Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation

Hériché, Jean‐Karim; Lees, Jon; Morilla, Ian; Walter, Thomas; Petrova, Boryana; Roberti, M. Julia; Hossain, M. Julius; Adler, Priit; Fernández, José Marı́a; Krallinger, Martin; Haering, Christian H.; Vilo, Jaak; Valencia, Alfonso; Ranea, Juan A. G.; Orengo, Christine A.; Ellenberg, Jan

doi:10.1091/mbc.e13-04-0221

Cited by 44 publications

(41 citation statements)

References 63 publications

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“…To validate the optimized clusters, three additional approaches demonstrated their biological significance. First, proteins that participate in the same pathway/cellular function are likely to share binding partners and thus are found together in protein–protein interaction (PPI) networks (see methods)27. Based on neighbour interacting partners of individual candidate proteins, randomized clusters containing the same number of proteins of each phenotypic cluster were produced (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…This was done in order to preserve the number and size of the clusters to be the same as the optimized experimental clusters obtained above. The network of PPIs of the candidate proteins was converted into a Kernel using a Commuter time Kernel (CK)276566 to measure the homogeneity of the clusters. For a given randomized cluster, the Commuter time (CK) score was calculated as following: for each protein in each cluster, the highest kernel similarity score to any other protein in the same cluster is obtained.…”

Section: Methodsmentioning

confidence: 99%

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Defining functional interactions during biogenesis of epithelial junctions

et al. 2016

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In spite of extensive recent progress, a comprehensive understanding of how actin cytoskeleton remodelling supports stable junctions remains to be established. Here we design a platform that integrates actin functions with optimized phenotypic clustering and identify new cytoskeletal proteins, their functional hierarchy and pathways that modulate E-cadherin adhesion. Depletion of EEF1A, an actin bundling protein, increases E-cadherin levels at junctions without a corresponding reinforcement of cell–cell contacts. This unexpected result reflects a more dynamic and mobile junctional actin in EEF1A-depleted cells. A partner for EEF1A in cadherin contact maintenance is the formin DIAPH2, which interacts with EEF1A. In contrast, depletion of either the endocytic regulator TRIP10 or the Rho GTPase activator VAV2 reduces E-cadherin levels at junctions. TRIP10 binds to and requires VAV2 function for its junctional localization. Overall, we present new conceptual insights on junction stabilization, which integrate known and novel pathways with impact for epithelial morphogenesis, homeostasis and diseases.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Defining functional interactions during biogenesis of epithelial junctions

et al. 2016

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“…Of particular interest is whether Ki67 is inactivated after this dephosphorylation as suggested for RCA. Researchers have identified several proteins involved in mitotic chromosome condensation in a condensin-independent manner (Petrova et al 2013;Heriche et al 2014;Robellet et al 2014;Nikalayevich & Ohkura 2015). The relevance of these proteins and Ki67 is also worth examining.…”

Section: Discussionmentioning

confidence: 99%

“…The significant contributions of condensin complexes and topoisomerase IIa (TopoIIa) to the assembly of mitotic chromosomes have been demonstrated in various biological and experimental contexts (Swedlow & Hirano 2003;Hirano 2012). On the contrary, certain cells reportedly manage to assemble mitotic chromosomes with a seemingly normal condensation state-albeit with imperfect shape-even in the absence of condensins or TopoIIa, which has prompted researchers to seek additional chromosome condensation factors (Vagnarelli et al 2006;Petrova et al 2013;Heriche et al 2014;Robellet et al 2014;Nikalayevich & Ohkura 2015). Although recent studies have shown that mitotic chromosome-like structures can be reconstituted in vitro with only six defined factors (Shintomi et al 2015), their relevance to authentic chromosomes formed in vivo remains to be verified.…”

Section: Introductionmentioning

confidence: 99%

Perichromosomal protein Ki67 supports mitotic chromosome architecture

et al. 2016

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Although the condensin complexes and topoisomerase IIa (TopoIIa) are the central players in mitotic chromosome formation, they are insufficient for its completion, and additional factors involved in the process have been extensively sought. In this study, we examined the possibility that Ki67, a perichromosomal protein widely used as a cell proliferation marker, is one such factor. Using a combination of auxin-inducible degron and CRISPR-Cas9-based gene editing technologies, we generated a human HCT116 cell line in which Ki67 is rapidly depleted in a few hours. The removal of Ki67 before mitotic entry did not impact the early mitotic chromosome assembly observed in prophase but subsequently resulted in the formation of misshapen mitotic chromosomes. When Ki67 was removed after mitotic entry, preassembled rod-shaped mitotic chromosomes became disorganized. In addition, we show that Ki67 and TopoIIa are reciprocally coimmunoprecipitated from mitotic cell extracts. These observations indicate that Ki67 aids the finalization of mitotic chromosome formation and helps maintain rod-shaped chromosome architecture, likely in collaboration with TopoIIa. Together, these findings represent a new model in which mitotic chromosome architecture is supported both internally and externally.

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“…A 3D Gaussian filter was applied to reduce the effects of noise. To detect chromosomal regions, the filtered image stack was binarized first using a multi-level thresholding method as described in 36 . In this approach, a global Otsu threshold 37 was determined for the entire stack and the threshold was then adapted for each 2D slice, validated by the connectivity of binary components in 3D.…”

Section: Figure Legendsmentioning

confidence: 99%

Experimental and computational framework for a dynamic protein atlas of human cell division

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Hossain

Hèriché

et al. 2017

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SUMMARYEssential biological functions, such as mitosis, require tight coordination of hundreds of proteins in space and time. Localization, timing of interactions and changes in cellular structure are all crucial to ensure correct assembly, function and regulation of protein complexes1-4. Live cell imaging can reveal protein distributions and dynamics but experimental and theoretical challenges prevented its use to produce quantitative data and a model of mitosis that comprehensively integrates information and enables analysis of the dynamic interactions between the molecular parts of the mitotic machinery within changing cellular boundaries.To address this, we generated a 4D image data-driven, canonical model of the morphological changes during mitotic progression of human cells. We used this model to integrate dynamic 3D concentration data of many fluorescently knocked-in mitotic proteins, imaged by fluorescence correlation spectroscopy-calibrated microscopy5. The approach taken here in the context of the MitoSys consortium to generate a dynamic protein atlas of human cell division is generic. It can be applied to systematically map and mine dynamic protein localization networks that drive cell division in different cell types and can be conceptually transferred to other cellular functions.

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Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation

Cited by 44 publications

References 63 publications

Defining functional interactions during biogenesis of epithelial junctions

Defining functional interactions during biogenesis of epithelial junctions

Perichromosomal protein Ki67 supports mitotic chromosome architecture

Experimental and computational framework for a dynamic protein atlas of human cell division

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