2011
DOI: 10.1074/jbc.m110.214486
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Structure-Function Analysis of Core STRIPAK Proteins

Abstract: We show that striatins and CCM3 regulate the Golgi localization of MST4 in an opposite manner. Consistent with a previously described function for MST4 and CCM3 in Golgi positioning, depletion of CCM3 or striatins affects Golgi polarization, also in an opposite manner. We propose that STRIPAK regulates the balance between MST4 localization at the Golgi and in the cytosol to control Golgi positioning.

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Cited by 137 publications
(101 citation statements)
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References 49 publications
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“…SETD6-associated peptides were affinity purified using an α-FLAG matrix. Then, SETD6-associated proteins were essentially identified by previously described mass spectrometric methods 24 , 25 . Interestingly, several SETD6-associated proteins identified have known biological functions related to nuclear hormone signaling (Fig.…”
Section: Resultsmentioning
confidence: 99%
“…SETD6-associated peptides were affinity purified using an α-FLAG matrix. Then, SETD6-associated proteins were essentially identified by previously described mass spectrometric methods 24 , 25 . Interestingly, several SETD6-associated proteins identified have known biological functions related to nuclear hormone signaling (Fig.…”
Section: Resultsmentioning
confidence: 99%
“…The high diversity of STRIPAK and STRIPAKlike complexes makes estimation of the molecular weight of the complex difficult. Human STRIPAK was found to play a role in Golgi apparatus polarization and is involved in mitosis by tether-ing Golgi vesicles to centrosomes and the nuclear membrane in a cell cycle-specific manner (22,23).…”
mentioning
confidence: 99%
“…Since HSP90 is one of the proteins most frequently detected in our standard AP-MS protocol, it would certainly have been filtered out by binary filters; however, when a newly developed tool called SAINT (Significance Analysis of INTeractome) [47], was applied, we were able to model the spectral count distribution for HSP90 (and all other proteins in the samples) across negative control samples, and provide a confidence value for each of the putative interactions detected. In addition to SAINT which has now been applied to large [15,69] and small [27,54] datasets and to intensity-based quantification [70], similar software tools that use label-free information to score the quality of protein-protein interactions have been developed [45,46,71]. All these tools were demonstrated to provide a more rigorous filtering of the contaminants than simple filters, leading to a better recovery of true interacting partners, though which tool performs best is dependent on the dataset at hand.…”
Section: From Identification and Relative Quantification To Interactionmentioning
confidence: 99%
“…In this case, the differential quantification between two or more samples is analyzed independently with sequential LC-MS runs, and software tools are used to calculate the relative area under the chromatographic peak. MS1-based quantification, when performed carefully, has proven very useful for the analysis of affinity-purified samples [52][53][54]. In addition to MS1-based quantification, another technique that is gaining increasing popularity is selected reaction monitoring (SRM).…”
Section: Quantitative Mass Spectrometrymentioning
confidence: 99%