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Mitotic chromosome condensation screen

Mitotic chromosome condensation screen


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If I want to screen for mutant yeast (S. cerevisiae) colonies that have defects in mitotic chromosome condensation, can I screen for colonies arrested in Mid-M phase? Would that work?


Mitotic Chromosome Condensation Requires Brn1p, the Yeast Homologue of Barren

Department of Pharmacology and Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, California 92093-0651.

Department of Pharmacology and Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, California 92093-0651.

Department of Embryology, Howard Hughes Medical Institute, Carnegie Institution of Washington, Baltimore, Maryland 21210 and

Department of Pharmacology and Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, California 92093-0651.

In vitro studies suggest that the Barren protein may function as an activator of DNA topoisomerase II and/or as a component of theXenopus condensin complex. To better understand the role of Barren in vivo, we generated conditional alleles of the structural gene for Barren (BRN1) in Saccharomyces cerevisiae. We show that Barren is an essential protein required for chromosome condensation in vivo and that it is likely to function as an intrinsic component of the yeast condensation machinery. Consistent with this view, we show that Barren performs an essential function during a period of the cell cycle when chromosome condensation is established and maintained. In contrast, Barren does not serve as an essential activator of DNA topoisomerase II in vivo. Finally,brn1 mutants display additional phenotypes such as stretched chromosomes, aberrant anaphase spindles, and the accumulation of cells with >2C DNA content, suggesting that Barren function influences multiple aspects of chromosome transmission and dynamics.


Abstract

In vitro studies suggest that the Barren protein may function as an activator of DNA topoisomerase II and/or as a component of theXenopus condensin complex. To better understand the role of Barren in vivo, we generated conditional alleles of the structural gene for Barren (BRN1) in Saccharomyces cerevisiae. We show that Barren is an essential protein required for chromosome condensation in vivo and that it is likely to function as an intrinsic component of the yeast condensation machinery. Consistent with this view, we show that Barren performs an essential function during a period of the cell cycle when chromosome condensation is established and maintained. In contrast, Barren does not serve as an essential activator of DNA topoisomerase II in vivo. Finally,brn1 mutants display additional phenotypes such as stretched chromosomes, aberrant anaphase spindles, and the accumulation of cells with >2C DNA content, suggesting that Barren function influences multiple aspects of chromosome transmission and dynamics.


DRUG-INDUCED PCC

As described in the previous section, PEG- or virus-induced PCC (cell fusion-mediated PCC) is a useful method for chromosome analysis, but its use is problematic. Due to these restrictions, virus or PEG-mediated fusion-PCC is used in very limited number of laboratories only.

Several approaches have been carried out to induce PCC using chemicals, which would make the procedure easier and reliable. First successes were obtained with caffeine and okadaic acid, but cells had to be synchronized in S-phase using DNA synthesis inhibitors such as hydroxyurea or thymidine (Schlegel and Pardee, 1986 Schlegel et al., 1990 Yamashita et al., 1990 ). The obtained chromosomes were, therefore, S-phase PCC only, which cannot be used for usual chromosome analysis, because they appear pulverized. The first report of PCC induction in somatic cells in any phase of cell cycle, without arresting the cells in S-phase, came from Gotoh et al. ( 1995 ). The authors used calyculin A or okadaic acid, specific inhibitors of type 1 and 2A protein phosphatases (Bialojan and Takai, 1988 Ishihara et al., 1989 Cohen et al., 1990 ) (Fig. 3). Calyculin A is, in particular, able to induce PCC in many kinds of cells, either in suspension or attached, and the PCC index is generally higher than with other protein phosphatase inhibitors. The method of drug-induced PCC is relatively easy whereby simply substitutes the use of colcemid to calyculin A in conventional chromosome preparation protocol (Gotoh et al., 1995 Durante et al., 1998a ). Incubation time of only approximately 30 min, much shorter than colcemid block (2–4 h) can induce a substantial number of PCCs. In addition, PCC index (corresponds to mitotic index) is usually much higher (>20%) than mitotic index of colcemid protocol (∼1–2%) (Gotoh et al., 1995 Asakawa and Gotoh, 1997 Durante et al., 1998a ). Examples of PCCs in different phases of the cell cycle, visualized by Giemsa-stained or fluorescence in situ hybridization (FISH) with whole-chromosome human DNA probes, are given in Figure 4. Drug-induced PCC is becoming increasingly popular, and a lot of reports in the literature using this technique have been recently published, where this technique is applied to several mammalian cells, either attached or in suspension (Gotoh and Asakawa, 1996 Asakawa and Gotoh, 1997 Coco-Martin and Begg, 1997 Durante et al., 1998a , 1999 Gotoh et al., 1999 , 2005 Prasanna et al., 2000 Ito et al., 2002 Bezrookove et al., 2003 Terzoudi et al., 2003 Malik et al., 2004 Blakely et al., 2005 El Achkar et al., 2005 Someya et al., 2006 ).

Molecular structures of calyculin A and okadaic acid, two powerful protein phosphatase inhibitors and PCC inducers. Chemical characteristics are: Calyculin A: Molecular Formula, C50H81N4O15P Molecular Weight, 1009.18 IC50 for PP1, 0.5–2 nM IC50 for PP2A, 0.1–1 nM Source, Discodermia Calyx. Okadaic Acid: Molecular Formula, C44H68O13 Molecular Weight, 805.2 IC50 for PPI, 10–60 nM IC50 for PP2A, 0.1–1 nM Source, Halichondria Okadai.

Examples of PCC in human mononuclear lymphocytes induced by calyculin A. A: A Giemsa-stained image of three cells in the same area of the slide in three different phases of the cell cycle: left, univalent G1 chromosomes center, bivalent G2 chromosomes right, pulverized S-phase cell. B: An insertion in chromosome 2, FISH-painted in spectrum orange (pointed by a white arrow) in a cell from a patient during radiotherapy. The normal chromosome 1 is painted in green. An S-phase cell is also visible in the same image, pointed by the yellow arrow. Drug-induced PCC is preferable to conventional colcemid-block for biodosimetry after partial-body exposure, such as in radiotherapy. C: A multi-aberrant cell, showing several complex rearrangements in both chromosomes 1 and 2, followed exposure to 1 Gy of high-energy iron ions. Severe damage induced by densely ionizing radiation may be underestimated using metaphase analysis.


DISCUSSION

Combined kernels on graphs of biological information are effective at information retrieval

We chose to view individual data types on gene function as graphs and measure functional similarity between genes as nodes of these graphs using kernels because of their attractive properties for data integration and mining. We limited our study to a few kernel functions with a preference for those that are parameter free. We demonstrated that the commute time was a powerful and parameter-free measure of similarity between genes across various biological data types viewed as graphs. It performed well in retrieving known functional relationships from various data sets, and among all kernels tested, it appeared the most robust, since it always gave the best or close to the best performance for each data type. In contrast, performance varied more widely for the other kernels depending on the data type. In particular, the diffusion kernel performed poorly for some values of its parameter, illustrating the importance of parameter choice for kernels with free parameters. Except for the diffusion kernel, the graph-derived kernels we used were less sensitive to bias introduced by highly connected genes. To our knowledge, our approach is the first to compare performances of different kernels and identify the best kernel for a particular data set before integrating it with other data. We furthermore showed that integration of several data types improved information retrieval power and that these data types were best integrated by combining the graph-derived kernels using the best kernel function for each data type rather than the graphs themselves as in GeneMANIA (Mostafavi and Morris, 2010). Therefore our approach compares favorably with state-of-the-art algorithms on information retrieval.

Combined kernels are powerful predictors of gene function

The interdependent nature of biological databases can lead to a good performance of computational methods in information retrieval but makes it difficult to assess performance for predicting new genes for biological functions. To test the kernel performance, we therefore tested new gene function predictions more stringently using data from genome-scale RNAi screens that were not included in our data sources. We could show that the top-ranked kernel-predicted genes are significantly enriched in the expected phenotypes for all five phenotypes queried with example genes (mitosis defect, cytokinesis defect, increased cell motility, DNA damage response, and NF-κ B activation). Nevertheless, many of the top kernel-predicted genes did not score as hits in the screens examined. This can be explained by either false positives in the predictions or false negatives in the screens. False-positive predictions could be produced if most of the genes in the query are not relevant. Therefore care has to be taken in the selection of query genes, and there may be better ways of selecting query genes for a particular process than using annotations from Gene Ontology as used here. In addition, it is likely that a significant fraction of the kernel-predicted genes that did not score correspond to false negatives in the screens. Indeed, false-negative rates between 8 and 34% have been reported in Drosophila (Liu et al., 2009 Booker et al., 2011) and human cells (Neumann et al., 2010). It is therefore likely that our virtual screen validation underestimated the kernel prediction power and instead provides a lower bound on the prediction performance.

It should also be noted that genes not represented in the source data are not accessible to the method. To be able to select completely uncharacterized genes, genome-wide experimental data sets or ab initio (e.g., sequence-derived) data would have to be included. However, our preliminary tests of genome-wide microarray data and sequence-based predicted interactions led us to exclude these data sets for making predictions because of poor performance.

Although the kernel combination approach slightly but consistently outperformed GeneMANIA, we note that it is difficult to demonstrate that any approach is the best possible without extensive experimental validation of all alternative methods. Nevertheless, the success rate of our predictions represents a fivefold increase over genome-wide screening, which in this context makes graph-derived, kernel-based gene ranking of practical value. For example, scaling up our high-resolution time-lapse imaging assay to cover the ∼21,000 protein-coding genes identified in the human genome would require more than 200 TB of disk space just to store the microscopy images, and the cost in reagents and consumables alone would reach several hundred thousand dollars. Therefore in silico genome-wide prescreening of genes to focus experimental testing on the top-ranked candidates can be an excellent alternative to costly and labor-intensive genome-wide experiments. Kernels on graph nodes represent a powerful method for gene function prediction, representing an easy-to-use “funnel” for the selection of candidate genes, and we therefore make our software freely available to the community at http://funl.org.

Kernels predict new genes that function in chromosome condensation

Finding new chromosome condensation genes has proven to be difficult for many years. This is possibly because condensation requires multiple contributing activities that, when singly inactivated, would produce only minor and transient condensation defects. Capturing these subtle phenotypes therefore requires quantitative monitoring of chromosome condensation in living cells, which is very difficult to do on the genome scale but is feasible with a candidate gene set. We therefore used this very sensitive phenotypic readout to screen the top-100-ranked kernel-predicted genes involved in mitotic chromosome condensation. Strikingly, these contained 32 new genes that caused a reproducible mitotic chromosome condensation phenotype upon knockdown that had not been previously described in mammalian cells. Eleven of the 32 genes score as high-confidence positives and therefore open new avenues for experiments. For example, TRAF3IP1 is involved in primary cilium formation (Berbari et al., 2011) and has also been implicated in signal transduction pathways (Niu et al., 2003 Ng et al., 2011) but not in chromosome condensation. Our study also clarifies several leads from the literature that had not been followed up. For example, histone deacetylases have been implicated in chromosome condensation with conflicting results (e.g., Cimini et al., 2003 Dowling et al., 2005) and without resolving the identity of the HDAC(s) involved. Similarly, DNA methylase DNMT3B was found associated with chromatin genes, including several condensin subunits (Geiman et al., 2004), but its role in mitotic chromosome condensation had not been demonstrated. Our work also highlights how a computational approach can find indirect connections between genes that would otherwise be difficult to find manually. For example, whereas PAPD5 is postulated to be a component of the human TRAMP complex involved in polyadenylation of RNAs and their subsequent targeting for degradation by the exosome (Schmidt and Butler, 2013), a mutation in trf4, a PAPD5 homologue in Saccharomyces cerevisiae, genetically interacts with top1 deletion to cause defects in ribosomal DNA condensation (Castaño et al., 1996).

Quantitative analysis of prophase chromosome condensation reveals a new functional aspect

Although mitotic chromosome condensation is inherently a dynamic process, very few studies have quantified it in live cells with a high temporal resolution, and, to our knowledge, no live-cell analyses of perturbations of the condensation process have been reported. Changes in the texture of fluorescently labeled chromatin between interphase and prometaphase are commonly used to define prophase. Our screen was based on the assumption that this definition of prophase reflects changes in chromatin volume. To test this assumption and further characterize chromosome condensation, we computationally analyzed high-resolution images from 3D time-lapse confocal microscopy to quantify chromatin volume during mitosis in control cells and in knockdowns of several hits from the screen. The observed variations in chromatin volume correlated well with the length of prophase as defined by texture classification under all conditions, confirming that chromatin texture is a good indicator of chromosome condensation. The volume measurements showed that gene knockdowns affected primarily the kinetics of compaction rather than the final compaction state of chromatin, consistent with the assumption of subtle phenotypes due to additive requirements of multiple factors. Volume analysis furthermore revealed that in the absence of prophase condensation, chromatin transiently expanded when the constraint of the nuclear envelope boundary was released by its breakdown at the end of prophase. Although the prompt prometaphase chromosome compaction rapidly reversed this expansion, this observation suggests a potential new function for prophase condensation, that is, to prevent chromatin leakage from the nucleus at NEBD.


An Extragenic Suppressor of the Mitosis-Defective Bimd6 Mutation of Aspergillus Nidulans Codes for a Chromosome Scaffold Protein

We previously identified a gene, bimD, that functions in chromosome segregation and contains sequences suggesting that it may be a DNA-binding protein. Two conditionally lethal mutations in bimD arrest with aberrant mitotic spindles at restrictive temperature. These spindles have one-third the normal number of microtubules, and the chromosomes never attach to the remaining microtubules. For this reason, we hypothesized that BIMD functioned in chromosome segregation, possibly as a component of the kinetochore. To identify other components that function with bimD, we conducted a screen for extragenic suppressors of the bimD5 and bimD6 mutations. We have isolated seven cold-sensitive extragenic suppressors of bimD6 heat sensitivity that represent three or possibly four separate sud genes. We have cloned one of the suppressor genes by complementation of the cold-sensitive phenotype of the sudA3 mutation. SUDA belongs to the DA-box protein family. DA-box proteins have been shown to function in chromosome structure and segregation. Thus bimD and the sud genes cooperatively function in chromosome segregation in Aspergillus nidulans.


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Chromosome condensation: maternal and paternal differences

Chromosome condensation: maternal and paternal differences. J Cell Sci 15 July 2002 115 (14): e1405. doi:


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