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Overview of mChIP-MS procedure and results. (A) Experimental platform for the large-scale characterization of chromatin-associated proteins by mChIP-MS in Saccharomyces cerevisiae. See Materials and methods section for complete details of the mChIP-MS pipeline. (B) Summary of the mChIP-MS data set. (C) Two-dimensional hierarchical clustering of bait interaction profiles between the 102 different bait analyzed by mChIP-MS. The overlap between the preys identified in individual mChIP-MS experiments was defined by first computing the distance measure based on the cosine function using the preys peptide count. Subsequently, the heat map was generated using the cluster software and visualized with Java Treeview from the computed bait–bait distance matrix. In addition, some known protein complexes were manually highlighted on the heat map. See Supplementary Figure S2 for a high-resolution image of the cluster.
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We previously reported a novel affinity purification (AP) method termed modified chromatin immunopurification (mChIP), which permits selective enrichment of DNA-bound proteins along with their associated protein network. In this study, we report a large-scale study of the protein network of 102 chromatin-related proteins from budding yeast that wer...
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... For example, that ACE2, SWI5 and SOK2 fall into the same group and share the same set of large coefficients provides useful biological insights, as it is well known that ACE2 and SWI5 are paralogs, meaning that they are related to each other through a gene duplication event and are highly conserved in yeast cell cycle gene progression, and according to Pan and Heitman (2000), with regards to nitrogen limitation, SOK2, along with ACE2 and SWI5, is essential in the pseudohyphal growth of yeast cells. Our analysis also provides a cluster with three TFs, namely HIR1, STP2 and SWI4, all of which are chromatin-associated transcription factors involved in regulating the expression of multiple genes at distinct phases of yeast cells (Lambert et al., 2010). ...
Modern high‐dimensional methods often adopt the ‘bet on sparsity’ principle, while in supervised multivariate learning statisticians may face ‘dense’ problems with a large number of nonzero coefficients. This paper proposes a novel clustered reduced‐rank learning (CRL) framework that imposes two joint matrix regularizations to automatically group the features in constructing predictive factors. CRL is more interpretable than low‐rank modelling and relaxes the stringent sparsity assumption in variable selection. In this paper, new information‐theoretical limits are presented to reveal the intrinsic cost of seeking for clusters, as well as the blessing from dimensionality in multivariate learning. Moreover, an efficient optimization algorithm is developed, which performs subspace learning and clustering with guaranteed convergence. The obtained fixed‐point estimators, although not necessarily globally optimal, enjoy the desired statistical accuracy beyond the standard likelihood setup under some regularity conditions. Moreover, a new kind of information criterion, as well as its scale‐free form, is proposed for cluster and rank selection, and has a rigorous theoretical support without assuming an infinite sample size. Extensive simulations and real‐data experiments demonstrate the statistical accuracy and interpretability of the proposed method.
... These differences detected between Li et al.'s PPIs and those we identified are most likely due to the transient nature of TF interactions and the use of different tagging strategies. Next, we compared our PPIs to public interaction databases such as PINA2 24 , STRING, IntAct, and BioGRID and several medium-to large-scale interactome studies such as Lambert et al. 25 , Malovannaya et al. 26 , and Huttlin et al. 27 (Tables S1B, C). Overall, 20% (1316/6703) of our BioID PPIs and 14% (220/1536) of our AP-MS interactions were also found in public databases or in the abovementioned interatomic studies. ...
... e Protein-protein interactions were identified using the AP-MS (1536) and BioID (6703) methods. Interactions were compared to interactions from the PINA2, IntAct, BioGRID, and String experimental protein interaction databases and to interactions from a study by Li et al. 6 , by Lambert et al. 25 , Malovannaya et al. 26 , and Huttlin et al. 27 ...
... Identified PPIs were compared to PPIs from public interaction databases such as PINA2 (version 2.0) 24 , STRING (version 11) 101 , IntAct 102 and BioGRID (version 4.4) 103 , and PPIs from several medium-to large-scale interactome studies such as Lambert et al. 25 , Malovannaya et al. 26 , and Huttlin et al. 27 . CORUM database 104 was used for protein complex analysis. ...
Transcription factors (TFs) interact with several other proteins in the process of transcriptional regulation. Here, we identify 6703 and 1536 protein–protein interactions for 109 different human TFs through proximity-dependent biotinylation (BioID) and affinity purification mass spectrometry (AP-MS), respectively. The BioID analysis identifies more high-confidence interactions, highlighting the transient and dynamic nature of many of the TF interactions. By performing clustering and correlation analyses, we identify subgroups of TFs associated with specific biological functions, such as RNA splicing or chromatin remodeling. We also observe 202 TF-TF interactions, of which 118 are interactions with nuclear factor 1 (NFI) family members, indicating uncharacterized cross-talk between NFI signaling and other TF signaling pathways. Moreover, TF interactions with basal transcription machinery are mainly observed through TFIID and SAGA complexes. This study provides a rich resource of human TF interactions and also act as a starting point for future studies aimed at understanding TF-mediated transcription.
... For example, that ACE2, SWI5 and SOK2 fall into the same group and share the same set of large coefficients provides useful biological insights, as it is well known that ACE2 and SWI5 are paralogs, meaning that they are related to each other through a gene duplication event and are highly conserved in yeast cell cycle gene progression, and according to Pan and Heitman (2000), with regards to nitrogen limitation, SOK2, along with ACE2 and SWI5, is essential in the pseudohyphal growth of yeast cells. Our analysis also provides a cluster with three TFs, namely HIR1, STP2 and SWI4, all of which are chromatin-associated transcription factors involved in regulating the expression of multiple genes at distinct phases of yeast cells (Lambert et al., 2010). ...
Modern high-dimensional methods often adopt the ``bet on sparsity'' principle, while in supervised multivariate learning statisticians may face ``dense'' problems with a large number of nonzero coefficients. This paper proposes a novel clustered reduced-rank learning (CRL) framework that imposes two joint matrix regularizations to automatically group the features in constructing predictive factors. CRL is more interpretable than low-rank modeling and relaxes the stringent sparsity assumption in variable selection. In this paper, new information-theoretical limits are presented to reveal the intrinsic cost of seeking for clusters, as well as the blessing from dimensionality in multivariate learning. Moreover, an efficient optimization algorithm is developed, which performs subspace learning and clustering with guaranteed convergence. The obtained fixed-point estimators, though not necessarily globally optimal, enjoy the desired statistical accuracy beyond the standard likelihood setup under some regularity conditions. Moreover, a new kind of information criterion, as well as its scale-free form, is proposed for cluster and rank selection, and has a rigorous theoretical support without assuming an infinite sample size. Extensive simulations and real-data experiments demonstrate the statistical accuracy and interpretability of the proposed method.
... Here, to understand the molecular link between ATAD2 function and chromatin dynamics and histone turnover, we re-examined our published proteomic data on endogenous Atad2-associated factors in mouse ES cells and identified the FACT histone chaperone complex. Likewise, a parallel examination of the available Yta7 interactome also highlighted FACT as a histone chaperone associated with this factor (Lambert et al, 2010;Morozumi et al, 2016). In addition, a functional relationship between Abo1 and FACT was also reported in S. pombe, where Abo1 was found in a complex with FACT (Gal et al, 2016). ...
... The data obtained on the suppressors of the abo1Δ cell growth defect prompted us to test the occurrence of a functional interplay between Atad2 and HIRA in mammalian cells. We also included FACT because of the published data on the interaction of Atad2/ Yta7/Abo1 with FACT (Lambert et al, 2010;Gal et al, 2016;Morozumi et al, 2016). ...
Taking advantage of the evolutionary conserved nature of ATAD2, we report here a series of parallel functional studies in human, mouse, and Schizosaccharomyces pombe to investigate ATAD2’s conserved functions. In S. pombe , the deletion of ATAD2 ortholog, abo1 , leads to a dramatic decrease in cell growth, with the appearance of suppressor clones recovering normal growth. The identification of the corresponding suppressor mutations revealed a strong genetic interaction between Abo1 and the histone chaperone HIRA. In human cancer cell lines and in mouse embryonic stem cells, we observed that the KO of ATAD2 leads to an accumulation of HIRA. A ChIP-seq mapping of nucleosome-bound HIRA and FACT in Atad2 KO mouse ES cells demonstrated that both chaperones are trapped on nucleosomes at the transcription start sites of active genes, resulting in the abnormal presence of a chaperone-bound nucleosome on the TSS-associated nucleosome-free regions. Overall, these data highlight an important layer of regulation of chromatin dynamics ensuring the turnover of histone-bound chaperones.
... The statistics of each dynamic network in DPPIN are summarized in Table 2. According to Eq. 5, generated dynamic networks in DPPIN [55] Genome-scale yeast-two-hybrid (Y2H) screen HT-Ito [27] Genome-scale Y2H with pooled library HT-Ho [25] Genome-scale affinity purification followed by mass spectrometry analysis of co-purified proteins (APMS) HT-Gavin [19] Genome-scale APMS HT-Krogan (LCMS) [31] Genome-scale APMS with liquid chromatography tandem mass spectrometry (LCMS) HT-Krogan (MALDI) [31] Genome-scale APMS with matrix-assisted laser desorption/ionization (MALDI) HT-Yu [62] Genome-scale Y2H HT-Breitkreutz [7] Large-scale APMS for kinase and phosphatase interactions HT-Babu [4] Large-scale APMS for membrane proteins HT-Lambert [32] Protein interactions for chromatin-related proteins HT-Tarassov [48] Genome-wide protein-protein interactions HT-Hazbun [23] Interactome for 100 essential genes are undirected and weighted. Hence, each edge is represented as ( , , , ), and are two nodes, denotes the current timestamp, and denotes the weight computed through Eq. 5 representing the active and co-expressed probability. ...
Nowadays, many network representation learning algorithms and downstream network mining tasks have already paid attention to dynamic networks or temporal networks, which are more suitable for real-world complex scenarios by modeling evolving patterns and temporal dependencies between node interactions. Moreover, representing and mining temporal networks have a wide range of applications, such as fraud detection, social network analysis, and drug discovery. To contribute to the network representation learning and network mining research community, in this paper, we generate a new biological dataset of dynamic protein-protein interaction networks (i.e., DPPIN), which consists of twelve dynamic protein-level interaction networks of yeast cells at different scales. We first introduce the generation process of DPPIN. To demonstrate the value of our published dataset DPPIN, we then list the potential applications that would be benefited. Furthermore, we design dynamic local clustering, dynamic spectral clustering, dynamic subgraph matching, dynamic node classification, and dynamic graph classification experiments, where DPPIN indicates future research opportunities for some tasks by presenting challenges on state-of-the-art baseline algorithms. Finally, we identify future directions for improving this dataset utility and welcome inputs from the community. All resources of this work are deployed and publicly available at https://github.com/DongqiFu/DPPIN.
... In yeast, either CAF-1 or Rtt106p can deposit histones onto newly synthesized DNA [1,4]. However, as Cac2p and Rtt106p co-precipitate in vitro and in vivo [1,35], it is possible that H3-H4 might also be transferred between CAF-1 and Rtt106p. Neither ASF1, CAC1, nor RTT106 are essential in budding yeast, and double or triple mutant combinations are also viable [1,4,36,37]. ...
... We next explored protein-protein interactions amongst these factors in live cells to examine their relationship during chromatin assembly. As the interactions between H3 and Cac1p or Rtt106p are promoted by H3 K56ac [4,21], we predicted that previously observed interactions between Asf1p and Cac1p or Rtt106p may require RTT109 [35,67]. To test this, we first assessed interactions between Asf1p and Rtt106p by measuring the fluorescent lifetime of GFP in live cells expressing either Asf1-GFPp alone, or Asf1-GFPp plus Rtt106-mCherryp, or negative control Spc29-mCherryp by FLIM-FRET. ...
... If correct, this would imply that transfer of histones H3/H4 from Asf1p to CAF-1 or Rtt106p typically occurs at the replication fork itself, which is consistent with our observations that Asf1p and PCNA interact in live cells (S3 Fig). This overall model is also supported by our previous observations that Rtt109p and SAS-I interact with wild type PCNA in live cells, but not with pol30p mutants with defects in ASF1-or CAF-1-dependent pathways [49] as well as the others' observations that Asf1p interacts with the PCNA loader RFC [55], CAF-1 interacts with PCNA [35,67,[117][118][119], Asf1p binds Cac2p weakly in vitro [67], and Rtt106p interacts with the PCNA unloader Elg1p [120]. ...
Replication-coupled chromatin assembly is achieved by a network of alternate pathways containing different chromatin assembly factors and histone-modifying enzymes that coordinate deposition of nucleosomes at the replication fork. Here we describe the organization of a CAF-1-dependent pathway in Saccharomyces cerevisiae that regulates acetylation of histone H4 K16. We demonstrate factors that function in this CAF-1-dependent pathway are important for preventing establishment of silenced states at inappropriate genomic sites using a crippled HMR locus as a model, while factors specific to other assembly pathways do not. This CAF-1-dependent pathway required the cullin Rtt101p, but was functionally distinct from an alternate pathway involving Rtt101p-dependent ubiquitination of histone H3 and the chromatin assembly factor Rtt106p. A major implication from this work is that cells have the inherent ability to create different chromatin modification patterns during DNA replication via differential processing and deposition of histones by distinct chromatin assembly pathways within the network.
... Recent advances in highthroughput (HTP) experimental techniques have allowed for the large-scale measurements of protein-protein interactions (PPIs) in various cellular compartments of different species. The complexity of interactions between proteins in human nuclei has been recently assessed, identifying hundreds of proteins interacting with different histone types [6][7][8][9][10][11][12][13] and histone post-translationally modified sites. [14][15][16][17][18][19] Although high-throughput approaches have been widely applied for mapping of protein interactomes, the identified PPIs still suffer from high falsepositive and false-negative rates and by inability to provide data of high resolution on physiological chromatin states. ...
To elucidate the properties of human histone interactions on the large scale, we perform a comprehensive mapping of human histone interaction networks by using data from structural, chemical cross-linking and high-throughput studies. Histone interactomes derived from different data sources show limited overlap and complement each other. It inspires us to integrate these data into the combined histone global interaction network which includes 5,308 proteins and 10,330 interactions. The analysis of topological properties of the human histone interactome reveals its scale free behavior and high modularity. Our study of histone binding interfaces uncovers a remarkably high number of residues involved in histone interactions, 80-90% of residues in histones H3 and H4 have at least one binding partner. Two types of histone binding modes are detected: interfaces conserved in most histone variants and variant specific interfaces. Finally, different types of chromatin factors recognize histones in nucleosomes via different localized binding modes, and many of these interfaces utilize acidic patches. Interaction networks are available at https://github.com/Panchenko-Lab/Human-histone-interactome.
... However, this method is not very efficient for proteomic analyses because it uses formaldehyde for in vivo crosslinking, which modifies proteins and then alters subsequent purification steps (Metz et al., 2004). ChIP combined with mass spectrometry after SDS separation has been used to identify proteins associated with enhancers in mouse embryonic stem cells or other model organisms (Lambert et al., 2010;Engelen et al., 2015). ...
... In budding yeast, in addition to cell fractionation which includes the use of zymolyase to obtain the spheroplast before separating the cytoplasm and the nucleus, to obtain chromatin fractions (Liang and Stillman, 1997;Frei and Gasser, 2000;Kubota et al., 2011), a method based on ChIP, termed mChIP, has been developed and enables the purification of protein complexes from chromatin (Lambert et al., 2009;Lambert et al., 2010). This method's main characteristic is that affinity purification is performed after chromatin sonication and solubilization, which allows protein-DNA complexes to be purified with their associated proteins in sufficient quantities to be analyzed by mass spectrometry. ...
... In order to evaluate and compare data from several experiments, a similar amount of the digested peptides obtained after protein solubilization per sample was subjected to a mass spectrometry analysis. The results confirmed that chromatin fractions were enriched in well-characterized chromatin proteins in yeast, such as RNA polymerases, replication factors, chromatin remodelers and transcription factors, as previously reported in other proteomic analyses (Carrillo Oesterreich et al., 2010;Lambert et al., 2010) (Fig. 3A). Similarly, their counterparts have also been identified as being associated with chromatin in other organisms (Tan et al., 2007). ...
We adapted and improved an approach that we named yChEFs (yeast Chromatin Enriched Fractions) for purifying chromatin fractions. yChEFs allows the easy, reproducible and scalable recovery of proteins associated with chromatin, and bypasses subcellular fractionation requirements that involve using zymolyase to obtain the spheroplast. The use of a small amount of culture cells and small volumes of solutions during the procedure greatly facilitates high-enriched chromatin purification, which is very useful when many samples need to be manipulated. It also reduces costs and efforts. yChEFs can be combined with mass spectrometry for proteomic analyses. We used yChEFs and mass spectrometry to identify the chromatin-associated proteome in Saccharomyces cerevisiae, and detected about 750 chromatin-bound proteins, many of which can be detected by other previous approaches. We also used this methodology to identify the RNA-dependent chromatin-associated proteome and identified about 500 proteins, including: RNA polymerase subunits, transcription regulators, such as helicases Sen1 and Rat1 that participate in transcription termination, components of nucleosome modifying complexes, chromatin remodelers complexes, and some proteins involved in RNA processing, among them, Dcp2, Xrn1 and Dhh1, which act in mRNA processing. Finally, yChEFs identified weak or transient chromatin-associated proteins.
... Depletion of Htl1 causes altered NPC morphology and nuclear envelope abnormalities We considered two models to explain how RSC promotes SPB insertion, one involving direct action of RSC at the SPB and the other involving RSC enhancing nuclear transport via the NPC. Although a large-scale study identified weak interactions between RSC and the SPB (Lambert et al., 2010) and the BAF180 subunit of the human RSC orthologue (PBAF) can be detected at spindle poles (Xue et al., 2000), we were unable to detect RSC-SPB interactions by yeast two-hybrid (Y2H; Fields and Song, 1989) or RSC at the SPB by bimolecular fluorescence complementation (BiFC; Sung and Huh, 2007;Fig. S3, d-f). ...
Ploidy is tightly regulated in eukaryotic cells and is critical for cell function and survival. Cells coordinate multiple pathways to ensure replicated DNA is segregated accurately to prevent abnormal changes in chromosome number. In this study, we characterize an unanticipated role for the Saccharomyces cerevisiae “remodels the structure of chromatin” (RSC) complex in ploidy maintenance. We show that deletion of any of six nonessential RSC genes causes a rapid transition from haploid to diploid DNA content because of nondisjunction events. Diploidization is accompanied by diagnostic changes in cell morphology and is stably maintained without further ploidy increases. We find that RSC promotes chromosome segregation by facilitating spindle pole body (SPB) duplication. More specifically, RSC plays a role in distributing two SPB insertion factors, Nbp1 and Ndc1, to the new SPB. Thus, we provide insight into a role for a SWI/SNF family complex in SPB duplication and ploidy maintenance.
... A second biological replicate was performed on a separate date. One-step affinity purification of 2xFLAG-Ydj1 was performed using anti-FLAG M2 Magnetic Beads (Sigma-Aldrich), as previously described [73] with some modifications. Briefly, Pellets from 250 ml of cultured C. albicans yeast cells were resuspended in 10 ml of lysis buffer (100 mM HEPES, pH 8.0, 20 mM magnesium acetate, 10% glycerol (v/v), 10 mM EGTA, 0.1 mM EDTA, 0.4% NP-40 supplemented with fresh protease inhibitors mixture (100 fold dilution; P8215 (Sigma-Aldrich) and 1 mM PMSF), and subsequently lysed by bead-beating for 3 x 2 minutes in a cold room. ...
Mitochondria underpin metabolism, bioenergetics, signalling, development and cell death in eukaryotes. Most of the ~1,000 yeast mitochondrial proteins are encoded in the nucleus and synthesised as precursors in the cytosol, with mitochondrial import facilitated by molecular chaperones. Here, we focus on the Hsp40 chaperone Ydj1 in the fungal pathogen Candida albicans, finding that it is localised to both the cytosol and outer mitochondrial membrane, and is required for cellular stress responses and for filamentation, a key virulence trait. Mapping the Ydj1 protein interaction network highlighted connections with co-chaperones and regulators of filamentation. Furthermore, the mitochondrial processing peptidases Mas1 and Mas2 were highly enriched for interaction with Ydj1. Additional analysis demonstrated that loss of MAS1, MAS2 or YDJ1 perturbs mitochondrial morphology and function. Deletion of YDJ1 impairs import of Su9, a protein that is cleaved to a mature form by Mas1 and Mas2. Thus, we highlight a novel role for Ydj1 in cellular morphogenesis, stress responses, and mitochondrial import in the fungal kingdom.
