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• W2004498900 endingPage "M111.011544" @default.
• W2004498900 startingPage "M111.011544" @default.
• W2004498900 abstract "A significant challenge in biology is to functionally annotate novel and uncharacterized proteins. Several approaches are available for deducing the function of proteins in silico based upon sequence homology and physical or genetic interaction, yet this approach is limited to proteins with well-characterized domains, paralogs and/or orthologs in other species, as well as on the availability of suitable large-scale data sets. Here, we present a quantitative proteomics approach extending the protein network of core histones H2A, H2B, H3, and H4 in Saccharomyces cerevisiae, among which a novel associated protein, the previously uncharacterized Ydl156w, was identified. In order to predict the role of Ydl156w, we designed and applied integrative bioinformatics, quantitative proteomics and biochemistry approaches aiming to infer its function. Reciprocal analysis of Ydl156w protein interactions demonstrated a strong association with all four histones and also to proteins strongly associated with histones including Rim1, Rfa2 and 3, Yku70, and Yku80. Through a subsequent combination of the focused quantitative proteomics experiments with available large-scale genetic interaction data and Gene Ontology functional associations, we provided sufficient evidence to associate Ydl156w with multiple processes including chromatin remodeling, transcription and DNA repair/replication. To gain deeper insights into the role of Ydl156w in histone biology we investigated the effect of the genetic deletion of ydl156w on H4 associated proteins, which lead to a dramatic decrease in the association of H4 with RNA polymerase III proteins. The implication of a role for Ydl156w in RNA Polymerase III mediated transcription was consequently verified by RNA-Seq experiments. Finally, using these approaches we generated a refined network of Ydl156w-associated proteins. A significant challenge in biology is to functionally annotate novel and uncharacterized proteins. Several approaches are available for deducing the function of proteins in silico based upon sequence homology and physical or genetic interaction, yet this approach is limited to proteins with well-characterized domains, paralogs and/or orthologs in other species, as well as on the availability of suitable large-scale data sets. Here, we present a quantitative proteomics approach extending the protein network of core histones H2A, H2B, H3, and H4 in Saccharomyces cerevisiae, among which a novel associated protein, the previously uncharacterized Ydl156w, was identified. In order to predict the role of Ydl156w, we designed and applied integrative bioinformatics, quantitative proteomics and biochemistry approaches aiming to infer its function. Reciprocal analysis of Ydl156w protein interactions demonstrated a strong association with all four histones and also to proteins strongly associated with histones including Rim1, Rfa2 and 3, Yku70, and Yku80. Through a subsequent combination of the focused quantitative proteomics experiments with available large-scale genetic interaction data and Gene Ontology functional associations, we provided sufficient evidence to associate Ydl156w with multiple processes including chromatin remodeling, transcription and DNA repair/replication. To gain deeper insights into the role of Ydl156w in histone biology we investigated the effect of the genetic deletion of ydl156w on H4 associated proteins, which lead to a dramatic decrease in the association of H4 with RNA polymerase III proteins. The implication of a role for Ydl156w in RNA Polymerase III mediated transcription was consequently verified by RNA-Seq experiments. Finally, using these approaches we generated a refined network of Ydl156w-associated proteins. The packaging of DNA in the nucleus of eukaryotic cells is achieved through the formation of nucleosomes, which predominantly consist of the core histones H2A, H2B, H3, and H4 (1Kornberg R.D. Chromatin structure: a repeating unit of histones and DNA.Science. 1974; 184: 868-871Crossref PubMed Scopus (1662) Google Scholar, 2Sahasrabuddhe C.G. van Holde K.E. The effect of trypsin on nuclease-resistant chromatin fragments.J. Biol. Chem. 1974; 249: 152-156Abstract Full Text PDF PubMed Google Scholar). Nucleosomes are highly dynamic structures, which are responsible for the state of chromatin, and may be found in either an extended (accessible = euchromatin) or condensed, (tightly packed = heterochromatin) state (3Huisinga K.L. Brower-Toland B. Elgin S.C. The contradictory definitions of heterochromatin: transcription and silencing.Chromosoma. 2006; 115: 110-122Crossref PubMed Scopus (132) Google Scholar). The chromatin state is highly regulated and therefore dynamically changed during all cellular processes involving genomic DNA like transcription, DNA replication, and DNA repair; each of these changes are accompanied by modifications of the four histones such as acetylation, methylation, or ubiquitination (reviewed in (4Murr R. Interplay between different epigenetic modifications and mechanisms.Adv. Gen. 2010; 70: 101-141Crossref PubMed Scopus (114) Google Scholar, 5Smith E. Shilatifard A. The chromatin signaling pathway: diverse mechanisms of recruitment of histone-modifying enzymes and varied biological outcomes.Mol. Cell. 2010; 40: 689-701Abstract Full Text Full Text PDF PubMed Scopus (179) Google Scholar, 6Kouzarides T. Chromatin modifications and their function.Cell. 2007; 128: 693-705Abstract Full Text Full Text PDF PubMed Scopus (8183) Google Scholar)). These events are critical cellular processes and will clearly remain under study in the future. Therefore, identifying new associations with histone proteins that participate in these processes is valuable. Many years of research have established specific interactions between corresponding protein complexes and histones (reviewed in (6Kouzarides T. Chromatin modifications and their function.Cell. 2007; 128: 693-705Abstract Full Text Full Text PDF PubMed Scopus (8183) Google Scholar, 7Bao Y. Shen X. INO80 subfamily of chromatin remodeling complexes.Mutation Res. 2007; 618: 18-29Crossref PubMed Scopus (87) Google Scholar). However, it is believed that these interactions are gene or locus specific, for example are only present when a gene is actively transcribed or at the site of DNA repair (8Bao Y. Shen X. Chromatin remodeling in DNA double-strand break repair.Curr. Opin. Genet. Dev. 2007; 17: 126-131Crossref PubMed Scopus (94) Google Scholar). In contrast, less is known about proteins that constitutively or predominantly interact with histones independent of the chromatin state. We therefore took the opposing approach and used affinity purification experiments followed by mass spectrometry using the Tandem Affinity Purification (TAP) 1The abbreviations used are:TAPtandem affinity purificationMudPITmultidimensional protein identification technologyFDRfalse discovery ratedNSAFdistributed normalized spectral abundance factorsSVDSingular Value DecompositionChIPchromatin immunoprecipitation. tagged histones in order to extend the core histone protein network, thereby identifying novel associations within core nucleosomes. One of the novel associations was with a previously poorly characterized protein, Ydl156w, which was initially identified in large scale analyses (9Gavin A.C. Aloy P. Grandi P. Krause R. Boesche M. Marzioch M. Rau C. Jensen L.J. Bastuck S. Dümpelfeld B. Edelmann A. Heurtier M.A. Hoffman V. Hoefert C. Klein K. Hudak M. Michon A.M. Schelder M. Schirle M. Remor M. Rudi T. Hooper S. Bauer A. Bouwmeester T. Casari G. Drewes G. Neubauer G. Rick J.M. Kuster B. Bork P. Russell R.B. Superti-Furga G. Proteome survey reveals modularity of the yeast cell machinery.Nature. 2006; 440: 631-636Crossref PubMed Scopus (2133) Google Scholar, 10Hellauer K. Lesage G. Sdicu A.M. Turcotte B. Large-scale analysis of genes that alter sensitivity to the anticancer drug tirapazamine in Saccharomyces cerevisiae.Mol. Pharmacol. 2005; 68: 1365-1375Crossref PubMed Scopus (23) Google Scholar). Ydl156w contains a WD40 repeat domain and has been shown to interact (directly or indirectly) with the histone H4-tail (11Suka N. Nakashima E. Shinmyozu K. Hidaka M. Jingami H. The WD40-repeat protein Pwp1p associates in vivo with 25S ribosomal chromatin in a histone H4 tail-dependent manner.Nucleic Acids Res. 2006; 34: 3555-3567Crossref PubMed Scopus (12) Google Scholar). Importantly, its human sequence homolog WDR76 (WD repeat protein 76) has a BLAST homology relation between sequences with an E-value of 5e-23 of their respective alignment (supplemental Fig. S1). WDR76 is also lacking any functional annotation and very limited information is available on its interaction partners as identified by STRING (http://string-db.org/). The study of Ydl156w could therefore also be a starting point for the future analysis of its human ortholog and might provide important information for the study of histone function. Through focused quantitative proteomics experiments combined with a bioinformatics approach utilizing genetic interaction data, gene deletions, and functional associations provided through the Gene Ontology consortium, we propose that the histone-associated protein Ydl156w is necessary for proper chromatin remodeling, transcription through RNA polymerase III, and DNA replication and repair. Follow-up studies using RNA-Seq further strengthened the importance of Ydl156w in RNA Polymerase III mediated transcription. Finally, using the information gained by the different approaches we also refined the network of Ydl156w-interacting proteins. tandem affinity purification multidimensional protein identification technology false discovery rate distributed normalized spectral abundance factors Singular Value Decomposition chromatin immunoprecipitation. All TAP-tagged strains were obtained from Open Biosystems (Huntsville, AL). Cells were grown in YPD to an absorbance of OD600 1.5–2.0. TAP was performed as previously described (12Puig O. Caspary F. Rigaut G. Rutz B. Bouveret E. Bragado-Nilsson E. Wilm M. Séraphin B. The tandem affinity purification (TAP) method: a general procedure of protein complex purification.Methods. 2001; 24: 218-229Crossref PubMed Scopus (1428) Google Scholar, 13Mosley A.L. Sardiu M.E. Pattenden S.G. Workman J.L. Florens L. Washburn M.P. Highly reproducible label free quantitative proteomic analysis of RNA polymerase complexes.Mol. Cell. Proteomics. 2011; 10 (M110.000687)Abstract Full Text Full Text PDF PubMed Scopus (58) Google Scholar). The ydl156w gene was deleted from a H4-TAP tag strain by homologous recombination using a kanamycin gene cassette flanked by 200 base pairs of gene specific sequence. In order to analyze the purified protein complexes, TCA-precipitation, LysC/Trypsin digestion, and multidimensional protein identification technology (MudPIT) analyses were performed as previously described (13Mosley A.L. Sardiu M.E. Pattenden S.G. Workman J.L. Florens L. Washburn M.P. Highly reproducible label free quantitative proteomic analysis of RNA polymerase complexes.Mol. Cell. Proteomics. 2011; 10 (M110.000687)Abstract Full Text Full Text PDF PubMed Scopus (58) Google Scholar, 14Florens L. Washburn M.P. Proteomic analysis by multidimensional protein identification technology.Methods Mol. Biol. 2006; 328: 159-175PubMed Google Scholar). Briefly, peptide mixtures were pressure-loaded onto a three-phase 100 μm fused silica microcapillary column packed with 2.0 centimeters 5-μm C18 reverse phase particles (Aqua, Phenomenex) followed by 4.0 centimeters strong cation exchange resin (Partisphere SCX, Whatman) followed by 8.0 centimeters of a reverse phase resin tip. Loaded columns were washed with buffer A (water/acetonitrile/formic acid (95:5:0.1, v/v/v), pH 2.6) for 5 min. After desalting, the triphasic column was placed in-line with a Quaternary Agilent 1100 series HPLC pump and a LTQ linear ion trap MS equipped with a nano-LC electrospray ionization source (ThermoFisher). A fully automated 12-step MudPIT run was performed as previously described (14Florens L. Washburn M.P. Proteomic analysis by multidimensional protein identification technology.Methods Mol. Biol. 2006; 328: 159-175PubMed Google Scholar). Each full MS scan (from 400 to 1600 m/z range) was followed by five MS/MS events using data-dependent acquisition, with the five most intense ions from each MS scan subjected to Collision Induced Dissociation (CID). The RAW files for each mass spectrometry run are publicly available for download through Tranche at https://proteomecommons.org under the following hash: eD+I7An5/vYZyfDwcR+qvMNi/Mfs47s5KlH7p4T6Ltrs5rw0BIi426CJANkur2FN+TIZM/H/uO9DN8+Evlb2bDh2IVcAAAAAAAACVA = = . RAW files were converted to the ms2 format using RAWDistiller v. 1.0, an in-house developed software. The ms2 files were subjected to database searching using SEQUEST (version 27 (rev.9) (15Eng J.K. McCormack A.L. Yates 3rd, J.R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database.J. Am. Soc. Mass Spectrom. 1994; 5: 976-989Crossref PubMed Scopus (5472) Google Scholar). No enzyme specificity was imposed during searches and the mass tolerance for precursor ions was set at 3 amu, whereas the mass tolerance for fragment ions was 0 amu. Tandem mass spectra were compared with 11,677 amino acid sequences consisting of 5880 nonredundant S. cerevisiae protein sequences obtained from the National Center for Biotechnology (2009-10-27 release). The database also included 176 common contaminant proteins including human keratins, IgGs, and proteolytic enzymes. The protein sequences for ubiquitin were pre-processed in order to reflect the mature processed form of ubiquitin expressed in the cell because the UBI4 gene contains multiple tandem repeats of the same sequence. The database also included randomized versions of each nonredundant protein entry to estimate the false discovery rates (FDR) (13Mosley A.L. Sardiu M.E. Pattenden S.G. Workman J.L. Florens L. Washburn M.P. Highly reproducible label free quantitative proteomic analysis of RNA polymerase complexes.Mol. Cell. Proteomics. 2011; 10 (M110.000687)Abstract Full Text Full Text PDF PubMed Scopus (58) Google Scholar). All SEQUEST searches were performed with a static modification of +57 Daltons added to cysteine residues to account for carboxamidomethylation, and dynamic searches of +16 Daltons for oxidized methionine; +14 Daltons for methylation of arginine and lysine residues; +28 Daltons for dimethylation of arginine and lysine residues; +42 Daltons for acetylation of alanine, lysine, serine and threonine residues; +80 Daltons for phosphorylation of serine, threonine and tyrosine; and +114 Daltons for ubiquitination of lysines. The process of performing post-translational modification searches on these purifications was not to identify new post-translational modifications, but to acquire additional spectra of the histone proteins because they are heavily modified. Spectra/peptide matches were filtered using DTASelect/CONTRAST (16Tabb D.L. McDonald W.H. Yates 3rd, J.R. DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics.J. Proteome Res. 2002; 1: 21-26Crossref PubMed Scopus (1144) Google Scholar). In this data set, spectrum/peptide matches only passed filtering if they were at least seven amino acids in length and fully tryptic. The DeltCn was required to be at least 0.08, with minimum XCorr values of 1.8 for singly, 2.0 for doubly, and 3.0 for triply charged spectra, and a maximum Sp rank of 10. Proteins that were subsets of others were removed using the parsimony option in DTASelect (16Tabb D.L. McDonald W.H. Yates 3rd, J.R. DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics.J. Proteome Res. 2002; 1: 21-26Crossref PubMed Scopus (1144) Google Scholar) on the proteins detected after merging all runs. Proteins that were identified by the same set of peptides (including at least one peptide unique to such protein group to distinguish between isoforms) were grouped together, and one accession number was arbitrarily considered as representative of each protein group. Quantitation was performed using label-free spectral counting. The number of spectra identified for each protein was used for calculating the distributed Normalized Spectral Abundance Factors (dNSAF) (17Zhang Y. Wen Z. Washburn M.P. Florens L. Refinements to label free proteome quantitation: how to deal with peptides shared by multiple proteins.Anal. Chem. 2010; 82: 2272-2281Crossref PubMed Scopus (297) Google Scholar). NSAF v7 (an in-house developed software) was used to create the final report on all non-redundant proteins detected across the different runs, estimate FDR, and calculate their respective dNSAF values. supplemental Tables S2, S4, S6, and S8 contain the dNSAF values, and estimated FDRs. Information on the identified peptides and protein assignment is given in supplemental Tables S1, S3, S6, and S7 and the total number of proteins with their corresponding sequence coverage, unique peptides, and spectral counts passing criteria is given for each purification in supplemental Tables S2, S4, S6, and S8. Across the histone TAP preparations, the spectral FDR ranged from 0.00 to 0.33%, the unique peptide FDR ranged from 0.00 to 3.25% and the protein FDR ranged from 0.00 to 7.44%. Across the Ydl156w replicates, the spectral FDR ranged from 0.00 to 0.28%, the unique peptide FDR ranged from 0.00 to 0.56% and the protein FDR ranged from 0.00 to 1.64%. The contaminant proteins were extracted from the data set as described in Mosley et al. (13Mosley A.L. Sardiu M.E. Pattenden S.G. Workman J.L. Florens L. Washburn M.P. Highly reproducible label free quantitative proteomic analysis of RNA polymerase complexes.Mol. Cell. Proteomics. 2011; 10 (M110.000687)Abstract Full Text Full Text PDF PubMed Scopus (58) Google Scholar). Basically, the nonspecific binding proteins were extracted from the data set by comparing the dNSAF value in each of the individual purifications with the dNSAF value from the mock controls. Seven mock control data sets were generated in which the mock controls consisted of yeast cell lysates from untagged BY4741 strains passed through the TAP purification to determine the background proteins in the data set that are a result of the purification protocol alone (supplemental Table S2). If the dNSAF value in the purification is less than twofold higher than the dNSAF in the mock control, the protein was considered nonspecific to that particular purification and the dNSAF was replaced by 0, otherwise the dNSAF value remained unchanged. The proteins that were shown to be nonspecific to all purifications were extracted from the data set. In addition, we also removed previously identified proteins that nonspecifically bind to the TAP tag (18Krogan N.J. Cagney G. Yu H. Zhong G. Guo X. Ignatchenko A. Li J. Pu S. Datta N. Tikuisis A.P. Punna T. Peregrin-Alvarez J.M. Shales M. Zhang X. Davey M. Robinson M.D. Paccanaro A. Bray J.E. Sheung A. Beattie B. Richards D.P. Canadien V. Lalev A. Mena F. Wong P. Starostine A. Canete M.M. Vlasblom J. Wu S. Orsi C. Collins S.R. Chandran S. Haw R. Rilstone J.J. Gandi K. Thompson N.J. Musso G. St Onge P. Ghanny S. Lam M.H. Butland G. Altaf-Ul A.M. Kanaya S. Shilatifard A. O’Shea E. Weissman J.S. Ingles C.J. Hughes T.R. Parkinson J. Gerstein M. Wodak S.J. Emili A. Greenblatt J.F. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae.Nature. 2006; 440: 637-643Crossref PubMed Scopus (2350) Google Scholar). Proteins from the data set that were considered as contaminants in the large scale TAP purification studies performed by Krogan et al. (18Krogan N.J. Cagney G. Yu H. Zhong G. Guo X. Ignatchenko A. Li J. Pu S. Datta N. Tikuisis A.P. Punna T. Peregrin-Alvarez J.M. Shales M. Zhang X. Davey M. Robinson M.D. Paccanaro A. Bray J.E. Sheung A. Beattie B. Richards D.P. Canadien V. Lalev A. Mena F. Wong P. Starostine A. Canete M.M. Vlasblom J. Wu S. Orsi C. Collins S.R. Chandran S. Haw R. Rilstone J.J. Gandi K. Thompson N.J. Musso G. St Onge P. Ghanny S. Lam M.H. Butland G. Altaf-Ul A.M. Kanaya S. Shilatifard A. O’Shea E. Weissman J.S. Ingles C.J. Hughes T.R. Parkinson J. Gerstein M. Wodak S.J. Emili A. Greenblatt J.F. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae.Nature. 2006; 440: 637-643Crossref PubMed Scopus (2350) Google Scholar). We chose this contaminant list because the strains used in the study were from the same S. cerevisiae strain library that we used. The similarity between the biological replicates was assessed by calculating the Pearson correlation using the R environment function cor(value1, value2, method = c(“pearson”)) . Singular vector decomposition was used to identify the most enriched proteins in our data set (19Sardiu M.E. Cai Y. Jin J. Swanson S.K. Conaway R.C. Conaway J.W. Florens L. Washburn M.P. Probabilistic assembly of human protein interaction networks from label-free quantitative proteomics.Proc. Natl. Acad. Sci. U. S. A. 2008; 105: 1454-1459Crossref PubMed Scopus (195) Google Scholar). The input matrix that was subjected to SVD consisted of 17 biological purifications corresponding to all known histone subunits and 556 pulled-down proteins. In principle, the proteins were sorted based on their coefficient values corresponding to the first singular vector and plotted in the linear-log and log-log scale (supplemental Fig. S2). The top proteins (located at the top of the distribution and separated by a dash line) with the highest coefficient values, which delimited from the remaining proteins were considered as forming a separate subnetwork. The SVD was performed using R environment function svd(matrix). Size exclusion chromatography was used to separate and identify large macromolecular complexes containing Ydl156w. The Ydl156w-TAP elution was concentrated, loaded onto a molecular weight calibrated Superose 6 10/300 GL column (high and low molecular weight calibration kit, GE Healthcare) at 0.2 ml/min, collecting 500 μl fractions. The Ydl156w protein was identified using an anti-TAP polyclonal antibody. After identifying fractions containing Ydl156w, selected fractions (fractions 5, 12, 17, and 23) were chosen for mass spectrometry analysis. GOstat (20Beissbarth T. Speed T.P. GOstat: find statistically overrepresented Gene Ontologies within a group of genes.Bioinformatics. 2004; 20: 1464-1465Crossref PubMed Scopus (986) Google Scholar) is a program that obtains the GO annotations for an analyzed list of genes and generates statistics on overrepresented genes found in a data set. GOstat was used to identify statistically overrepresented genes in the biological processes found in MudPIT data. GO terms were considered statistically significant if the p value was less than 0.01. In addition to the GOstat analysis, we also used Go Slim (http://www.yeastgenome.org/cgi-bin/GO/goSlimMapper.pl) to separate the proteins into different complexes. Like GOstat, Go Slim Mapper maps the GO annotations to a list of genes. The Slim set “Macromolecular complex Terms: Component” was used for determining whether the proteins in our data set are members of a particular complex. For the purpose of graphical representation we listed the top 20 mostly significant biological processes. In order to measure the similarity between the GO terms obtained from the GOstat analysis we used the function mgoSim (21Yu G. Li F. Qin Y. Bo X. Wu Y. Wang S. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products.Bioinformatics. 2010; 26: 976-978Crossref PubMed Scopus (667) Google Scholar) from the R environment. This function is generally used to compute the semantic similarity among sets of GO terms using the topology of the GO structure graph. The output value of the basic function mgoSim is between 0 and 1, with a higher value indicating higher similarity between the GO terms. To better understand the cellular role of Ydl156w, the transcriptome in deletion mutants was compared with wild-type using RNA-Seq. Three biological replicates of wild-type and Ydl156w deletion mutants were grown in YPD and total RNA was extracted and purified using the MasterPure™ Yeast RNA Purification Kit (http://www.epibio.com/item.asp?id = 426). Total RNA from wild-type and mutant strains was generated in triplicate and used to prepare individually barcoded libraries with the TruSeq RNA Sample Preparation Kit (Illumina, cat# FC-122–1001) according to manufacturer specifications. Briefly, 1250 ng Total RNA was enriched for poly(A)+ RNA by oligo(dT) selection. The Poly(A)+ RNA was then fragmented, and first-strand cDNA synthesis performed using random hexamer priming. Following second-strand synthesis, the ends were cleaned up, a nontemplated 3′ A was added, and Illumina indexed adapters were ligated to the ends. The libraries were enriched by 15 rounds of PCR. Purified libraries were quantified using the High Sensitivity DNA assay on an Agilent 2100 Bioanalyzer. All six libraries were pooled at equal molarities for multiplex sequencing. Pooled libraries were run single read with a 40 nt read length on two lanes of an Illumina GAIIx instrument (http://www.illumina.com/systems/genome_analyzer_iix.ilmn). The resulting fastq files were mapped to the yeast genome (UCSC, sacCer2) using tophat aligner version 1.3.1 (22Trapnell C. Pachter L. Salzberg S.L. TopHat: discovering splice junctions with RNA-Seq.Bioinformatics. 2009; 25: 1105-1111Crossref PubMed Scopus (9110) Google Scholar). The alignments for sample pairs split across two lanes were merged. Cuffdiff version 1.0.3 was used to quantify gene expression for yeast genes using Ensembl version 63 in which the ratios between samples were quantified using the cuffdiff tool from the cufflinks suite (23Roberts A. Pimentel H. Trapnell C. Pachter L. Identification of novel transcripts in annotated genomes using RNA-Seq.Bioinformatics. 2011; 27: 2325-2329Crossref PubMed Scopus (739) Google Scholar). The results of the RNA level comparison between ydl156w deletion mutant and wild-type were visually represented using a MA plot (i.e. intensity versus average intensity). We subsequently subjected the up- and down-regulated transcripts to a GOstat analysis, from which the significantly enriched functional classes were determined. In order to determine the functional role of Ydl156w in transcription, in particular with RNA-Polymerase III mediated transcription, we made use of the data set generated by Roberts et al. (24Roberts D.N. Stewart A.J. Huff J.T. Cairns B.R. The RNA polymerase III transcriptome revealed by genome-wide localization and activity-occupancy relationships.Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 14695-14700Crossref PubMed Scopus (142) Google Scholar). To define the RNA Polymerase III targets in yeast, Roberts et al. performed genome-wide chromatin immunoprecipitation using subuits of RNA Polymerase III, TFIIB and TFIIC. For each segment (i.e. transcriptional unit), a ratio between Cy5 (for ChIP enriched) and Cy3 (for input) was recorded and normalized. Based on this ratio, which reflected the relative enrichment of the corresponding segment, a percentile rank (0–100%) was assigned. In order to obtain the segments occupied by RNA Polymerase III, we merely employed the data derived from its two subunits, Rpc40 and Rpc82 (i.e. left out TFIIB and TFIIC subunits). To ensure that the segments of changing RNA expression are indeed occupied by RNA Polymerase III, we required that the average percentile for each of the Rpc40 or Rpc82 occupied segments was at least 50% (i.e. medium to high percentage) greater than the average percentile obtained from the control (i.e. untagged strain). The hypergeometric distribution (equivalent to Fisher’s one tailed exact test) was used to determine the degree of enrichment between transcriptional units occupied by RNA Polymerase III (i.e. genes occupied by RNA Polymerase III according to Roberts et al.) and those identified in our RNA-Seq study as being up- or down-regulated. The hypergeometric probability distribution is defined as follows: where k is the number of overlapping genes (i.e. genes that were up or down regulated and identified as occupied by Pol III with a particular average percentile cutoff), s is the sample size (i.e. number of genes that were up- (335 genes) or down-regulated (109 genes). M is the number of genes that were occupied by Pol III as determined by Roberts et al. (i.e. 2626 with an average percentile greater than 50%, 2122 with a 60% or more percentile, 1549 having 70% or higher average percentile, and 927 with an average greater than 80%), N is the population size (the total number of 3207 ORFs in the list of Robert et al.), and the brackets indicate the binomial coefficient: View Large Image Figure ViewerDownload Hi-res image Download (PPT) The hypergeometric distribution was calculated in the R environment using the function dhyper(k, M, N-M, s). Note that, the hypergeometric distribution was calculated applying different average percentile cutoffs between 50 and 80%. All histones of the core nucleosome, i.e. H2A, H2B, H3, and H4 were TAP-tagged (hereafter referred to as “baits”), expressed and purified by affinity purification (supplemental Table S1 and S2). The proteins specifically binding to the respective histones (i.e. prey protein) were analyzed by MudPIT (14Florens L. Washburn M.P. Proteomic analysis by multidimensional protein identification technology.Methods Mol. Biol. 2006; 328: 159-175PubMed Google Scholar) and quantified using the distributed normalized spectral abundance fac" @default.
• W2004498900 created "2016-06-24" @default.
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• W2004498900 date "2012-04-01" @default.
• W2004498900 modified "2023-09-28" @default.
• W2004498900 title "Characterization of a Highly Conserved Histone Related Protein, Ydl156w, and Its Functional Associations Using Quantitative Proteomic Analyses" @default.
• W2004498900 cites W1508125605 @default.
• W2004498900 cites W1520268659 @default.
• W2004498900 cites W1520588455 @default.
• W2004498900 cites W154063391 @default.
• W2004498900 cites W1550639365 @default.
• W2004498900 cites W1849539769 @default.
• W2004498900 cites W1971377179 @default.
• W2004498900 cites W1979417055 @default.
• W2004498900 cites W1981429455 @default.
• W2004498900 cites W1983983428 @default.
• W2004498900 cites W1984919526 @default.
• W2004498900 cites W1990299806 @default.
• W2004498900 cites W1995796643 @default.
• W2004498900 cites W1995806503 @default.
• W2004498900 cites W2001221569 @default.
• W2004498900 cites W2002085309 @default.
• W2004498900 cites W2008351972 @default.
• W2004498900 cites W2013571070 @default.
• W2004498900 cites W2023370625 @default.
• W2004498900 cites W2026465178 @default.
• W2004498900 cites W2028138760 @default.
• W2004498900 cites W2029698013 @default.
• W2004498900 cites W2031819924 @default.
• W2004498900 cites W2039757117 @default.
• W2004498900 cites W2046696981 @default.
• W2004498900 cites W2048186818 @default.
• W2004498900 cites W2050721857 @default.
• W2004498900 cites W2055650542 @default.
• W2004498900 cites W2055718828 @default.
• W2004498900 cites W2063650222 @default.
• W2004498900 cites W2069481584 @default.
• W2004498900 cites W2070867144 @default.
• W2004498900 cites W2078138443 @default.
• W2004498900 cites W2082395806 @default.
• W2004498900 cites W2093825780 @default.
• W2004498900 cites W2096083625 @default.
• W2004498900 cites W2096630319 @default.
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• W2004498900 cites W2099415899 @default.
• W2004498900 cites W2100761975 @default.
• W2004498900 cites W2102481268 @default.
• W2004498900 cites W2102992354 @default.
• W2004498900 cites W2105831706 @default.
• W2004498900 cites W2116117181 @default.
• W2004498900 cites W2133102806 @default.
• W2004498900 cites W2134320468 @default.
• W2004498900 cites W2142460132 @default.
• W2004498900 cites W2146724295 @default.
• W2004498900 cites W2149676146 @default.
• W2004498900 cites W2155142785 @default.
• W2004498900 cites W2156102046 @default.
• W2004498900 cites W2159675211 @default.
• W2004498900 cites W2161385174 @default.
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