29 research outputs found

    Overexpression of the Linker Histone-binding Protein tNASP Affects Progression through the Cell Cycle

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    NASP is an H1 histone-binding protein that is cell cycle-regulated and occurs in two major forms: tNASP, found in gametes, embryonic cells, and transformed cells; and sNASP, found in all rapidly dividing somatic cells (Richardson, R. T., Batova, I. N., Widgren, E. E., Zheng, L. X., Whitfield, M., Marzluff, W. F., and O'Rand, M. G. (2000) J. Biol. Chem. 275, 30378-30386). When full-length tNASP fused to green fluorescent protein (GFP) is transiently transfected into HeLa cells, it is efficiently transported into the nucleus within 2 h after translation in the cytoplasm, whereas the NASP nuclear localization signal (NLS) deletion mutant (NASP-DeltaNLS-GFP) is retained in the cytoplasm. In HeLa cells synchronized by a double thymidine block and transiently transfected to overexpress full-length tNASP or NASP-DeltaNLS, progression through the G(1)/S border is delayed. Cells transiently transfected to overexpress the histone-binding site (HBS) deletion mutant (NASP-DeltaHBS) or sNASP were not delayed in progression through the G(1)/S border. By using a DNA supercoiling assay, in vitro binding data demonstrate that H1 histone-tNASP complexes can transfer H1 histones to DNA, whereas NASP-DeltaHBS cannot. Measurement of NASP mobility in the nucleus by fluorescence recovery after photobleaching indicates that NASP mobility is virtually identical to that reported for H1 histones. These data suggest that NASP-H1 complexes exist in the nucleus and that tNASP can influence cell cycle progression through the G(1)/S border through mediation of DNA-H1 histone binding

    Natural Variation in Fish Transcriptomes: Comparative Analysis of the Fathead Minnow (Pimephales promelas) and Zebrafish (Danio rerio).

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    Fathead minnow and zebrafish are among the most intensively studied fish species in environmental toxicogenomics. To aid the assessment and interpretation of subtle transcriptomic effects from treatment conditions of interest, better characterization and understanding are needed for natural variation in gene expression among fish individuals from lab cultures. Leveraging the transcriptomics data from a number of our toxicogenomics studies conducted over the years, we conducted a meta-analysis of nearly 600 microarrays generated from the ovary tissue of untreated, reproductively mature fathead minnow and zebrafish samples. As expected, there was considerable batch-to-batch transcriptomic variation; this batch-effect appeared to differentially impact subsets of fish transcriptomes in a nonsystematic way. Temporally more closely spaced batches tended to share a greater transcriptomic similarity among one another. The overall level of within-batch variation was quite low in fish ovary tissue, making it a suitable system for studying chemical stressors with subtle biological effects. The observed differences in the within-batch variability of gene expression, at the levels of both individual genes and pathways, were probably both technical and biological. This suggests that biological interpretation and prioritization of genes and pathways targeted by experimental conditions should take into account both their intrinsic variability and the size of induced transcriptional changes. There was significant conservation of both the genomes and transcriptomes between fathead minnow and zebrafish. The high degree of conservation offers promising opportunities in not only studying fish molecular responses to environmental stressors by a comparative biology approach, but also effective sharing of a large amount of existing public transcriptomics data for developing toxicogenomics applications

    Within-batch variation as measured by the maximum (% of transcriptome) and minimum number of DEGs per permutation.

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    <p>There were 250 permutations conducted under individual factors. Between-batch variation was controlled statistically in these analyses. PPR, fathead minnow; DRE, zebrafish.</p><p>Within-batch variation as measured by the maximum (% of transcriptome) and minimum number of DEGs per permutation.</p

    Between-batch variation as measured by average number of DEGs (standard deviations where N>3).

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    <p>The DEGs were identified in paired comparisons of N–1 batches against a batch designated as a common reference. N is the total number of batches in a factor. The comparisons were made in conjunction with the analysis of within-batch effects involving 250 permutations. There were little variations in between-batch effects among permutations so their calculations were made only from the first permutation. DRE, zebrafish; PPR, fathead minnow.</p><p>Between-batch variation as measured by average number of DEGs (standard deviations where N>3).</p

    Within-batch variation as measured by coefficient of variation (CV).

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    <p>The CVs were calculated for each probe by individual batches under the Experiment factor and averaged over all batches. Further averaging these CVs across the entire transcriptome yielded an overall CV of 0.056 for fathead minnow (PPR) and 0.051 for zebrafish (DRE). The total number of orthologous genes identified between Agilent 015064 and 019597 was 6617, represented by 9311 and 6950 unique probes respectively. The PPR probes mapped to their EST sequences and ESTs to NCBI databases by BLAST all had a minimum E-value of E-06.</p><p>Within-batch variation as measured by coefficient of variation (CV).</p

    Interspecific correlation by average intensity and average coefficient of variation of individual pathways.

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    <p>A total of 84 (three outliers excluded) KEGG pathways were calculated for their average intensities (A), and 53 (two outliers excluded) pathways for their average CVs (B), based on a combined total of 6617 orthologous genes. To be included, each pathway must have at least five orthologs and a p-value of ≤0.1 for the correlation of the intensities or CVs of its member genes as estimated within a batch. The CCs were 0.86 and 0.80 for the average intensity and average CV by pathway respectively, with the both p-values = 0. The p-values of normality test of error distribution for linear regressions were 0.045 and 0.585 respectively.</p
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