Multiple mechanisms determine ER network morphology during the cell cycle inXenopusegg extracts
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ABSTRACT:
SUBMITTER: Songyu Wang
PROVIDER: S-JCBD-201308001 | bioimages |
REPOSITORIES: bioimages
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Project description:In metazoans the endoplasmic reticulum (ER) changes during the cell cycle, with the nuclear envelope (NE) disassembling and reassembling during mitosis and the peripheral ER undergoing extensive remodeling. Here we address how ER morphology is generated during the cell cycle using crude and fractionated Xenopus laevis egg extracts. We show that in interphase the ER is concentrated at the microtubule (MT)-organizing center by dynein and is spread by outward extension of ER tubules through their association with plus ends of growing MTs. Fusion of membranes into an ER network is dependent on the guanosine triphosphatase atlastin (ATL). NE assembly requires fusion by both ATL and ER-soluble N-ethyl-maleimide-sensitive factor adaptor protein receptors. In mitotic extracts, the ER converts into a network of sheets connected by ER tubules and loses most of its interactions with MTs. Together, these results indicate that fusion of ER membranes by ATL and interaction of ER with growing MT ends and dynein cooperate to generate distinct ER morphologies during the cell cycle.
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Project description:BACKGROUND:Cell division is central to the physiology and pathology of all eukaryotic organisms. The molecular machinery underpinning the cell cycle has been studied extensively in a number of species and core aspects of it have been found to be highly conserved. Similarly, the transcriptional changes associated with this pathway have been studied in different organisms and different cell types. In each case hundreds of genes have been reported to be regulated, however there seems to be little consensus in the genes identified across different studies. In a recent comparison of transcriptomic studies of the cell cycle in different human cell types, only 96 cell cycle genes were reported to be the same across all studies examined. RESULTS:Here we perform a systematic re-examination of published human cell cycle expression data by using a network-based approach to identify groups of genes with a similar expression profile and therefore function. Two clusters in particular, containing 298 transcripts, showed patterns of expression consistent with cell cycle occurrence across the four human cell types assessed. CONCLUSIONS:Our analysis shows that there is a far greater conservation of cell cycle-associated gene expression across human cell types than reported previously, which can be separated into two distinct transcriptional networks associated with the G1/S-S and G2-M phases of the cell cycle. This work also highlights the benefits of performing a re-analysis on combined datasets.
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Project description:Cyclin-dependent kinase (Cdk) phosphorylation of the Retinoblastoma protein (Rb) drives cell proliferation through inhibition of Rb complexes with E2F transcription factors and other regulatory proteins. We present the first structures of phosphorylated Rb that reveal the mechanism of its inactivation. S608 phosphorylation orders a flexible "pocket" domain loop such that it mimics and directly blocks E2F transactivation domain (E2F(TD)) binding. T373 phosphorylation induces a global conformational change that associates the pocket and N-terminal domains (RbN). This first multidomain Rb structure demonstrates a novel role for RbN in allosterically inhibiting the E2F(TD)-pocket association and protein binding to the pocket "LxCxE" site. Together, these structures detail the regulatory mechanism for a canonical growth-repressive complex and provide a novel example of how multisite Cdk phosphorylation induces diverse structural changes to influence cell cycle signaling.
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Project description:To better characterize group IE like human breast cancer based on the gene profiles of estrogen actions through estrogen receptor alpha (ER alpha), we identified an ER alpha transcriptional regulatory network for cell cycle in silico. We used two datasets from cell line (Data 1) and clinical samples (Data 2), respectively. Analyses on Data 1 via trajectory clustering and Pathway-Express confirmed the significant estrogen effect on up-regulating cell cycle activities. The gene expression relationships between ER alpha and cell cycle genes were re-identified in Data 2 by three statistical methods – Galton-Pearson’s correlation coefficient, Student’s t-test and the coefficient of intrinsic dependence. They were mostly (56.09%)(46/82) re-confirmed by literature search. E2F1 was found to be the major ER alpha target in regulating cell cycle gene expressions (83.72%)(36/43) via suppressive mode. However, enhanced cell cycle progression via up-regulating some cell cycle genes was predicted in silico possibly involving E2F2, in part. Both tumorigenic and tumor suppressing activities indicated by this network were predicted. This network clearly provides a robust way for uncovering estrogen actions in an ER(+) subtype specific manner.
2009-07-24 | GSE17040 | GEO
Project description:To better characterize group IE like human breast cancer based on the gene profiles of estrogen actions through estrogen receptor alpha (ER alpha), we identified an ER alpha transcriptional regulatory network for cell cycle in silico. We used two datasets from cell line (Data 1) and clinical samples (Data 2), respectively. Analyses on Data 1 via trajectory clustering and Pathway-Express confirmed the significant estrogen effect on up-regulating cell cycle activities. The gene expression relationships between ER alpha and cell cycle genes were re-identified in Data 2 by three statistical methods – Galton-Pearson’s correlation coefficient, Student’s t-test and the coefficient of intrinsic dependence. They were mostly (56.09%)(46/82) re-confirmed by literature search. E2F1 was found to be the major ER alpha target in regulating cell cycle gene expressions (83.72%)(36/43) via suppressive mode. However, enhanced cell cycle progression via up-regulating some cell cycle genes was predicted in silico possibly involving E2F2, in part. Both tumorigenic and tumor suppressing activities indicated by this network were predicted. This network clearly provides a robust way for uncovering estrogen actions in an ER(+) subtype specific manner. Experiment Overall Design: Two clinical datasets were used in this study. One, the 37 clinical arrays (abbreviated as 37A) consist of 26 A for patients positive in estrogen receptor alpha (ER) and in progesterone receptor (PR) immunohistochemical stain (IHC) and 11A for patients negative in ER IHC. This dataset was designated as Data 2. The 31 clinical arrays (31A) consist of 20A for patients positive in ER status but negative in PR status and 11A which are the same as in 37A. This dataset was used for data comparison. All the signals from the mRNA profile of each sample in the experiments were normalized using the internal control RNA- Stratagene's human common reference RNA via statistical method 'rank consistant lowess. Finally, those ratios were transformed by Log2.
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