Gene expression is an intricate process tightly linked from gene activation to the nuclear export of mRNA. Recent studies have indicated that the proteasome is essential for gene expression regulation. The proteasome regulatory particle binds to the SAGA complex and affects transcription in an ATP-dependent manner. Here we report that a specific interaction between the proteasomal ATPase, Rpt2p and Sgf73p of the SAGA complex leads to the dissociation of the H2Bub1-deubiquitylating module (herein designated the Sgf73-DUBm) from SAGA both in vitro and in vivo. We show that the localization of the Sgf73-DUBm on chromatin is perturbed in rpt2-1, a strain of Saccharomyces cerevisiae that is specifically defective in the Rpt2p-Sgf73p interaction. The rpt2-1 mutant also exhibits impaired localization of the TREX-2 and MEX67-MTR2 complexes and is defective in mRNA export. Our findings collectively demonstrate that the proteasome-mediated remodelling of the SAGA complex is a prerequisite for proper mRNA export.
Given a massive graph, how can we exploit its hierarchical structure for concisely but exactly summarizing the graph? By exploiting the structure, can we achieve better compression rates than state-of-the-art graph summarization methods?The explosive proliferation of the Web has accelerated the emergence of large graphs, such as online social networks and hyperlink networks. Consequently, graph compression has become increasingly important to process such large graphs without expensive I/O over the network or to disk. Among a number of approaches, graph summarization, which in essence combines similar nodes into a supernode and describe their connectivity concisely, protrudes with several advantages. However, we note that it fails to exploit pervasive hierarchical structures of realworld graphs as its underlying representation model enforces supernodes to be disjoint.In this work, we propose the hierarchical graph summarization model, which is an expressive graph representation model that includes the previous one proposed by Navlakha et al. as a special case. The new model represents an unweighted graph using positive and negative edges between hierarchical supernodes, each of which can contain others. Then, we propose SLUGGER, a scalable heuristic for concisely and exactly representing a given graph under our new model. SLUGGER greedily merges nodes into supernodes while maintaining and exploiting their hierarchy, which is later pruned. SLUGGER significantly accelerates this process by sampling, approximation, and memoization. Our experiments on 16 real-world graphs show that SLUGGER is (a) Effective: yielding up to 29.6% more concise summary than stateof-the-art lossless summarization methods, (b) Fast: summarizing a graph with 0.8 billion edges in a few hours, and (c) Scalable: scaling linearly with the number of edges in the input graph. * Equal contribution.
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