Cell biology: Import and nuclear size.

Nature (Impact Factor: 42.35). 11/2010; 468(7323):513-6. DOI: 10.1038/468513a
Source: PubMed

ABSTRACT The size of a cell's nucleus is usually proportional to the size of the cell itself. How are the two linked? The answer lies, at least in part, in the import of one or more cytoplasmic cargoes into the nucleus.

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    ABSTRACT: Background High Content Screening (HCS) has become an important tool for toxicity assessment, partly due to its advantage of handling multiple measurements simultaneously. This approach has provided insight and contributed to the understanding of systems biology at cellular level. To fully realize this potential, the simultaneously measured multiple endpoints from a live cell should be considered in a probabilistic relationship to assess the cell's condition to response stress from a treatment, which poses a great challenge to extract hidden knowledge and relationships from these measurements. Method In this work, we applied a text mining method of Latent Dirichlet Allocation (LDA) to analyze cellular endpoints from in vitro HCS assays and related to the findings to in vivo histopathological observations. We measured multiple HCS assay endpoints for 122 drugs. Since LDA requires the data to be represented in document-term format, we first converted the continuous value of the measurements to the word frequency that can processed by the text mining tool. For each of the drugs, we generated a document for each of the 4 time points. Thus, we ended with 488 documents (drug-hour) each having different values for the 10 endpoints which are treated as words. We extracted three topics using LDA and examined these to identify diagnostic topics for 45 common drugs located in vivo experiments from the Japanese Toxicogenomics Project (TGP) observing their necrosis findings at 6 and 24 hours after treatment. Results We found that assay endpoints assigned to particular topics were in concordance with the histopathology observed. Drugs showing necrosis at 6 hour were linked to severe damage events such as Steatosis, DNA Fragmentation, Mitochondrial Potential, and Lysosome Mass. DNA Damage and Apoptosis were associated with drugs causing necrosis at 24 hours, suggesting an interplay of the two pathways in these drugs. Drugs with no sign of necrosis we related to the Cell Loss and Nuclear Size assays, which is suggestive of hepatocyte regeneration. Conclusions The evidence from this study suggests that topic modeling with LDA can enable us to interpret relationships of endpoints of in vitro assays along with an in vivo histological finding, necrosis. Effectiveness of this approach may add substantially to our understanding of systems biology.
    BMC Bioinformatics 10/2013; 14(14). DOI:10.1186/1471-2105-14-S14-S11 · 2.67 Impact Factor
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    ABSTRACT: Evolution of metabolic rates of multicellular organisms is hypothesized to reflect the evolution of their cell architecture. This is likely to stem from a tight link between the sizes of cells and nuclei, which are expected to be inversely related to cell metabolism. Here, we analysed basal metabolic rate (BMR), internal organ masses and the cell/nucleus size in different tissues of laboratory mice divergently selected for high/low mass-corrected BMR and four random-bred mouse lines. Random-bred lines had intermediate levels of BMR as compared to low- and high-BMR lines. Yet, this pattern was only partly consistent with the between-line differences in cell/nucleus sizes. Erythrocytes and skin epithelium cells were smaller in the high-BMR line than in other lines, but the cells of low-BMR and random-bred mice were similar in size. On the other hand, the size of hepatocytes, kidney proximal tubule cells and duodenum enterocytes were larger in high-BMR mice than other lines. All cell and nucleus sizes were positively correlated, which supports the role of the nucleus in cell size regulation. Our results suggest that the evolution of high BMR involves a reduction in cell size in specialized tissues, whose functions are primarily dictated by surface-to-volume ratios, such as erythrocytes. High BMR may, however, also incur an increase in cell size in tissues with an intense transcription and translation, such as hepatocytes.
    Journal of Evolutionary Biology 01/2014; 27(3). DOI:10.1111/jeb.12306 · 3.48 Impact Factor
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    ABSTRACT: The chromatin-associated proteome (chromatome) regulates cellular gene expression by restricting access of transcriptional machinery to template DNA, and dynamic re-modeling of chromatin structure is required to regulate critical cell functions including growth and replication, DNA repair and recombination, and oncogenic transformation in progression to cancer. Central to the control of these processes is efficient regulation of the host cell cycle, which is maintained by rapid changes in chromatin conformation during normal cycle progression. A global overview of chromatin protein organization is therefore essential to fully understand cell cycle regulation, but the influence of the chromatome and chromatin binding topology on host cell cycle progression remains poorly defined. Here we used partial MNase digestion together with iTRAQ-based high-throughput quantitative proteomics to quantify chromatin-associated proteins during interphase progression. We identified a total of 481 proteins with high confidence that were involved in chromatin-dependent events including transcriptional regulation, chromatin re-organization, and DNA replication and repair, while the quantitative data revealed the temporal interactions of these proteins with chromatin during interphase progression. When combined with biochemical and functional assays, these data revealed a strikingly dynamic association of protein HP1BP3 with the chromatin complex during different stages of interphase, and uncovered a novel regulatory role for this molecule in transcriptional regulation. We report that HP1BP3 protein maintains heterochromatin integrity during G1-S progression and regulates the duration of G1 phase to critically influence cell proliferative capacity.
    Molecular &amp Cellular Proteomics 05/2014; 13(9). DOI:10.1074/mcp.M113.034975 · 7.25 Impact Factor


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