Unknown,Transcriptomics,Genomics,Proteomics

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Mouse Normal Variation

ABSTRACT: Cancer of the prostate is influenced both by genetic predisposition and environmental factors. The identification of genes capable of modulating cancer development has the potential to unravel disease heterogeneity and aid diagnostic and prevention strategies through improved understanding of gene-environment interactions. To this end, mouse models have been developed to isolate the influences of individual genetic lesions in the context of consistent genotypes and environmental exposures. However, the extent of normal prostatic phenotypic variability dictated by a genetic background potentially capable of influencing the process of carcinogenesis has not been established. In this study we used microarray analysis to quantitate transcript abundance levels in the prostates of five commonly studied inbred mouse strains. We applied a multiclass response t-test to identify genes whose expression in each strain significantly differed from the other four strains. Approximately 13% (932 genes) exhibited significant differential expression (range 1.3 to 190-fold) in one strain relative to other strains (FDRM-bM-^IM-$10%). The pattern of variability in transcript levels did not result from variations unique to a particular strain, but rather represented genetic variability across all five strains assessed. Expression differences were confirmed by qRT-PCR, or immunohistochemistry for several genes previously shown to influence cancer progression such as Psca, Mmp7, and Clusterin. Analyses of human prostate transcripts orthologous to variable murine prostate genes identified differences in gene expression in benign epithelium that correlated with the differentiation state of adjacent tumors. For example, the expression of apolipoprotein D, a gene known to enhance resistance to cell stress in Drosophila, was expressed at significantly greater levels in benign epithelium associated with high-grade versus low-grade cancers. These data support the concept that the cellular, tissue, and organismal context contribute to oncogenesis and suggest that a predisposition to a sequence of events leading to pathology may be determined prior to cancer initiation. Prostates from twelve mice from each of five strains of Mus musculus: C57BL/6J, 129X1/SvJ, BALB/cAnCrl, FVB/NJ and DBA/2NCrl, were resected and individual lobes were dissected: DP: dorsal prostate; LP: lateral prostate; VP: ventral prostate; AP: anterior prostate. Each experimental sample represents a pool of equal amounts of RNA for each prostatic lobe from 3 animals. Four independent experimental samples were created per strain: 12 mice divided into 4 pools of 3 mice each for a total of 4 microarray experiments per strain. Amplified RNA from each experimental sample was hybridized against a reference sample (created by combining equal amounts of RNA from all the samples from all strains) onto custom mouse prostate cDNA microarrays using alternate dye-labeling to account for dye-specific effects.

ORGANISM(S): Mus musculus

SUBMITTER: Denise Mauldin

PROVIDER: E-GEOD-5962 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Histone modifications affect DNA-templated processes ranging from transcription to genomic replication. In this study, we examine the cell cycle dynamics of the trimethylated form of histone H3 lysine 4 (H3K4me3), a mark of active chromatin that is viewed as â??long-livedâ?? [1], and that is involved in memory during cell state inheritance in metazoans [2]. We synchronized yeast using two different protocols, then followed H3K4me3 patterns as yeast passed through subsequent cell cycles. While most H3K4me3 patterns were conserved from one generation to the next, we found that methylation patterns induced by alpha factor or high temperature were erased within one cell cycle, during S phase. Early-replicating regions were erased before late-replicating regions, implicating replication in H3K4me3 loss. However, incomplete H3K4me3 erasure occurred at the majority of loci even when replication was prevented, suggesting that most erasure results from an active process. Indeed, deletion of the demethylase Jhd2 slowed erasure at most loci. Together, these results indicate overlapping roles for passive dilution and active enzymatic demethylation in erasing ancestral histone methylation states in yeast. References: [1] Ng HH, Robert F, Young RA, Struhl K (2003) Targeted recruitment of Set1 histone methylase by elongating Pol II provides a localized mark and memory of recent transcriptional activity. Mol Cell 11: 709-719. [2] Ringrose L, Paro R (2004) Epigenetic regulation of cellular memory by the Polycomb and Trithorax group proteins. Annu Rev Genet 38: 413-443. The overall design of the experiment consists of two cell cycle experiments, each consisting of the following subsets: gene expression, ChIP with anti-H3K4Me3 antibody, and ChIP input. The experiments are as follows: CCA - BY4741 bar1- cells synchronized by alpha factor arrest; CCTS - BY4741 cdc28(ts) cells synchronized by arrest at non-permissive temperature. The individual time courses are enumerated as follows: CCA gene expression (array title "Synchronized cells, xxx min, cell cycle CCA"), 18 arrays; CCA ChIP for anti-H3K4Me3 (array title "H3K4Me3 ChIP cell cycle CCA, xxx min"), 17 arrays, 1 replicate; CCA ChIP input (array title "ChIP Input cell cycle CCA, xxx min"), 18 arrays, 1 replicate; CCTS gene expression (appears in a different dataset as biological replicate "I"; array title "Synchronized cells, xxx min, biological replicate I"), 18 arrays; CCTS ChIP for anti-H3K4Me3 (array title "H3K4Me3 cell cycle CCTS, xxx min"), 2 technical replicates, 18 arrays per replicate; CCTS ChIP input (array title "ChIP Input cell cycle CCTS, xxx min"), 2 technical replicates, 18 arrays per replicate. Gene expression arrays were run against a reference of unsynchronized cells, ChIP-chip anti-H3K4Me3 samples were run against an IP (of the same epitope) of unsynchronized cells, and ChIP input samples were run against either a pool of all the time points in the time course (CCTS) or against sonicated DNA isolated from unsynchronized cells (CCA). Four additional experiments have been performed. They are referred to as series X in the title. Series 1: anti H3K4me3 CHIP time course of BY4741 bar1 delete cells (wt) synchronized with alpha factor and released in the cell cycle, 9 arrays, 1 replicate. Series 2: anti H3K4me3 CHIP time course of BY4741 bar1 jhd2 delete cells (jhd2 delete) synchronized with alpha factor and released in the cell cycle, 7 arrays, 1 replicate. Series 3: anti H3K4me3 CHIP time course of CYM36 cells (cdc7ts) synchronized with alpha factor and released in the cell cycle at the permissive 24C or at the restrictive 37C temperature, 22 arrays, 2 replicates. Series 4: anti H3K4me3 CHIP time course of BY4741 bar1 delete cells (wt) grown in galactose and synchronized with alpha factor and either released in the cell cycle in glucose media or alpha factor arrested and switched to glucose media, 6 arrays, 1 replicate.

Project description:Significant insight about biological networks arises from the study of network motifs âoverly abundant network subgraphs, but such wiring patterns do not specify when and how potential routes within a cellular network are used. To address this limitation, we introduce activity motifs, which capture patterns in the dynamic use of a network. Using this framework to analyze transcription in Saccharomyces cerevisiae metabolism, we find that cells use different timing activity motifs to optimize transcription timing in response to changing conditions: forward activation to produce metabolic compounds efficiently, backward shutoff to rapidly stop production of a detrimental product and synchronized activation for co-production of metabolites required for the same reaction Measuring protein abundance over a time course reveals that mRNA timing motifs also occur at the protein level. Timing motifs significantly overlap with binding activity motifs, where genes in a linear chain have ordered binding affinity to a transcription factor, suggesting a mechanism for ordered transcription. Finely timed transcriptional regulation is therefore abundant in yeast metabolism, optimizing the organism's adaptation to new environmental conditions. We generated a set of 13 time courses by measuring gene expression after a metabolic change. Yeast strain KCN118 (MATalpha ade2) was grown at 28 Â°C in 400 ml of synthetic complete media with 2% dextrose (SCD) to an OD600 of 0.6. Synthetic complete was prepared using the standard recipe, except 75 uM inositol was included. At OD600 of 0.6, 100 ml of cells were collected by centrifugation and frozen as a reference sample, and the remaining cells were rapidly collected by filtration, washed with distilled water and resuspended in 300 ml of one of the following media: SCE (SC + 2% ethanol), SCG (SC + 2% galactose), SM1 (SCD lacking amino acids A, R, N, C, Q, G, K, P, S, F and T), SM2 (SCD lacking amino acids L, I, V, W, H and M), S0 (SCD lacking all amino acids), S0G (no amino acids, 2% galactose) or S0E (no amino acids, 2% ethanol). The data appears in Figures 2 and 4 of the manuscript, as it relates to the global analysis of all the arrays used in the dataset. All time courses consist of the following time points (in min): 15, 30, 60, 120, 240, and were hybridized against the t = 0 time point of cells grown in SCD. Each time course was performed as one single biological replicate and one technological replicate, except where noted below. Specifically, the 13 time courses break down into the following groups: Media key: SCD (synthetic complete, not including inositol) SCE (SC + 2% ethanol) SCG (SC + 2% galactose) SM1 (SCD lacking amino acids A, R, N, C, Q, G, K, P, S, F and T) SM2 (SCD lacking amino acids L, I, V, W, H and M) S0 (SCD lacking all amino acids) S0G (no amino acids, 2% galactose) S0E (no amino acids, 2% ethanol). ino = inositol aa = all amino acids supplemented The following group descriptions are the media as described above, followed by the description as indicated in the long title of the individual arrays: 1. S0 (5 arrays) = SD 2. SCD (6 arrays, t = 240 min has 2 tech replicates) = SD+aa 3. SM2 (5 arrays) = SD+aa:ARNCQGKPSDEFTY+ino 4. SM1 + ino (5 arrays) = SD+aa:LIVWHM+ino 5. S0 + ino (6 arrays, t = 240 min has 2 tech replicates) = SD+ino 6. S0E (5 arrays) = SEtOH 7. S0E + aa (7 arrays, t = 240 min and 60 min each have 2 tech replicates) = SEtOH+aa 8. S0E + aa + ino (5 arrays) = SEtOH+aa+ino 9. S0E + ino (8 arrays, t = 240 min, 15 min and 30 min each have 2 tech replicates) = SEtOH+ino 10. S0G (5 arrays) = Sgal 11. S0G + aa (5 arrays) = Sgal+aa 12. S0G + aa + ino (5 arrays) = Sgal+aa+ino 13. S0G + ino (6 arrays, t = 60 min has 2 tech replicates) = Sgal+ino

Project description:The use of genome-wide RNA abundance profiling by microarrays and deep sequencing has spurred a revolution in our understanding of transcriptional control. However, changes in mRNA abundance reflect the combined effect of changes in RNA production, processing, and degradation, and thus, mRNA levels provide an occluded view of transcriptional regulation. To partially disentangle these issues, we carry out genome-wide RNA Polymerase II (“Pol2”) localization profiling in budding yeast in two different stress response time courses. While mRNA changes largely reflect changes in transcription, there remains a great deal of variation in mRNA levels that is not accounted for by changes in Pol2 abundance. We find that genes exhibiting “excess” mRNA produced per Pol2 are enriched for those with overlapping cryptic transcripts, indicating a pervasive role for nonproductive or regulatory transcription in control of gene expression. Finally, we characterize changes in Pol2 localization when Pol2 is genetically inactivated using the rpb1-1 temperature-sensitive mutation. We find that Pol2 is lost from chromatin after roughly an hour at the restrictive temperature, and that there is a great deal of variability in the rate of Pol2 loss at different loci. Together, these results provide a global perspective on the relationship between Pol2 and mRNA production in budding yeast. The array studies consisted of 4 time courses in which the genome-wide location of RNA Polymerase II was mapped via chromatin immunoprecipitation (ChIP) in BY4741 S. cerevisiae cells or in such cells bearing the temperature sensitive mutation rpb1, in response to heat shock at 37 degrees C and/or 1.5 mM diamide. All samples were hybridized as the ChIP sample (Cy3 labeled, channel 1) versus its cognate ChIP input sample (Cy5 labeled, channel 2). The first time course was a heat shock in wild-type BY4741 cells. The second time course was a heat shock in BY4741 rpd1 cells. The third time course was a diamide treatment of BY4741 rpd1 cells. The fourth time course was a 10 minute heat shock in BY4741 rpd1 cells followed by treatment from 15-60 minutes with diamide (3 samples). In the first three time courses the time range is 0-120 minutes (5 samples). A total of 18 arrays were used in this study. No additional replicates or dye-flip experiments were performed.

Project description:Acetylation of histone H3 lysine 56 is a covalent modification best-known as a mark of newly-replicated chromatin, but has also been linked to replication-independent histone replacement. Here, we measured H3K56ac levels at single-nucleosome resolution in asynchronously growing yeast cultures, as well as in yeast proceeding synchronously through the cell cycle. We developed a quantitative model of H3K56ac kinetics, which shows that H3K56ac is largely explained by the genomic replication timing and the turnover rate of each nucleosome, suggesting that cell cycle profiles of H3K56ac should reveal most first-time nucleosome incorporation events. However, since the deacetylases Hst3/4 prevent use of H3K56ac as a marker for histone deposition during M phase, we also directly measured M phase histone replacement rates. We report a global decrease in turnover rates during M phase, and a further specific decrease in turnover among early origins of replication, which switch from rapidly-replaced in G1 phase to stable-bound during M phase. Finally, by measuring H3 replacement in yeast deleted for the H3K56 acetyltransferase Rtt109 and its two co-chaperones Asf1 and Vps75, we find evidence that Rtt109 and Asf1 preferentially enhance histone replacement at rapidly-replaced nucleosomes, whereas Vps75 appears to inhibit histone turnover at those loci. These results provide a broad perspective on histone replacement/incorporation throughout the cell cycle, and suggest that H3K56 acetylation provides a positive feedback loop by which replacement of a nucleosome enhances subsequent replacement at the same location. To characterize incorporation of H3K56ac in yeast, several sets of ChIP-chip experiments were performed, along with a set of gene expression microarrays. The following describes each individual set, the number of biological and array replicates performed, dye-flip replicates if performed, the total number of arrays, and the applicable figures in the manuscript. Except for Item D (gene expression), all arrays were ChIP-chip experiments. A. Mid-log H3K56ac measurement. 3 biological replicates, 1 array replicate each of K56Ac ChIP with Grunstein Lab antibody. 2 biological replicates, 2 array replicates each of K56Ac ChIP with Upstate antibody. 7 total arrays. Figure 1A-C, 3, S1, S2A, S6, S7. B. Cell cycle - H3K56ac measurements. 1 biological replicate, 3 array replicates each of K56Ac ChIP with Upstate antibody. 17 time points (10 min. unavailable in third array replicate). 50 total arrays. Figures 2, 3, 4, S4, S5, S7, S8. C. G1 phase turnover. 1 biological replicate (3 time points) with 1 dye-flip replicate each time point (45 minutes replaced with 60 minutes for dye-flip replicate). 6 total arrays. Tables S2, S3. D. Cell cycle - mRNA measurements. 2 biological replicates, 1 array replicate each. 34 total arrays. Figure S3. E. M phase turnover rates. 2 biological replicates, second biological replicate has one dye-flip replicate. 17 total arrays. Figure 5. F. Deletion mutants in H3K56ac pathway for G1-arrested cells. Strains: wild-type parental strain (PKY4212; 2 biological replicates), asf1D (strain 2 biological replicates), vps75D (3 biological replicates, third biological replicate having two array replicates), rtt109D (2 biological replicates, each with one dye-flip replicate). 12 total arrays. Figure 6.

Project description:Animals: Specific pathogen-free male Wistar-Kyoto rats weighing 300 to 350 g (Charles River Japan, Kanagawa, Japan) were used. Experiments were performed according to institutional guidelines. Anti-GBM GN: GBM antigen for the rats was prepared as described previously(1,2). Five albino rabbits were immunized subcutaneously with GBM antigen emulsified with Freund's complete adjuvant (Difco Laboratories, Detroit, MI, USA). A booster was given three times every two weeks using the same antigen. Four days after the final booster, the rabbits were bled from the carotid artery under anesthesia. Anti-GBM sera were heat-decomplemented for 30 min at 56 degrees Celsius and absorbed with freshly harvested rat erythrocytes. Rats were divided into several groups, each of which consisted of 4 to 8 rats. The rats assigned to the GN groups were injected in the dorsal tail vein with 3 ml/kg of anti-GBM serum diluted ten-fold with saline under ether anesthesia. The day of the anti-GBM serum injection was defined as day 0. The rats assigned to the control groups were injected intravenously with the same volume of non-immune rabbit control serum, for comparison with the anti-GBM GN rats. The general condition and body weight of the rats was observed over the course of the experiments. Drug Treatment: Prednisolone (Shionogi Pharmaceutical, Osaka, Japan) was administered orally at 1 mg/kg body weight twice a day from day 14 of anti-GBM serum injection until sacrifice. cDNA microarray analysis: NCH_RAT_SLK array contains about 3000 distinct cDNAs from a normalized rat kidney cDNA library. cDNAs were spotted on the slide glass with MAS coated surface (Matsunami, Osaka, Japan) and irradiate UV for cross-linking. Non-spotted surface was blocked with succinic anhydride. Total RNA was prepared from each renal cortex by a guanidinium isothiocyanate method using ISOGEN reagent (Nippon Gene, Tokyo, Japan), and mRNA was prepared by using Oligotex-dT30 (Takara, Shiga, Japan), according to the manufacturer's instructions. We hybridized labeled target DNAs on the cDNA microarrays with overnight incubation at 65 degrees Celsius. Hybridization images were scanned using a GenePix 4000A scanner (Axon Instruments, Union City, CA), and the signal intensities were quantified with GenePix Pro 3.0 software (Axon Instruments). Data analysis was performed(3,4). In brief, quantified signal intensities were converted by taking logarithms in base two. Using transformed data derived from each pair of competitive hybridization images, we drew scatter diagrams to compare sample signal intensities with those derived from controls, and executed regression analysis. The given residuals explain logarithmic gene expression ratios. Therefore, we selected genes whose average residuals were more than 1 or less than â1, i.e. they represented a two-fold difference in expression level. References 1. Yamada, M., Sasaki, R., Sato, N., Suzuki, M., Tamura, M., Matsushita, T., Kurumatani, H.Amelioration by beraprost sodium, a prostacyclin analogue, of established renal dysfunction in rat glomerulonephritis model. Eur. J. Pharmacol. 449, 167-176 (2002). 2. Krakower, C.A., Nicholes, B.K., Greenspon, S.A. Proteinuria and the fragility of normal and diseased glomerular basement membrane. Proc. Soc. Exp. Biol. Med. 159, 324-334 (1978). 3. Katsuma, S., Shiojima, S., Hirasawa, A., Suzuki, Y., Takagaki, K., Murai, M., Kaminishi, Y., Hada, Y., Koba, M., Muso, E., Miyawaki, S., Ohgi, T., Yano, J., Tsujimoto, G. Genomic analysis of a mouse model of immunoglobulin A nephropathy reveals an enhanced PDGF-EDG5 cascade. Pharmacogenomics J. 1, 211-217 (2001). 4. Dong, G., Loukinova, E., Chen, Z., Gangi, L., Chanturita, T.I., Liu, E.T., Van Waes, C. Molecular profiling of transformed and metastatic murine squamous carcinoma cells by differential display and cDNA microarray reveals altered expression of multiple genes related to growth, apoptosis, angiogenesis, and the NF-kappaB signal pathway. Cancer Res. 61, 4797-4808 (2001).

Project description:Algal photo-bio hydrogen production, a promising method for producing clean and renewable fuel in the form of hydrogen gas, has been studied extensively over the last few decades. In this study, microarray analyses were used to obtain a global expression profile of mRNA abundance in the green alga Chlamydomonas reinhardtii at five different time points before the onset and during the course of sulphur depleted hydrogen production. The present work confirms previous findings on the impacts of sulphur deprivation but also provides new insights into photosynthesis, sulphur assimilation and carbon metabolism under sulphur starvation towards hydrogen production. For instance, while a general trend towards repression of transcripts encoding photosynthetic genes was observed, the abundance of Lhcbm9 (encoding a major light harvesting polypeptide) and LhcSR1 (encoding a chlorophyll binding protein) was strongly elevated throughout the experiment, suggesting remodeling of the photosystem II light harvesting complex as well as an important function of Lhcbm9 under sulphur starvation. This study presents the first global transcriptional analysis of C. reinhardtii during hydrogen production using five major time points at Peak Oxygen, Mid Oxygen, Zero Oxygen, Mid Hydrogen and Peak Hydrogen. Keywords: Time course, sulfur deprivation, hydrogen production. Time course microarray analyses were used to analyze the global gene expression in sulfur depleted hydrogen producing C. reinhardtii. When depleted of sulfur, a sealed an illuminated C. reinhardtii culture slowly becomes anaerobic and produces hydrogen gas. Based on the changes in the dissolved O2 level and H2 production rate, the whole course of H2 production from the starting of S deprivation to the end of H2 production can be divided into five phases including an Aerobic Phase (I) in which the dissolved O2 level goes up (I), an O2 Consumption Phase (II), an Anaerobic Phase (III), a H2 Production Phase (IV) and a Termination phase (V). Samples were taken from three different bioreactors (biological replicates) at the following time points after the start of S depletion: 6 h, 16 h, 21 h, 37 h and 52 h corresponding to Peak O2, Mid O2, Zero O2, Mid H2 and Peak H2.

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Mouse Normal Variation

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