Proteomics of a fuzzy organelle: interphase chromatin
2014; Springer Nature; Volume: 33; Issue: 6 Linguagem: Inglês
10.1002/embj.201387614
ISSN1460-2075
AutoresGeorg Kustatscher, Nadia Hégarat, Karen L H Wills, Cristina Furlan, Jimi Wills, Helfrid Hochegger, Juri Rappsilber,
Tópico(s)RNA and protein synthesis mechanisms
ResumoResource17 February 2014Open Access Proteomics of a fuzzy organelle: interphase chromatin Georg Kustatscher Georg Kustatscher Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, UK Search for more papers by this author Nadia Hégarat Nadia Hégarat Genome Damage and Stability Centre, University of Sussex, Brighton, UK Search for more papers by this author Karen L H Wills Karen L H Wills Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, UK Search for more papers by this author Cristina Furlan Cristina Furlan Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, UK Search for more papers by this author Jimi-Carlo Bukowski-Wills Jimi-Carlo Bukowski-Wills Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, UK Search for more papers by this author Helfrid Hochegger Corresponding Author Helfrid Hochegger Genome Damage and Stability Centre, University of Sussex, Brighton, UK Search for more papers by this author Juri Rappsilber Corresponding Author Juri Rappsilber Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, UK Department of Biotechnology, Technische Universität Berlin, Berlin, Germany Search for more papers by this author Georg Kustatscher Georg Kustatscher Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, UK Search for more papers by this author Nadia Hégarat Nadia Hégarat Genome Damage and Stability Centre, University of Sussex, Brighton, UK Search for more papers by this author Karen L H Wills Karen L H Wills Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, UK Search for more papers by this author Cristina Furlan Cristina Furlan Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, UK Search for more papers by this author Jimi-Carlo Bukowski-Wills Jimi-Carlo Bukowski-Wills Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, UK Search for more papers by this author Helfrid Hochegger Corresponding Author Helfrid Hochegger Genome Damage and Stability Centre, University of Sussex, Brighton, UK Search for more papers by this author Juri Rappsilber Corresponding Author Juri Rappsilber Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, UK Department of Biotechnology, Technische Universität Berlin, Berlin, Germany Search for more papers by this author Author Information Georg Kustatscher1,‡,‡, Nadia Hégarat2,‡,‡, Karen L H Wills1, Cristina Furlan1, Jimi-Carlo Bukowski-Wills1, Helfrid Hochegger 2 and Juri Rappsilber 1,3 1Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, UK 2Genome Damage and Stability Centre, University of Sussex, Brighton, UK 3Department of Biotechnology, Technische Universität Berlin, Berlin, Germany ‡These authors contributed equally to this work. *Corresponding author. Tel: +44 1273 877510; Fax: +44 1273 678121; E-mail: [email protected] *Corresponding author. Tel: +44 131 651 7056; E-mail: [email protected] The EMBO Journal (2014)33:648-664https://doi.org/10.1002/embj.201387614 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Chromatin proteins mediate replication, regulate expression, and ensure integrity of the genome. So far, a comprehensive inventory of interphase chromatin has not been determined. This is largely due to its heterogeneous and dynamic composition, which makes conclusive biochemical purification difficult, if not impossible. As a fuzzy organelle, it defies classical organellar proteomics and cannot be described by a single and ultimate list of protein components. Instead, we propose a new approach that provides a quantitative assessment of a protein's probability to function in chromatin. We integrate chromatin composition over a range of different biochemical and biological conditions. This resulted in interphase chromatin probabilities for 7635 human proteins, including 1840 previously uncharacterized proteins. We demonstrate the power of our large-scale data-driven annotation during the analysis of cyclin-dependent kinase (CDK) regulation in chromatin. Quantitative protein ontologies may provide a general alternative to list-based investigations of organelles and complement Gene Ontology. Synopsis A probabilistic definition of interphase chromatin captures its dynamic and regulated composition with a new proteomics-based quantitative protein ontology approach. This resource is subsequently applied for the identification of novel factors whose interphase chromatin association depends on cyclin-dependent kinase (CDK). Static protein lists in organellar proteomics are replaced by degrees of chromatin involvement. Chromatin components are defined by biological co-behavior instead of biochemical co-purification. Interphase chromatin probabilities (ICPs) for 7635 proteins focus datasets on likely chromatin factors. Chromatin enrichment for proteomics (ChEP) defines a simple but efficient procedure to enrich chromatin for proteomic analysis. Proteomic analysis of CDK-dependent chromatin composition reveals novel cell cycle-regulated chromatin factors. Introduction Some of the first achievements of proteomics were to define the protein composition of organelles following isolation or quantitative enrichment (Mootha et al, 2003; Schirmer et al, 2003). A second generation of strategies has studied subcellular compartments based on co-fractionation with marker proteins on density gradients (Andersen et al, 2003; Dunkley et al, 2004; Foster et al, 2006). Defining organelles in this way critically depends on biochemical procedures and inherently introduces a series of purification artifacts. We have recently circumvented some of these by an approach called Multiclassifier Combinatorial Proteomics (MCCP; Ohta et al, 2010). A common feature of these investigations is that they attempt to completely separate genuine components from contaminants through biochemical and/or bioinformatics approaches. Crucially, the underlying assumption is that definite component lists can accurately describe complex biological structures. In light of the dynamic nature of organelles, an alternative concept may be needed. Through examination of human interphase chromatin, we develop an approach to capture the dynamic composition of biological structures, rather than enforcing static binary protein annotation. Our analysis of interphase chromatin followed a three-stage process. (i) We developed a new protocol to biochemically isolate chromatin-enriched fractions. (ii) We employed MCCP (Ohta et al, 2010) with a refinement to encapsulate different degrees of functional involvement of proteins in chromatin. (iii) We then derived for each protein its probability of having a general chromatin-based function. The final result is a quantitative protein ontology term "interphase chromatin" that complements manually curated Gene Ontology (GO; Ashburner et al, 2000) and network-extracted ontology (NeXO; Dutkowski et al, 2013). We then apply this method to analyze changes in chromatin mediated by Cdk1 and Cdk2 cyclin-dependent kinase (CDK) activities in S-phase, and identify novel cell cycle-regulated chromatin proteins that play a role in S-phase entry and progression. Results and Discussion A new chromatin enrichment procedure As a first step, we optimized the proteomic coverage of human interphase chromatin, that is, the DNA/histone fiber and all proteins associating with it. For this, we developed a new procedure, which we call chromatin enrichment for proteomics (ChEP). We fix proteins in chromatin by in vivo formaldehyde cross-linking and remove non-covalently associated proteins by washing under extremely stringent conditions (Fig 1 and 4). These initial conditions relate to standard chromatin immunoprecipitation (ChIP) experiments (Solomon et al, 1988) and were also employed as starting point of a recent proteomic analysis of telomeres (Déjardin & Kingston, 2009). However, our approach then uses simple centrifugation to collect whole chromatin for subsequent mass spectrometric analysis of the associated cross-linked proteins. This should allow quantitative analyses of processes that affect chromatin globally. Figure 1. Chromatin enrichment for proteomics (ChEP) Outline of the ChEP procedure, see 4 for details. SDS–PAGE gel of a typical chromatin fraction obtained using this procedure, compared to a whole-cell lysate and a classical chromatin pellet. Proteomic analysis of a typical ChEP chromatin sample. 3522 proteins were identified and classified manually according to their function. The number of proteins per category is indicated in brackets. SILAC-based quantitative proteomics comparing ChEP chromatin with a classical chromatin pellet, demonstrating that ChEP enriches for chromatin players more efficiently. The 1024 known chromatin proteins (red) and 1706 proteins with no expected chromatin function (blue) were annotated manually based on literature evidence. Uncharacterized proteins are not shown. Download figure Download PowerPoint In a representative proteomic analysis, we identified 3522 proteins comprising typical chromatin-associated processes such as transcription, histone modification, and DNA repair (Fig 1C). The protocol enriches for chromatin factors in a considerably more efficient way than the classical chromatin pellet (Fig 1D). Nonetheless, nearly half of the proteins identified at this proteomic scale have no apparent chromatin-related function. Additional DNA-directed isolation steps, such as hydroxyapatite chromatography, failed to reduce co-purifying unrelated proteins further (not shown). For mitotic chromosomes, we achieved a seemingly clear-cut separation of genuine components and purification background using MCCP (Ohta et al, 2010). However, such a separation may be artificial in the case of interphase chromatin. A multitude of highly dynamic and regulated biological processes take place in interphase chromatin, such as replication, gene expression, and DNA repair. Many proteins only associate with chromatin under specific physiological conditions. For others, only part of their cellular pool is active in chromatin. To emphasize these dynamic aspects of interphase chromatin, we call it a "fuzzy" organelle. The ability to describe these varying degrees of contribution is essential for an accurate understanding of chromatin plasticity. Rather than qualitatively separating chromatin from background proteins in ChEP fractions, we therefore aimed to quantify the contribution of each protein toward chromatin. We hypothesized that MCCP would also be able to address this. A novel type of classifier to infer protein function MCCP combines multiple "classifier" experiments, which individually separate two protein groups incompletely, into a powerful super-ranking using a random forest (RF; Breiman, 2001) machine learning algorithm. This algorithm learns to distinguish between chromatin-like or chromatin-unlike behavior in classifier experiments on the basis of well-described training proteins. Classifier experiments used here include standard approaches such as comparing a chromatin-enriched fraction with whole-cell lysates or other subcellular fractions (Fig 2A–C and Table 1). We applied six "biochemical classifiers" that infer function from physical association and invariably carry the risk of purification artifacts (contaminants and losses). Moreover, these classifiers do not take into account different physiological states of chromatin and consequently cannot integrate regulated changes in chromatin composition. To circumvent this problem, we developed an alternative type of classifier experiment. We compared ChEP preparations differing as a result of regulated physiological changes within the cell. This "biological classifier" approach aims to reduce purification artifacts while at the same time encapsulating biological complexity (Fig 2E). Function is inferred by machine learning from co-behavior with known reference proteins. In our case, these are 486 proteins linked to chromatin and 582 not (see 4). Figure 2. Defining interphase chromatin through biochemical or biological classifiers A. Traditional biochemical classifiers compare chromatin with other subcellular fractions. B, C. SILAC plots showing enrichment of known chromatin (red) over non-chromatin proteins (blue) for two such experiments. The distributions of chromatin and non-chromatin proteins are displayed as overlaid graphs. D. Individual experiments are integrated by machine learning for a more powerful distinction of chromatin and non-chromatin proteins, as assessed by receiver operating characteristic-like curves. E–H. Schematics and SILAC plots of representative biological classifiers showing the uneven distribution of chromatin and non-chromatin proteins in chromatin fractions prepared from distinct physiological conditions (see Table 1 for full list of experiments). The following number of proteins were quantified and plotted after median normalization: (B), 1441 chromatin proteins/2882 non-chromatin proteins; (C), 1373/2636; (F), 1130/1709; (G), 933/1193; (H), 1156/1774. Download figure Download PowerPoint Table 1. Experiments used to infer the composition of interphase chromatin ID Sample 1 (SILAC light) Sample 2 (SILAC heavy) Sample 3 (SILAC medium) Cell line Proteins Comment Biochemical classifier experiments (comparing ChEP chromatin with other biochemical fractions) a Whole-cell lysate ChEP chromatin / MCF-7 5227 See note 1 b ChEP chromatin Whole-cell lysate / HeLa 5650 See note 1 c Whole-cell lysate ChEP chromatin / HepG2 2262 d Nuclei lysate ChEP chromatin / HeLa 5121 e Chromatin pellet ChEP chromatin / HeLa 4852 f Chromatin pellet ChEP chromatin / HepG2 1676 g Whole-cell lysate ChEP chromatin / HeLa 5231 Label-swap of (b) Biological classifier experiments (comparing ChEP fractions from different biological conditions) a Untreated TNF-α, 5 min / HeLa 3789 b Untreated TNF-α, 10 min / HeLa 3658 c Untreated TNF-α, 30 min / HepG2 3615 d α-Amanitin DMSO Trichostatin A MCF-7 1546 e Untreated Camptothecin / U20S 2769 f Untreated Ionizing radiation, 10 Gy / U20S 2793 g Untreated Ionizing radiation, 30 Gy / U20S 2742 h Untreated E2 and 4-OHT E2 MCF-7 2768 See note 2 i From HEK293 cells From HepG2 cells / / 3719 J Cell cycle Gl/S Cell cycle phase M Cell cycle phase G2 HeLa 3720 See note 3 k 10% rat serum 1 10% dialyzed FCS / HepG2 3038 See note 4 l 10% rat serum 2 10% dialyzed FCS / HepG2 3297 See note 4 m Untreated 1 μg/ml doxycycline / HeLa 2271 See note 5 n Untreated 1 μg/ml doxycycline 0.1 μg/ml doxycycline HeLa 1843 See note 5 o α-Amanitin DMSO Trichostatin A MCF-7 1517 Replica of (d) p Cell cycle phase G2 Cell cycle Gl/S Cell cycle phase M HeLa 4088 See note 6 q Untreated E2 and 4-OHT E2 MCF-7 3394 Replica of (h) r E2 and 4-OHT E2 Untreated MCF-7 2883 Label-swap of (h) s E2 Untreated E2 and 4-OHT MCF-7 1301 Label-swap of (h) t E2 and 4-OHT E2 Untreated MCF-7 3481 Label-swap of (h) u E2 Untreated E2 and 4-OHT MCF-7 2914 Label-swap of (h) v Untreated 1 μg/ml doxycycline / HeLa 3123 Replica of (m) w 1 μg/ml doxycycline Untreated / HeLa 3187 Label-swap of (m) Additional classifier experiments a Purified w/o RNase Purified with RNase / HepG2 1749 See note 7 b Cell cycle Gl/S Cell cycle phase M / HeLa 1183 5 min fixation c Cell cycle Gl/S Cell cycle phase M / HeLa 1196 10 min fixation d Cell cycle Gl/S Cell cycle phase M / HeLa 1273 15 min fixation e Cell cycle Gl/S Cell cycle phase M / HeLa 1372 20 min fixation Note 1 Mixed 1:4 to increase detection of chromatin factors (affects all proteins, so no impact on classifier performance). Note 2 E2 is 17-β-estradiol; 4-OHT is 4-hydroxytamoxifen. Note 3 Cells arrested by thymidine (Gl/S), RO-3306 (G2), nocodazole (M). Note 4 Rat serum replaced FCS in cell culture medium. Note 5 Stable cell line expressing macroH2Al.l from a doxycycline-induced Tet-ON promoter. Note 6 Label-swap replica of (j), but only fixed for 5 min with formaldehyde. Note 7 Standard ChEP procedure includes RNase treatment, see 4. Our biological classifiers rely on global, systemic perturbations. This ensures that a broad range of chromatin processes is affected and in many ways, even though such changes might be subtle. For example, ChEP preparations from cells treated with or without TNF-α show subtle, global changes that affected chromatin and non-chromatin proteins differently (median 1.12-fold difference; Fig 2F). We subsequently added data on chromatin-enriched fractions from another 18 different biological conditions, including cell types, cell cycle phases, and drug treatments (see Table 1 for full list). In all these cases, we observe general, coordinated alterations, such that the densities of chromatin and non-chromatin proteins vary slightly throughout all SILAC plots (Fig 2F–H). Even though detectable through the high accuracy of quantitative proteomics (Ong et al, 2002), such small bulk changes are usually overlooked or dismissed in favor of large changes in few proteins. For some experiments, no coordinated differences between chromatin and non-chromatin proteins were observed, for example protein turnover (not shown), and these were not included here. We integrate co-fractionation changes in response to many in vivo perturbations instead of suggesting function from biochemical co-fractionation alone. As a consequence, the composition of the organelle is defined in its native environment. Accordingly, abundant contaminants of chromatin purifications are correctly identified as false positives by biological classifiers, since these proteins do not respond to physiological changes in the same way as genuine chromatin components (Supplementary Fig S1). Note that a virtually unlimited number of biological classifiers can be conceived. Even treating cells with TNF-α for 5 min rather than 10 min provides additional information (Supplementary Fig S2). Importantly, perturbations do not need to target the structure in question directly or selectively, as long as they induce global biological changes that affect the structure. An integrated chromatin score The output, an integrated chromatin score, was validated using 5795 proteins that we manually annotated as either "chromatin proteins" (any reported function on chromatin) or "non-chromatin proteins" (well-characterized proteins without indication of involvement with chromatin; Fig 2D). Notably, the combined set of global perturbation experiments discriminates chromatin from non-chromatin players better than a classic biochemical enrichment experiment, such as comparing a chromatin fraction with a whole-cell lysate (Supplementary Fig S1). For the remainder of this study, we integrated all experiments that showed some bulk separation (see Table 1). This optimized performance as judged by receiver operating characteristic (ROC)-like curves (Fig 2D) and maximized the number of proteins observed. From machine learning score to interphase chromatin probability A protein with integrated chromatin score of 0.8 received a chromatin vote from 80% of the trees in the RF. The score provides a ranking but gives no indication on how likely the protein has a chromatin function. To provide dimension and scale, we calibrated the score distribution making use of the 5795 annotated evaluation proteins in our dataset. We calculated the fraction of proteins with reported chromatin functions among all characterized proteins within score windows. We described the result as a sigmoid function (Fig 3A, see 4 for details). In this way, we integrate knowledge on proteins with similar scores into the probability of any given protein to have a chromatin function. This translation is robust and reproducible (Supplementary Fig S3). A calibrated score of 0.8 for instance means that eight of 10 reference proteins with this value have a reported chromatin function, thus providing a probability for the function of this protein. We refer to this value as interphase chromatin probability (ICP; Fig 3B, Supplementary Table 1). ICPs provide a general annotation on how similar a protein behaves experimentally to archetypal chromatin proteins. We provide ICPs for 7635 human proteins and protein isoforms, including the 5795 evaluation proteins (1823 proteins with literature evidence linking them to chromatin and 3972 non-chromatin proteins) and 1840 previously uncharacterized proteins. Proteins were classified as "uncharacterized" based on absence of literature but also had low GO coverage and weak domain-based prediction (Supplementary Fig S4). Of the 1840 uncharacterized proteins described in this study, 576 have a chromatin probability >0.5, indicating that hundreds of chromatin components are presently still uncharacterized. The large number of novel chromatin proteins is in line with a recent report that used alternative technology to test more than 100 proteins and found 42 previously unknown chromatin components (van Bemmel et al, 2013). ICPs integrate large-scale data for quantitative gene function prediction and can help systematically fill current annotation gaps. Figure 3. Toward a probabilistic chromatin definition Using 5795 proteins of known function, the percentage of chromatin (red) and non-chromatin proteins (blue) was calculated for overlapping windows (e.g., gray box) of the random forest (RF) machine learning score. Fitting a sigmoid curve through the percentages translates the RF score into "interphase chromatin probabilities" (ICPs). 1823 evaluation proteins are known chromatin players, while 3972 have no expected chromatin function. Scatterplot showing ICPs for 7635 human proteins (the 5795 evaluation proteins and 1840 uncharacterized proteins). Pie charts show protein categories above and below ICP 0.5, including uncharacterized proteins (white). Download figure Download PowerPoint ICPs are consistent with the function of protein domains To validate ICPs, we performed several tests based on literature knowledge and bioinformatics and finally applied the method to elucidate cell cycle regulation of chromatin. As a first validation step, we investigated the correlation between ICPs and the presence of protein domains that have been linked to interphase chromatin (Supplementary Fig S5). As expected, proteins with canonical chromatin domains (e.g., chromo, bromo, JmjC) invariably have high ICPs. Conditional and regulated chromatin proteins such as transcription factors with sequence-specific DNA-binding domains indeed cover a broad range of ICPs. Proteins with a Ras domain have no reported chromatin involvement and receive low ICPs. This suggests that ICPs can capture a dynamic range of involvement in chromatin. ICPs capture diverse biological behavior of proteins We next investigated whether ICPs accurately reflect the biological behavior of well-described proteins and their complexes. Typical chromatin-associated protein complexes such as MCM2-7 and FACT have consistently high ICPs for all their subunits (Fig 4A). In contrast, subunits with multiple functions correctly have different ICP values from core subunits. This includes the MLL histone methyltransferase subunits Dpy-30L (Xu et al, 2009) and HCF2 (Johnson et al, 1999; Fig 4A) and multiple NuRD components (Supplementary Fig S6). Different isoforms of NuRD subunits with redundant function receive similar ICPs, indicating large accuracy of ICP values. Ribosomes, commonly found contaminants in biochemical purifications, have low ICP values (Fig 4B). This indicates that ICPs successfully integrate biological rather than biochemical behavior. ICPs also match the dynamic chromatin association of the condensin complex (Fig 4A). In interphase, condensin I subunits diffuse into the cytoplasm (very low ICP) and condensin II subunits remain nuclear with a low chromatin affinity (Gerlich et al, 2006; medium ICP). Common condensin subunits show an intermediate behavior. This is consistent with ICPs being a parameter that describes the average behavior when multiple pools are present. Similarly, Cdk1 has a low ICP (0.15) as its main pool is bound to cytoplasmic cyclin B, while a minor fraction competes with Cdk2 for nuclear cyclin A and E and acts on chromatin (Santamaría et al, 2007). Different from ribosomes, factors of ribosome biogenesis associate with pre-rRNA co-transcriptionally and thus have some chromatin association. This is reflected in intermediate to high ICP values (Fig 4B). Interestingly, splicing factors show a large spread from low to high ICP values. This distribution is not random; all Sm and LSm proteins have low ICP values, while SR-rich splicing factors, which can act co-transcriptionally (Zhong et al, 2009), consistently have high ICP values (Fig 4C). Similarly, ICPs allow distinguishing canonical from conditional chromatin proteins. For example, SMAD and STAT transcription factors are normally absent from chromatin due to their signal-dependent nuclear localization and have consistently low ICPs (Fig 4A). In conclusion, ICPs provide a quantitative annotation that captures the subtle biological behavior of diverse proteins and functions, rather than providing classical "all or nothing" scores to distinguish between true and false positives. Figure 4. Interphase chromatin probabilities describe chromatin plasticity A. Protein complexes can have uniform interphase chromatin probabilities (ICPs; MCM2–7, FACT) but also heterogeneous ICPs (MLL, condensin) in agreement with additional roles of individual subunits. Consistently low ICPs are assigned to cytoplasmic, signal-dependent SMAD/STAT transcription factors. B, C. ICPs also set apart ribosome biogenesis, which happens in nucleoli as part of or vicinal to chromatin, from core ribosome components (B), as well as core splicing factors (Sm/LSm) from SR proteins, which can act co-transcriptionally (C). Data information: The number of proteins in each group is indicated in brackets. Download figure Download PowerPoint ICPs as quantitative annotation of the multifunctional proteome As a final test, we asked whether ICPs could identify predominantly chromatin-based proteins among those 248 proteins in our dataset that are both cytoplasmic and chromosomal according to the GO database (Ashburner et al, 2000). ICPs can successfully reveal these proteins' main activities as shown by the examples in Fig 5. Looking at the most extreme ICP values, 16 proteins with highest ICP are well-described chromatin proteins, while 13 proteins with lowest ICP have a main function elsewhere. Note that proteins with low chromatin ICP are not indicating GO artifacts, for example septin filaments interact with kinetochores during mitosis (Spiliotis et al, 2005; Zhu et al, 2008). This demonstrates that ICPs may help to address one of the large problems of protein annotation. Protein annotation databases are challenged by an increasing amount of data on proteins leading to an accumulation of proteins with multiple, apparently unrelated, annotations. For example, according to GO, 40% of all human nuclear proteins are also found in the cytoplasm. Many of these proteins will be multifunctional. However, increasingly sensitive analyses will decrease the value of protein localization for function prediction, for example chromatin proteins may be observed while they are translated in the cytoplasm. This ultimately reduces the value of such annotations. Quantitative protein ontologies, as suggested here, have the potential to solve these issues by providing a probabilistic dimension to protein annotations. Figure 5. Interphase chromatin probabilities (ICPs) as quantitative annotation for multifunctional proteins ICP distribution of 248 proteins with both cytoplasmic and chromosomal localization according to Gene Ontology (red/blue circles) reveals a protein's core function. Download figure Download PowerPoint ICPs as adjustable focus for Cdk-dependent chromatin regulation ICPs could be used for guidance when looking for bona fide chromatin proteins. ICPs do not define specific chromatin functions of individual proteins. Therefore, we envision ICPs as a form of large-scale data-derived and quantitative GO term to allow focusing other datasets onto chromatin function. We undertook two studies to exemplify this. First, we analyzed changes in chromatin composition driven by Cdk-dependent cell cycle progression through S-phase (Fig 6A). Initiation and completion of DNA replication has a major impact on chromatin (Khoudoli et al, 2008), but how core chromatin processes are cell cycle regulated in somatic cells remains poorly understood. To address this question, we conducted a quantitative proteomics study that took advantage of an analogue-sensitive mutation in Cdk1 that we previously established in wild-type (WT) and Cdk2-knockout chicken DT40 cells (Hochegger et al, 2007). This mutation allows the rapid and highly specific inactivation of Cdk1 by the bulky ATP analogue 1NMPP1. Neither Cdk1 nor Cdk2 is requi
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