Human cells have evolved elaborate mechanisms for responding to DNA damage to maintain genome stability and prevent carcinogenesis. For instance, the cell cycle can be arrested at different stages to allow time for DNA repair. The APC/C(C) (dh1) ubiquitin ligase mainly regulates mitotic exit but is also implicated in the DNA damage-induced G2 arrest. However, it is currently unknown whether APC/C(C) (dh1) also contributes to DNA repair. Here, we show that Cdh1 depletion causes increased levels of genomic instability and enhanced sensitivity to DNA-damaging agents. Using an integrated proteomics and bioinformatics approach, we identify CtIP, a DNA-end resection factor, as a novel APC/C(C) (dh1) target. CtIP interacts with Cdh1 through a conserved KEN box, mutation of which impedes ubiquitylation and downregulation of CtIP both during G1 and after DNA damage in G2. Finally, we find that abrogating the CtIP-Cdh1 interaction results in delayed CtIP clearance from DNA damage foci, increased DNA-end resection, and reduced homologous recombination efficiency. Combined, our results highlight the impact of APC/C(C) (dh1) on the maintenance of genome integrity and show that this is, at least partially, achieved by controlling CtIP stability in a cell cycle- and DNA damage-dependent manner.
Stable isotope labeling is widely used to encode and quantify proteins in mass-spectrometry-based proteomics. We compared metabolic labeling with stable isotope labeling by amino acids in cell culture (SILAC) and chemical labeling by stable isotope dimethyl labeling and find that they have comparable accuracy and quantitative dynamic range in unfractionated proteome analyses and affinity pull-down experiments. Analyzing SILAC- and dimethyl-labeled samples together in single liquid chromatography-mass spectrometric analyses minimizes differences under analytical conditions, allowing comparisons of quantitative errors introduced during sample processing. We find that SILAC is more reproducible than dimethyl labeling. Because proteins from metabolically labeled populations can be combined before proteolytic digestion, SILAC is particularly suited to studies with extensive sample processing, such as fractionation and enrichment of peptides with post-translational modifications. We compared both methods in pull-down experiments using a kinase inhibitor, dasatinib, and tagged GRB2-SH2 protein as affinity baits. We describe a StageTip dimethyl-labeling protocol that we applied to in-solution and in-gel protein digests. Comparing the impact of post-digest isotopic labeling on quantitative accuracy, we demonstrate how specific experimental designs can benefit most from metabolic labeling approaches like SILAC and situations where chemical labeling by stable isotope-dimethyl labeling can be a practical alternative.
Interest in protein methylation has grown rapidly in recent years. Mass spectrometry-based proteomics is ideally suited to characterize protein modifications, but the multiplicity of methylated residues and the lack of efficient methods to enrich methylated proteins have limited the proteomic identification of protein methylation sites. In this protocol, we compare two metabolic labeling approaches, stable isotope labeling by amino acids in cell culture (SILAC) and its variant heavy methyl SILAC, for studying protein methylation. Instead of heavy lysine and arginine in the typical SILAC experiment, heavy methyl SILAC uses (13)C, (2)H methionine as the labeling amino acid. As cells convert methionine to S-adenosylmethionine, heavy methyl SILAC encodes a 4 Da mass tag for each methyl group, distinguishing between degrees of methylation is possible from mass difference alone. We provide a protocol for SILAC-based analyses of protein methylation and highlight the strengths and weaknesses of each method for targeted and proteomic analyses.
Poly(ADP-ribose), or PAR, is a cellular polymer implicated in DNA/RNA metabolism, cell death, and cellular stress response via its role as a post-translational modification, signaling molecule, and scaffolding element. PAR is synthesized by a family of proteins known as poly(ADP-ribose) polymerases, or PARPs, which attach PAR polymers to various amino acids of substrate proteins. The nature of these polymers (large, charged, heterogeneous, base-labile) has made these attachment sites difficult to study by mass spectrometry. Here we propose a new pipeline that allows for the identification of mono(ADP-ribosyl)ation and poly(ADP-ribosyl)ation sites via the enzymatic product of phosphodiesterase-treated ADP-ribose, or phospho(ribose). The power of this method lies in the enrichment potential of phospho(ribose), which we show to be enriched by phosphoproteomic techniques when a neutral buffer, which allows for retention of the base-labile attachment site, is used for elution. Through the identification of PARP-1 in vitro automodification sites as well as endogenous ADP-ribosylation sites from whole cells, we have shown that ADP-ribose can exist on adjacent amino acid residues as well as both lysine and arginine in addition to known acidic modification sites. The universality of this technique has allowed us to show that enrichment of ADP-ribosylated proteins by macrodomain leads to a bias against ADP-ribose modifications conjugated to glutamic acids, suggesting that the macrodomain is either removing or selecting against these distinct protein attachments. Ultimately, the enrichment pipeline presented here offers a universal approach for characterizing the mono- and poly(ADP-ribosyl)ated proteome.
The ability to determine structure-activity relationships (SAR) and identify cellular targets from cell lysates and tissues is of great utility for kinase inhibitor drug discovery. We describe a streamlined mass spectrometry-based chemoproteomics workflow to examine the SAR and target profiles of a small library of kinase inhibitors that consists of the drug dasatinib and a panel of general type II inhibitors. By combining a simplified affinity enrichment and on-bead protein digestion workflow with quantitative proteomics, we achieved sensitive and specific enrichment of target kinases using our small molecule probes. We applied the affinity matrices in competition experiments with soluble probes in HeLa cell lysates using less than 1 mg of protein per experiment. Each pull-down experiment was analyzed in a single nano LC-MS run. Stringent selection criteria for target identification were applied to deduce 28 protein targets for dasatinib and 31 protein targets for our general type II kinase inhibitor in HeLa cell lysate. Additional kinase and protein targets were identified with the general type II inhibitor analogs, with small structural changes leading to divergent target profiles. We observed surprisingly high sequence coverage on some proteins, enabling further analyses of phosphorylation sites for several target kinases without additional sample processing. Our rapid workflow profiled cellular targets for six small molecules within a week, demonstrating that an unbiased proteomics screen of cellular targets yields valuable SAR information and may be incorporated at an early stage in kinase inhibitor development.
Mitochondria are centers of metabolism and signaling whose content and function must adapt to changing cellular environments. The biological signals that initiate mitochondrial restructuring and the cellular processes that drive this adaptive response are largely obscure. To better define these systems, we performed matched quantitative genomic and proteomic analyses of mouse muscle cells as they performed mitochondrial biogenesis. We find that proteins involved in cellular iron homeostasis are highly coordinated with this process and that depletion of cellular iron results in a rapid, dose-dependent decrease of select mitochondrial protein levels and oxidative capacity. We further show that this process is universal across a broad range of cell types and fully reversed when iron is reintroduced. Collectively, our work reveals that cellular iron is a key regulator of mitochondrial biogenesis, and provides quantitative data sets that can be leveraged to explore posttranscriptional and posttranslational processes that are essential for mitochondrial adaptation.
Labeling of primary amines on peptides with reagents containing stable isotopes is a commonly used technique in quantitative mass spectrometry. Isobaric labeling techniques such as iTRAQ™ or TMT™ allow for relative quantification of peptides based on ratios of reporter ions in the low m/z region of spectra produced by precursor ion fragmentation. In contrast, nonisobaric labeling with mTRAQ™ yields precursors with different masses that can be directly quantified in MS1 spectra. In this study, we compare iTRAQ- and mTRAQ-based quantification of peptides and phosphopeptides derived from EGF-stimulated HeLa cells. Both labels have identical chemical structures, therefore precursor ion- and fragment ion-based quantification can be directly compared. Our results indicate that iTRAQ labeling has an additive effect on precursor intensities, whereas mTRAQ labeling leads to more redundant MS2 scanning events caused by triggering on the same peptide with different mTRAQ labels. We found that iTRAQ labeling quantified nearly threefold more phosphopeptides (12,129 versus 4,448) and nearly twofold more proteins (2,699 versus 1,597) than mTRAQ labeling. Although most key proteins in the EGFR signaling network were quantified with both techniques, iTRAQ labeling allowed quantification of twice as many kinases. Accuracy of reporter ion quantification by iTRAQ is adversely affected by peptides that are cofragmented in the same precursor isolation window, dampening observed ratios toward unity. However, because of tighter overall iTRAQ ratio distributions, the percentage of statistically significantly regulated phosphopeptides and proteins detected by iTRAQ and mTRAQ was similar. We observed a linear correlation of logarithmic iTRAQ to mTRAQ ratios over two orders of magnitude, indicating a possibility to correct iTRAQ ratios by an average compression factor. Spike-in experiments using peptides of defined ratios in a background of nonregulated peptides show that iTRAQ quantification is less accurate but not as variable as mTRAQ quantification.
Target identification remains challenging for the field of chemical biology. We describe an integrative chemical genomic and proteomic approach combining the use of differentially active analogs of small molecule probes with stable isotope labeling by amino acids in cell culture-mediated affinity enrichment, followed by subsequent testing of candidate targets using RNA interference-mediated gene silencing. We applied this approach to characterizing the natural product K252a and its ability to potentiate neuregulin-1 (Nrg1)/ErbB4 (v-erb-a erythroblastic leukemia viral oncogene homolog 4)-dependent neurotrophic factor signaling and neuritogenesis. We show that AAK1 (adaptor-associated kinase 1) is a relevant target of K252a, and that the loss of AAK1 alters ErbB4 trafficking and expression levels, providing evidence for a previously unrecognized role for AAK1 in Nrg1-mediated neurotrophic factor signaling. Similar strategies should lead to the discovery of novel targets for therapeutic development.
We report the application of quantitative mass spectrometry to identify plasma membrane proteins differentially expressed in melanoma cells with high vs. low metastatic abilities. Using stable isotope labeling with amino acids in culture (SILAC) coupled with nanospray tandem mass spectrometry, we identified CUB-domain-containing protein 1 (CDCP1) as one such differentially expressed transmembrane protein. CDCP1 is not only a surface marker for cells with higher metastatic potential, but also functionally involved in enhancing tumor metastasis. Overexpression of CDCP1 also correlates with activation of Src. Pharmacological reagents, PP2 and Dasatinib, which block Src family kinase activation, blocked scattered growth of CDCP1-overexpressing cells in 3D Matrigel culture, suggesting that CDCP1 might function through the activation of Src-family kinases (SFKs). This hypothesis was further supported by mutational studies of CDCP1. Whereas wild-type CDCP1 enhances Src activation, point mutation Y734F abolishes in vitro dispersive growth in 3D culture and in vivo metastasis-enhancing activities of CDCP1. In addition, the Y734F mutation also eliminated enhanced Src activation. Thus, this work provides molecular mechanisms for the metastasis-enhancing functions of CDCP1.
As mass-spectrometry-based quantitative proteomics approaches become increasingly powerful, researchers are taking advantage of well established methodologies and improving instrumentation to pioneer new protein expression profiling methods. For example, pooling several proteomes labeled using the stable isotope labeling by amino acids in cell culture (SILAC) method yields a whole-proteome stable isotope-labeled internal standard that can be mixed with a tissue-derived proteome for quantification. By increasing quantitative accuracy in the analysis of tissue proteomes, such methods should improve integration of protein expression profiling data with transcriptomic data and enhance downstream bioinformatic analyses. An accurate and scalable quantitative method to analyze tumor proteomes at the depth of several thousand proteins provides a powerful tool for global protein quantification of tissue samples and promises to redefine our understanding of tumor biology.
Following genotoxic stress, cells activate a complex kinase-based signaling network to arrest the cell cycle and initiate DNA repair. p53-defective tumor cells rewire their checkpoint response and become dependent on the p38/MK2 pathway for survival after DNA damage, despite a functional ATR-Chk1 pathway. We used functional genetics to dissect the contributions of Chk1 and MK2 to checkpoint control. We show that nuclear Chk1 activity is essential to establish a G(2)/M checkpoint, while cytoplasmic MK2 activity is critical for prolonged checkpoint maintenance through a process of posttranscriptional mRNA stabilization. Following DNA damage, the p38/MK2 complex relocalizes from nucleus to cytoplasm where MK2 phosphorylates hnRNPA0, to stabilize Gadd45? mRNA, while p38 phosphorylates and releases the translational inhibitor TIAR. In addition, MK2 phosphorylates PARN, blocking Gadd45? mRNA degradation. Gadd45? functions within a positive feedback loop, sustaining the MK2-dependent cytoplasmic sequestration of Cdc25B/C to block mitotic entry in the presence of unrepaired DNA damage. Our findings demonstrate a critical role for the MK2 pathway in the posttranscriptional regulation of gene expression as part of the DNA damage response in cancer cells.
The use of recombinant proteins, antibodies, small molecules, or nucleic acids as affinity reagents is a simple yet powerful strategy to study the protein-bait interactions that drive biological processes. However, such experiments are often analyzed by Western blotting, limiting the ability to detect novel protein interactors. Unbiased protein identification by mass spectrometry (MS) extends these experiments beyond the study of pairwise interactions, allowing analyses of whole networks of protein-bait interactions. With the latest advances in MS, it is not uncommon to identify thousands of proteins from complex mixtures. Paradoxically, the improved sensitivity of proteomic analyses can make it more difficult to distinguish bait-specific interactions from the large background of identified proteins. In quantitative proteomics, MS signals from protein populations labeled with stable isotopes such as (13)C and (15)N can be identified and quantified relative to unlabeled counterparts. Using quantitative proteomics to compare biochemical enrichments with the bait of interest against those obtained with control baits allows sensitive detection and discrimination of specific protein-bait interactions among the large number of nonspecific interactions with beads. Ad hoc optimization of enrichment conditions is minimized, and mild purification conditions preserve secondary or high-order protein-protein interactions. The combination of biochemical enrichment and quantitative proteomics allows rapid characterization of molecular baits with their interacting proteins, providing tremendous insight into their biological mechanisms of action.
DNA damage checkpoints arrest cell cycle progression to facilitate DNA repair. The ability to survive genotoxic insults depends not only on the initiation of cell cycle checkpoints but also on checkpoint maintenance. While activation of DNA damage checkpoints has been studied extensively, molecular mechanisms involved in sustaining and ultimately inactivating cell cycle checkpoints are largely unknown. Here, we explored feedback mechanisms that control the maintenance and termination of checkpoint function by computationally identifying an evolutionary conserved mitotic phosphorylation network within the DNA damage response. We demonstrate that the non-enzymatic checkpoint adaptor protein 53BP1 is an in vivo target of the cell cycle kinases Cyclin-dependent kinase-1 and Polo-like kinase-1 (Plk1). We show that Plk1 binds 53BP1 during mitosis and that this interaction is required for proper inactivation of the DNA damage checkpoint. 53BP1 mutants that are unable to bind Plk1 fail to restart the cell cycle after ionizing radiation-mediated cell cycle arrest. Importantly, we show that Plk1 also phosphorylates the 53BP1-binding checkpoint kinase Chk2 to inactivate its FHA domain and inhibit its kinase activity in mammalian cells. Thus, a mitotic kinase-mediated negative feedback loop regulates the ATM-Chk2 branch of the DNA damage signaling network by phosphorylating conserved sites in 53BP1 and Chk2 to inactivate checkpoint signaling and control checkpoint duration.
Advances in mass spectrometry-based proteomics have enabled the incorporation of proteomic data into systems approaches to biology. However, development of analytical methods has lagged behind. Here we describe an empirical Bayes framework for quantitative proteomics data analysis. The method provides a statistical description of each experiment, including the number of proteins that differ in abundance between 2 samples, the experiments statistical power to detect them, and the false-positive probability of each protein.
Most small-molecule probes and drugs alter cell circuitry by interacting with 1 or more proteins. A complete understanding of the interacting proteins and their associated protein complexes, whether the compounds are discovered by cell-based phenotypic or target-based screens, is extremely rare. Such a capability is expected to be highly illuminating--providing strong clues to the mechanisms used by small-molecules to achieve their recognized actions and suggesting potential unrecognized actions. We describe a powerful method combining quantitative proteomics (SILAC) with affinity enrichment to provide unbiased, robust and comprehensive identification of the proteins that bind to small-molecule probes and drugs. The method is scalable and general, requiring little optimization across different compound classes, and has already had a transformative effect on our studies of small-molecule probes. Here, we describe in full detail the application of the method to identify targets of kinase inhibitors and immunophilin binders.
The innate immune system senses viral DNA that enters mammalian cells, or in aberrant situations self-DNA, and triggers type I interferon production. Here we present an integrative approach that combines quantitative proteomics, genomics and small molecule perturbations to identify genes involved in this pathway. We silenced 809 candidate genes, measured the response to dsDNA and connected resulting hits with the known signaling network. We identified ABCF1 as a critical protein that associates with dsDNA and the DNA-sensing components HMGB2 and IFI204. We also found that CDC37 regulates the stability of the signaling molecule TBK1 and that chemical inhibition of the CDC37-HSP90 interaction and several other pathway regulators potently modulates the innate immune response to DNA and retroviral infection.
Stable isotope labeling by amino acids (SILAC) metabolically encodes cell populations for protein quantification by mass spectrometry. SILAC was introduced in 2002 and the field of mass spectrometry based proteomics has changed dramatically over the last decade. Increased sensitivity and speed of mass spectrometry instruments coupled with significantly improved mass resolution and precision have led to much higher rates of peptide identification and deeper coverage of proteomic samples. Several proteomics approaches are now available for quantifying proteins and their post-translational modifications, each with their strengths and weaknesses. The simplicity and robustness of SILAC have led to its widespread adoption and new applications have emerged that play to its particular strengths as a metabolic labeling approach.
The adipocyte-derived hormone leptin is a critical regulator of many physiological functions, ranging from satiety to immunity. Surprisingly, very little is known about the transcriptional pathways that regulate adipocyte-specific expression of leptin. Here, we report studies in which we pursued a strategy integrating BAC transgenic reporter mice, reporter assays, and chromatin state mapping to locate an adipocyte-specific cis-element upstream of the leptin (LEP) gene in human fat cells. Quantitative proteomics with affinity enrichment of protein-DNA complexes identified the transcription factor FOS-like antigen 2 (FOSL2) as binding specifically to the identified region, a result that was confirmed by ChIP. Knockdown of FOSL2 in human adipocytes decreased LEP expression, and overexpression of Fosl2 increased Lep expression in mouse adipocytes. Moreover, the elevated LEP expression observed in obesity correlated well with increased FOSL2 levels in mice and humans, and adipocyte-specific genetic deletion of Fosl2 in mice reduced Lep expression. Taken together, these data identify FOSL2 as a critical regulator of leptin expression in adipocytes.
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