In‐depth and 3‐dimensional exploration of the budding yeast phosphoproteome
2021; Springer Nature; Volume: 22; Issue: 2 Linguagem: Inglês
10.15252/embr.202051121
ISSN1469-3178
AutoresMichael C. Lanz, Kumar Yugandhar, Shagun Gupta, Ethan J. Sanford, Vítor M. Faça, Stephanie Vega, Aaron M.N. Joiner, J. Christopher Fromme, Haiyuan Yu, Marcus B. Smolka,
Tópico(s)Genomics and Phylogenetic Studies
ResumoResource25 January 2021free access Transparent process In-depth and 3-dimensional exploration of the budding yeast phosphoproteome Michael C Lanz Michael C Lanz orcid.org/0000-0001-8175-7627 Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USAThese authors contributed equally to this work Search for more papers by this author Kumar Yugandhar Kumar Yugandhar Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USAThese authors contributed equally to this work Search for more papers by this author Shagun Gupta Shagun Gupta Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author Ethan J Sanford Ethan J Sanford Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author Vitor M Faça Vitor M Faça Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author Stephanie Vega Stephanie Vega Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author Aaron M N Joiner Aaron M N Joiner Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author J Christopher Fromme J Christopher Fromme Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author Haiyuan Yu Corresponding Author Haiyuan Yu [email protected] orcid.org/0000-0001-7597-6049 Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author Marcus B Smolka Corresponding Author Marcus B Smolka [email protected] orcid.org/0000-0001-9952-2885 Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author Michael C Lanz Michael C Lanz orcid.org/0000-0001-8175-7627 Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USAThese authors contributed equally to this work Search for more papers by this author Kumar Yugandhar Kumar Yugandhar Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USAThese authors contributed equally to this work Search for more papers by this author Shagun Gupta Shagun Gupta Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author Ethan J Sanford Ethan J Sanford Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author Vitor M Faça Vitor M Faça Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author Stephanie Vega Stephanie Vega Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author Aaron M N Joiner Aaron M N Joiner Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author J Christopher Fromme J Christopher Fromme Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author Haiyuan Yu Corresponding Author Haiyuan Yu [email protected] orcid.org/0000-0001-7597-6049 Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author Marcus B Smolka Corresponding Author Marcus B Smolka [email protected] orcid.org/0000-0001-9952-2885 Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA Search for more papers by this author Author Information Michael C Lanz1,3, Kumar Yugandhar2, Shagun Gupta2, Ethan J Sanford1, Vitor M Faça1, Stephanie Vega1, Aaron M N Joiner1, J Christopher Fromme1, Haiyuan Yu *,2 and Marcus B Smolka *,1 1Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA 2Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA 3Present address: Department of Biology, Stanford University, Stanford, CA, USA *Corresponding author. Tel: +1 607 2550259; E-mail: [email protected] *Corresponding author (Lead contact). Tel: +1 607 2550274; E-mail: [email protected] EMBO Reports (2021)22:e51121https://doi.org/10.15252/embr.202051121 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 Phosphorylation is one of the most dynamic and widespread post-translational modifications regulating virtually every aspect of eukaryotic cell biology. Here, we assemble a dataset from 75 independent phosphoproteomic experiments performed in our laboratory using Saccharomyces cerevisiae. We report 30,902 phosphosites identified from cells cultured in a range of DNA damage conditions and/or arrested in distinct cell cycle stages. To generate a comprehensive resource for the budding yeast community, we aggregate our dataset with the Saccharomyces Genome Database and another recently published study, resulting in over 46,000 budding yeast phosphosites. With the goal of enhancing the identification of functional phosphorylation events, we perform computational positioning of phosphorylation sites on available 3D protein structures and systematically identify events predicted to regulate protein complex architecture. Results reveal hundreds of phosphorylation sites mapping to or near protein interaction interfaces, many of which result in steric or electrostatic “clashes” predicted to disrupt the interaction. With the advancement of Cryo-EM and the increasing number of available structures, our approach should help drive the functional and spatial exploration of the phosphoproteome. SYNOPSIS This study compiles a large set of independent experiments into a comprehensive phosphoproteome resource for the budding yeast community. 3D analysis of protein interaction interfaces and other strategies are used to predict functionality amongst the ≥ 40,000 reported phosphorylation events. 75 independent phosphoproteomic experiments were consolidated into a comprehensive resource of over 40,000 budding yeast phosphorylation sites. Multiple strategies were used to infer functional phosphorylation events. Mapping phosphorylation sites to protein interaction interfaces revealed phosphorylation sites that regulate protein-protein interactions. Introduction Post-translational modification of proteins by phosphorylation controls virtually every cellular process. Regulatory mechanisms based on phosphorylation have been widely explored and characterized. In classical approaches, phosphorylation sites are often biochemically identified on substrate proteins of interest and then mutated to either prevent or constitutively mimic a phosphorylation event. The phenotypes associated with these “phosphomutant” proteins inform on the biological purpose of phosphorylation at that site. In the last 15 years, technological advances in mass spectrometry, along with the development of enrichment methods for phosphorylated peptides (Ficarro et al, 2002; Gruhler et al, 2005; Larsen et al, 2005; Bodenmiller et al, 2007), have greatly expanded our ability to identify phosphorylation events, leading to large phosphoproteomic datasets (Aebersold & Mann, 2003; Olsen et al, 2006; Olsen et al, 2010; Swaney et al, 2013; Sharma et al, 2014; Bastos de Oliveira et al, 2015; Hu et al, 2019). However, our ability to probe the biological relevance of the identified phosphorylation events still relies on low-throughput methods. As a consequence, the functional importance of most cataloged phosphorylation events has not yet been determined (Needham et al, 2019). Notably, given the overwhelming number of identified phosphorylation events, over 100,000 in the case of a human cell (Hornbeck et al, 2019; Ochoa et al, 2020), and the likely promiscuity in kinase actions, it is debatable whether all of these events are functionally relevant (Lienhard, 2008; Landry et al, 2009). In many cases where attempts have been made to investigate the role of specific phosphorylation events, the results are often negative (Dephoure et al, 2013), consistent with the notion that many phosphorylation events may be extensively redundant in nature or, perhaps, not functional (Landry et al, 2009; Levy et al, 2012). These issues highlight the necessity for strategies to predict functional phosphorylation sites from large phosphoproteome datasets. While guidelines for interpreting phosphoproteomic datasets to identify candidate sites for mutational analysis are available (Dephoure et al, 2013), strategies to efficiently and systematically identify functional phosphorylation events are lacking, especially in the case of budding yeast. Here, we present an in-depth phosphoproteome for budding yeast that constitutes the largest collection of phosphorylation sites for this organism. Over 10.6 million high-resolution MS/MS spectra were acquired in our mass spectrometer. In addition, we utilized two independent methods for scoring phosphosite localization and employed an in-house algorithm to capture ambiguous phosphosites that fall within clusters of consecutive, phosphorylate-able residues. When considering the new phosphorylation events identified by this study, the aggregated budding yeast phosphoproteome currently constitutes over 46,000 phosphosites. In addition to performing cell cycle- and DNA damage-related analyses, we computationally positioned phosphorylation onto all available 3D protein structures in order to systematically identify potentially functional phosphorylation events. Results reveal many phosphorylation sites that map to or near protein interaction interfaces, some of which result in steric or electrostatic “clashes” predicted to disrupt the interaction. Phosphorylation site mutants experimentally validate our predictions and establish roles for phosphorylation in negatively regulating protein–protein interactions. We have compiled our in-depth phosphoproteome into an online database open to the budding yeast community. This resource should help drive the functional and spatial exploration of the yeast phosphoproteome. Results In-depth phosphoproteome of budding yeast We sought to generate an in-depth phosphoproteomic database for the model system budding yeast. The spectra used to assemble the dataset were generated from 75 independent SILAC-based experiments conducted in our laboratory. These experiments were originally performed for various unrelated biological inquiries and explored a range of conditions, including distinct cell cycle stages, DNA damage treatment, and carbon deprivation (Fig 1A, Dataset EV1). Phosphopeptides enriched from proteolytically digested whole cell lysates were subsequently pre-fractionated by HILIC chromatography (Fig 1A). Our data are sourced strictly from high-resolution spectral data acquired using a single, in-house mass spectrometer and processed through a unified data processing pipeline. A fraction of this dataset was previously published (See Dataset EV1 (Lanz et al, 2018)). In all, the dataset consists of fragmentation spectra acquired from over 825 LC-MS/MS injections (1,500+ h of data-dependent acquisition time). To identify peptide spectrum matches (PSMs) from our library of fragmentation spectra, we utilized two prominent search engines—Sequest and Andromeda (MaxQuant; Appendix Fig S1, Datasets EV2 and EV3; see details under Materials and Methods). The search parameters for the Andromeda and Sequest searches were mostly the same, the primary difference being the semi-specificity for tryptic ends in the Sequest search (we were unable to search large amounts of data with semi-specific digestion using our MaxQuant platform). Nearly 2,300 sites were identified with highest confidence in semi-tryptic phosphopeptides (Dataset EV4), demonstrating the utility of the secondary search. We performed one phosphoproteomic experiment using chymotrypsin as the digestive enzyme (Dataset EV5 and Appendix Fig S1). In all, our dataset consists of ~ 3.5 million PSMs, representing ~ 45,000 called phosphosites detected within ~ 4,000 proteins (Fig 1A). Figure 1. In-depth analysis of the budding yeast phosphoproteome A generalized workflow for mapping the budding yeast phosphoproteome using mass spectrometry. Dataset EV1 contains detailed information on the experiments included in this dataset. Appendix Fig S1 contains a decision tree that describes how the primary and secondary searches were compiled into a final dataset. Rationale for phosphosite localization analysis. Site localization probabilities were determined using MaxQuant and the PhosphoRS node within Proteome Discoverer. Hypothetical fragmentations illustrate how fragment ion information impacts the ability to resolve phosphorylated residues within phosphopeptides. Clustered sites were identified using an in-house algorithm. Overlay of this study with YeastMine, the public repository for phosphosites utilized by the Saccharomyces Genome Database (SGD). Localization criteria for phosphosites identified by this study are relaxed in descending Venn diagrams. Our lower cutoff reflects a balance between confidence in localization and the prevention of false negatives. In the comparison marked with the asterisk, phosphosites that we derived from non-unique phosphopeptides were included in the overlay if already present in YeastMine. These non-unique sites were excluded from the analysis if not present in YeastMine. Download figure Download PowerPoint A critical challenge in the analysis of peptide-centric phosphoproteomic workflows is the need to properly assign the phosphorylated STY residue within a fragmented phosphopeptide (Thompson et al, 2012). The use of multiple search engines allowed us to employ two prominent algorithms for determining phosphosite localization, PhosphoRS and PTM-Score, each of which utilize distinct methods to identify “site-determining” ions (Taus et al, 2011; Sharma et al, 2014). We present our dataset with a range of cutoffs for localization probability (Fig 1B, Dataset EV2). We also implemented an in-house clustering algorithm to capture several thousand “phosphosites” whose site localization probabilities were distributed within consecutive STY residues (Fig 1B illustrates how clustered phosphosites differ from other phosphosites with ambiguous localization). As phosphosite localization confidence decreases, the total number reported phosphosites is inflated by false positives because, in some cases, multiple ambiguous phosphosites are called from phosphopeptides that may only harbor a single phosphorylated residue. With this fact in mind, we overlaid our dataset with the primary public repository for budding yeast phosphosites, YeastMine. YeastMine contains over 21,000 “unique” phosphosites (Balakrishnan et al, 2012) and is the contributing repository for the Saccharomyces Genome Database (SGD), an online resource used by nearly all budding yeast biologists. YeastMine is comprised of phosphosites identified from high-throughput MS-based studies in addition to phosphosites identified from low-throughput investigations of individual proteins or protein complexes. When considering only the phosphosites we identified with greater than 90 localization probability, we detect over 60% of the sites contained within YeastMine. As the stringency for phosphosite localization is relaxed, the overlap of our dataset with YeastMine increases. This is true even as poorly localized sites are considered, as well as phosphosites identified within non-unique phosphopeptides that map to multiple different proteins (Fig 1C). This comparative analysis suggests that public phosphosite repositories may contain many mis-localized phosphorylation sites that, although originating from a true PSM, result from differences in how individual contributors account for phosphosite localization. Our suggested cutoff (Fig 1C; dashed line) aims to strike a balance between the false positives associated with poorly localized sites and the false negatives resulting from a strict reliance on highly localized sites. We found that phosphosite localization probability tolerance also impacted the proportionality of STY phosphorylation within our dataset and that the fraction of phospho-tyrosine residues doubles as the threshold for site localization probability is relaxed (Fig 2A). This observation could in part explain the slightly higher proportion of tyrosine phosphorylation reported on YeastMine (Fig 2B). In fact, the average quality of phospho-tryosine sites identified in our dataset is lower than that of phospho-serine or phospho-threonine (Fig 2C). Because sites identified as phospho-tyrosine in our study (and possibly YeastMine) are prone to represent mis-localized phospho-serine or phospho-threonine, we encourage the careful consideration of the PSM quality metrics when investigating tyrosine phosphorylation. We found that filtering based on the number of phosphosite identifications (PSMs) increases overall data quality (Fig 2C and D) and reduces the false discovery rate (Fig 2E, Appendix Fig S2). Figure 2. S, T, Y phosphorylation proportionality and quality control in the budding yeast phosphoproteome A. Proportionality of S, T, Y phosphorylation as a function of phosphosite localization probability. Clustered sites are excluded. B. Proportionality of S, T, Y phosphorylation in the indicated datasets. Clustered sites are excluded. C, D. (C) MaxQuant's site localization scores for and (D) total number of S, T, and Y phosphosites with at least one, two, or three identifications (PSMs). Only sites identified by the primary search (Maxquant) were included. E. FDR approximation for the final dataset (Dataset EV2) when considering phosphosites with at least one, two, or three identifications (PSMs). FDR was estimated by a Target/Decoy analysis designed to track the Target/Decoy ratio at each step of the analysis pipeline, both before and after combining results from the primary and secondary search engines (Appendix Fig S2). Download figure Download PowerPoint To contextualize the depth of our study, we plotted our identified phosphosites as a function of protein abundance (Ho et al, 2018). Despite the fact that the enrichment of phosphopeptides directly from cell lysates can hinder the detection of phosphorylation events that occur in low abundant proteins (Solari et al, 2015), we readily identified novel phosphorylation events in very low abundant yeast proteins, and the distribution of phosphosite discovery was mostly independent of the estimated protein abundance (Fig 3A). The scale of our analysis (825 independent MS runs) produced a large dynamic range of phosphopeptide identifications (i.e. PSMs) per phosphorylation site. By plotting the number of PSMs for each phosphosite as a function of the harboring protein's estimated copy number (Fig 3B), we highlighted ~ 500 phosphosites with highest PSM# -to- protein abundance ratios, which could potentially serve as crude indicators of high stoichiometry phosphorylation within low abundance proteins, despite the noted caveats of using number of PSMs and generalizing estimated protein abundances for such types of inferences (Fig 3B, see “#identifications” and “ProteinAbundance” columns in Dataset EV2). Figure 3. Contextualizing the depth of the budding yeast phosphoproteome Histogram depicting the distribution of identified phosphosites as a function of protein copy number (estimated from Ho et al, 2018). Bars representing the number of phosphosites identified in this study are plotted behind (not on top of) the bars representing YeastMine. The PSM count for identified phosphosites as a function of protein copy number (estimated from Ho et al, 2018). Only phosphosites within proteins with copy number estimations are depicted. Blue dots highlight a small subset of sites with a high PSM#/protein copy number ratio. Coverage maps comparing the Yen1 and Mrc1 phosphosites identified in this study (above, in black) with the sites identified in low-throughput studies (below, in gray). For the low-throughput MS analyses, phosphopeptides were enriched after affinity purification of Yen1 or Mrc1 from yeast lysates. The current state of the budding yeast phosphoproteome (Dataset EV2). This study is combined with YeastMine and another recent large-scale analysis, Hu et al, 2019, which used a localization probability cutoff of 75 for their dataset. See Dataset EV3 for a dataset sourced exclusively from the Sequest searches. In the comparison marked with the asterisk, phosphosites that we derived from non-unique phosphopeptides were included in the overlay if already present in YeastMine. These non-unique sites were excluded from the analysis if not present in YeastMine. Dot graph examining saturation in the ability to identify novel phosphoproteins and phosphosites from the budding yeast phosphoproteome. Unique, non-redundant phosphosites from this study were iteratively added to an aggregate set (left–right) in randomized chunks (localization probability of > 70, clustered sites are excluded). Dataset generated using chymotrypsin (Dataset EV5) is the ultimate addition to the plot. Download figure Download PowerPoint Because it was previously demonstrated that the extent of phosphorylation identified in high-throughput studies is less than that which can be detected from affinity-purified proteins (Albuquerque et al, 2008), we next compared our coverage to various low-throughput MS analyses. One such low-throughput study identified 25 phosphosites in Yen1 (Blanco et al, 2014), a nuclease regulated by cyclin-dependent kinase. We were able to detect 18 phosphosites in Yen1 (Fig 3C), nearly all of which were identified by Blanco and colleagues. Only four Yen1 phosphosites are contained within YeastMine. Another study identified 39 phosphosites in the replisome protein Mrc1 (Albuquerque et al, 2008), a number comparable to the 36 sites identified in our study (Fig 3C). Together, these examples illustrate that, in some cases, our depth of coverage compares to the depth achieved in the analysis of affinity-purified proteins. In addition, we note that our analysis confirmed the presence of phosphorylation at putative phosphosites, whose mutation was previously shown to preclude phosphorylation-dependent mobility shifts and disrupt genuine phospho-mediated regulation (Appendix Fig S3, bolded dark blue sites with asterisk) (Kono et al, 2008; Rossi et al, 2015). We next sought to define the current state of the budding yeast phosphoproteome by aggregating our dataset with two prominent publicly available datasets. In addition to YeastMine, we also incorporated a recent large-scale phosphoproteomic screen performed by Hu et al (2019). Unlike our study, Hu et al, utilized high-pH reverse phase chromatography for phosphopeptide prefractionation (Batth et al, 2014). Aggregation of these three datasets (our dataset, Hu et al, and YeastMine) resulted in a composite dataset containing 46,553 phosphosites (Fig 3D, Dataset EV2). Similar to what has been done previously (Amoutzias et al, 2012), and using the YeastMine and Hu et al, datasets as a foundation, we iteratively incorporated our dataset in a randomized “chunk”-wise manner. As the final portions of our tryptic dataset were considered, our ability to detect new phosphoproteins and phosphosites was approaching saturation (Fig 3E). However, the ultimate addition of a dataset derived from phosphopeptides generated by chymotryptic digestion broke the plateau of the saturation curve (Fig 3E), suggesting that the size of the yeast phosphoproteome can still be significantly expanded using alternative digestive enzymes. It is also likely that the phosphoproteome can also be expanded by exploring a more diverse set of cellular states. For example, our dataset lacks spectra acquired from meiotic conditions and, therefore, may not contain phosphorylation events mediated by meiosis-specific kinases, like Ime2 (Foiani et al, 1996; Guttmann-Raviv et al, 2002). Moreover, our search pipeline does not capture phosphorylation that occurs on non-canonical residues, which has recently been identified in other eukaryotes (Hardman et al, 2019). Functional and regulatory exploration of the budding yeast phosphoproteome Despite the large quantity of phosphorylation revealed by mass spectrometry, the inability to distinguish meaningful phosphorylation events from “noise” within the phosphoproteome represents a fundamental limitation of the technology. To address this limitation, we employed a variety of strategies to systematically reveal potentially meaningful phosphorylation events. First, we took advantage of an extensive compilation of temperature sensitive (ts) budding yeast mutants (Li et al, 2011). We reasoned that, since ts mutations fall within chemically sensitive regions of a protein's structure, phosphorylation events which occur at or near these ts residues are more likely to be impactful. Our analysis revealed 50 phosphorylation events that occur in immediate proximity (± 3 a.a.) to residues that harbor ts mutations (Dataset EV6), and in several cases the ts residue is itself phosphorylated. In one such case, rsp5-T104A, the sensitizing mutation, is the substitution of a threonine to alanine, which suggests that the phosphorylation of the Rsp5 ubiquitin ligase at T104 is somehow important for its function. Because phosphorylation that is subjected to dynamic regulation is more likely to be functionally important (Kanshin et al, 2015), we next aimed to extract regulatory information for the phosphorylation events we identified. A unique feature of our dataset is the multitude of analyses performed on yeast treated with DNA damaging agents, synchronized to distinct cell cycle stages, or both. We selected from our dataset a set of 11 independent SILAC experiments that probe the response to DNA damage in different stages of the cell cycle. This curation contains measurements for the behavior of over 23,000 phosphosites (Dataset EV7). In budding yeast, DNA damage signaling is mediated by the sensor kinases Mec1 and Tel1 and the downstream checkpoint kinase Rad53 (see recent reviews (Giannattasio & Branzei, 2017; Pardo et al, 2017; Cussiol et al, 2019; Lanz et al, 2019)), while the cyclin-dependent kinase orders the progression of the budding yeast cell cycle. Phosphopeptides whose abundance is dependent on the action of these kinases have been identified previously (Holt et al, 2009; Bastos de Oliveira et al, 2015), and the behavior of these “substrate” phosphopeptides can be used to track kinase activity (Fig 4A) (Hustedt et al, 2015; Bastos de Oliveira et al, 2018; Lanz et al, 2018). We assessed changes in the activity of these kinases, along with changes to the rest of the phosphoproteome, in response to DNA damage and cell cycle progression (Fig 4B–D, Appendix Fig S4). The treatment of G1-arrested cells with a UV-mimicking drug, 4-Nitroquinoline N-oxide (4NQO), results in short tracts of ssDNA exposure (as a byproduct of nucleotide excision repair pathway (Giannattasio et al, 2004)) and is sufficient for the activation of both the apical and downstream checkpoint kinases (Fig 4B). We found that signaling from the DNA damage response kinases uncouples during unperturbed S phase, where Mec1 and Tel1 exhibit an activity independent of Rad53 (Fig 4C), a finding consistent with previous work (Bastos de Oliveira et al, 2015; Lanz et al, 2018). However, replication in the presence of a DNA alkylating agent, MMS, strongly induces Rad53 activity during S phase, revealing more efficient signal transduction from Mec1 to Rad53. Strikingly, in addition to the established targets of Mec1, Tel1, and Rad53 previously reported, the data presented here highlight many unexplored DNA damage- and cell cycle-regulated phosphorylation events. Figure 4. Probing regulation: DNA damage and cell cycle-induced changes to the phosphoproteome Tracking the activity of DNA damage and cyclin-dependent kinase signaling by monitoring the behavior of substrate phosphopeptides. In brief, “substrate” sites are kinase-dependent phosphopeptides and harbor phosphorylated residues that lie in consensus target sequence of the indicated kinase. These putative substrate sites were defined previously by (Holt et al, 2009; Bastos de Oliveira et al, 2015). SILAC quantitation for the indicated experiment in the form of a volca
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