in the chromatin architecture specified by their DNA sequences. Pol II pausing is prominent at highly-regulated genes whose sequences inherently disfavor nucleosome formation within the gene, but favor nucleosomal occlusion of the promoter. Pausing of polymerase maintains these genes in an active state by inhibiting the formation of repressive promoter chromatin. In contrast, promoters of housekeeping genes that lack paused Pol II are deprived of nucleosomes regardless of polymerase binding, but show higher nucleosome occupancy downstream. Our results suggest that the â??defaultâ?? chromatin state of a gene instructs its regulation, and that highly-regulated promoters have evolved to encourage competition between nucleosomes and paused Pol II for promoter occupancy. Drosophila melanogaster S2 cells (Drosophila Genomics Resource Center) were untreated, or treated with dsRNA for 96 hours, as described in Armknecht, S. et al. (2005) Methods in Enzymology, 392: pp. 55-73. Total RNA was then extracted using the RNeasy RNA extraction kit, with on column DNAse digestion (Qiagen), according to manufacturerâ??s protocol. Gene expression analysis was conducted using Drosophila Genome 2.0 GenechipÂ® arrays (Affymetrix, Santa Clara, CA). Starting with 1ug of total RNA, biotin-labeled cRNA was produced using the Affymetrix 3â?? Amplification One-Cycle Target labeling kit according to manufacturerâ??s protocol. For each array, 10ug of amplified cRNAs were fragmented and hybridized to the array for 16 hours in a rotating hybridization oven using the Affymetrix Eukaryotic Target Hybridization Controls and protocol. Slides were stained and washed as indicated in the Antibody Amplification Stain for Eukaryotic Targets protocol using the Affymetrix Fluidics Station FS450. Arrays were then scanned with an Affymetrix Scanner 3000 and data was obtained using either GenechipÂ® Operating Software (Version 1.2.0.037) or Affymetrix GeneChip Command Console (V. 1.1) and imported into the Rosetta Resolver system (Version 6.0) as outlined in the scan and data processing protocols.

Project description:The use of calcineurin inhibitor (CI) immunosuppressants has significantly improved the early allograft survival rate in organ transplantation. However, CI therapy has been associated with chronic nephrotoxicity, which limits their long-term utility. In order to understand the mechanisms of the toxicity, we analyzed the gene expression changes that underlie the development of CI immunosuppressant-mediated nephrotoxicity, in male Sprague-Dawley (SD) rats dosed daily with cyclosporine (CsA), FK506 or rapamycin (Rapa) for 1 to 28 days. We identified a group of genes, whose expression in rat kidney is quantitatively correlated with CI-induced kidney injury as observed in changes in blood urea nitrogen (BUN) levels and kidney histopathology. These genes include both up-regulated genes, such as Ren1 and Klks3, and down-regulated genes, such as Calb1, Egf, NCC, and kidney specific Wnk1 (KS-Wnk1). Using the down-regulated genes alone we successfully predicted CI immunosuppressant-mediated kidney injury in rats following 7 days of treatment. Among these genes are two mechanism-related genes, NCC and KS-Wnk1, both of which are involved in the sodium transport in the distal nephrons. The down-regulation of both genes at the mRNA and protein level in rat kidney following CI treatment was confirmed by quantitative RT-PCR and immunohistochemical staining, respectively. We hypothesize that decreased expression of NCC may cause reduced sodium chloride reabsorption in the distal tubules, and contribute to the prolonged activation of the Renin-Angiotensin-System (RAS), a demonstrated contributor to the development of CI-induced nephrotoxicity in both animal models and clinical settings. Therefore, NCC and KS-Wnk1 could potentially be used as biomarkers for early detection and prevention of CI-related nephrotoxicity in clinical practice. Male SD rats approximately 5-7 weeks old were maintained on certified rodent chow and dosed i.p. with olive oil, CsA (CAS # 59865-13-3, 2.5 or 25 mg/kg/day), FK506 (CAS # 104987-11-3, 0.6 or 6 mg/kg/day), or Rapa (CAS # 53123-88-9, 1 or 10 mg/kg/day) (LC Laboratories, Woburn, MA) for 1, 7, 14 or 28 consecutive days. Blood was collected at the terminal necropsy (24 hours after 1, 7, 14 or 28 days of treatment) for clinical chemistry analysis. Each kidney was cut in half (one cross section and the other longitudinal). One half of the left kidney and one half of the right kidney were used for histopathological evaluation and the remaining portions of the kidney were processed for gene expression analysis. Total RNA was isolated from rat kidney using QIAGEN RNeasy kits (QIAGEN Inc., Valencia, CA) following manufacture’s instruction. The rat genome array (RAT 230 2.0) GeneChip (Affymetrix, Santa Clara, CA) was used for gene expression profiling.

Project description:A causal role of mutations in genes encoding for multiple general transcription factors in neurodevelopmental disorders including autism suggested that alterations at the global level of gene expression regulation might also relate to disease risk in sporadic cases of autism. This premise can be tested by evaluating for global changes in the overall distribution of gene expression levels. For instance, in mice, we recently showed that variability in hippocampal-dependent behaviors was associated with variability in the pattern of the overall distribution of gene expression levels, as assessed by variance in the distribution of gene expression levels in the hippocampus. We hypothesized that a similar change in the variance in gene expression levels might be found in children with autism. Gene expression microarrays covering greater than 47,000 unique RNA transcripts were done on purified RNA from peripheral blood lymphocytes of children with autism (n=82) and controls (n=64). The variance in the distribution of gene expression levels from each microarray was compared between groups of children. Also tested was whether a risk factor for autism, increased paternal age, was associated with variance in the overall distribution of gene expression levels. A decrease in the variance in the distribution of gene expression levels in peripheral blood lymphocytes (PBL) was associated with the diagnosis of autism and a risk factor for autism, increased paternal age. Traditional approaches to microarray analysis of gene expression suggested a possible mechanism for decreased variance in gene expression. Gene expression pathways involved in transcriptional regulation were down-regulated in the blood of children with autism and children of older fathers. Thus, results from global and gene specific approaches to studying microarray data were complimentary and supported the hypothesis that alterations at the global level of gene expression regulation are related to autism and increased paternal age. Regulation of transcription, thus, represents a possible point of convergence for multiple etiologies of autism and other neurodevelopmental disorders. The study was designed to compare gene expression profiles in peripheral blood lymphocytes of children with autism (n=82) and controls(n=64). Expression profiling: Expression profiling was performed at Translational Genomics (TGen), a member of the NIMH Neuroscience Microarray Consortium. Total RNA was extracted from peripheral blood lymphocytes (PBL) within 30 minutes of the blood draw using the Qiagen Qiaquick kit (Germantown, MD). Isolated total RNA was double round amplified, cleaned, and biotin-labeled using Affymetrix’s GeneChip Two-Cycle Target Labeling kit (Santa Clara, CA) with a T7 promoter and Ambion’s MEGAscript T7 High Yield Transcription kit (Austin, TX) as per manufacturer’s protocol. Amplified and labeled cRNA was quantified on a spectrophotometer and run on a 1% TAE gel to check for an evenly distributed range of transcript sizes. Twenty micrograms of cRNA was fragmented to approximately 35-200bp by alkaline treatment (200 mM Tris-acetate, pH 8.2, 500 mM KOAc, 150 mM MgOAc) and run on a 1% TAE gell to verify fragmentation. Separate hybridization cocktails were made using 15 micrograms of fragmented cRNA from each sample as per Affymetrix’s protocol. Two hundred microliters (containing 10 micrograms of fragmented cRNA) of each cocktail was separately hybridized to an Affymetrix Human Genome U133 Plus 2.0 Array for 16h at 45 degree Celsius in the Hybridization Oven 640. The Affymetrix Human Genome Arrays measure the expression of over 47,000 transcripts and variants, including 38,500 characterized human genes. Arrays were washed on Affymetrix’s GeneChip Fluidics Station 450 using a primary streptavidin phycoerythrin (SAPE) stain, subsequent biotinylated antibody stain, and secondary SAPE stain. Arrays were scanned on Affymetrix’s GeneChip Scanner 3000 7G with AutoLoader. Scanned images obtained by the Affymetrix GeneChip Operating Software (GCOS) v1.2 were used to extract raw signal intensity values per probe set on the array. A scaling factor of 150 was used to normalize array signal intensity in MAS 5.0. Arrays were scanned over 1 day on 2 different machines. Arrays scanned on the same machine and in the same day were considered to be from the same scan batch. Rescanning of a limited number of samples indicated that there were no significant differences between machines, nonetheless, for all comparisons groups were balanced for the scan batch. Gene expression levels were not adjusted for possible batch effects as algorithms that attempt to adjust for batch effects also alter the gene expression distribution. When samples could not be prepped simultaneously they were balanced for group membership (autism vs. control). To statistically control for possible confounds related to scan batches in our analysis of gene expression variance, batch number was entered into an analysis of covariance. For traditional analysis of gene expression, experimental groups were balanced with respect to batch membership. Microarray data analysis: Affymetrix .cel files were imported into Affymetrix Expression Console version 1.1. Data was pre-processed and summarized by Microarray Analysis Suite (MAS) 5.0 and Robust Multiarray Analysis (RMA). For the analysis of gene expression distributions, MAS 5.0 was used because the algorithm does not alter the gene expression distribution, whereas, RMA utilizes quantile normalization of probes prior to summarization and, therefore, has the potential to remove group level differences in gene expression distributions. Because of the numerous advantages in its handling of noise in gene expression and background subtraction, RMA was used for traditional gene expression analyses looking for specific gene expression differences between groups. Because, we found group level differences in the distribution of gene expression levels between groups, for traditional gene expression analyses summarized gene expression levels were also quantile normalized after the summarization step. Quantile normalization adjusts all data sets such that they have identical distribution patterns. Probesets were then filtered for those that were called present in at least 50 out of the 146 subjects (n = 25,146 probesets). A p-value of .05 was used as a threshold for significance. A fold-change of 1.1 was used as a cut off for magnitude of change. All microarrays met manufacturers recommended quality control criteria. Present calls ranged from 37.4% to 49%, mean 43.7%, SD 2.7%. Actin 3’to5’ ratios ranged from .726 to 5.15, mean1.37, SD 0.5. There were no significant group level differences in quality control measures.

Project description:Toll like receptor 4 (TLR4), an innate immunity gene, is involved in responses to several pulmonary agonists including endotoxin (LPS; Poltorak et al.,1998), ozone (O3 ,Kleeberger et. al., 2001), Pseudomonas aeruginosa (Faure et al, 2004), and hyperoxia (Zhang et al, 2005). TLR4 appears to partially mediate the response to LPS- and O3-induced lung injury, however, TLR4 is protective for prevention of injury in Pseudomonas aeruginosa infection and against acute lung injury (hyperoxia). The mechanism behind this protection is unclear. We previously demonstrated that TLR4 was also protective against BHT-induced chronic inflammation and tumor promotion (Bauer et al, 2005). C.C3H-Tlr4Lps-d (BALBLps-d) mice, congenic for a 10 cM region of C3H/HeJ chromosome 4 that contains Tlr4 (Vogel et al, 1994), have a missence mutation that renders TLR4 dysfunctional. The Tlr4 mutation likely abrogates signaling by disrupting a direct point of contact with other signaling molecules (Akira S, Takeda K. Toll-like receptor signalling. Nat Rev Immunol 2004;4(7):499-511.). Bronchoalveolar lavage fluid (BALF) alveolar macrophages, lymphocytes, and total protein content were significantly elevated in BALBLps-d mice compared to BALB/c (BALB; Tlr4 sufficient) mice following chronic BHT (Bauer et al., 2005). BALBLps-d mice also had a significant increase in tumor multiplicity (60%) over that of BALB mice in response to an MCA/BHT tumor promotion protocol. While this was the first model to demonstrate a protective role for TLR4 in chronic lung inflammation and tumorigenesis, the downstream mechanism regulating this protective response remains unknown. Using Affymetrix microarray analysis followed by GeneSpring and Ingenuity pathway analyses, we herein identified known and novel downstream pathways and their interactions that may be involved in the protective mechanism elicited by TLR4. We therefore hypothesize that these pathways and interactions amongst the genes identified during the tumor promotion/chronic inflammation stage are in part influencing the differential strain response observed during tumorigenesis. Keywords: time course, tumor study Protocol 1 - 3 biological replicates after chronic dosing in each mouse strain Protocol 2 - multiple replicates after MCA/BHT tumor progression model used

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MRNA expression in female Drosophila melanogaster heads at 10 days and 40 days

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