Functional and Structural Characterization Reveals Novel FBXW7 Biology
by
Tonny Chao Huang
A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Medical Biophysics University of Toronto
© Copyright by Tonny Chao Huang 2018
Functional and Structural Characterization Reveals Novel FBXW7 Biology
Tonny Chao Huang
Master of Science
Department of Medical Biophysics University of Toronto
2018 Abstract
This thesis aims to examine aspects of FBXW7 biology, a protein that is frequently mutated in a variety of cancers. The first part of this thesis describes the characterization of FBXW7 isoform and mutant substrate profiles using a proximity-dependent biotinylation assay. Isoform-specific substrates were validated, revealing the involvement of FBXW7 in the regulation of several protein complexes. Characterization of FBXW7 mutants also revealed site- and residue-specific consequences on the binding of substrates and, surprisingly, possible neo-substrates.
In the second part of this thesis, we utilize high-throughput peptide binding assays and statistical modelling to discover novel features of the FBXW7-binding phosphodegron. In contrast to the canonical motif, a possible preference of FBXW7 for arginine residues at the +4 position was discovered. I then attempted to validate this feature in vivo and in vitro on a novel substrate discovered through BioID.
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Acknowledgments
The past three years in the Department of Medical Biophysics have defied expectations. I not only had the opportunity to conduct my own independent research, but also to work with distinguished collaborators and to explore exciting complementary fields. I experienced the freedom to guide my own academic development, as well as to pursue my extracurricular interests. Perhaps most significantly, the amount of personal development I have experienced during this journey has significantly changed my views of my myself and my place within the world.
For this, there are those who I will be forever grateful for their guidance, mentorship, and friendship. I would like to first thank my supervisor, Brian. His knowledge and expertise helped guide my research, but it was his kindness and support that allowed me the freedom to complete my graduate studies in my own way. I would also like to thank all members of the Raught Lab that I have had the privilege to cross paths with. The camaraderie shared between the graduate students in the Lab has helped me greatly in completing my degree, so for that I would like to thank Meg, Aaron, Deb, Diana, and Adam. I would also like to thank Étienne, Estelle, and Faith for their technical support and for helping me to get started working in the Lab. I have also befriended many wonderful people within the Department who have helped me along the way, including Nina, Parasvi, Justin, Stanley, Javier, and Danton, to name a few. Lastly, I would like to thank my family for their unending support, without which none of this would have been possible.
If any regrets were to be had, it would be that I was ultimately unsuccessful in what I had sought out to accomplish at the outset of my studies. While my goals and ambitions now lie elsewhere, I remain hopeful that the contents contained within this thesis may one day be shared beyond the limits of this document.
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Table of Contents
Acknowledgments...... iii
Table of Contents ...... iv
List of Figures ...... vii
List of Tables ...... ix
Abbreviations ...... x
List of Appendices ...... xii
Chapter 1 – Introduction ...... 1
Introduction ...... 2
1.1 The ubiquitin-proteasome system ...... 2
1.1.1 Ubiquitin-mediated proteolysis ...... 3
1.1.2 The ubiquitylation cascade ...... 9
1.1.3 Ubiquitin E3 ligase types ...... 12
1.2 The FBXW7 protein ...... 18
1.2.1 F-box proteins and the SCF complex...... 18
1.2.2 Structure and organization of the FBXW7 protein ...... 26
1.2.3 FBXW7 substrates in health and disease ...... 30
1.3 Proteomic approaches in studying protein-protein interactions...... 35
1.3.1 Strategies for the study of protein-protein interactions in vivo ...... 35
1.3.2 Proximity-dependent biotinylation assays ...... 40
1.3.3 Protein identification via mass spectrometry ...... 45
1.4 Thesis motivation and outline ...... 48
Chapter 2 – Elucidation of substrate profiles of FBXW7 and mutants through BioID ...... 49
Elucidation of substrate profiles of FBXW7 isoforms and mutants through BioID ...... 50
2.1 Chapter overview ...... 50
2.2 Contributions...... 50 iv
2.3 Materials and methods ...... 52
2.3.1 Plasmids ...... 52
2.3.2 Cell lines ...... 52
2.3.3 BioID and biotin-streptavidin affinity purification ...... 52
2.3.4 Mass spectrometry ...... 53
2.3.5 Immunoblotting...... 54
2.3.6 Substrate validation via cycloheximide chase ...... 54
2.3.7 Immunofluorescence imaging ...... 55
2.3.8 Data analysis and visualization ...... 55
2.4 Results ...... 56
2.4.1 Expression and localization of FlagBirA-FBXW7 isoforms ...... 56
2.4.2 FBXW7 isoforms exhibit distinct substrate profiles ...... 59
2.4.3 CHX chase reveals novel FBXW7 interactors...... 64
2.4.4 Effect of hotspot mutations on substrate binding is site- and residue-specific ...... 70
2.5 Discussion ...... 72
Chapter 3 – Discovery of novel FBXW7 phosphodegron features ...... 77
Discovery of novel FBXW7 phosphodegron features ...... 78
3.1 Chapter overview ...... 78
3.2 Contributions...... 78
3.3 Materials and methods ...... 79
3.3.1 Generation of peptide-binding models...... 79
3.3.2 Peptide array synthesis and binding ...... 79
3.3.3 Mutant phosphodegron cloning and CHX chase ...... 80
3.3.4 Fluorescence polarization ...... 80
3.4 Results ...... 81
3.4.1 Performance of the Cdc4 model ...... 81 v
3.4.2 Analysis of the FBXW7 peptide-binding array ...... 84
3.4.3 Evaluation of phosphodegron features...... 88
3.5 Discussion ...... 92
References ...... 94
Appendices ...... 112
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List of Figures
Figure 1.1: Comparison of lysosomal and proteasomal protein degradation ...... 4
Figure 1.2: Structure and function of the 26S proteasome ...... 7
Figure 1.3: Generalized overview of the ubiquitylation cascade ...... 9
Figure 1.4: Overview of three primary types of E3 ligases ...... 13
Figure 1.5: Examples of different RING type E3 ligases...... 15
Figure 1.6: The SCF E3 ligase complex and types of FBPs ...... 20
Figure 1.7: Generalized dynamics of the SCF complex in substrate ubiquitylation ...... 23
Figure 1.8: Structural organization and localization of the FBXW7 isoforms ...... 28
Figure 1.9: Missense mutations in FBXW7 in the MSK-IMPACT sequencing cohort ...... 31
Figure 1.10: Comparison of two common strategies used to study PPIs...... 37
Figure 1.11: Overview of the BioID assay ...... 41
Figure 1.12: Generalized workflow of a bottom-up mass spectrometry experiment...... 46
Figure 2.1: Expression and knockdown of FlagBirA-tagged FBXW7 isoforms in Flp-In TREx 293 cells ...... 56
Figure 2.2: Epifluorescence and confocal microscopy of FlagBirA-tagged FBXW7 isoforms reveal isoform-specific localization ...... 57
Figure 2.3: Bait-bait Pearson correlation coefficients reveal relationship between FBXW7 BioID datasets ...... 61
Figure 2.4: FBXW7 isoforms exhibit distinst substrate profiles ...... 63
Figure 2.5: Visualization of nucleoplasmic, cytoplasmic, and nucleolar FBXW7 isoform interactor profiles ...... 65
Figure 2.6: Putative substrates identified by BioID are stabilized with FBXW7 knockdown ...... 67
Figure 2.7: Components of the SAGA, ATAC, ASCOM, and DREAM complexes discovered by BioID are stabilized in FBXW7 knockdown conditions...... 68
Figure 2.8: Effect of hotspot mutation on nucleoplasmic FBXW7 substrate binding is mutant- specific ...... 71
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Figure 3.1: Visualization of Cdc4 model performance in the human proteome for the training dataset ...... 82
Figure 3.2: Binding intensities in separate peptide-binding assays are generally reproducible ....85
Figure 3.3: Sequence logos of peptides with the top 200 highest and lowest intensities in FBXW7 peptide binding arrays ...... 87
Figure 3.5: Evaluation of TAF6L phosphodegrons ...... 91
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List of Tables
Table 2.1: BioID identifies validated substrates and interactors of FBXW7 ...... 62
Table 2.2: BioID identifies components of the SAGA, ATAC, ASCOM, and DREAM complex components as putative substrates ...... 66
Table 3.1: Cdc4 peptide-binding model outperforms other strategies for finding FBXW7- binding peptides in the human proteome for the training dataset...... 81
Table 3.2: Binding of select validated FBXW7 phosphodegrons in the training dataset ...... 83
Table 3.3: Binding of select validated FBXW7 phosphodegrons in proteome-wide screening. ...86
Table 3.4: Structural feature prediction of TAF6L candidate phosphodegrons ...... 90
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Abbreviations
AP Affinity purification APC/C Anaphase-promoting complex/cyclosome APEX Engineered ascorbate peroxidase ASCOM Activating signal co-integrator 2 (complex) ATAC Ada Two A-containing (complex) ATP Adenosine triphosphate BET Bromodomain and extra-terminal motif BiFC Bimolecular fluorescence complementation BSR Bimolecular sensor/reporter CHX Cycloheximide CML Chronic myeloid leukemia co-IP Co-immunoprecipitation CPD Cdc4 phosphodegron CRL Cullin-RING ligase DD Dimerization domain DREAM Dimerization partner, Rb-like, E2F and multivulval class B (complex) DSB Double-strand break DUB Deubiquitylating enzyme ESI Electrospray ionization FBP F-box protein FBXL F-box/LRR protein FBXO F-box only protein FBXW F-box/WD40 repeat-containing protein FRET Förster resonance energy transfer HECT Homologous to E6AP C-terminus HERC HECT domain and RCC1-like domain-containing IBR In between RING (domain) IFN Interferon LC Liquid chromatography LIC Leukemia-initiating cells LRR Leucine rich repeats m/z Mass to charge ratio MALDI Matrix-assisted laser desorption/ionization MSK- Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer IMPACT Targets NICD Notch intracellular domain NLS Nuclear localization signal OE-PCR Overlap-extension polymerase chain reaction PCA Protein fragment complementation assay PCR Polymerase chain reaction x
PLA Proximity ligation assay PMA Phorbol 12-myristate 13-acetate POI Protein of interest PPI Protein-protein interaction PSM Peptide-spectrum match PTM Post-translational modification RBR RING-between-RING RING Really interesting new gene SAGA Spt-Ada-Gcn5 acetyltransferase (complex) SAINT Significance analysis of interactome Sc Saccharomyces cerevisiae gene name SCF SKP1-CUL1-FBP complex SVM Support vector machine T-ALL T-cell acute lymphocytic leukemia TAP Tandem affinity purification TKB Tyrosine kinase binding (domain) TPP Trans-Proteomic Pipeline UBC Ubiquitin conjugating (domain) UFD Ubiquitin fold domain UPS Ubiquitin-proteasome system v-ATPase Vacuolar-type proton-ATPase WD40 Tryptophan-aspartic acid repeats (domain) Y2H Yeast two-hybrid
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List of Appendices
Table S1: A curated overview of validated FBXW7 substrates and phosphodegrons ...... 113
Table S2: List of primers used for the generation of FlagBirA-tagged or 3xHA-tagged expression vectors ...... 117
Table S3: List of antibodies used in immunoblotting (IB) and immunofluorescence microscopy (IF) ...... 119
Figure S1: Binding intensities in separate peptide-binding assays are generally reproducible ...120
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Chapter 1 – Introduction
1 2
Introduction
The ubiquitin-proteasome system is a major mode of selective protein degradation in eukaryotic organisms. Within this system, the F-box and WD repeat-containing protein 7 (FBXW7) has been implicated in the regulation of many important processes. This thesis focuses on the discovery of novel FBXW7 isoform-specific substrates and other aspect of FBXW7 biology to better characterize its role in health and disease.
1.1 The ubiquitin-proteasome system
From cell division to apoptosis, proteins facilitate essentially all processes in biological systems. However, prior to the mid-1900s, it was widely assumed that proteins were largely stable and rarely replaced (Ciechanover 2005). This paradigm began to shift when radioactive amino acids were found to be rapidly incorporated into the tissues of rats that consumed them, instead of being fully metabolized and excreted (Schoenheimer et al. 1939). Today, protein turnover is understood to be an important process in biological systems. With a median half-life of 46 h, proteins may often outlast their usefulness in shorter processes like cell cycling, which generally take less than half as long in humans (Schwanhäusser et al. 2011; Eden et al. 2011). Errors in transcription, translation, or physical stress like oxidation or heat can also result in misfolded proteins that not only lose their functions, but can form aggregates that are toxic to the cell (Amm et al. 2014). Protein degradation, or proteolysis, is therefore crucial not only for the regulation of biological processes, but also for protein quality control, which includes the rapid destruction of upwards of 30% of newly-translated proteins that fail to fold properly (Schubert et al. 2000). The ubiquitin-proteasome system (UPS) is one of the primary pathways of targeted proteolysis and, unsurprisingly, its dysregulation underlies many human diseases. In cancers, mutations in UPS machinery can lead to inappropriately stabilized oncoproteins, an example of which can be found within the interaction between the von Hippel-Lindau disease tumour suppressor (VHL) and the hypoxia-inducible factor 1-alpha (HIF1A) in highly vascularized tumours such as clear cell renal cell carcinoma (Baldewijns et al. 2010). Neurodegenerative disorders such as Huntington’s disease and Alzheimer’s disease produce protein aggregates that impair the function of the UPS, resulting in abnormal synaptic function (Davies et al. 2007; Upadhya and Hegde 2007). The UPS has also been implicated in a variety of other disorders, including cystic fibrosis, Paget’s disease of bone, viral oncogenesis, muscle wasting disorders,
3 hereditary hypertension, and diabetes (Turnbull et al. 2007; Layfield and Shaw 2007; Shackelford and Pagano 2007; Nury et al. 2007; Rotin 2008; Wing 2008). Given the increasing awareness of its role in health and disease, pharmacological intervention in the UPS has also been a topic of interest. The following sections will provide an overview of key processes in the UPS and their involvement in health and disease.
1.1.1 Ubiquitin-mediated proteolysis
Intracellular proteolysis in mammalian cells is primarily mediated via lysosomes or proteasomes. Lysosomal proteolysis relies on proteases that are active within the acidic environment of the lysosome. In contrast, proteasomal proteolysis is facilitated by the proteasome, a 33-component multiprotein complex found throughout the nucleus and cytoplasm. Both proteolytic pathways utilize ubiquitin, a highly-conserved 76 residue protein that exists in all eukaryotes that can be covalently linked to substrate proteins as a post-translational modification (PTM) in a process referred to as ubiquitylation (as well as ubiquitination). Ubiquitin may also be linked to other ubiquitin molecules at its N-terminal methionine or one of its seven lysine residues, producing ubiquitin oligomers (or “chains”) of various lengths and branching patterns. The mode in which the substrate protein is ubiquitylated dictates the effect on the substrate, a review of which can be found in Yau & Rape, 2016. In general, homotypic chains linked through lysine 48 (K48) or K11 target substrates for proteasomal degradation, while other forms of mono- and polyubiquitylation are involved in non-proteasome related functions, including lysosomal degradation. The following section provides an overview of the role of ubiquitin in these proteolytic pathways.
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Figure 1.1: Comparison of lysosomal and proteasomal protein degradation. (Top) Simplified overview of ubiquitin function in intracellular proteolysis. K48/K11 polyubiquitin chains facilitate degradation via the proteasome, while other types of ubiquitin chains, including linkage through K27 and K63, facilitate proteolysis via the lysosome, including autophagy, which is mediated by sequestosome 1. (Bottom) Comparison of proteolytic machinery structures. Electron micrographs depict A) the formation of an autophagosome around depolarized mitochondria in mouse embryonic fibroblasts and B) lysosomes containing bovine serum albumin conjugated to colloidal gold. Arrows indicate the respective organelles and scale bars represent 500nm in both figures. Structure of the 29S proteasome is depicted in C), as determined through cryo-electron microscopy at a resolution of 3.9Å. Micrographs of autophagosome and lysosome were modified from (Pickrell and Youle 2015) and (Luzio et al. 2007), respectively. Structure of the 29S proteasome was accessed through the Protein Data Bank (PDB) (5L4G) (Schweitzer et al. 2016).
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Discovered by Christian de Duve in 1953, lysosomes were once thought to be the primary mediator of protein degradation prior to the discovery of proteasomal proteolysis (de Duve et al. 1953; Ciechanover 2005). These specialized vesicles contain a variety of acid hydrolases that cleave chemical bonds in the acidic environment of the lysosome, which is maintained through the cleavage of adenosine triphosphate (ATP) to generate hydrogen ions by vacuolar-type proton-ATPases (v-ATPase). Lysosomes can fuse with mature endosomes and autophagosomes, resulting in the degradation of their contents through the acid hydrolases. While lysosomal proteolysis relies primarily on the endomembrane system for vesicle trafficking and fusion, ubiquitin regulates the sorting of proteins to the lysosome, notably for plasma membrane proteins (Clague & Urbé, 2010). Monoubiquitylation of several receptor tyrosine kinases, such as the epidermal growth factor receptor (EGFR) by E3 ubiquitin-protein ligase CBL (also Casitas B- lineage lymphoma proto-oncogene, CBL), leads to their internalization from the plasma membrane to early sorting endosomes, thereby downregulating their signaling functions (Clague & Urbé, 2006; Haglund et al., 2003). The endosomal sorting complexes required for transport (ESCRT) machinery is needed for subsequent processing of these receptors, but the affinity of the ESCRT machinery for monoubiquitin is low compared to its affinity for K63-linked tetraubiquitin chains (Ren & Hurley, 2010). Efficient lysosomal degradation of EGFR thus requires both monoubiquitylation and subsequent K63 polyubiquitylation to facilitate endosomal maturation via ESCRT machinery (Lauwers, Jacob, & André, 2009). Human ESCRT machinery is also associated with at least two deubiquitylating enzymes (DUBs), ubiquitin carboxyl- terminal hydrolase 8 (USP8) and STAM-binding protein (STAMBP), that provide an additional layer of regulation in the proteolysis of EGFR via cleavage of K63-linked ubiquitin molecules (Clague & Urbé, 2006). Lysosomal degradation also underlies autophagy, a metabolic process whereby organelles and other intracellular components are encapsulated by a double-membraned structure known as an autophagosome, which later fuses with lysosomes for bulk degradation of its contents. In autophagy, ubiquitin is important in marking proteins and organelles for degradation, which are subsequently recognized by sequestosome 1 protein (SQSTM1), which acts as an adaptor between the ubiquitylated proteins and the preautophagosomal membrane (Kirkin, McEwan, Novak, & Dikic, 2009). An example of the importance of ubiquitin in autophagy can be observed in the clearance of depolarized mitochondria, which have lost their proton gradients between the matrix and the intermembrane space and, hence, their ability to produce ATP. Failure to clear damaged mitochondria contributes to the formation of Lewy
6 bodies, protein aggregates that are a hallmark of Parkinson’s disease, the most common neurodegenerative movement disorder. The E3 ubiquitin ligase parkin (PRKN) ubiquitylates voltage-dependent anion-selective channel protein 1 (VDAC1) of depolarized mitochondria with K27 polyubiquitin chains, which allows for the recruitment of SQSTM1 to form autophagosomes around the mitochondria (Geisler et al., 2010). Pathogenic mutations in PRKN fail to ubiquitylate VDAC1, resulting in impairment of autophagic clearance of depolarized mitochondria.
Soon after its discovery, it became increasingly clear that the lysosome cannot be the sole facilitator of proteolysis. Evidence from multiple groups suggested the existence of an ATP- dependent proteolytic machinery that selectively targeted intracellular proteins and was unaffected by the inhibition of lysosomes (Ciechanover, 2005; Poole, Ohkuma, & Warburton, 1977). Work on cell-free proteolytic systems derived from reticulocytes, which lack lysosomes, resulted in the discovery of the ATP-dependent ubiquitin cascade by Hershko, Ciechanover and colleagues, paving the way for the eventual discovery of the proteasome in 1986 by Ronald Hough and colleagues (Ciechanover, Hod, & Hershko, 1978; Hershko, Heller, Elias, & Ciechanover, 1983; Hough, Pratt, & Rechsteiner, 1986).
The proteasome describes several large holoenzymes minimally comprising a catalytic core particle, commonly referred to as the 20S proteasome (Saeki & Tanaka, 2012). It is composed of two outer heptameric α-rings and two inner heptameric β-rings stacked axially to form a cylinder-like structure with a central proteolytic chamber formed by the two β-rings and two ante-chambers formed between the α-rings and β-rings. Substrates pass through the narrow α- rings into the antechambers, which are thought to maintain the polypeptide chain in an unfolded state, and then into the central proteolytic chamber. The β-rings each contain three catalytically- active threonines on subunits β1, β2, and β5 that confer caspase-like, trypsin-like, and chymotrypsin-like activity, respectively, to the central proteolytic chamber. Together, these subunits generate oligopeptides, typically 3-22 residues long, from substrate polypeptide chains (Kisselev et al. 1999). Variants of the catalytic core also exist in tissue-specific contexts. In immune cells, the β1, β2, and β5 subunits are replaced with interferon (IFN) γ-induced β subunits to form the immunoproteasome, which exhibits elevated chymotrypsin-like and trypsin- like activities that support the generation of peptides for antigen display and IFN-induced oxidative stress (Tanaka & Kasahara, 1998). Thymic epithelial cells also express a thymus-
7 specific β5 subunit with weaker chymotrypsin activity that is suggested to be essential for positive selection of CD8+ T cells (Murata et al., 2007; Murata, Takahama, & Tanaka, 2008). The 20S proteasome was thought to be latent in the absence of additional regulatory proteins in cells, but it may be capable of degrading intrinsically unstructured proteins or proteins with oxidative damage (Baugh, Viktorova, & Pilipenko, 2009). The 20S proteasome has also been a target of pharmacological inhibition. Notably, the proteasome inhibitor MG132 was instrumental in advancing research of proteasome function in health and disease and led to the discovery of bortezomib, a proteasome inhibitor used for the treatment of multiple myeloma and other cancers (Goldberg, 2012). MG132 is a carboxybenzyl-peptide aldehyde that potently inhibits the chymotrypsin-like β5 subunit, as well as the caspase-like β1 subunit at higher concentrations.
Figure 1.2: A) Structure of the 26S proteasome and important processes are highlighted. B) Three catalytically- active threonines confer proteolytic capability to the β-rings of the 20S proteasome. Proteasome inhibitors MG132 and bortezomib inhibits chymotrypsin-like β5 subunit, as well as the caspase-like β1 subunit at higher concentrations. Figure reproduced from (Goldberg 2012) with permission of Rockefeller University Press.
Activity of the 20S proteasome is limited in the absence of a regulatory particle. The predominant form of the proteasome contains one or two 19S regulatory particles (also known as PA700), which assemble onto the α-rings of the 20S proteasome to form the 26S proteasome. This allows the holoenzyme to recognize polyubiquitin chains, recycle ubiquitin molecules, unfold substrate proteins, and drive the polypeptide into the 20S proteasome, the latter two of which are ATP-dependent (Saeki & Tanaka, 2012). Recognition of ubiquitylated substrates is
8 facilitated by the 26S proteasome non-ATPase regulatory subunit 4 (PSMD4) and the proteasomal ubiquitin receptor ADRM1 (ADRM1) (Deveraux, Jensen, & Rechsteiner, 1995; Husnjak et al., 2008). Ubiquitin chains are removed by non-ATPase regulatory subunit 14 (PSMD14) prior to substrate engagement with the “base” of the 19S regulatory particle, which includes 6 ATPases that facilitate both unfolding of the substrate and the opening of the α-rings for entry into the 20S proteasome (Rechsteiner, Realini, & Ustrell, 2000; Verma et al., 2002). The introduction of substrates into the 20S can also be facilitated via other regulatory particles. Notably, the IFNγ-induced 11S regulatory particle (also known as PA28) is implicated in the processing of specific antigens for immune display as part of the immunoproteasome (Murata et al. 2001). The proteasome activator complex subunit 4 (PSME4, also PA200) is another regulatory particle that mediates the degradation of histones during spermatogenesis and DNA damage repair (Khor et al., 2006; Tanaka & Kasahara, 1998). Hybrid proteasomes have also been detected in cells, where two different types of regulatory particles bind a single 20S proteasome. Binding of both the 19S and 11S regulatory particles have been observed to generate peptides distinct from the 26S proteasome, possibly to enhance antigen presentation for immune cells (Cascio et al. 2002).
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1.1.2 The ubiquitylation cascade
Ubiquitylation requires three families of protein acting in a cascade: ubiquitin-activating enzymes, ubiquitin-conjugating enzymes, and ubiquitin ligases. These proteins are also commonly referred to as ubiquitin E1 enzymes, E2 enzymes, and E3 ligases, respectively. With over 600 encoded in the human genome, E3 ligases are the most abundant family of proteins in the UPS, followed by 32 E2 enzymes and two E1 enzymes. Therefore, the UPS relies on a relatively small number of general E1 and E2 enzymes to provide the machinery necessary for a large number of E3 ligases, which confer the UPS specificity in target ubiquitylation. The E1- E2-E3 system of nomenclature is also used in the activation cascades of ubiquitin-like modifiers such as the small ubiquitin-like modifier (SUMO) and the neural precursor cell expressed, developmentally down-regulated 8 (NEDD8) proteins. Ubiquitin can also be removed from substrates by DUBs. The following section provides an overview of processes and proteins in the ubiquitylation cascade.
Figure 1.3: Generalized overview of the ubiquitylation cascade. Inactive ubiquitin is activated by and E1 enzyme in an ATP-dependent manner, followed by its transfer from the E1 enzyme to an E2 enzyme. The ubiquitin-bound E2 enzyme is recruited to an E3 ligase through an E2-coordinating domain, which varies depending on the type of E3 ligase. The E3 ligase coordinates the final ubiquitylation of the substrate.
The first enzymatic step in ubiquitylation, activating ubiquitin molecules, is mediated by the E1 enzymes. E1 enzymes first bind inactive ubiquitin and ATP in complex with a divalent magnesium ion (Mg2+) to catalyze the formation of a high-energy adenylate-ubiquitin bond on the C-terminal glycine residue of the ubiquitin molecule (Schulman & Harper, 2009). This high- energy bond is then attacked by a catalytic cysteine residue on the E1 enzyme to form a high- energy thioester bond between the E1 enzyme and the ubiquitin molecule, thereby completing
10 the activation of ubiquitin. Another inactive ubiquitin molecule is then bound by the E1 to form the adenylate-ubiquitin intermediate, resulting in an E1 enzyme bound to two separate ubiquitin molecules at different sites, which is thought to aid in the transfer of the activated ubiquitin to an E2 enzyme. Two ubiquitin E1 enzymes have been identified in humans, ubiquitin-like modifier- activating enzyme 1 (UBA1) and ubiquitin-like modifier-activating enzyme 6 (UBA6). As with all E1 enzymes, both UBA1 and UBA6 contain three canonical E1 enzymatic domains: an adenylation domain, a catalytic cysteine domain, and a C-terminal ubiquitin fold domain (UFD). UBA1 is largely considered to be the primary E1 enzyme in humans while evidence suggests that UBA6 may play a role in the activation of ubiquitin for a small subset of E2 enzymes (Jin, Li, Gygi, & Harper, 2007). The regulation of E1 enzymes in vivo is poorly understood.
Activated ubiquitin remains bound to the E1 enzymes until it is transferred to a cognate ubiquitin E2 conjugating enzyme. This process is facilitated through the UFD on the E1 enzyme and a series of coordinated conformational changes on both enzymes, ultimately resulting in the transfer of the thioester-bonded ubiquitin from the E1 enzyme to a cysteine residue on the E2 enzyme (Lee & Schindelin, 2008). The E2 enzyme then facilitates the reaction of the conjugated ubiquitin onto substrates, though the reactivity is generally low in the absence of an E3 ligase (Stewart, Ritterhoff, Klevit, & Brzovic, 2016). The human genome encodes approximately 32 E2 enzymes for ubiquitin, all of which contain a catalytic ubiquitin conjugating (UBC) domain that encompasses both the E2 active site and the E3 binding site. In cases where the activated ubiquitin is transferred directly from the E2 enzyme to the substrate, it is the E2 enzyme that confers specificity to the ubiquitin system by determining both the initial residue for ubiquitylation and the branching pattern of ubiquitin chains. In cases where the activated ubiquitin is transferred onto the E3 ligase first, it is thought that the E3 ligase determines the ubiquitylation product. The different types of ubiquitin chains that are formed by E2 enzymes drive the substrate towards different fates. As previously mentioned, K11 and K48 homotypic ubiquitin chains target substrate proteins for proteolysis via the proteasome (Yau & Rape, 2016). Other ubiquitin chains typically facilitate processes unrelated to the proteasome, such as lysosomal degradation by facilitating the trafficking of proteins through the endosomal system. Given their importance in determining the outcome of ubiquitylation, E2 enzymes are regulated through multiple mechanisms, such as through the binding of inactive ubiquitin at an allosteric
11 site in the UBC domain or the ubiquitylation of E2 enzymes themselves for degradation via the proteasome (Brzovic, Lissounov, Christensen, Hoyt, & Klevit, 2006; Stewart et al., 2016).
The transfer of ubiquitin from the E2 enzymes to the substrate is facilitated by E3 ligases, which are categorized into three different families: the really interesting new gene (RING) type, the RING-between-RING (RBR) type, and the homologous to E6AP C-terminus (HECT) type. While the specific enzymatic mechanism differs between the three types of E3 ligases, all typically result in the transfer of the active ubiquitin from the cysteine residue on the E2 enzyme to an ε-amino group of a lysine residue on the target protein, including other ubiquitin molecules, through an isopeptide bond. Ubiquitin can also be conjugated to the N-terminus methionine residue of proteins via a peptide bond, as well as to cysteine residues via a thioester bond. Conjugation of ubiquitin to hydroxyl groups of serine and threonine have also been reported (Wang et al. 2009). With over 600 putative E3 coding genes in the human genome, E3 ligases constitute the largest family of proteins in the ubiquitin cascade and consequently confer greater specificity to the system than E1 or E2 enzymes. E3 ligases also play an important role in determining ubiquitylation products by recruiting different E2 enzymes to the substrate. E3 ligases also typically contain a domain that recognizes and binds a specific substrate motif or molecule.
Lastly, ubiquitin can be cleaved from other proteins through DUBs. There are approximately 100 putative DUBs encoded in the human genome, most of which are papain-like cysteine proteases while the remainder are zinc-dependent metalloproteases (Nijman et al., 2005). By removing ubiquitin and ubiquitin chains from substrates, DUBs play an important role in rescuing ubiquitylated substrates from downstream processes such as degradation. DUBs are also important for several other processes in the ubiquitin cascade, such as generating ubiquitin from precursor proteins, recycling ubiquitin from unanchored ubiquitin chains, and regenerating ubiquitin from adventitiously formed thiol esters (Reyes-Turcu, Ventii, & Wilkinson, 2009).
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1.1.3 Ubiquitin E3 ligase types
Though the existence of the ubiquitin system predates eukaryotes, it was during early eukaryotic evolution when the repertoire of ubiquitylation machinery, particularly the amongst the E3 ligases, expanded greatly (Grau-Bové et al. 2015). HECT type E3 ligases appeared during this period, along with the rapid diversification of RING type ligases and complexes. Presumably, this expansion was both driven by and necessary for the evolution of the relatively complex features of eukaryotic organisms from prokaryotes. The following section provides an overview of the diversity of E3 ligases.
The largest family of E3 ligases, and the family to which FBXW7 belongs, are the RING type ligases, which are so named for their inclusion of the RING finger domain. First described by Freemont and colleagues in 1991, the RING finger domain utilizes several conserved cysteine residues and a histidine residue to coordinate the binding of two zinc ions in a cross-braced fashion (Freemont, Hanson, & Trowsdale, 1991). Understanding of the RING finger domain was greatly expanded in 1999, when several groups discovered that the zinc-coordinating domain was essential for the recruitment of E2 enzymes to RING finger domain-containing E3 ligases for subsequent substrate ubiquitylation (Kamura et al., 1999; Ohta, Michel, Schottelius, & Xiong, 1999; P. Tan et al., 1999). Further structural studies of the RING finger domain revealed that the binding of the domain to E2 enzymes drives the E2 towards a closed conformation, where the conjugated ubiquitin is primed for transfer onto a substrate lysine residue (Pruneda et al. 2012). RING type E3 ligases also include the U-box type E3 enzymes, which were named after a domain found on the yeast E4 ubiquitin-protein ligase (Ufd2) (Hatakeyama & Nakayama, 2003). Though the U-box domain lacks the coordination of zinc ions, the structure and mechanism through which ubiquitin is transferred are similar to the RING finger domain.
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Figure 1.4: Overview of three primary types of E3 ligases. RING-type E3 ligases facilitate the transfer of activated ubiquitin directly from the E2 enzyme onto the substrate protein, while HECT-type and RBR-type E3 ligases first transfer the activated ubiquitin onto themselves to form a reactive intermediate prior to substrate ubiquitylation. RING, really interesting new gene domain; N-HECT, homologous to E6AP C-terminus domain, N-terminal lobe; C- HECT, homologous to E6AP C-terminus domain, C-terminal lobe; IBR, in between ring domain.
With approximately 600 member proteins, RING type E3 ligases exhibit diverse mechanisms for facilitating the transfer of ubiquitin from the E2 enzymes to the substrate (Buetow and Huang 2016). Some RING type E3 ligases facilitate ubiquitylation as a monomer, such as the E3 ubiquitin ligase CBL, which targets many activated receptor tyrosine kinases for degradation (Joazeiro et al., 1999). Other RING type E3 ligases function as components of multiprotein complexes. For example, homodimerization of the E3 ubiquitin-protein ligase RNF4 (RNF4) is essential for the recruitment of E2 enzymes to ubiquitylate substrates such as the centromere protein I (CENPI), which is crucial for the control of kinetochore assembly (Mukhopadhyay, Arnaoutov, & Dasso, 2010; Plechanovová et al., 2011). The breast cancer type 1 susceptibility protein (BRCA1) and the BRCA1-associated RING domain protein 1 (BARD1) both contain
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RING finger domains, but have very low ubiquitin ligase activities unless a heterodimer is formed (Hashizume et al., 2001). The same paper found that a breast cancer-associated mutation on BRCA1 abrogates dimerization with BARD1. In addition to dimers, some RING type E3 ligases exist in the form of multi-subunit complexes formed around cullin proteins and are, hence, referred to as cullin-RING ligase (CRL) complexes. Whereas substrate specificity of monomeric and dimeric E3 ligase complexes is usually dictated by specific domains on the RING finger domain-containing protein itself, such as the tyrosine kinase binding (TKB) domain on CBL (Thien & Langdon, 2001), the substrate specificity of CRL complexes is typically conferred by a subunit that contains no RING finger domain itself. This allows CRL complexes to behave in a modular fashion, allowing for different substrate recognition subunit proteins to be assembled with the same core components. This can be observed in the anaphase-promoting complex or cyclosome (APC/C), which contains at least 14 core components (Zhang et al. 2014). The APC/C E3 ligase complex forms around the cullin-like scaffold protein anaphase-promoting complex subunit 2 (ANAPC2) and binds E2 enzymes through anaphase-promoting complex subunit 11 (ANAPC11), the only protein in the complex with a RING finger domain. The specificity of substrate binding by APC/C is determined by its association to either cell division cycle protein 20 homolog (CDC20) or the Fizzy-related protein homolog (FZR1) through cell division cycle protein 23 homolog (CDC23), allowing the complex to ubiquitylate different sets of substrates, which is crucial for APC/C-dependent progression of the cell cycle.
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Figure 1.5: Examples of different RING type E3 ligases, including A) the monomeric E3 ligase CBL, B) the homodimeric E3 ligase RNF4, C) the heterodimeric E3 ligase BRCA1-BARD1, and D) the cullin-RING ligase complex APC/C. Full structures are shown for A) and D), while B) and C) shows the dimerized RING domains. Colours in the protein structures reflect the domains or subunits in their respective diagrams. Structures were obtained from the PDB through the following PDB IDs: CBL, 2Y1M; RNF4, 3NG2; BRCA1-BARD1, 1JM7; APC/C, 5KHR. CBL, E3 ubiquitin-protein ligase CBL; TKB, tyrosine kinase binding domain; RNF4, E3 ubiquitin- protein ligase RNF4; PSB, poly-SUMO binding domain; BRCA1, breast cancer type 1 susceptibility protein; BARD1, BRCA1-associated RING domain protein 1; BRCT, BRCA1 C-terminus domain; APC/C, anaphase- promoting complex/cyclosome; ANAPC11, anaphase-promoting complex subunit 11; ANAPC2, anaphase- promoting complex subunit 2; CDC23, cell division cycle protein 23 homolog; CDC20, cell division cycle protein 20 homolog; FZR1, Fizzy-related protein homolog.
The HECT type E3 ligases are the second most common type of E3, with 28 genes identified in humans. As was the case with RING type E3 ligases, HECT type E3 ligases are so named for their inclusion of the HECT domain, which was first reported in 1995 by Howley and colleagues (Huibregtse, Scheffner, Beaudenon, & Howley, 1995). The eponymous E6-associated protein
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(E6-AP, also ubiquitin-protein ligase E3A, UBE3A) was found to form a complex with the human papillomavirus E6 protein, which targets the tumour suppressor cellular tumor antigen p53 (TP53) for ubiquitin-mediated proteolysis, driving the oncogenic potential of the virus (Scheffner et al. 1993). Since then, a total of 28 proteins have been identified to contain HECT domains in combination with a substrate-binding domain. The HECT domain itself comprises two lobes connected by a flexible hinge: an N-terminal lobe that binds an E2 enzyme and a C- terminal lobe containing a catalytic cysteine residue (Buetow and Huang 2016). Unlike RING type E3 ligases, where the ubiquitin is directly transferred from the E2 enzyme onto the substrate, HECT type E3 ligases facilitate ubiquitylation through two separate catalytic steps. First, an E2 enzyme with an attached ubiquitin binds the N-terminal lobe, allowing for the cysteine residue on the C-terminal lobe to come into close proximity with the ubiquitin molecule for the transfer of the ubiquitin molecule onto the C-terminal lobe, after which the E2 enzyme is released. Upon substrate binding to the HECT E3 ligase, the ubiquitin molecule is transferred from the C-terminal lobe to a lysine residue on the substrate. HECT type E3 ligases have been implicated in the regulation of membrane proteins through the neural precursor cell expressed developmentally down-regulated protein 4 (NEDD4) subfamily, which includes 9 E3 ligases. NEDD4 ubiquitylates amiloride-sensitive sodium channel subunits (SCNN1B/G), which controls the reabsorption of sodium in kidneys (Staub et al. 1996). Deletion of proline-rich regions on SCNN1B/G associated with Liddle’s syndrome abrogates binding of NEDD4, leading to constitutive resorption of sodium ions and hypertension. NEDD4 also targets receptor tyrosine kinase erbB-3 (ERBB3) and the fibroblast growth factor receptor 1 (FGFR1) for proteasomal and lysosomal degradation, respectively (Huang et al., 2015; Persaud et al., 2011). Both ERBB3 and FGFR1 are growth factor receptors that are important in development and dysregulated in cancers, thereby implicating NEDD4 in these processes. Another subfamily of HECT E3 ligases are the HECT domain and RCC1-like domain-containing (HERC) proteins. Within this subfamily, one of the better characterized proteins is probable E3 ubiquitin-protein ligase HERC1, which ubiquitylate tuberin (TSC2) for degradation (Chong-Kopera et al. 2006). As TSC2 is involved in the regulation of the mammalian target of rapamycin (mTOR) pathway, the HERC1-TSC2 interaction may be important in promoting cell growth.
The last and smallest family of E3 ligases are the RBR type E3 ligases, which are defined by the inclusion of two distinct RING finger-like domains separated by an in between RING (IBR)
17 domain. The N-terminal RING finger (RING1) domain resembles the canonical RING finger domain in sequence and structure, whereas the C-terminal RING finger (RING2) domain does not, though it still coordinates two zinc ions, albeit in a linear fashion instead of a cross-braced configuration (Buetow and Huang 2016). The IBR domain resembles the RING2 domain in structure and coordinates two zinc ions in a similar fashion. Though the discovery of RBR type E3 ligases dates back to 1999, they were long thought to be a subfamily of the RING type E3 ligases with a similar mechanism for ubiquitylation (van der Reijden, Erpelinck-Verschueren, Löwenberg, & Jansen, 1999). It would not be until 2011 when Klevit’s group demonstrated that RBR type E3 ligases functioned somewhat like a hybrid between RING type and HECT type E3 ligases. It was revealed through studies of two RBR type E3 ligases, parkin and E3 ubiquitin- protein ligase ARIH1, that the RING1 domain recruits an E2 enzyme to transfer the conjugated ubiquitin onto a conserved cysteine residue in the RING2 domain, which is subsequently transferred to a substrate (Wenzel et al. 2011). The RING1 domain lacks a conserved arginine residue that that promotes ubiquitin transfer from the E2. Furthermore, mutation of the conserved cysteine residue in the RING2 domain or deletion of the domain eliminates the transfer of ubiquitin. Together, these studies demonstrate that both RING1 and RING2 are necessary for the function of RBR E3 ligases. The function of the IBR domain is not well understood, but structural studies into the RBR E3 ligase RNF31 have revealed an allosteric site where a free ubiquitin molecule can bind to enhance the recruitment of an E2 enzyme and the transthioesterification of the RING2 domain. Fourteen RBR type E3 ligases have been identified in the human genome, the most studied of which is the E3 ligase parkin. Mutations in parkin are associated with Parkinson’s disease, which is thought to disrupt its ability to target α-synuclein for ubiquitin-mediated proteolysis, leading to increased fibrillization of the microtubule- associated protein tau (Shimura et al., 2001). Parkin is implicated in mitochondrial clustering and fusion through the ubiquitylation of mitofusin 1 and 2, two important proteins in the process (Gegg et al., 2010). The aforementioned E3 ligase ARIH1 has been implicated in the regulation of protein synthesis through the ubiquitylation of the eukaryotic translation initiation factor 4E type 2 (EIF4E2), and RNF31, another RBR type E3 ligase, is crucial in inflammation by forming linear ubiquitin chains on the NFκB essential modulator NEMO (Niu, Shi, Iwai, & Wu, 2011; N. G. S. Tan et al., 2003).
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1.2 The FBXW7 protein
The FBXW7 protein is the substrate recognition subunit of a CRL complex, where it facilitates ubiquitylation of targeted proteins, primarily for proteasomal degradation. Human FBXW7 was identified by three separate groups in 2001 based on homology to the Saccharomyces protein cell division control protein 4 (Cdc4), the Drosophila protein Archipelago (Ago), and the Caenorhabditis protein suppressor/enhancer of lin-12 protein 10 (sel-10) (Strohmaier et al. 2001; Moberg et al. 2001; Gupta-Rossi et al. 2001). Since its discovery, FBXW7 has been implicated in targeting many proto-oncoproteins for degradation and has hence been commonly referred to as a tumour suppressor in multiple contexts. The following sections provide an overview of F- box proteins (FBP), the structure of FBXW7, and its role in health and disease.
1.2.1 F-box proteins and the SCF complex
The F-box family of proteins comprises ~69 genes in the human genome, all of which contain the eponymous F-box domain, a ~50-amino acid structure first identified in cyclin F (Jin 2004; Bai et al. 1996). This domain allows F-box proteins to assemble into the S-phase kinase- associated protein (SKP1), cullin 1 (CUL1), and FBP-containing (SCF) ubiquitin E3 ligase complex. As a subfamily of CRLs, most FBPs appear to assemble into SCF complexes, though at least one FBP, FBXW8, can assemble with cullin 7 instead (Mészáros et al. 2017). Much of the early work on FBPs and SCF-mediated proteolysis originated in Saccharomyces. In particular, the cell division cycle (cdc) proteins cdc4, cdc34, and cdc53 were implicated in phosphorylation- dependent, ubiquitin-mediated proteolysis of cyclin-dependent kinase inhibitor Sic1 with Skp1 and Hrt1 (also Roc1) (Patton et al. 1998). Further investigations revealed that the degradation of different proteins relied on a core set of common proteins, such as Cdc34, Cdc53, and Skp1, as well as substrate-specific proteins such as Cdc4 and Grr1. This modularity reflects our current understanding of SCF complexes, formed of a common core complex composed of CUL1 (Sc: Cdc53), SKP1, and RING-box protein 1 (RBX1, Sc: Hrt1/Roc1). Both Cdc4 and Cdc34 are also present in humans and are known as FBXW7 and the ubiquitin-conjugating enzyme E2 R1 (UBE2R1), respectively.
Within the SCF complex, the FBP acts primarily as the substrate recognition subunit and, hence, nomenclature of FBPs was standardized in 2004 to reflect the domain that mediates the protein- protein interactions (PPI). The two most common protein interaction domains are the tryptophan-
19 aspartic acid (WD40) repeats with 10 members and leucine rich repeats (LRR) with 21 (Jin 2004). These F-box proteins are designated as F-box/WD40 repeat-containing proteins (FBXWs) and F-box/LRR proteins (FBXLs), respectively. All other F-box proteins with other or no recognizable protein interaction domains are designated as F-box only proteins (FBXOs). The majority of FBPs are poorly characterized with few established substrates (Skaar et al. 2014). Of the few well-characterized FBPs, the S-phase kinase-associated protein 2 (SKP2, also FBXL1) is one of the best understood. SKP2 is best characterized as the SCF component that targets the tumour suppressor cyclin-dependent kinase inhibitor 1B (CDKN1B, also p27, Sc: Kip1) for proteasomal degradation, driving quiescent cells into S phase (Sutterlüty et al. 1999; Carrano et al. 1999). Consequently, SKP2 has been reported as an oncoprotein in a wide variety of cancers, where its overexpression is correlated with high tumour grade and poor prognosis (Frescas and Pagano 2008). SKP2 has also been demonstrated to facilitate the degradation of other tumour suppressors such as cyclin-dependent kinase inhibitors 1A (CDKN1A, also p21), 1C (CDKN1C, also p57), and retinoblastoma-like 2 (RBL2, also p130) (Yu et al. 1998; Kamura et al. 2003; Tedesco et al. 2002). The beta-transducin repeat containing protein (ꞵ-TrCP) refers to two paralogous FBPs (FBXW1/11, also ꞵ-TrCP1/2) that are also well-characterized. ꞵ-TrCP has been implicated in the regulation of both tumour promoters and suppressors, notably ꞵ-catenin (CTNNB1) and NFκB inhibitors (IκB, also NFKBIA/B/E) (Winston et al. 1999; Shirane et al. 1999). Interestingly, both upregulation and mutation of ꞵ-TrCP have been observed in cancers. For example, elevated levels of ꞵ-TrCP have been found with constitutive NFκB signaling in chemoresistant pancreatic cancer cell lines and a study of 95 gastric cancer specimens showed mutations within the WD40 repeat domain of ꞵ-TrCP correlated with elevated ꞵ-catenin levels (Müerköster et al. 2005; Kim et al. 2007). Ongoing research into FBPs has revealed other interesting functions. FBXL3 has been implicated in the regulation of circadian rhythm oscillations through the degradation of cryptochrome 1 (CRY1) and 2 (CRY2) (Busino et al. 2007). FBXL2 is one of two FBPs to contain a motif for geranylgeranylation, which may be important for its role in regulating phosphoinositide 3-kinase signaling, as well as facilitating the productive infection of the hepatitis C virus (Kuchay et al. 2013; Wang et al. 2005). Some FBPs like FBXL10 (also KDM2B) and FBXL11 (KDM2A) also possess histone demethylase activity and their overexpression correlates with poor prognosis in cancers (Shen and Spruck 2017).
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Figure 1.6: The SCF E3 ligase complex and types of FBPs. (Left) The elongated structure of the core SCF complex. Subunits within the complex are coloured according to the schematic representation. The structure includes only an F-box domain bound to SKP1. (Right) Structures of SKP2, BTRC, and FBXO31 as representatives of the three types of FBPs, categorized by their substrate-binding domains. Within each structure, the F-box domains are coloured in green while the substrate-binding domains are in blue. Accordingly, the LRR domain and the WD40 repeat domains are highlighted for SKP2 and BTRC, respectively. For FBXO31, a unique ꞵ-barrel structure that has been found to facilitate the binding of G1/S-specific cyclin-D1 (CCND1) is highlighted (Li et al. 2018). Structures were obtained from the PDB through the following PDB IDs: SCF complex, 1LDK; SKP2, 2ASS; BTRC, 1P22; FBXO31, 5VZT. SKP1, S-phase kinase-associated protein 1; CUL1, cullin-1; FBP, F-box protein; RBX1, E3 ubiquitin-protein ligase RBX1; SKP2, S-phase kinase-associated protein 2; BTRC, F-box/WD repeat-containing protein 1A; FBXO31, F-box only protein 31.
The sequences recognized by FBPs on substrates are referred to as degrons. Many degrons require phosphorylation for proper recognition by FBPs and are hence termed phosphodegrons. By observing the consensus sequence of degrons on multiple substrates, canonical degrons have been identified for many of the well-characterized FBPs, including SKP2 and ꞵ-TrCP (Skaar et
21 al. 2013). Variations of the phosphodegron are often serine-threonine substitutions or phosphomimicking amino acids. For example, the canonical ꞵ-TrCP phosphodegron is described as DpSGXXpS, which is found on ꞵ-catenin and IκBα/β in the form of DpSGIHpS, DpSGLDpS, and DpSGLGpS, respectively (Frescas and Pagano 2008). The protein aurora borealis (BORA), another substrate of ꞵ-TrCP, contains instead the phosphodegron DpSGYNpT, where the phosphothreonine is necessary for BORA degradation (Seki et al. 2008). ꞵ-TrCP-mediated degradation of the Wee1-like protein kinase (WEE1) relies on another non-canonical motif, EEGFGpS, where both negatively-charged glutamates are required for ꞵ-TrCP binding (Watanabe et al. 2004). Binding of phosphodegrons is the most common mechanism for FBP substrate binding, though a wide variety of alternative mechanisms have also been observed. For some degrons, phosphorylation has the opposite effect where it inhibits binding of FBPs to the unmodified degron, as observed in the binding of the Denticleless protein homolog (DTL, also CDT2) by FBXO11 (Abbas et al. 2013). Access to an unmodified degron can also be regulated via phosphorylation at another site of the protein, as observed in the regulation of the ribonucleoside-diphosphate reductase subunit M2 (RRM2) by cyclin F (D'Angiolella et al. 2012). Certain FBPs require additional cofactors to bind their substrates, as observed in binding of SKP2 to p27, which requires the cyclin-dependent kinases regulatory subunit 1 (CKS1B), not SKP2, to bind the phosphodegron on p27 for efficient ubiquitylation (Hao et al. 2005). Some degrons have also be shown to require other forms of PTM, such as glycosylation-mediated binding of FBXO2 and FBXO6 to incorrectly-folded glycoproteins to facilitate glycoprotein endoplasmic reticulum-associated degradation (GERAD) (Glenn et al. 2008). Lastly, some FBPs bind entirely unmodified domains, observed in the extended contact between the LRR domain of FBXL3 and the surface of CRY1/2, as well as the insertion of the C-terminus into a conserved pocket in CRY proteins (Xing et al. 2013). Taken together, the diverse range of substrate binding mechanisms of FBPs poses a considerable challenge to in silico discovery of FBP substrates.
FBPs assemble into SCF complexes via SKP1. Structural analysis of the interface between the F- box domain and SKP1 reveals a hydrophobic trihelical structure that fits into a hydrophobic pocket on SKP1 (Schulman et al. 2000). SKP1, in turn, binds to CUL1 through hydrophobic surfaces in the N-terminal domain of CUL1, a long stalk-like structure composed mainly of helices (Zheng et al. 2002; Cardozo and Pagano 2004). CUL1 acts as a rigid scaffold to bridge SKP1-FBP with E2 enzymes through RBX1, which binds to the globular C-terminal domain of
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CUL1 through the formation of intermolecular ꞵ-sheet structure. Whereas the rigidity of CUL1 is required for its function, RBX1 is inherently flexible through a linker between its N-terminal CUL1-binding strand and C-terminal RING domain (Zheng et al. 2002; Rahighi and Dikic 2011). SCF-mediated ubiquitylation is a complex process that is dependent on coordination between complex components, notably CUL1 and RBX1, as well as a number of external regulatory proteins that facilitate the cycling of CUL1 with other complex components. The SCF-mediated ubiquitylation cycle, summarized simplistically, begins with the modification of CUL1 with NEDD8 at a lysine residue at amino acid position 720. This is facilitated by RBX1, which acts as the E3 enzyme for neddylation by binding the NEDD8-conjugating enzyme (UBE2M, Sc: Ubc12) (Calabrese et al. 2011). Though RBX1 alone is sufficient for CUL1 neddylation, binding of another NEDD8 E3 enzyme, DCN1-like protein (DCUN1D1, Sc: Dcn1), potentiates this reaction. The neddylation of CUL1 induces a conformational change that allows RBX1 to be released from the winged-helix B region of CUL1 that previously locked the complex in a closed conformation (Duda et al. 2008). In this open conformation, not only is RBX1 binding to ubiquitin E2 enzymes like UBE2R1 increased, but the RBX1-bound ubiquitin E2 enzyme can also extend out towards the N-terminal end of CUL1, which is highly important for initiating ubiquitin chains on unmodified substrates (Kawakami et al. 2001; Saha and Deshaies 2008). At around the same time the ubiquitin E2 enzyme is recruited, SKP1 and FBPs assemble into the complex. The fully assembled SCF complex then mono- or polyubiquitylates the substrate protein. Eventually, NEDD8 is removed from CUL1 by the constitutive photomorphogenesis 9 (COP9) signalosome (CSN) (Wei and Deng 2003). This potentiates the binding of the cullin-associated NEDD8-dissociated protein 1 (CAND1), which in turn causes the dissociation of the SKP1-FBP from CUL1 (Liu et al. 2018). Binding of CAND1 to CUL1 also promotes the binding of DCUN1D1, increasing the speed of CUL1 re-neddylation and allows the SCF complex to reform with new FBPs. Recent computational simulations and in vivo experiments revealed that the SCF complex is constantly undergoing this cycle through CAND1, which may explain the tolerance of the complex to large changes in FBP populations during the development of multicellular organisms (Liu et al. 2018).
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Figure 1.7: Generalized dynamics of the SCF complex in substrate ubiquitylation. (1) Full assembly of the SCF complex, including CUL1 neddylation that drives E2 enzyme binding and substrate ubiquitylation. (2) Following substrate ubiquitylation, CUL1 is deneddylated by CSN. (3) CUL1 deneddylation enables the competitive binding of CAND1, removing the FBP-SKP1 subunits from the SCF complex. (4,5) Binding of new FBP-SKP1 and DCUN1D1 drives SCF complex back to a catalytically-active form. The rapid cycling of the SCF complex requires ~87s for one full cycle. NEDD8, neural precursor cell expressed, developmentally down-regulated 8; CSN, constitutive photomorphogenesis 9 (COP9) signalosome; CAND1; cullin-associated NEDD8-dissociated protein 1; DCUN1D1, DCN1-like protein. Figure adapted from (Liu et al. 2018).
Lastly, given the involvement of the SCF complex in a variety of important biological processes, there have been ongoing efforts to target the SCF complex for pharmacological intervention. One candidate compound currently in phase II trials is pevonedistat (TAK-924, also MLN4924), which binds and inhibits the NEDD8-activating enzyme E1 (UBA3), resulting in an inhibition of cullin neddylation (Soucy et al. 2009). Though pevonedistat broadly affects CRL functions, it has been shown to be effective at growth inhibition in a variety of cancer cell lines and xenografts (Skaar et al. 2014). Efforts have also been made to target FBPs specifically, either to
24 disrupt FBP interaction with SKP1 or with substrate proteins. One compound discovered through virtual screening was reported in 2013 to selectively inhibit the binding of SKP2 to SKP1, resulting in increased chemosensitivity in xenograft models (Chan et al. 2013). Another compound was designed to bind to the pocket formed by SKP2 and CKS1B, inhibiting the SKP2-mediated degradation of CDKN1B and arresting cell cycle in cancer cell lines. Beyond inhibiting the SCF complex, attempts have also been made to retarget FBPs artificially to other substrates through proteolysis targeting chimeras (PROTACs). PROTACs generally are small molecules with two distinct chemical moieties, one for binding to CRLs and another to the desired substrate protein, thereby potentiating the ubiquitylation of the new substrate. PROTACs-based strategies attempt to circumvent two major limitations of occupancy-based approaches to protein inhibition (Toure and Crews 2016). First, a lower intracellular concentration of the therapeutic agent may be needed to achieve an inhibitory effect as each PROTAC molecule can facilitate the degradation of multiple target proteins, decreasing the likelihood of adverse off-target effects. Secondly, as PROTAC molecules do not function by occupying an enzyme’s active site, they can be used to target non-enzymatic proteins, including transcription factors and scaffolding proteins. Attempts to generate PROTACs date back at least to as early as 2001, when a molecule containing the IκB degron was joined with ovalicin, allowing for the retargeting of ꞵ-TrCP to methionine aminopeptidase 2 (METAP2) (Sakamoto et al. 2001). Clinical application of PROTACs was validated recently with lenalidomide, a derivative of thalidomide that has been used for the treatment of multiple myeloma since 2004 through an unknown mechanism (Pan and Lentzsch 2012). Lenalidomide was found to simultaneously bind the CRL complex containing cereblon (CRBN) as the substrate recognition subunit and the transcription factors Ikaros (IKZF1) or Aiolos (IKZF3) to facilitate their degradation, as well as inhibiting the ubiquitylation of natural CRBN substrate MEIS2 (Fischer et al. 2014). Since the elucidation of mechanism of action of lenalidomide, other efforts have been underway to retarget CRLs to substrates such as the bromodomain-containing protein 4 (BRD4) and steroid hormone receptor ERR1 (ESRRA) (Lai and Crews 2017). Preclinical studies of PROTAC molecules have recently demonstrated the effectiveness of redirecting the CRLs associated with VHL and CRBN to target BRD4, resulting in increased chemosensitivity of mantle cell lymphoma cells compared to BRD4 inhibitor (Sun et al. 2018). Similar to retargeting FBPs through PROTACs, studies of the Arabidopsis F-box protein Transport Inhibitor Response 1 (TIR1) has also revealed the possibility of rescuing the function of mutant FBPs through auxin-
25 like molecules, which stabilize substrate interactions by binding the mutant substrate interaction domain (Skaar et al. 2014). There are currently no FDA approved interventions targeting FBPs, but a better understanding of FBP function may accelerate the drug discovery process.
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1.2.2 Structure and organization of the FBXW7 protein
The FBXW7 gene is located on chromosome 4q31.3, a region that is frequently deleted in a variety of cancers (Knuutila et al. 1999). The gene includes 13 protein-coding exons, which generate three isoforms of FBXW7 through alternative splicing that differ only in the 5’ exon, resulting in different N-termini that localize the isoforms to either the nucleoplasm, cytoplasm, or nucleolus (Welcker and Clurman 2008). These isoforms are commonly referred to as isoform 1/α, isoform 2/β, and isoform 3/ɣ, respectively. The common exons encode for a nucleolar/nuclear localization signal, a dimerization domain (DD), the F-box domain, and the WD40 repeat domain. The nucleoplasmic isoform 5’ exon encodes an additional nuclear localization signal (NLS) while the cytoplasmic isoform exon encodes a transmembrane domain. The nucleolar isoform exon does not appear to encode any additional localization signals. The transmembrane domain of cytoplasmic FBXW7 has been found to penetrate the endoplasmic reticulum membrane in at least one cell line, with the WD40 repeat domain facing the cytoplasm (Matsumoto et al. 2011). FBXW7 localizes to the nucleolus in the absence of isoform-specific 5’ exons, indicating that the nucleoplasmic and cytoplasmic isoform-specific 5’ exons override the nucleolar localization signal on the shared exons, though mutation of the NLS on the nucleoplasmic isoforms retains the protein in the nucleoplasm (Welcker et al. 2004).
In addition to localization, the FBXW7 isoforms are also differentially expressed and regulated. The isoforms are driven by different promoters and exhibit different levels of expression in cell lines and primary cells, with the nucleoplasmic isoform being the most highly expressed in most systems, followed by the cytoplasmic isoform (Welcker and Clurman 2008; Grim et al. 2008). The nucleoplasmic isoform is also the most stable with a half-life of over 6 hours, whereas the other two isoforms have half-lives less of than an hour. Studies of FBXW7 isoform expression levels throughout the cell cycle showed that all three isoforms have the highest expression during gap 1 phase (G1) (Sionov et al. 2013; Matsumoto et al. 2006). Expression of the nucleoplasmic and cytoplasmic isoforms are significantly reduced as cells enter the synthesis phase (S) with expression gradually increasing through gap 2 phase (G2) and mitosis. In contrast, the decrease in expression of the nucleolar isoform is much more drastic and does not begin to recover until mitosis. The difference in expression in different phases of the cell cycle can likely be attributed to differential targeting of substrates by FBXW7 isoforms, namely G1/S-specific cyclin E (CCNE1/2) and the Myc proto-oncogene protein (MYC), which will be discussed in greater
27 detail later. FBXW7 isoforms also respond differently to genotoxic and replicative stress, under which cytoplasmic FBXW7 is mostly strong induced in p53-dependent and -independent manners, whereas the other isoforms are unresponsive (Sionov et al. 2013; Matsumoto et al. 2006). Lastly, FBXW7 isoforms have also been reported to be differentially expressed in specific tissues. In one analysis of mouse tissues, cytoplasmic FBXW7 mRNA was only detected in the brain and testes, while nucleolar FBXW7 mRNA was detected in heart and skeletal muscle. Meanwhile, nucleoplasmic FBXW7 mRNA was found in all tissues (Matsumoto et al. 2006).
Substrate recognition by FBXW7 is facilitated by the WD40 repeat domain, an eight-bladed β- propeller structure. Structural studies of Cdc4 revealed three key arginine residues (R485, R467, R534) within the third and fourth blades that form direct electrostatic interactions with the CCNE1 T380 phosphodegron, which has the sequence LLpTPPQSGK (Orlicky et al. 2003). The mechanism of phosphodegron binding through the arginine residues is conserved in FBXW7 through R465, R479, and R505 (Hao et al. 2007). Mutagenesis of the T380 phosphodegron defined the original high-affinity Cdc4 phosphodegron (CPD) as I/L-I/L/P-pT-P-
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Figure 1.8: Structural organization and localization of the FBXW7 isoforms. A) FBXW7 is encoded in the genome across 13 exons that includes, in order from the N-terminus, the dimerization domain (DD), the F-box domain, and the WD40 repeat domain. Alternative splicing of the first three exons results in different isoforms that share the aforementioned domains, but different localization within the cell. B) Localization of Flag-tagged FBXW7 isoforms in U2OS cells. C) Structure of the FBXW7 protein from the F-box domain to the C-terminus, obtained with PDB ID 2OVR. (1) Profile of FBXW7. The F-box domain is coloured in green while the WD40 repeats are colored in blue. (2) A closer examination of the WD40 repeats domain, which forms a β-propeller structure. (3) Coulombic surface mapping of the WD40 repeats. Red colouring indicates more positively charged while blue indicates more negatively charged. The site of substrate phosphodegron binding, highlighted by the yellow box. Immunofluorescent image of FBXW7 localization modified from (Welcker and Clurman 2008).
Other than the F-box domain, the DD is the last domain of interest within the shared regions of FBXW7 isoforms. This domain, which also exists on Cdc4 and ꞵ-TrCP, allows for two SCF complex to dimerize (Tang et al. 2007; Hao et al. 2007). Dimerization of these FBPs have been shown to increase the robustness of binding to multiple suboptimal degrons on the same substrate. In yeast, dimerization of Cdc4 is not required for Sic1 recognition, but is required for
29 its robust polyubiquitylation (Tang et al. 2007). In the same study, Cdc4 with dimerization defect readily ubiquitylated Sic1 when one of its suboptimal degron was changed to a high-affinity degron, despite the deletion of all other degrons from the protein, demonstrating the importance of dimerization in degrading substrates with suboptimal degrons. Degradation of CCNE1 by dimerization-deficient FBXW7 can be mediated by the T380 phosphodegron, but not when the +4 position serine is mutated to an alanine (Welcker and Clurman 2007). Meanwhile, wild type FBXW7 tolerates mutation of the T380 phosphodegron through dimerization with a separate FBXW7 that binds to another phosphodegron centered at T62 (Welcker et al. 2013). When the T62 phosphodegron was mutated to be similar to the high-affinity T380 phosphodegron, both monomeric and dimeric FBXW7 could facilitate the degradation of CCNE1/2, even with a T380A mutation. FBXW7 dimerization therefore accommodates complex regulation of substrate degradation through binding multiple independently-regulated phosphodegrons.
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1.2.3 FBXW7 substrates in health and disease
FBXW7 has been demonstrated to be one of the most frequently mutated proteins in pan-cancer analyses and is commonly defined as a general tumour suppressor. One of the landmark genetic studies of FBXW7 in over 1,500 primary tumours samples across fifteen tumour types revealed a pan-cancer mutation rate of 5.6%, with mutation rates as high 35% in cholangiocarcinomas and 31% in T-cell acute lymphocytic leukemia (T-ALL) (Akhoondi et al. 2007). Of all the FBXW7 mutations, 94% were missense mutants with ~43% at two of the three arginine residues that facilitate phosphodegron binding, R465 (29%) and R479 (13%). The same study also found that these hotspot mutations abrogated binding to CCNE1 and exhibited a dominant negative effect in a heterozygotic system, likely due to the formation of heterodimeric SCF complexes, in addition to earlier reports of FBXW7 being a haploinsufficient tumour suppressor (Mao et al. 2004). The high rate of FBXW7 mutation across a wide variety of cancers can also be observed in the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK- IMPACT) sequencing study of over 10,000 metastatic tumours across 62 different cancer types (Zehir et al. 2017). FBXW7 was found to be mutated in 3.2% of all samples, approximately the same rate as CTNNB1 (3.3%). Of the cancer types with at least 100 samples, FBXW7 missense mutations occurred most frequently in endometrial cancer (15.14% of 218 cases), colorectal cancer (11.42% of 1007), and bladder cancer (9.46% of 423). The most frequent missense mutations are at the three key arginine residues, together accounting for 45% of all missense mutations. Of the three arginine hotspots, mutations at R465 were the most common, followed by R505, in contrast to the aforementioned 2007 study. In corroboration with these reports, FBXW7 was also recently highlighted as a top somatic driver of cancer within the ubiquitin system, based on data from The Cancer Genome Atlas (Ge et al. 2018)
Given that the known substrate profile of FBXW7 includes many well-established oncoproteins, it follows that FBXW7 exerts its tumour suppressive effects by facilitating the degradations of those oncoproteins and that, in the event of a hotspot arginine mutation, FBXW7 loses that ability by failing to bind those oncoproteins (for a curated list of FBXW7 substrates, see Table S1). As alluded to in previous sections, CCNE1 is one of the best studied substrates of FBXW7. Through its interaction with cyclin-dependent kinase 2 (CDK2), CCNE1 facilitates the phosphorylations of substrate proteins such as the retinoblastoma-associated protein (RB1) and CDKN1B for progression of the cell cycle through G1 phase into the S phase (Hwang and
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Clurman 2005). Despite the infrequency of CCNE1 gene amplification, CCNE1 is frequently reported to be overabundant in cancers, increasing genetic instability due to impaired cell cycle regulation. Given that FBXW7 also targets other cell cycle-related proteins for degradation, the exact effect of CCNE1 binding abrogation can be difficult to deconvolute and some studies have found a poor correlation between FBXW7 loss and CCNE1 abundance in certain cancers, though there is an enrichment for phosphorylated CCNE1 (Spruck et al. 2002). FBXW7-mediated degradation of CCNE1 has been suggested to rely on the sequential action of both the nucleoplasmic and nucleolar isoforms in some cell lines (Bhaskaran et al. 2012). The nucleoplasmic isoform facilitates the binding of peptidyl-prolyl cis-trans isomerase NIMA- inacting 1 (PIN1), which converts the P-P bond within the T380 phosphodegron from cis to trans. This allows for the sequestration of the CCNE1 into the nucleolus, possibly via nucleophosmin (NPM1), where CCNE1 is polyubiquitylated by the nucleolar isoform of FBXW7.
Figure 1.9: Missense mutations in FBXW7 in the MSK-IMPACT sequencing cohort. Data derived from (Zehir et al. 2017) and visualization adapted from cBioPortal (Cerami et al. 2012).
Another noteworthy oncogenic substrate of FBXW7 is MYC, a transcription factor that has been found to bind 22.4% of all promoters and is one of the most frequently dysregulated proteins across cancers (Perna et al. 2012; Dang 2012). Binding of MYC by FBXW7 is facilitated through GSK3-mediated phosphorylation of a T58 phosphodegron, which is frequently mutated in cancers (Thomas and Tansey 2011). Interestingly, while the phosphorylation of the +4 position serine (S62) by mitogen-activated protein kinase 1 (MAPK1) primes the phosphorylation of T58, S62 phosphorylation itself has been reported to antagonize MYC degradation (Welcker and Clurman 2008). Dephosphorylation of S62 appears to rely on the activity of PIN1 and degradation of MYC is facilitated by the nucleolar isoform of FBXW7, whereas nucleoplasmic MYC is stabilized through ubiquitin carboxyl-terminal hydrolase 28
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(USP28), further demonstrating the complexity of MYC regulation (Thomas and Tansey 2011; Welcker et al. 2004; Popov et al. 2007). MYC is important for cell proliferation and is one of the four factors discovered by Takahashi and Yamanaka to induce pluripotency in embryonic and adult fibroblasts, the other three being POU domain class 5 transcription factor 1 (POU5F1, also OCT3/4), transcription factor SOX-2 (SOX2), and Krueppel-like factor 4 (KLF4) (Takahashi and Yamanaka 2006). Deletion of FBXW7 increases efficiency of induced pluripotency, even in the absence of MYC overexpression (Okita et al. 2012). FBXW7-mediated proteolysis has also been demonstrated to be a primary form of MYC regulation in hematopoietic stem cells and is crucial for their differentiation program. Deletion of FBXW7 in animal models lead to lower numbers of lymphoid-committed progenitor cells. This phenotype is rescued by the hemizygous deletion of MYC (Reavie et al. 2010). The importance of the FBXW7-MYC axis in the maintenance of quiescence and stemness has implications for the treatment of blood cancers like T-ALL and chronic myeloid leukemia (CML). Leukemia-initiating cells (LICs) remain in quiescence through the degradation of MYC and are consequently resistant to chemotherapeutics such as imatinib (Corbin et al. 2011). Ablation of FBXW7 leads to accumulation of MYC, which correlates with driving LICs out of quiescence and sensitizing them towards p53-induced apoptosis and chemotherapeutics such as imatinib and bromodomain and extra-terminal motif (BET) inhibitors (Takeishi et al. 2013; King et al. 2013). Moreover, these interventions appear to affect LICs more strongly than normal hematopoietic stem cells.
While FBXW7 isoforms appear to cooperate in the degradation of CCNE1 and possibly MYC, they have also been reported to act antagonistically in the degradation of peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PPARGC1A) (Trausch-Azar et al. 2015). PPARGC1A serve as a coactivator to many transcription factors in the control of cellular metabolism and mitochondrial biogenesis. In the cytoplasm, PPARGC1A is degraded via interaction with cytoplasmic FBXW7. Curiously, interaction with nucleoplasmic FBXW7 appears to stabilize PPARGC1A. When examined further, it was discovered that nucleoplasmic FBXW7 formed ubiquitin chains of lower total molecular weight than cytoplasmic FBXW7. It is possible that FBXW7 isoforms, through differences in structure or by virtue of differential localization, can facilitate the formation of different polyubiquitin chains. This is supported by recent evidence demonstrating the formation of K63-linked chains by nucleoplasmic FBXW7 at sites of DNA damage (Zhang et al. 2016).
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FBXW7-mediated proteolysis of the neurogenic locus notch homolog protein 1 (NOTCH1) is another well-studied interaction with significant implications for cell fate decisions and cancer. NOTCH1 is a transmembrane receptor which, upon exposure to its extracellular ligands, is cleaved by two separate proteases (Bray 2016). Notably, cleavage by the ɣ-secretase complex releases the Notch intracellular domain (NICD) from the plasma membrane, which then enters the nucleus to drive transcription of target genes. FBXW7 binding to NICD is controlled by cyclin-dependent kinase 8 (CDK8) phosphorylation of a degron centered around T2512 (O'Neil et al. 2007; Thompson et al. 2007). Both NOTCH1 and FBXW7 mutations are notably common in primary T-ALL samples and cell lines and mutations in either protein can abrogate binding of the other. Consequently, hotspot mutations in FBXW7 also protects T-ALL cell lines from ɣ- secretase inhibitors as residual NOTCH1 activity cannot be properly suppressed. The significance of the FBXW7-NOTCH1 interaction has been highlighted to be valuable in the prognosis of T-ALL (Asnafi et al. 2008; Trinquand et al. 2013). However, as with the aforementioned FBXW7 substrate, the significance of this interaction can be difficult to distinguish from the activity of FBXW7 with other proteins. Notably, NOTCH1 induces the expression of both MYC and CCNE1 (Witkowski et al. 2015; Zender et al. 2013). FBXW7 has also been reported to degrade presenilin-1 (PSEN1), a component of the ɣ-secretase complex (Li et al. 2002).
Though FBXW7 is commonly designated as a tumour suppressor, evidence is emerging that it may have other functions in specific contexts. The first of such evidence is the report that FBXW7 targets nuclear factor NF-kappa-B p100 subunit (NFKB2) for proteolysis in B cells. NFKB2 is the primary inhibitor of the alternative NFκB pathway and, upon phosphorylation by inhibitor of nuclear factor kappa-B kinase subunit alpha (CHUK, also IKKα) at S866, is degraded by ꞵ-TrCP, consequently releasing NFκB components and activating transcription of pro-survival target genes (Busino et al. 2012). Nucleoplasmic FBXW7 was found to target a different NFKB2 phosphodegron centered around S707, which is phosphorylated by GSK3 instead. The FBXW7-NFKB2 interaction appears to be B cell-specific and is of particular interest in the context of multiple myeloma, where constitutive NFκB signaling drives growth and proliferation (Annunziata et al. 2007). Thus, FBXW7 can function as an oncoprotein in multiple myeloma by contributing to NFκB activity.
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As mentioned previously, nucleoplasmic FBXW7 has also recently been demonstrated to facilitate K63 ubiquitin chains in DNA double-strand break (DSB) repair via non-homologous end-joining (NHEJ) (Zhang et al. 2016). Within a minute of radiation damage, nucleoplasmic FBXW7 is phosphorylated by serine-protein kinase ATM (ATM) at S26, resulting in the recruitment of FBXW7 to DSB sites where it facilitates the K63 polyubiquitylation of DNA repair protein XRCC4 (XRCC4), possibly via ubiquitin-conjugating enzyme E2 N (UBE2N, also UBC13). This facilitates the interaction of XRCC4 with other NHEJ machinery for proper DSB repair. FBXW7 binds XRCC4 at a phosphodegron centered around S325/326 and phosphorylation is mediated by DNA-dependent protein kinase catalytic subunit (PRKDC, also DNA-PKcs). Mutation of S325/326 or inhibition of neddylation results in drastically reduced XRCC4 polyubiquitylation and sensitized cells to radiation damage. The involvement of NHEJ in cancer is inherently complex as impairment contributes to not only increased genomic instability and higher mutation rates, but also higher sensitivity to genotoxic stresses (Srivastava and Raghavan 2015). Upregulation or hyperactivation of DSB repair mechanisms can thus also support tumourigenesis and protect cancers from radio- and chemotherapies. Thus, FBXW7’s contribution to cancer development via NHEJ is similarly complex and can both contribute and protect from disease progression.
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1.3 Proteomic approaches in studying protein-protein interactions
While it is widely accepted today that essentially all biological processes are facilitated by proteins, the study of proteins began less than two centuries ago, when chemists discovered that certain isolates of cells could remain functional independent of intact cells (Braun and Gingras 2012). At around the same time, Berzelius coined the term “protein” to describe a common organic material found within plants and animals. By the early 1900s, it became increasingly apparent that proteins may be the “chemical basis of life”. Over the next century, the interaction of proteins with other proteins was shown to be vital in a variety of biological processes, such as muscle contraction, the citric acid cycle, and signal transduction. In the 1980s, the first fusion proteins and epitope tags were described, enhancing protein purification and enabling proteome- wide studies of PPIs. The following sections will discuss some common techniques for the study of PPIs, including proximity-dependent biotinylation assays, and the coupling of mass spectrometry.
1.3.1 Strategies for the study of protein-protein interactions in vivo
Common approaches for studying PPIs in vivo can be generally classified into “bimolecular sensor/reporter” (BSR) strategies and pull-down strategies. BSR assays generally require both the protein of interest (POI), commonly termed the bait, and the substrate, termed the prey, to be separately fused or probed with proteins. When the bait and the prey are in close proximity, the attached proteins also interact to produce some measurable effect. An example of such assays is the yeast two-hybrid (Y2H) system, where the regulatory protein GAL4 is split into two non- functional fragments that can be fused with the bait and prey (Fields and Song 1989). Upon bait- prey interaction, the fragmented GAL4 can reconstitute GAL4, which then drives the expression of a reporter, commonly a metabolic protein, by binding a specific upstream activation sequence. This type of approach is also referred to as protein an fragment complementation assay (PCAs) and has also been applied in mammalian cells through the murine dihydrofolate reductase protein (Dhfr), which is crucial in the biosynthesis of purines and thymidylate (Pelletier et al. 1998; Remy and Michnick 1999). Dhfr can be similarly split into two non-functional protein fragments that allow Dhfr-negative cells to survive in nucleotide-free media upon reconstitution of the functional Dhfr. In addition to the survival effect, the PPI can also be visualized and localized via fluorescence microscopy with fluorescein-conjugated methotrexate, which is rapidly expelled
36 from cells unless it binds reconstituted Dhfr. A variety of other proteins have also been utilized in a similar fashion, including the green fluorescent protein (GFP), hygromycin B phosphotransferase, and ubiquitin (Michnick et al. 2007). There are also techniques that can bypass the requirement for genetic engineering, such as Förster resonance energy transfer (FRET) assays, where antibodies conjugated with fluorescent molecules against the bait and prey can be used, in addition to fusing the bait and prey with fluorescent proteins. Upon stimulation of the donor fluorophore, energy can be transferred to an acceptor fluorophore within ~10nm, resulting in an emission from the acceptor molecule (Lönn and Landegren 2017). Another technique that uses antibodies to probe pairs of interacting proteins is the proximity ligation assay (PLA), which relies on the rolling amplification of antibody-conjugated oligonucleotides and fluorescent probes that target the resulting DNA.
BSR assays have also been used for high-throughput screening of interactomes. Notably, large Y2H screens of human PPIs have been instrumental in establishing early human interactomes (Brückner et al. 2009; Stelzl et al. 2005; Rual et al. 2005). Development of Y2H systems have continued to improve screening of PPIs in their native environment and for membrane-bound proteins. As yeast cells lack human chaperones and PTM machinery, there has also been efforts to recreate the Y2H system in mammalian cells, notably with a split ubiquitin sensor for the study of membrane proteins (Petschnigg et al. 2014). Bimolecular fluorescence complementation (BiFC) assays, a PCA utilizing split fluorescent proteins, has also been used in high-throughput screening to uncover novel PPIs in plant and mammalian cells (Miller et al. 2015). High- throughput screens, however, have a few key limitations for the study of large interactomes. First, many screens are limited by the comprehensiveness of the clone library as each protein- sensor fusion needs to be generated separately. Exhaustive complementary DNA (cDNA) libraries have been used to improve coverage, but this increases the cost of screens as identification of interactions would require sequencing or polymerase chain reaction (PCR) analysis. Second, overexpression of the fusion proteins can also to false positives due to improper localization and non-specific interactions of the sensors. Additionally, in the case of selection screens, false positives can also arise if the prey itself confers a survival advantage. Lastly, as these screens are limited to probing binary interactions, they cannot provide information on secondary interactors or dynamic protein complexes like the SCF complex. These limitations contribute to the low accuracy and reproducibility between large screens (Ito et al.
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2001; von Mering et al. 2002). Despite these issues, these BSR screens continue to be utilize due to their relative inexpensiveness and improvements in techniques and data analysis have improved upon their limitations.
Figure 1.10: Comparison of two common strategies used to study PPIs: (A) “bimolecular sensor/reporter” (BSR) assays and (B) pull-down strategies. (A) In BSRs, binary interactions are commonly detected through the reconstitution of a functional protein by non-functional protein fragments (F1 and F2) attached to the putative interactors. BSRs can also be performed with fluorescent proteins instead of protein fragments, which can be either fused with the substrate proteins or bind to the substrate proteins through conjugated antibodies. (B) Pull-down strategies aim to isolate interactions and complexes formed with the protein of interest (POI) and removing non- interacting proteins prior to the elution of the POI.
Whereas BSR strategies focus on probing the interaction between specific bait and the prey pairs, pull-down strategies focus on purifying interactions between the bait and any prey in vivo from all other proteins through affinity purification (AP) or chromatography ex vivo. As an overview, cells expressing the endogenous or exogenous POI are lysed under gentle conditions, typically with weak non-ionic or zwitterionic non-denaturing detergents that preserve the
38 interactions between the POI and other proteins in complex (Brown and Audet 2008). The POI is then immobilized by an affinity ligand onto a support, typically in a column or on agarose beads, resulting in the immobilization of bound interactors as well (Hage and Matsuda 2015). Some common affinity tag-ligand pairs for AP include polyhistidine tags and nickel ions, glutathione S-transferase (GST) tag and glutathione, and, in co-immunoprecipitation (co-IP), antibodies against an epitope tag or the POI itself. Proteins that are not in complex with the POI are removed in the mobile phase from the system through washing with the application buffer. Finally, POI-interactor complexes are dissociated or “pulled down” from the support with elution buffers that disrupt the binding of the POI to the affinity ligand through changes in pH, ionic strength, polarity, temperature, or by the inclusion of a competing agent. The eluted proteins can then be identified via techniques such as immunoblotting or mass spectrometry.
Compared to BSR strategies, pull-down strategies generally require little to no genetic manipulation, can provide information about complexes and secondary interactors, and are better suited for high-throughput interactome studies. Pull-down assays also have a few key weaknesses that need to be considered. First, compared to Y2H, AP assays offer greater coverage of the proteome, but are also be more biased towards abundant proteins (von Mering et al. 2002). Second, suboptimal washing conditions may result in both false positives and false negatives in the eluted protein population, as insufficient washing results in non-specific protein binding to the affinity ligands, but overwashing can disrupt the POI-interactor complexes or POI- affinity ligand interactions. To address this issue, technologies have been developed to increase assay sensitivity. One such technology is tandem affinity purification (TAP), where POIs are fused with two affinity tags separated by a proteolytic cleavage site (Rigaut et al. 1999). POI- interactor complexes can then be purified through the first affinity tag-ligand interaction, followed by cleavage to reveal the second target sequence for another round of AP with the second affinity ligand, increasing the purity of the resulting sample and improving sensitivity and specificity (Brückner et al. 2009). Another approach leverages the interaction between biotin and streptavidin, which is the strongest non-covalent interaction known in nature (de Boer et al. 2003). Additionally, there few proteins are naturally biotinylated, reducing the possibility for non-specific binding to streptavidin. Target sequences fused to POIs can be biotinylated by the E. coli bifunctional ligase/repressor BirA, followed by stringent purification with streptavidin. These techniques improve the purification of POI-bound protein complexes, but ultimately do
39 not stabilize the binding of proteins to the POI. Consequently, such methods may be suboptimal for the detections of PPIs that are transient or low affinity. Furthermore, gentle lysis conditions do not fully solubilize membranes and the nucleus, affecting the ability for conventional AP to detect PPIs in these locations. These limitations have driven the development of novel techniques, such as assays that rely on the proximity ligation of biotin.
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1.3.2 Proximity-dependent biotinylation assays
The proximity-dependent biotinylation assay described by Roux and colleagues in 2012, commonly referred to as BioID, generally relies on abortive biotin ligase mutants that lose their ability for directed biotinylation (Roux et al. 2012). The first version of BioID relies on the R118G BirA mutant, which was first reported in 2000 (Kwon and Beckett 2000). The arginine residue in question is part of a conserved motif in many biotin ligases that mediate binding of biotinyl-5’-AMP, an intermediate in the biotinylation process. The R118G mutant is unable to retain the reactive biotin-5’-AMP, which is instead released into the surrounding environment, where it is free to react with any primary amines, typically the ε-amino group of lysine (Choi- Rhee et al. 2004). In the first application of BioID, BirA R118G was fused with prelamin-A/C (LMNA), a component of the nuclear lamina (Roux et al. 2012). Expression of the BirA-protein fusion in the presence of biotin facilitated the biotinylation of proteins proximal to LMNA, which can then be purified following cell lysis through AP with streptavidin. In addition to identifying many known interactors with LMNA, BioID also identified novel components of the nuclear lamina, including the soluble lamin-associated protein of 75kDa (FAM169A, also SLAP75). The advantages of BioID over conventional AP assays hinge mostly on the covalent modification of biotin to proteins proximal to the POI, thus abrogating the requirement to maintain POI-interactor complexes as capture of interacting proteins is direct. Consequently, proteins that interact with the POI transiently or with weak affinity can be detected, even if they are not bound to the POI at the time of cell lysis. Using BioID, cells can also be fully solubilized through denaturing detergents such as sodium dodecyl sulfate (SDS), improving detection of nuclear, membrane-bound, and insoluble proteins like LMNA. Additionally, binding of the biotinylated interactors to streptavidin columns is strong and direct, permitting rigorous washing of the column. While BioID labels proteins around the POI regardless of actual binding, the functional radius of BioID is quite small and is comparable to FRET at ~10nm, based on studies of the nuclear pore complex (Kim et al. 2014). When modeling proteins as perfect spheres and interactions in a linear array, this functional radius essentially captures approximately two proteins between 50-100kDa in mass (Erickson 2009).
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Figure 1.11: Overview of the BioID assay. Mutant BirA is fused with the POI and is expressed in vivo. Upon exposure to biotin, the fusion protein generates a cloud of activated biotin around the fusion protein that reacts with any primary amines on proteins that interact with the POIs. Biotinylated proteins are then purified from cell lysates with streptavidin. The biotinylated proteins are then digested for analysis via mass spectrometry.
BioID has been used to study a variety of systems that have been previously inaccessible to AP, such as the largely-insoluble human centrosome-cilium interface (Gupta et al. 2015). Centrosomes function as the major microtubule organizing center and coordinates a variety of important cellular processes including cell division, polarity, and cilia formation (Bettencourt- Dias et al. 2011). Most cells contain a primary cilium that has been demonstrated to be crucial in facilitating sensory and signaling pathways. Dysregulation of centrosome or cilium function has been linked to a variety of disorders including aneuploidy, neurodevelopmental disorders, and a collection of organ-specific disorders known collectively as “ciliopathies”. BioID of 58 different proteins at the centrosome-cilium interface identified over 1,700 proteins, only 25% of which
42 have been previously implicated with centrosome or cilium function. Of the 500 newly- discovered proteins that were subject to additional characterization, putative functions can be ascribed to over 300 of them. Additionally, topological analysis BioID datasets between non- ciliated and ciliated cells provided insight into high-level changes during ciliogenesis. In addition to enabling the study of insoluble cellular compartments, BioID has also been utilized to study exceptionally labile proteins like MYC in both cell culture and xenograft models (Dingar et al. 2015). In addition to its short half-life, MYC is also mostly associated with chromatin, further limiting AP experiments. BioID of MYC revealed over 100 MYC-interacting proteins, ~25% of which have been previously reported. Comparatively, AP studies of MYC capture reported interactors at a rate of 2-5%. The BioID dataset was also more enriched for nuclear proteins, while comparable AP studies are much less enriched, demonstrating BioID’s ability to probe previously-inaccessible cellular compartments. Within the BioID results, chromodomain- helicase-DNA-binding protein 8 (CHD8) was validated as a novel interactor of MYC.
BioID has also been validated as a powerful technique for the proteomic study of ubiquitin E3 ligases (Coyaud et al. 2015). Interactions between E3 ligases and substrates are often at a low affinity and thus are prone to perturbation. Furthermore, E3-substrate interactions are also transient, as K48 and K11 polyubiquitylated substrates are degraded by the proteasome. Lastly, many E3 ligases exert their functions in poorly-soluble compartments. To circumvent these challenges, BioID was utilized to study ꞵ-TrCP. To prevent the degradation of polyubiquitylated proteins, cells were cultured in MG132-containing media during the BioID process and a protein was considered a putative substrate if it is enriched under MG132 conditions as compared to MG132-free conditions. BioID identified ~300 ꞵ-TrCP-interacting proteins, which included components of the SCF complex. as validated substrates such as CTNNB1. The study also identified 77 putative substrates that were enriched under MG132 conditions. Further functional studies of the putative substrates implicated ꞵ-TrCP in several new processes, such as the maintenance of nuclear membrane integrity through targeting of SUN domain-containing protein 2 (SUN2), the regulation of P-body abundance, and the regulation of eukaryotic translation initiation through targeting of protein phosphatase 1 regulatory subunit 15B (PPP1R15B).
Since its initial description, improvements and alterations have also been made to the BioID system. The first notable alteration is the application of a mutant BirA protein derived from Aquifex aeolicus instead of E. coli in the BioID2 system (Kim et al. 2016). Despite being 25%
43 smaller, the A. aeolicus BirA protein is more efficient and requires less biotin than the E. coli BirA. The reduced biotin requirement may allow BioID2 to be used in model systems with more difficult biotin supplementation, as well as improving its usage in time-sensitive experiments. The overall smaller structure of BirA in BioID2 can also reduce the possibility of adversely affecting the POIs, such as mislocalization or misfolding of the fusion protein. In an attempt to fuse the PCA and pull-down approaches, split-BioID strategies have also been recently reported (De Munter et al. 2017; Schopp et al. 2017). It is shown here that BirA can be split into two non- functional fragments that reconstitute the functional BirA upon bait-prey binding. These systems have been used to study protein phosphatase holoenzymes and miRNA-related pathways. Compared to BioID alone, these systems provide both a scalable assay for validating binary PPIs with easy readout, as well as biotinylated proteins for downstream identification of proteins that interact with the tagged dimer. Split-BioID could also be applicable for the study of proteins in highly-dynamic protein complexes by limiting biotinylation to when a specific complex is formed.
Several peroxidase-based proximity-dependent biotinylation assays have also been developed in parallel for applications in proteomics (Kim and Roux 2016). These approaches generally rely on peroxidases to generate reactive radicals that react with electron-rich amino acids like tyrosine and tryptophan. Horseradish peroxidase (PRXC1A, also HRP) has been used to study lipid rafts and cell surface proteins by catalyzing the formation of biotin-labeled radicals in the presence of hydrogen peroxide (Miyagawa-Yamaguchi et al. 2014; Li et al. 2014). HRP is largely limited to studies of the cell surface as it is largely non-functional inside the cell. Instead, an engineered ascorbate peroxidase (APEX) system can be used to study PPIs inside the cell (Rhee et al. 2013). Originally designed for applications in electron microscopy for the catalysis of diaminobenzidine for contrast purposes, APEX can also catalyze biotin-phenol with hydrogen peroxide for proximity-dependent biotinylation (Martell et al. 2012). APEX has been successfully applied in characterizing the proteomes of organelles such as the mitochondria and endoplasmic reticulum, as well as PPIs in a fashion comparable to BioID (Lam et al. 2015; Lee et al. 2016; Jing et al. 2015). When compared, the crucial difference between APEX and BioID is the duration of labeling. Whereas BioID requires 6-18h for optimal biotinylation, APEX captures a snapshot of proximal proteins acquired over a minute. Thus, APEX is well-suited for short time-sensitive studies, such as comparative studies following pharmacological treatment, but adverse effects of
44 exposure to oxidative damage makes it undesirable for the prolonged capture of all possible PPIs throughout cell cycling. Conversely, BioID is ill-suited for the study of PPIs within specific timeframes, but can detect PPIs over long periods of observation. Recently, variants of BioID have been described to efficiently label proximal proteins within an hour, reducing the gap in required time between BioID and APEX (Branon et al. 2017).
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1.3.3 Protein identification via mass spectrometry
Unlike BSR assays, which provide simple readouts to binary interactions, AP produces a complex mixture of different polypeptides. Subsequent protein sequencing is thus needed to elucidate the identity of proteins within the mixture. Up until the 1990s, protein sequencing relied primarily on separating proteins via two-dimensional gel electrophoresis, followed by sequencing via Edman degradation, a process that progressively cleaved the N-terminal residue (Aebersold 2003). While mass spectrometry was commercially available for use in analytical chemistry since the 1940s, it would not be until the 1990s when key technological developments enabled the application of mass spectrometry in protein sequencing (Griffiths 2008). Since then, mass spectrometry has steadily become the preferred technique for high-throughput protein sequencing in proteomic research.
Briefly, mass spectrometry separates and detects molecules based on their mass-to-charge ratio (m/z). In the protein sequencing workflow, this has traditionally proceeded first through the enzymatic digestion of the proteins prior to mass spectrometry, such as by trypsin, to generate a mixture of peptides (Aebersold and Mann 2003). This approach, also referred to as bottom-up proteomics, is in contrast to top-down proteomics, where proteins are not digested prior to mass spectrometry (Smith et al. 2013). While top-down proteomics can offer improved sequence coverage, variant detection, and variant-specific PTM association in the characterization of proteoforms, it requires advanced instrumentation and information tools that have only become more readily available recently (Catherman et al. 2014). Regardless of the approach, the next step is to ionize the peptides, which can be accomplished via matrix-assisted laser desorption/ionization (MALDI) or electrospray ionization (ESI). Both methods for peptide ionization enabled protein mass spectrometry in the 1990s, though ESI is today the preferred system for the analysis of complex peptide mixtures as it could be coupled to liquid chromatography (LC) with ease, allowing for analysis of the eluent over time in a single experiment (Aebersold 2003). LC can also be used to separate samples for MALDI, but they are deposited onto the sample target plate as individual spots that need to be ionized separately. However, interpretation of the MALDI-generated mass spectra is simpler than ESI as MALDI predominately generates singly charged ions. MALDI has also been used to ionize tissue samples for mass spectrometry imaging (Aichler and Walch 2015).
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Figure 1.12: Generalized workflow of a bottom-up mass spectrometry experiment.
Following enzymatic digestion, peptides are commonly lyophilized and resuspended in acid, such as formic acid, followed by LC. The sample is then ionized at the electrospray tip, where high voltage is applied to the solution as it exists the tip, aerosolizing and charging the peptides for entry into the mass spectrometer. Once inside the mass spectrometer, charged peptides are moved through electrical fields generated by multipole ion guides and can be retained in ion traps. Peptide ions then pass onto mass analyzers that separate and measures the ions by their m/z before they are registered by a detector that counts the number of ions at each m/z. This process generates the first mass spectra (MS, also MS1) from which ions are selected for additional analysis, typically based on their abundance. These selected precursor ions can then be further fragmented via collision-induced dissociation, where the peptides are collided with inert gas at high velocities. The resulting product ions are analyzed once more to produce a second mass spectra (MS/MS, also MS2).
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For bottom-up proteomic peptide identification, MS/MS spectra are commonly searched against a database of theoretical spectra generated from peptide sequences derived from in silico digestion of protein sequences (Matthiesen and Jensen 2008). Some common sequence databases search algorithms include X!Tandem, Mascot, and Comet. MS/MS spectra can also be searched against libraries by algorithms like SpectraST, which contains empirically observed spectra, or with de novo search engines like PEAKS, which derive peptide sequences without reference spectra. Spectral matching algorithms output probabilities of correct peptide-spectrum matches (PSMs), which can be refined through statistical modelling with tools like PeptideProphet, which uses an expectation maximization algorithm to calculate global false discovery rates and probabilities of correctness. Refined PSM probabilities can then be used to infer protein identities with ProteinProphet, which takes into account all matched peptides and produces the simplest list of proteins to account for all observations, producing a probability for each protein to actually be present in the sample. Many tools have been developed to streamline data analysis into one environment, such as the Trans-Proteomic Pipeline (TPP) (Keller et al. 2005; Deutsch et al. 2010). In addition to incorporating multiple search algorithms, PeptideProphet, and ProteinProphet, TPP also utilizes iProphet to combine search results from multiple search engines to improve PSM accuracy. More recently, the ProHits laboratory information management system was developed as a pipeline for interaction proteomics (Liu et al. 2010). In addition to full TPP integration, ProHits also incorporates the significance analysis of interactome (SAINT) tool, which evaluates the probability of detecting a protein against a collection of user-defined negative controls by modeling spectral count distributions for true and false interactions.
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1.4 Thesis motivation and outline
The FBXW7 protein is important in the regulation of several cellular processes. Furthermore, FBXW7 is frequently mutated at hotspots in a variety of cancers. However, the substrate profile of FBXW7 isoforms and isoform-specific functions are poorly described outside of a few notable substrates like CCNE1, MYC, and PPARGC1A, likely due to limitations in conventional AP-MS techniques for the study of FBPs. A recent study of FBXW1/11 with BioID coupled to mass spectrometry revealed several new substrates, validating BioID as a powerful technique for the study of FBP PPIs. In chapter two, BioID was used to map the substrate profiles of FBXW7 isoforms. With this information, several isoform-specific putative substrates were validated, revealing the involvement of FBXW7 in the regulation of several protein complexes. BioID was also performed on several proteoforms of nucleoplasmic FBXW7 with hotspot mutations to study the effect on substrate binding. To our surprise, the effect on substrate binding appears to be mutant-specific, and certain mutants appear to gain interactors not found in the substrate profiles of wild type FBXW7.
FBXW7 is thought to mediate substrate-binding via the CPD, but the phosphodegron of many FBXW7 substrate strongly deviate from this motif. In chapter three, we utilize high-throughput peptide binding assays and statistical modelling in an attempt to discover novel features of the FBXW7-binding phosphodegron. In contrast to the CPD, a possible preference of FBXW7 for arginine residues at the +4 position was discovered. We then attempt to validate this feature in vivo and in vitro on a novel substrate discovered through BioID.
Chapter 2 – Elucidation of substrate profiles of FBXW7 and mutants through BioID
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Elucidation of substrate profiles of FBXW7 isoforms and mutants through BioID 2.1 Chapter overview
The ubiquitin E3 ligase FBXW7 is crucial in the regulation of many cellular processes through the degradation of substrates like CCNE1, MYC, and NOTCH1. Consequently, mutations that are believed to perturb the binding of FBXW7 to its substrates are frequently observed in a variety of cancers. FBXW7 substrates continue to slowly be discovered, each one expanding our understanding of the importance of FBXW7 in health and disease. Mass spectrometry holds great potential for the study of PPIs, but has been limited in the study of FBXW7 due to the transience of FBXW7-substrate interactions and the difficulty in applying pull-down assays to relatively insoluble cellular compartments, where two isoforms of FBXW7 reside. As a result, understanding of isoform-specific FBXW7 function and the effect of hotspot mutations remains limited. BioID has been previously demonstrated to be effective for the study of FBP PPIs, unveiling new biological functions at a proteome level. In this chapter, the substrate profiles of FBXW7 isoforms and hotspot mutants are examined with BioID. The interactions between FBXW7 and many important protein complexes involved in histone modification and transcriptional regulation are highlighted, which were further validated through CHX chase experiments. The discovery of FBXW7 mutants that appear to gain mutant-specific substrates is also discussed.
2.2 Contributions
Jeremy Benedetti (former exchange student) generated the FBXW7 isoform cell lines and generated preliminary BioID data with MG132. Monika Mis (Ph.D graduate from S. Angers’ group) generated the FBXW7 mutant cell lines. Estelle Laurent (laboratory manager) assisted with cloning and AP of BioID samples. Faith Au-Yeung (laboratory technician) assisted with optimizing immunoblotting and siRNA transfection protocol. Étienne Coyaud (scientific associate) performed the MS experiments and processed the MS data. Christopher Mogg (laboratory technician) generated the MBIP BioID dataset. Avik Basu (post-doctoral fellow) and Parasvi Patel (Ph.D student from R. Hakem’s group) assisted with fluorescent microscopy. Tonny Huang optimized concentrations of MG132 and PMA for BioID experiments and performed additional BioID on FBXW7 isoforms and mutants with the two chemicals,
51 performed AP of the BioID samples, generated the samples for immunofluorescence microscopy, analyzed and visualized the MS data, generated 3xHA vectors for putative substrates, optimized procedures cycloheximide (CHX) chase experiments and performed the experiments, optimized immunoblotting procedures, and generated and quantified the immunoblots.
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2.3 Materials and methods 2.3.1 Plasmids
FBXW7 isoforms were amplified via PCR from either human cDNA or plasmids from the Mammalian Gene Collection and were subsequently cloned into pcDNA5 FRT/TO FlagBirA* vector with the appropriate restriction enzymes (NEB) (for primers, see Table S2). Generation of mutants were performed via overlap extension PCR. Putative substrates and mutants were cloned into pcDNA3 3xHA vector in the same fashion.
2.3.2 Cell lines
Flp-In TREx 293 cells (also referred to as HEK293 cells hereafter) were maintained in complete media composed of DMEM (Gibco) supplemented with 10% FBS and 1% penicillin- streptomycin. Cells were dissociated from culture plates with trypsin for passaging. Cell lines conditionally expressing FlagBirA-tagged FBXW7 isoforms or mutants were generated using the Flp-In system, followed by selection with 200μg/mL hygromycin B added to culture media.
2.3.3 BioID and biotin-streptavidin affinity purification
FlagBirA-tagged FBXW7 isoforms or mutants cell lines are culture in 20x 50mm dishes. At ~60% confluency, culture media is replaced with fresh complete media supplemented with 1μg/mL tetracycline (Sigma), 50μM biotin (BioShop). Fifteen plates were additionally supplemented with either 5μM MG132 (American Peptide Company), 100nM phorbol 12- myristate 13-acetate, or both. Following incubation for 24h, cells were collected and pelleted by centrifugation, then washed twice with PBS. Dried cell pellets were then snap frozen. The above process was repeated for each cell line to generate two biological replicates.
Cell pellets were resuspended in 10mL of lysis buffer containing 50mM Tris-HCl pH 7.5, 150mM NaCl, 1mM EDTA, 1mM EGTA, 1% Triton X-100, 0.1% SDS, 1:500 protease inhibitor cocktail (Sigma-Aldrich), 1:1000 benzonase nuclease (Novagen), and 0.5% sodium deoxycholate and incubated at 4°C with end-over-end rotation for an hour. Samples were then sonicated to disrupt any visible aggregates, followed by centrifugation at 16,000g and 4°C for 30 minutes. Supernatants were transferred to new conical tubes and incubated for 3h with 30μL of packed, pre-equilibrated streptavidin sepharose beads (GE) at 4°C with end-over-end rotation. Beads were pelleted by centrifugation at 2,000rpm for 2 minutes, then settled by place the tubes upright
53 for 2 minutes. Supernatant was then removed from the beads, which were resuspended with 1mL of 50mM ammonium bicarbonate pH 8.3 solution and transferred to microcentrifuge tubes. Beads were washed 4 times with ammonium bicarbonate solution, then transferred into new microcentrifuge tube and washed 2 more times. Beads were then incubated overnight at 37°C with 1μg of MS-grade TPCK-treated trypsin (Promega) dissolved in 200μL of ammonium bicarbonate solution and end-over-end rotation. Following overnight incubation, 0.5μg of additional MS-grade TPCK-treated trypsin was added for an additional incubation of 2h. Beads were then pelleted by centrifugation at 2,000rpm for 2 minutes and supernatant was transferred to new microcentrifuge tubes. The beads were washed with 300μL of ammonium bicarbonate solution and pelleted by centrifugation at 14,000rpm for 10 minutes. The resulting supernatant was removed and pooled with the first eluates. The supernatants were then lyophilized and resuspended in buffer A (0.1% formic acid) and 1/6th of each sample was analyzed per MS analysis.
2.3.4 Mass spectrometry
High-performance liquid chromatography was conducted using a 2cm precolumn (Acclaim PepMap 20mm x 75μm inner diameter (ID)) and 50cm analytical column (Acclaim PepMap, 500mm x 75μm diameter; C18; 2μm; 100 Å, Thermo Fisher Scientific). Peptides were subjected to LC-ESI-MS/MS using a 120min reverse-phase (5-30% acetonitrile, 0.1% formic acid) buffer gradient at 225nL/min on a Proxeon EASY-nLC 1000 pump in-line with a Thermo Q-Exactive HF quadrupole-Orbitrap mass spectrometer. A precursor ion scan was performed using a resolving power of 60,000 and up to the twenty most intense peaks were selected for MS/MS (minimum ion count of 1,000 for activation) using higher energy collision induced dissociation fragmentation. Precursor ions are dynamically excluded from analysis for 5 seconds (m/z within a range of 10ppm, exclusion list size up to 500). MS data analysis was performed on the ProHits platform. Thermo .RAW files were converted to .mzXML format using Proteowizard, then searched using X!Tandem and Comet against the Human RefSeq Version 45 database. Search parameters specified a precursor ion mass tolerance of 15ppm and a product ion tolerance of 0.2Da, with up to 2 missed cleavages allowed for trypsin. Modifications were allowed for +1@N, +1@Q, +16@M, and +114@K. Proteins identified with an iProphet cut-off of 0.9 (≤1% FDR) and at least two unique peptides were analyzed with SAINT Express v3.3. Twenty-seven control runs from cells expressing the FlagBirA tag only were compared to two biological and
54 two technical replicates of each FBXW7 BioID experiment. High confidence interactors were identified as those with Bayesian FDR < 2%.
2.3.5 Immunoblotting
Cells for immunoblotting were collected with boiling Laemmli sample buffer, followed by incubation at 95°C for 5 minutes and brief sonication. Samples were loaded on 10% bis- acrylamide gels for electrophoresis, then transferred onto nitrocellulose membranes via tank electrotransfer. Membranes were blocked in either PBS or TBS containing 0.1% Tween 20 (PBS-T/TBS-T) and 5% skim milk. Antibodies were diluted in blocking buffer (for antibodies, dilutions, and time, see Table S3). Membranes were washed with PBS-T/TBS-T for 1.5h between primary and secondary antibody binding and after secondary antibody binding. Protein were visualized on autoradiography film through enhanced chemiluminescence with Clarity Western ECL Blotting Substrate (Bio-Rad). Protein abundances were quantified using ImageJ (Schneider et al. 2012).
2.3.6 Substrate validation via cycloheximide chase
On day zero, two wells per substrate are seeded with 300,000 low-passage HEK293 cells on 6- well plates. From this point, cells are cultured only in antibiotic-free DMEM with 10% FBS. On day one, cells are transfected with 20nM of either ON-TARGETplus FBXW7 siRNA SMARTpool or Non-targeting Control siRNA #2 with DharmaFECT 1 Transfection Reagent (Dharmacon) according to manufacturer’s instructions. Sixteen hours post-transfection on day two, siRNA transfection media is replaced with fresh media. After eight hours of incubation, cells are transfected with 1μg of 3xHA-tagged substrate expression vectors using Polyjet In Vitro Transfection Reagent (SignaGen) according to manufacturer’s instructions. Sixteen hours post- transfection on day three, vector transfection media is replaced with fresh media and cells were left to incubate for eight hours. Cells were then dissociated from culture plates with trypsin and each well was passaged into six wells on 24-well plates. Sixteen hours post-passaging on day four, cells were treated with CHX at a final concentration of 100μg/mL for 0-8h in a reverse time-course. Cells were then lysed with Laemmli sample buffer. Half-lives were calculated via linear regression of quantified protein abundances.
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2.3.7 Immunofluorescence imaging
Coverslips were prepared by first washing with ethanol and PBS, then incubated in 5% polylysine for 30 minutes. Coverslips were then washed 8 times with PBS and complete media. 50,000 FlagBirA-FBXW7 isoform cells were seeded onto the coverslip. Expression of fusion proteins was induced with tetracycline for 18h, after which the coverslips are transferred into new wells and washed with PBS. Cells were then fixed with 4% paraformaldehyde (PFA) in PBS at pH 7.5 for 10 minutes at room temperature, followed by permeabilization with 0.1% Triton X- 100 in PBS for 10 minutes at room temperature. Coverslips were blocked with 5% BSA, 10% FBS, 0.25% Triton X-100, and 1% fish skin gelatin for 1 hour. Coverslips were then washed with PBS, followed by incubation with 1:1000 anti-Flag M2 antibody (Sigma) in blocking buffer for 1 hour (for details, see Table S3). Cells expressing the nucleolar FBXW7 fusion were also probed with anti-fibrillarin antibody in the same manner. After incubation with primary antibodies, coverslips were washed with PBS and incubated with 1:5000 DAPI, anti-mouse Alexa 488 for the visualization of Flag, or anti-rabbit Texas Red for the visualization of fibrillarin. Coverslips were mounted with ProLong Gold Antifade (Thermo). Epifluorescence microscopy was performed using a Leica DM4000B fluorescence microscope equipped with digital camera (Leica DFC490). Images were acquired under × 100 magnification (oil) using Leica Image Manager software. Confocal microscopy was performed using a Nikon A1R confocal microscope equipped with 60X oil immersion objective.
2.3.8 Data analysis and visualization
MS data was processed in Microsoft Excel with additional analysis performed in R. Venn diagrams were generated in VennDIS (Ignatchenko et al. 2015). Data visualization in R were performed using the BoutrosLab.plotting.general package. Gene enrichment analysis was performed with g:Profiler and Metascape (Reimand et al. 2007; Tripathi et al. 2015). Visualization of mass spectrometry dataset correlations performed in ProHits-viz (Knight et al. 2017). Sankey diagrams were generated with the SankeyMATIC web tool (http://sankeymatic.com). UpSet plot was generated in the UpSetR Shiny App (Lex et al. 2014).
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2.4 Results 2.4.1 Expression and localization of FlagBirA-FBXW7 isoforms
To confirm the presence of FlagBirA-tagged FBXW7 isoforms at the protein level (Gry et al. 2009). Tetracycline was used to induce the expression of the fusion protein in stable BioID cell lines and immunoblotting for the Flag tag confirmed the expression of the fusion proteins (Figure 2.1). The cytoplasmic isoform is detected at lower levels, however, when compared to the nuclear isoforms, which are detected at similar levels. This is in contrast to previous reports of ectopic FBXW7 expression in 293A cells, where cytoplasmic and nucleolar isoforms had comparably low protein levels (Grim et al. 2008). Notably, all three stable BioID cell lines were generated in the Flp-In TREx 293 cells, which contains only one integration site at a genetically- active locus. It is therefore possible that the separate Flp-In TREx cell lines are better for observing differential isoform regulation post-translation as expression levels of transgenes are better controlled compared to co-transfection of plasmids. Alternatively, fusion with FlagBirA may differentially affect the stability of FBXW7 isoforms. Efficient knockdown of all three isoforms with siRNA is also demonstrated (Figure 2.1).
Figure 2.1: Expression and knockdown of FlagBirA-tagged FBXW7 isoforms in Flp-In TREx 293 cells. Cells were transfected with non-targeting (NT) or anti-FBXW7 (FBXW7) siRNAs, followed by induction of fusion protein with tetracycline (+) for 24h and subsequent immunoblotting.
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Figure 2.2: (Top) Epifluorescence and (Bottom) confocal microscopy of FlagBirA-tagged FBXW7 isoforms reveal isoform-specific localization. Expression of FlagBirA was induced with tetracycline in Flp-In TREx 293 cell lines for 24h before fixing. Images were acquired with the same camera settings across isoforms.
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To study the localization of FlagBirA-tagged FBXW7 isoforms, cells expressing the fusion proteins were probed with α-Flag antibodies for immunofluorescence microscopy (Figure 2.2). Epifluorescent microscopy revealed proper localization of the nucleoplasmic isoform to the nucleus. While the nucleolar isoform was also found throughout the nucleus, foci were found to colocalize with fibrillarin, a protein associated with nucleoli. Compared to the nuclear isoforms, signal from the cytoplasmic isoform is detected throughout the cells and does not appear to be excluded from the nucleus. Confocal microscopy revealed localization of the cytoplasmic isoform to the nucleus in some cells comparable to previous reports (Figure 1.8B), but could not find significant colocalization between nucleolar FBXW7 and fibrillarin. Exogenous expression of FlagBirA-tagged FBXW7 isoforms thus appears to generally localize to their reported compartments with the cytoplasmic and nucleolar isoforms also permeating into nearby compartments. Analysis of putative substrates shared by two or more isoforms in BioID is consequently difficult given the overlap in localization of the fusion proteins.
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2.4.2 FBXW7 isoforms exhibit distinct substrate profiles
BioID and SAINT analysis identified 836 unique high-confidence interactors in total between the FBXW7 isoforms. Correlation analysis of the interactor profiles between different FBXW7 isoforms under different conditions confirmed segregation based on localization and MG132 status with no outliers (Figure 2.3). Stable interactors such as SCF complex components SKP1 and CUL1, as well as NEDD8, were detected by all three isoforms (Table 2.1). Other proteins involved in SCF function, including components of the CSN and paralogues of DCUN1D1, were detected by certain FBXW7 isoforms. CAND1, which antagonizes the binding of FBP-SKP1 to CUL1, was not detected by any isoforms. Meanwhile, RBX1 was detected by mass spectrometry, but was not considered a high-confidence interactor with a BFDR of 2%. Taken together, it appears that the FlagBirA-tagged FBXW7 isoforms are properly assembling into SCF complexes in vivo. In addition to the SCF complex, known substrates of FBXW7 were also detected as high-confidence interactors, including MYC, NOTCH1, MED13L, and SREBF1 (Table S1). Several known substrates, including MYC and MED13L, were significantly detected only when PMA was used in conjunction with MG132, validating the application of PMA in conjunction with MG132 in BioID as a valid strategy for increasing the detection of FBP substrates.
To identify putative substrates, a threshold of log2 ratio > 1.85 between the total spectral counts of BioID samples treated with MG132 over non-treated samples was defined, which was based on the ratio observed for EBNA1BP2, which is a validated interactor but not a substrate. Additionally, a t-test p threshold of -log(p) > 2, or p < 0.01, between the observed spectral counts was also defined. This rigorous filtering results in 485 putative substrates between the three isoforms (Figure 2.4). Only ~19% of putative substrates were shared by more than one isoform of FBXW7 with the rest being isoform-specific. Gene ontology (GO) enrichment analysis revealed that the total nucleoplasmic substrate profile was highly enriched for proteins in the nucleoplasm (p=1.03x10-60), while being unenriched for nucleolar and cytoplasmic proteins (p=1.00). The total nucleolar substrate profile was also highly enriched for nucleoplasmic proteins and unenriched for cytoplasmic proteins, but was also enriched for nucleolar proteins (p=4.55x10-13). Meanwhile, the total cytoplasmic substrate profile was enriched for both cytoplasmic and nucleoplasmic proteins (p=1.95x10-8 and p=2.25x10-10, respectively). Cytoplasmic FBXW7 has been found to be anchored at the endoplasmic reticulum membrane
60 through a transmembrane domain at its N-terminus (Matsumoto et al. 2011). In agreement with this observation, there was no enrichment for any compartments of the endoplasmic reticulum. Analysis of the cytoplasmic-specific fraction alone revealed that it was unenriched for nucleoplasmic proteins (p=1.00) while the enrichment for cytoplasmic proteins did not change. There is ~10x more nucleolar substrates than there are nucleoplasmic-specific substrates, suggesting that there are significant specific processes differentially regulated by the nuclear isoforms. BioID also identifies isoform-specific degradation of several validated substrates (Table 2.1), including MYC, which was identified as a substrate by the nucleolar isoform, in agreement with previous reports. The forkhead box protein M1 (FOXM1) was previously reported as a substrate of FBXW7, but it was unreported which isoform mediated its degradation (Chen et al. 2016). In our BioID dataset, FOXM1 was only detected as substrate for the nucleolar isoform. Therefore, our BioID not only correctly identifies previously reported substrates with isoform specificity, but also provides insight into substrates for which no specific isoform of FBXW7 was identified as the E3 ligase.
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Figure 2.3: Bait-bait Pearson correlation coefficients reveal relationship between FBXW7 BioID datasets. FBXW7 isoforms are denoted as 7.1 nucleoplasmic, 7.2 cytoplasmic, and 7.3 nucleolar. Two non-FBXW7 BioID datasets from serine-beta-lactamase-like protein, mitochondrial (LACTB) and serine protease HTRA2, mitochondrial (HTRA2) are included.
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FBXW7 isoforms Symbol Interactor Substrate Max log2 enrichment ratio -log(p) PMA required
CREB3L2 2 4.46 1.59 Yes
EBNA1BP2 1, 2, 3 1.85 (2) 5.83 No
FOXM1 3 3 4.46 2.40 Yes
JUNB 1, 3 1, 3 7.06 (1) 5.14 Yes
KLF5 1, 2, 3 2 4.00 3.16 Yes
MCL1 2 4.09 1.86 Yes
MED13 1, 2, 3 1, 2, 3 6.19 (1) 2.16 No
MED13L 2 3.46 1.07 Yes
MYC 3 3 2.58 2.07 Yes
NCOA3 3 -2.32 N/A Yes
NFKB2 2 2.81 1.59 Yes
NOTCH1 2 2 4.64 2.90 No
NR1D1 2 3.00 1.36 Yes
SREBF1 2 2 4.70 3.67 Yes
SKP1 1, 2, 3 1, 2, 3 2.24 (1) 14.96 No
CUL1 1, 2, 3 1.22 (2) 4.20 No
NEDD8 1, 2, 3 2, 3 4.25 (3) 2.91 No
Table 2.1: BioID identifies validated substrates and interactors of FBXW7. Isoforms are indicated in the following manner: (1) nucleoplasmic; (2) cytoplasmic; (3) nucleolar. A protein is designated as an interactor should it pass SAINT analysis in any condition, and is designated as a substrate should its spectral counts be enriched with MG132 treatment with a log2 ratio > 1.85 and a t-test p < 0.01. A protein requires PMA should it be designated as an interactor only with MG132 and PMA treatment and not with MG132 alone. The max enrichment ratio is presented for the isoforms that designate the protein as a substrate, then as an interactor should no isoform identify the protein as a substrate.
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Figure 2.4: FBXW7 isoforms exhibit distinct substrate profiles, as revealed by BioID. (Top) Overview of FBXW7 substrate profiles as revealed by BioID. Enrichment analysis for cellular compartments were performed with gProfiler. (Bottom) Detailed breakdown of FBXW7 BioID dataset.
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2.4.3 CHX chase reveals novel FBXW7 interactors
To validate putative isoform-specific substrates, a strategy was adapted that focused on substrates that were highly-enriched in MG132-containing samples, as well as detection of multiple proteins that are known to be part of the same complex (Figure 2.5). Proteins that interact with previously validated FBXW7 substrates, such as components of the mediator complex, were avoided. To screen for putative substrates, cells were transfected with 3xHA- tagged substrate expression vectors with either FBXW7-targeting siRNAs or non-targeting siRNAs to characterize changes in steady state protein levels. Increases in abundance of several proteins were observed, including FOXM1 (Figure 2.6). Other stabilized proteins include the human Spt-Ada-Gcn5 acetyltransferase (SAGA) complex components TAF5-like RNA polymerase II p300/CBP-associated factor-associated factor 65kDa subunit 5L (TAF5L), TAF6- like RNA polymerase II p300/CBP-associated factor-associated factor 65kDa subunit 6L (TAF6L), and transcription initiation protein SPT3 homolog (SUPT3H). Other complexes of interest include Ada Two A-containing (ATAC), activating signal co-integrator 2 (ASCOM), and the dimerization partner, Rb-like, E2F and multivulval class B (DREAM) complex. The screen also showed that several putative substrates did not increase in protein levels significantly following FBXW7 knockdown, including DREAM complex components protein lin-9 homolog (LIN9), protein lin-54 homolog (LIN54), and retinoblastoma-like protein 1 (RBL1) (data not shown), demonstrating that depletion of FBXW7 does not generally increase all protein levels.
To further validate the substrates of interest, CHX chase experiments were performed over 8h in conditions of FBXW7 knockdown conditions and non-targeting controls (Figure 2.7). For the SAGA complex, while TAF5L, TAF6L, and SUPT3H were all stabilized upon FBXW7 knowndown, TAF6L was most significantly stabilized. Significant stabilization of other proteins was also observed, including ATAC complex component MAP3K12 binding inhibitory protein 1 (MBIP), ASCOM complex component PAX-interacting protein 1 (PAXIP1), and DREAM complex component Myb-related protein B (MYBL2).
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A) B)
C) Figure 2.5: Visualization of (A) nucleoplasmic, (B) cytoplasmic, (C) and nucleolar FBXW7 isoform interactor profiles and substrate thresholds. Putative substrates are designated should its spectral counts be enriched with MG132 treatment with a log2 ratio > 1.85 and a t-test p < 0.01 (-logp = 2), as indicated in the grey boxes. Validated substrates of FBXW7, as well as putative substrates described in this thesis, are highlighted.
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FBXW7 isoforms Associated Designated Designated Max log2 - PMA Symbol complex interactor substrate enrichment ratio log(p) required
TAF6L SAGA 1 1 2.35 7.27 No
TAF5L SAGA 1 3.17 N/A No
SUPT3H SAGA 1 1 3.91 6.72 No
TRRAP SAGA 1, 3 1, 3 3.26 (1) 7.5 No
MBIP ATAC 3 3 5.43 2.81 Yes
YEATS2 ATAC 3 3 5.48 5.49 No
PAXIP1 ASCOM 1 1 4.86 6.12 No
NCOA6 ASCOM 1 1.70 7.81 No
FOXM1 DREAM 3 3 4.46 2.4 Yes
MYBL2 DREAM 3 3 8.21 9.78 No
LIN37 DREAM 2, 3 3 2.58 2.07 No
LIN9 DREAM 2, 3 3 6.88 7.38 No
LIN54 DREAM 3 3 2.64 2.36 No
LIN52 DREAM 1, 2, 3 1, 3 5.32 (3) 4.72 No
RBL1 DREAM 3 3 4.25 2.62 Yes
E2F4 DREAM 1, 2 1, 2 4.58 2.69 No
E2F5 DREAM 3 3 3.70 5.26 No
Table 2.2: BioID identifies components of the SAGA, ATAC, ASCOM, and DREAM complex components as putative substrates. Notation of isoforms and designations are described in Table 2.3. SAGA, Spt-Ada-Gcn5 acetyltransferase; TAF6L, TAF6-like RNA polymerase II p300/CBP-associated factor-associated factor 65kDa subunit 6L; TAF5L, TAF5-like RNA polymerase II p300/CBP-associated factor-associated factor 65kDa subunit 5L; SUPT3H, transcription initiation protein SPT3 homolog; TRRAP, transformation/transcription domain- associated protein; ATAC, Ada Two A containing; MBIP, MAP3K12 binding inhibitory protein 1; YEATS2, YEATS domain-containing protein 2; ASCOM, activating signal co-integrator 2; PAXIP1, PAX-interacting protein 1; NCOA6, nuclear receptor coactivator 6; DREAM, dimerization partner, Rb-like, E2F and multivulval class B; FOXM1, forkhead box protein M1; MYBL2, Myb-related protein B; LIN37, protein lin-37 homolog; LIN9, protein lin-9 homolog; LIN54, protein lin-54 homolog; LIN52, protein lin-52 homolog; RBL1, retinoblastoma-like protien 1; E2F4, transcription factor E2F4; E2F5, transcription factor E2F5.
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Figure 2.6: Putative substrates identified by BioID are stabilized with FBXW7 knockdown. HEK293 cells were transfected with either non-targeting (-) or FBXW7-targeting (+) siRNA and 3xHA-tagged substrate expression vectors. Cells were prepared for immunoblotting ~48h post-transfection. Bands of proper size are indicated with an underscoring asterisk. Results are representative of duplicate experiments.
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A) SAGA complex
B) ATAC complex
C) ASCOM complex
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D) DREAM complex-associated
Figure 2.7: Components of the SAGA, ATAC, ASCOM, and DREAM complexes discovered by BioID are stabilized in FBXW7 knockdown conditions. HEK293 cells were transfected with siRNAs and 3xHA-tagged substrate expression vectors as described previously. Transfected cells were then subjected to CHX treatment for 0h, 1h, 2h, 4h, 6h, or 8h, followed by lysis and immunoblotting. Band intensities were quantified with ImageJ and half- lives were calculated via linear regression. Results are representative of 2-3 replicates.
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2.4.4 Effect of hotspot mutations on substrate binding is site- and residue- specific
To investigate the effect of hotspot mutations on substrate binding, BioID was performed on 5 different nucleoplasmic FBXW7 mutants with the threshold for putative substrate set at log2 ratio >1.81 between MG132-treated and untreated samples. These mutants are, in decreasing order of occurrence in the MSK-IMPACT study, R465H (26% of all hotspot mutants), R465C (21%), R505C (20%), R479Q (14%), and R505G (5%). The consequence on the substrate profile varied greatly between the mutants (Figure 2.8). Mutants with the greatest losses in substrate detection were R465C and R505C with 6 and 7 putative substrates, respectively. This level of substrate binding abrogation is followed closely by the R479Q mutant, which has 17 putative substrates. Based on these observations, it is possible that cysteine mutations are most deleterious to FBXW7 function. Between these three mutant substrate profiles, only 6 putative substrates were shared with wild type nucleoplasmic FBXW7, almost all of which were proteins related with SCF complex function. This is unsurprising as the mutants retain an unmutated F- box domain. The putative substrates of these mutants also do not overlap with the substrate profiles of other wild type FBXW7 isoforms. Together, these abrogative mutants represent 55% of hotspot mutations in MSK-IMPACT patients, suggesting that the contribution of FBXW7 mutation to disease in these patients may be largely due to loss of substrate binding.
In comparison to the abrogative mutants, the R465H and R505G mutants of FBXW7 do not appear to lose all substrate binding. The R465H mutant shares 17% (9/53) of its substrate profile with wild type FBXW7, while R505G shares 23% (22/95). Interestingly, the R465H and R505G mutants appear to have sizable mutant-specific substrate profiles, especially R505G, which detects 54 putative substrates not shared by wild type nucleoplasmic or any other mutants of FBXW7. Accounting for 31% of patient mutations, it is possible that some of the substrates gained by R465H and R505G mutants could actively contribute to disease.
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Figure 2.8: Effect of hotspot mutation on nucleoplasmic FBXW7 substrate binding is mutant-specific. (Top) UpSet plot showing extent of overlap between wild type nucleoplasmic FBXW7 and five hotspot mutants, as well as substrate profile sizes. (Bottom) Heatmap of MG132-induced enrichment in spectral counts.
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2.5 Discussion
FBXW7 is an important protein in health and disease, but its substrate profile has been poorly explored due to limitations in conventional assays to study FBPs. Our BioID results distinctly maps the interaction landscape of FBXW7 isoforms, identifying over 450 new putative substrates and ascribing isoform-specific regulation of known substrates. Through CHX chase experiments, several new putative substrates of FBXW7 was identified, including TAF6L, MBIP, PAXIP1, and MYBL2. Additionally, BioID of FBXW7 harboring hotspot mutations reveal potential distinctions between abrogative mutants and neomorphic mutants. This is in agreement with reports where FBXW7 mutants drive disease progression in xenograft mice with NOTCH1, resulting in worse survival rates than deletion of FBXW7 (King et al. 2013).
Our validation of putative substrates suggests a novel role for FBXW7 in the regulation of histone modification through acetyltransferase and methyltransferase complexes. The 2MDa SAGA complex is an evolutionarily conserved transcription coactivator complex (Helmlinger and Tora 2017). SAGA facilitates the acetylation of histones on H3K9 and H3K14 through histone acetyltransferase KAT2A (Sc: GCN5), opening the chromatin structure to allow the formation of the pre-initiation complex, a critical rate-limiting step for transcription. This is facilitated through the binding of the SAGA complex to transcription factors like MYC, BRCA1, and E2F proteins through transformation/transcription domain-associated protein (TRRAP) (Nagy and Tora 2007). In addition to transcription factor-guided histone acetylation, the SAGA complex has been implicated in the global acetylation of chromatin, though the effect is less well understood. The core structural module (CSM) of the SAGA complex contains several TBP- associated factors (TAFs) and transcription factor SPT homologs (SUPTs). Though some components of the CSM are shared with the general transcription factor II D (TFIID) complex, TAF6L, and SUPT3H are found exclusively in the SAGA complex. TAF5L, TAF6L, and SUPT3H are specifically identified as substrates of the nucleoplasmic isoform. BioID of nucleoplasmic FBXW7 also identified TAF5L as an interactor, but not as a substrate. Additionally, BioID identified TRRAP as a substrate of all FBXW7 isoforms. Thus, BioID was able to detect ~¼ of the proteins within the SAGA complex. Based on our present understanding of the structure of the SAGA complex, the enrichment of detected proteins within the CSM suggests that nucleoplasmic FBXW7 binds here (Müller and Tora 2004, and based on personal communications with L. Tora). Though all three proteins were found to be stabilized in FBXW7
73 knockdown conditions, TAF6L exhibited the greatest increase in half-life, suggesting it is likely to be the true substrate and TAF5L and SUPT3H are stabilized through increased CSM stability. Transfection of 3xHA-tagged TAF6L into cells with active FlagBirA-tagged FBXW7 followed by streptavidin pull-down revealed detection of TAF6L by both nucleoplasmic and nucleolar FBXW7, but not cytoplasmic FBXW7 (data not shown). Hence, ectopic expression of substrates in BioID cells may be an imperfect method to validate BioID data as substrates may be present in compartments in greater quantities than endogenous levels. TAF6L has been shown to be essential for proper SAGA complex function in D. melanogaster, though not for the recruitment of Gcn5 to SAGA-regulated genes (Weake et al. 2009). Instead, TAF6L appears to be crucial for some non-catalytic function, as larvae harbouring TAF6L mutations or deletions are not viable. The importance of TAF6L appears to be entirely unexplored in humans, but regulation of the SAGA complex through TAF6L by FBXW7 may be important for transcriptional regulation.
BioID also revealed the regulation of the ATAC acetyltransferase and ASCOM methyltransferase complexes. The ATAC complex is the other KAT2A-containing acetyltransferase complex in humans and is evolutionarily related to the SAGA complex, sharing the entirety of the histone acetyltransferase module with the SAGA complex (Helmlinger and Tora 2017; Spedale et al. 2012). Two components of the ATAC complex, MBIP and YEATS2, were identified as substrates specifically by nucleolar FBXW7. Similar to the SAGA complex, the ATAC complex is also involved in histone acetylation, though the function appears to be distinct from the SAGA complex (Spedale et al. 2012). The role of YEATS2 in the ATAC complex is to bind acetylated and crotonylated histones, while MBIP is required for the structural integrity of the complex (Zhao et al. 2016; Guelman et al. 2008). Unfortunately, BioID of MBIP failed to detect FBXW7 (data not shown), demonstrating the difficulty of reciprocal detection of PPIs with FBPs. ATAC also contains a second histone acetyltransferase, KAT14, that is important for complex stability and mammalian development (Guelman et al. 2008). Meanwhile, the ASCOM complex facilitates H3K4 trimethylation through histone-lysine N- methyltransferase 2B/C (KMT2B/C, also MLL4/3), a mark for transcriptionally-active chromatin (Lee et al. 2008). Within the ASCOM complex, NCOA6 and PAXIP1 was specifically identified with nucleoplasmic FBXW7. CHX chase experiments also supported the hypothesis that PAXIP1 is a substrate of FBXW7, while NCOA6 could not be detected through immunoblotting. Deletion of NCOA6 results in ablation of KMT2B/C-directed histone trimethylation, resulting in
74 failed adipogenesis in cell lines and embryonic lethality in mice (Lee et al. 2008). In addition to being part of the ASCOM complex, PAXIP1 has also been implicated in DNA repair by binding to phosphorylated 53BP1 to promote NHEJ (Escribano-Diaz and Durocher 2013). Despite the apparent regulation of the ATAC, ASCOM, and SAGA complexes by FBXW7, preliminary histone immunoblots failed to show differences in histone acetylation and methylation marks in FBXW7 knockdown conditions (data not shown).
Another major complex identified through BioID of FBXW7 is the DREAM complex and associated proteins, which coordinates gene expression during various phases of the cell cycle (Sadasivam and DeCaprio 2013). Briefly, the DREAM complex consists of a MuvB core complex that is bound by repressive E2Fs such as E2F4 and E2F5 during quiescence. Upon entry into the cell cycle, the MuvB core dissociates from the repressive E2Fs to allow for transcription driven by activator E2Fs. Meanwhile, the MuvB core sequentially binds MYBL2 and FOXM1 for the expression of late cell cycle genes. MYBL2 undergoes proteasomal degradation through SKP2 during S/G2 phase transition, as does FOXM1 through APC during M/G1 phase transition. Of the 14 proteins associated with the DREAM complex, 9 were detected, including components of the MuvB core, MYBL2, E2F4/5, and FOXM1. Furthermore, several of these proteins were identified as substrates only by nucleolar FBXW7. FOXM1 has been previously identified as a substrate of FBXW7, but as a part of the Wnt signaling pathway, where it recruits CTNNB1 to target gene promoters (Chen et al. 2016). In the BioID data, FOXM1 was detected with nucleolar FBXW7, contrary to the previous proposed model of cytoplasmic degradation. Meanwhile, MYBL2 is important in cell cycling, cell survival, and the maintenance of stem cell properties (Musa et al. 2017). MYBL2 expression is repressed by the DREAM complex and overexpression of MYBL2 is observed in a variety of cancers. It is possible that dysregulation of MYBL2 and FOXM1 in FBXW7-mutant cancers can contribute to the progression of disease.
BioID exploration of FBXW7 has greatly expanded our understanding of the involvement of FBXW7 isoforms in various cellular processes, but there exist some discrepancies within the data. Many validated substrates are not detected by the BioID experiments, most notably CCNE1. Closer examination of the MS data revealed that while CCNE1 was detected by the nucleoplasmic and cytoplasmic isoforms, it was not sufficiently detected to be considered an interactor by SAINT when compared to the control samples. This demonstrates the rigorousness of data processing by SAINT, which still identified many validated substrates of FBXW7, but
75 may still generate some false negatives due to low protein abundance or conduciveness of peptides to MS analysis (Jarnuczak et al. 2016). Interestingly, PIN1 and NPM1 were insignificantly detected by all isoforms via MS, but CCNE1 is completely undetected by nucleolar FBXW7. While none of the CCNE1-associated proteins passed filtering by SAINT, these results seems to be contrary to previous reports of CCNE1 degradation by nucleolar FBXW7 (Bhaskaran et al. 2012).
It should also be noted that our BioID approach may have been suboptimal for the study of cytoplasmic FBXW7, which contains a single-pass transmembrane domain. By tagging the N- terminus of cytoplasmic FBXW7 with FlagBirA, the anchoring of the protein to the endoplasmic reticulum membrane may have been disrupted. This may account for an enrichment in genes involved in the response and regulation of endoplasmic reticulum stress specifically in the cytoplasmic substrate profile. However, it is unclear what effect, if any, this disruption has on the BioID-derived substrate profile of cytoplasmic FBXW7. Instead, several lines of evidence suggest that substrate detection by FlagBirA-tagged cytoplasmic FBXW7 to be relatively unperturbed, despite a putative disruption in membrane anchoring. First, immunofluorescence microscopy of tagged cytoplasmic FBXW7 appears diffuse throughout the cytoplasm comparable to previous reports with smaller epitope tags. Second, BirA appears to be exposed to the cytoplasm and not the endoplasmic reticulum lumen as gene enrichment analysis shows that the cytoplasmic substrate profile is enriched for the cytoplasm, but not at all for endoplasmic reticulum lumen. Third, cytoplasmic FBXW7 detected several validated substrates of FBXW7, of which at least NOTCH1 and SREBF1 are known to exist in the cytoplasm. It should also be noted that tagging cytoplasmic FBXW7 at the C-terminal is unfeasible as it would likely disrupt the function of the WD40 repeat domain. Ideally, the BirA coding sequence could be inserted between the transmembrane domain and the other FBXW7 domains.
A wealth of data has been presented in this section, but much validation and study of the biological relevance of the putative substrates remain. Firstly, we have so far been unable to identify a novel cytoplasmic substrate due to issues with cloning or protein expression. Given the comparatively large size of the cytoplasmic putative substrate profile, continued validation of proteins should generate positive results. Secondly, in order to further validate the substrates identified in this section, putative phosphodegrons should be mutated to observe if it abrogates FBXW7 binding. Some of this work will be presented in the next section. Thirdly, despite the
76 validation of many new substrates, there is currently a lack of evidence to link these novel interactions to a biological effect. As FBXW7 is known to target several well-known oncoproteins for degradation, it may be difficult to demonstrate the significance of novel interactions separate from the impact of known interactions. With regards to the effect on chromatin modification, we look to obtain FBXW7 knockout cell lines to compare levels of histone acetylation and methylation at a global level and at SAGA/ATAC/ASCOM-associated loci, and whether any effect can be rescued via the reconstitution of specific FBXW7 isoform. Lastly, we have not yet explored gain of substrates by some FBXW7 hotspot mutants, or interactors that are not enriched by MG132. Validation of the former can provide insight into the role of FBXW7 mutation in cancers, and validation of the latter can identify neo-substrates and interactors of mutant FBXW7 proteins.
In summary, BioID was used to identify isoform-specific substrate profiles of FBXW7. Within this set of data, multiple novel substrates were validated, expanding our understanding of FBXW7-regulated processes. It is also reveal that the effect of FBXW7 hotspot mutation on substrate binding is site- and residue-specific. Further analysis of the BioID datasets can elucidate the biology of FBXW7 in health and disease contexts, which may enable novel therapeutic avenues in cancer.
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Chapter 3 – Discovery of novel FBXW7 phosphodegron features
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Discovery of novel FBXW7 phosphodegron features 3.1 Chapter overview
Like many FBPs, binding of FBXW7 to substrates is facilitated through phosphodegrons. Extensive characterization of the yeast homolog of FBXW7, Cdc4, has resulted in the CPD with the sequence I/L-I/L/P-pT-P-
3.2 Contributions
Sebastien Giguere (post-doctoral fellow from M. Tyers’ group) generated the Cdc4 and FBXW7 peptide-binding models, as well validated the Cdc4 model. Gerald Gish (staff scientist at the Lunenfeld-Tanenbaum Research Institute) synthesized the peptide arrays for FBXW7 binding experiments. Stephen Orlicky (research associate from F. Sicheri’s group) performed most of the fluorescent polarization assays, as well as analyzing the data. Tonny Huang contributed to discussions regarding the peptide-binding assays, selected peptides for peptide-binding arrays, analyzed the results from the peptide-binding assays, and performed some fluorescent polarization assays.
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3.3 Materials and methods 3.3.1 Generation of peptide-binding models
Peptide-binding models are based on the Generic String (GS) kernel, as previously described in Giguère et al. 2013, and the support vector regression algorithm, also known as a support vector machine (SVM). Briefly, the GS kernel analyzes the features of peptides based on a set of weights to output how similar the peptides are. By using a SVM, training data in the form of peptide-binding arrays can be used to tune the set of weights so the GS kernel can correctly classify peptides into different categories of binding based on how similar they are. In the case of the Cdc4 peptide-binding model, results from a previously-reported peptide-binding array was classified manually into non-binding, weakly binding, intermediate binding, and strongly binding. The peptide sequences and their classifications were then used to train the model, which achieved an accuracy of 81% through 10-fold cross-validation. This model was then applied to the human proteome to identify Cdc4/FBXW7-binding peptides. A second peptide-binding array was synthesized that includes 760 singly- and doubly-phosphorylated peptides predicted by the Cdc4 model, as well as 240 randomly-selected phosphopeptides and validated FBXW7 phosphodegrons. Results from the second peptide-binding array was used to generate an FBXW7-specific model in the same manner as the Cdc4 model. The FBXW7 model was then used to predict possible phosphodegrons in the human proteome.
3.3.2 Peptide array synthesis and binding
Peptide arrays were constructed via Fmoc solid phase peptide synthesis onto cellulose membranes, as described in Tang et al. 2005. Briefly, fluorenylmethyloxycarbonyl (Fmoc)- protected amino acids are activated with N,N’-diisopropylcarbodiimide, a coupling reagent that forms a highly electrophilic group with the carboxylate group of the amino acid. This allows the activated amino acid to form a peptide bond with the terminal primary amine group of an immobilized peptide chain. The Fmoc protecting can then be removed (deprotection) with piperidine for subsequent amino acid coupling. Peptides were synthesized in a 16x24 format onto 130mm x 90mm membranes with automation. Membranes were incubated with GST-tagged FBXW7 complexed with His-tagged SKP1, which was probed using an anti-His antibody for visualization. Spot intensities were measured with a ChemiDoc imaging system (Bio-Rad).
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3.3.3 Mutant phosphodegron cloning and CHX chase
Mutation of predicted phosphodegrons on substrates was performed using overlap extension polymerase chain reaction (OE-PCR). Briefly, internal primers flanking the site of mutation were used to separately amplify two fragments overlapping at the site of mutation (see Table 2.1 for primers). The two fragments were then annealed and amplified to be cloned into the pcDNA3 3xHA expression vector. Sequences were confirmed via capillary-based Sanger sequencing at The Centre for Applied Genomics (TCAG). In cases where OE-PCR failed to produce correct vectors, mutants were ordered from BioBasic. Serine and threonine residues were mutated to glycines (GCC). CHX chase experiments were performed as described previously.
3.3.4 Fluorescence polarization
Oligopeptides 11 amino acids in length were synthesized with phosphorylated serines and threonines, as well as N-terminal FITC and C-terminal amidation by BioBasic. Lyophilized peptides were resuspended in HEPES pH 7.5 and concentrations were determined via spectrophotometry. Resuspended peptides were dissolved in a buffer containing 10mM HEPES, 1mM DTT, 0.01% Brij 35, 0.1mg/mL BSA, and 50mM NaCl to a final concentration of 5nM. Purified FBXW7-SKP1 was serially diluted over 12 concentrations using the same buffer. Diluted peptides and proteins were then mixed in a 1:1 ratio and transferred to a 384-well black microplate. The samples were incubated at room temperature for 30 minutes and then read by a Synergy NEO HTS multimode reader (BioTek).
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3.4 Results 3.4.1 Performance of the Cdc4 model
10-fold cross-validation of the Cdc4 model with the Cdc4 peptide-binding array revealed that the model correctly predicted Cdc4-binding with an accuracy of 81%. When applied to the human proteome, the model outperformed randomly-selected phosphopeptides and phosphodegrons in FBXW7-SKP1-binding assays (Table 3.1). Results from triplicate peptides on separate arrays are reproducible with only 7.89% disagreement in binding scoring. Here, binding probabilities generated by the Cdc4 model correlated weakly with actual peptide binding assay intensities, though there also exists a large amount of predicted binders that failed to bind in the assay (Figure 3.1). Surprisingly, almost no validated phosphodegrons bound FBXW7-SKP1 in the peptide-binding assay, including phosphodegrons from CCNE1, MCL1, and MYC (Table 3.2). Curiously, binding of the T58-centered MYC phosphodegron was weaker than the S62-centered phosphodegron, in conflict with previous reports that it is the T58 phosphodegron that facilitates FBXW7 binding. These observations may indicate that the peptide-binding assay cannot accurately model substrate binding in vivo. Nevertheless, the resulting FBXW7 peptide-binding assay was used to train an FBXW7 peptide-binding model.
Number of peptides % binding
Validated FBXW7 phosphodegrons 40 7.5
Of which are phosphorylated 23 13.0
Sequences from proteome that matches consensus FBXW7 phosphodegron 100 4.0
Random sequences from proteome that are phosphorylated 100 6.0
Cdc4 prediction model 760 56.0
Table 3.1: Cdc4 peptide-binding model outperforms other strategies for finding FBXW7-binding peptides in the human proteome for the training dataset. Binding determination was made by visual inspection of the peptide- binding arrays. The consensus FBXW7 phosphodegron is defined here as O-O-O-pS/pT-P-X-X-pS/pT/E, as per (Wertz et al. 2011). Random phosphorylated sequences from the proteome retain the position 0 and +4 phosphosites.
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Figure 3.1: Visualization of Cdc4 model performance in the human proteome for the training dataset. CPD is used here to refer to the consensus FBXW7 phosphodegron motif. 1P and 2P denote singly- or doubly-phosphorylated peptides. Spearman’s rank correlation coefficient and associated p value is indicated.
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Gene Peptide Binding probability (%) Intensity Binding determination
CCNE1 SGLLTPPQSGK 0 11232 Non-binding
SGLLpTPPQSGK 85.31 6624 Non-binding
SGLLpTPPQpSGK 0 6076 Non-binding
MYC ELLPTPPLSPS 0 1032 Non-binding
ELLPpTPPLSPS 84.63 72 Non-binding
ELLPpTPPLpSPS 0 3828 Non-binding
TPPLSPSRRSG 0 1548780 Intermediate binding
TPPLpSPSRRSG 45.87 2901248 Strongly binding
MCL1 DAIMSPEEELD 0 3768 Non-binding
DAIMpSPEEELD 45.04 686 Non-binding
SLPSTPPPAEE 0 0 Non-binding
SLPSpTPPPAEE 54.10 8604 Non-binding
Table 3.2: Binding of select validated FBXW7 phosphodegrons in the training dataset. Binding status is determined manually by inspecting the peptide-binding array.
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3.4.2 Analysis of the FBXW7 peptide-binding array
Proteome-wide screening of FBXW7 peptide-binding was performed over several arrays, including two sets of arrays each containing over 600 peptides derived from the FBXW7 BioID dataset with strong predicted binding in the FBXW7 model. To examine the reproducibility of peptides between arrays, 131 peptides of varying intensities were selected to be reproduced based on intensities of the first array. There were classified as high intensity (110 peptides with normalized intensities > 700,000), medium intensity (10 peptides, 400,000-500,000), and low intensity (11 peptides, < 5,000) repeat controls. When examining the repeat controls by normalized intensities, it is apparent that intensity values cannot be compared between different sets of arrays without adjustments (Figure 3.2). There is, however, clear segregation between the three controls groups and intensities between the high intensity controls can be generally correlated between the two blots.
Due to the poor performance of the peptide-binding assay for validated phosphodegrons in the previous experiment, the screen was expanded to examine more phosphodegrons from validated FBXW7 substrates. Surprisingly, most validated phosphodegrons bound FBXW7 in these assays, though they exhibited a great range of signal intensities (Table 3.3). These phosphodegrons required at least a single phosphothreonine or serine in order to bind FBXW7 with the exception of AURKA, which lost binding with phosphorylation. This conflicts with previous reports that identified this phosphodegron as the primary site of FBXW7 binding (Kwon et al. 2012). The results from the peptide-binding assay suggest that the AURKA phosphodegron may be important in the regulation of AURKA stability, but not the primary binding site.
Sequence logo of peptides with the highest intensities revealed a preference for arginine residues in the +4 position (Figure 3.3). Meanwhile, the peptides with the lowest intensities were enriched for arginine residues in the +5 to +6 position. Additionally, strongly-binding peptides did not discriminate between serine and threonine residues at position zero, suggesting that FBXW7 may be less discriminatory towards serines than Cdc4 (Nash et al. 2001).
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Figure 3.2: Binding intensities in separate peptide-binding assays are generally reproducible. Concordance between two separate rounds of FBXW7 peptide-binding assays based on repeated peptides. Spearman’s rank correlation coefficient and associated p value are indicated. Two peptides selected for subsequent fluorescent polarization assays are highlighted.
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Gene Peptide Intensity
NOTCH1 HPFLTPSPESP 41612
HPFLpTPSPESP 1164024
CCNE1 SGLLTPPQSGK 20928
SGLLpTPPQSGK 177740
SGLLpTPPQpSGK 734504
MYC ELLPTPPLSPS 3680
ELLPpTPPLSPS 63588
ELLPpTPPLpSPS 63000
FOXM1 EEWPSPAPSFK 4316
EEWPpSPAPSFK 3352
EEWPpSPAPsFK 21524
MED13L GMPLTPPTSPE 19540
GMPLpTPPTSPE 162408
GMPLpTPPTpSPE 123708
AURKA APLGTVYRELQ 61748
APLGpTVYRELQ 2072
Table 3.3: Binding of select validated FBXW7 phosphodegrons in proteome-wide screening.
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Figure 3.3: Sequence logos of peptides with the top 200 highest (Top) and lowest (Bottom) intensities in FBXW7 peptide binding arrays. Analysis was performed with the WebLogo 3 web portal (Crooks et al. 2004).
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3.4.3 Evaluation of phosphodegron features
To investigate the discrimination of phosphothreonine and phosphoserine at position zero for binding to FBXW7, we evaluated the binding of the CCNE1 T380 and T380S phosphodegron in solution via fluorescence polarization assays. The T380S CCNE1 phosphodegron bound FBXW7 with lower affinities than the natural T380 phosphodegron (Figure 3.4). This is in concordance with similar experiments with Cdc4, indicating that phosphothreonines and phosphoserines are not interchangeable and are phosphodegron-specific (Nash et al. 2001).
To investigate the existence of a possible +4 arginine feature in the FBXW7-binding phosphodegron, binding of FBXW7 to a putative phosphodegron of TBC domain family member 4 (TBC1D4), a protein involved in insulin-dependent transport of glucose, was examined (Cartee 2015). TBC1D4 was detected as a substrate by all isoforms of FBXW7 and a phosphodegron centered around S666 with a +4 arginine was predicted to bind FBXW7 by the FBXW7 model. S666 has been previously reported to be phosphorylated in vivo, though the purpose of the phosphorylation remains unclear. The TBC1D4 S666 phosphodegron was found to reproducibly bind FBXW7-SKP1 in peptide binding assays, but not in fluorescence polarization assays (Figures 3.2, 3.4). Investigations of the position zero serine, position +4 arginine, and +5 arginine on the TBC1D4 peptide all produced similar negative results.
The binding of FBXW7 to putative phosphodegrons on TAF6L, a substrate discovered with BioID and validated through CHX chase experiments, were also examined. Three candidate phosphodegrons can be identified on TAF6L centered around S495, S501, and S589, all of which have been previously reported to be phosphorylated in vivo. While no structure of TAF6L exists, structural prediction suggests all three residues reside within solvent-exposed loops or bends (Table 3.3). Furthermore, S495 and S501 are predicted to reside within disordered regions, while S589 was not. Notably, the S589 candidate phosphodegron contains a +4 arginine residue and was most strongly predicted to bind FBXW7 in the FBXW7-binding model, followed by the S501 candidate. The S589 was also evaluated via peptide-binding assays and was found to bind FBXW7 reproducibly (Figure 3.2). Based on this evidence, alanine mutants of all three phosphodegron candidates were generated and evaluated for their effect on TAF6L stability in CHX chase experiments. All three mutants stabilized TAF6L in both normal and knockdown conditions of FBXW7 (Figure 3.5). Half-lives of the S589A and S495A mutants were more
89 increased than the S501A mutant in conditions of FBXW7 knockdown, suggesting that these may be phosphodegrons that are not primarily under FBXW7 regulation. Fluorescence polarization assays exploring the binding of the S589 phosphodegron in solution revealed binding to FBXW7 with micromolar affinity (Figure 3.4, 3.6). The binding signal, however, could not be outcompeted by a CCNE1 pT380 “cold peptide” (data not shown).
Figure 3.4: Validation of novel FBXW7 phosphodegron features via fluorescence polarization. (Top) Comparison of polarization signal between T380 and T380S phosphodegron. The TAF6L S589 phosphodegron was also probed but produced a weak binding signal. Dissociation constants (Kd) are indicated where applicable. (Bottom) Binding signal of putative TBC1D4 phosphodegron and variants compared to CCNE1 phosphodegron binding signal.
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Candidate phosphodegron
Prediction (%) S589 S495 S501
Disordered 2.5 100 100
Solvent accessibility
Exposed 51.8 64.8 80.2
Medium 36.5 22.5 13.5
Secondary structure
Coil 98.8 92.8 99.8
α-helix 0.7 0.1 0.1
β-sheet 0.5 7.1 0.1
Table 3.4: Structural feature prediction of TAF6L candidate phosphodegrons. Predictions were generated with the RaptorX web portal by inputting the TAF6L protein sequence (Källberg et al. 2012).
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Figure 3.5: Evaluation of TAF6L phosphodegrons. (Top) FBXW7 phosphodegron mutants are stabilized in non- targeting siRNA and FBXW7 knockdown conditions. 3xHA-tagged TAF6L and mutant expression vectors and siRNA were transfected into HEK293 cells as described in previous sections. Cells were treated with CHX for 0h, 2h, 4h, and 6h prior to sample preparation for immunoblotting. Ratios of half-lifes between FBXW7 knockdown conditions and non-targeting conditions are indicated for each variant. Results are representative of duplicate experiments. (Bottom) Binding of TAF6L S589 phosphodegron to FBXW7 in fluorescence polarization assays over greater concentrations of FBXW7.
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3.5 Discussion
A significant portion of our understanding of FBXW7-substrate interaction is based on studies of Cdc4 and the CPD. As more substrates of FBXW7 are reported, it is increasingly apparent that FBXW7 can interact with phosphodegrons that deviate from the CPD. Here, we attempted to discover features of an FBXW7-binding phosphodegron through statistical modelling and peptide binding assays. Through fluorescence polarization assays, it was confirmed that FBXW7 discriminates between position zero phosphothreonines and phosphoserines on CCNE1, in agreement with similar analyses with Cdc4. Furthermore, a possible enrichment of arginine residues at the +4 position was revealed. Lastly, validation of TAF6L phosphodegrons that interacts with FBXW7 was performed through peptide-binding assays, CHX chase experiments, and fluorescence polarization assays.
One major feature of our approach was the reliance of peptide-binding assays, as it was used in both the training and validation of the models. However, as demonstrated by our experiments with the TBC1D4 S666 peptide, it is clear that binding in such assays may not translate to binding of peptides in solution in vitro, much less in vivo. The intensities of validated FBXW7 phosphodegrons also varied greatly (Table 3.2). This may be explained by the ability of FBXW7 to dimerize for simultaneous binding of multiple weak phosphodegrons on a protein, but makes it difficult when evaluating bona fide phosphodegrons by intensities in the peptide-binding array. Interestingly, some non-phosphorylated versions of validated phosphodegrons produced higher intensities than some phosphorylated phosphodegrons, possibly suggesting that FBXW7-binding may not necessarily depend on degron phosphorylation. Furthermore, closer examination of the arrays revealed that some artefacts in intensity scoring exist when peptides with oversaturated signals alter the scoring of nearby peptides. The usefulness of the model may thus be limited by both the biological complexity of FBXW7-substrate interaction and technical artefacts of the peptide-binding assay.
It is possible that the +4 arginine feature is an artefact of the peptide-binding assays and statistical model. The combination of a pT/pS-P-X-X-R motif may be inherently conducive to FBXW7 binding when immobilized on a membrane. Of the two phosphodegrons that were examined with this feature, only the TAF6L S589 produced a signal in fluorescence polarization assays that could not be outcompeted by a cold peptide. If one accepts the model that the three
93 arginine residues within the WD40 repeat containing domain are necessary for interactions with phosphodegrons, then it is unlikely that FBXW7 can accommodate two separate phosphopeptides to explain this observation. Nevertheless, the S589A TAF6L mutant is significantly stabilized in vivo. As TAF6L is still degraded over time in FBXW7 knockdown conditions, it is clear that it is also regulated by other processes. Therefore, alterations to S589 could stabilize TAF6L through the disruption of interactions with other E3 ligases independent of FBXW7. Of the three possible phosphodegron sites on TAF6L, the S589 and S495 mutants were most stabilized in conditions of FBXW7 knockdown, suggesting that these are likely not the primary sites of FBXW7-binding. Instead, our data suggest that S501A, which follows the canonical CPD motif, is the primary site of FBXW7 binding. Further validation through fluorescence polarization would confirm this hypothesis. It is inconclusive whether the +4 arginine residue is a possible feature or an artefact as only two peptides following this motif were tested in solution. While there is currently no known FBXW7 substrate that harbours this feature, there are validated phosphodegrons that deviate greatly from the CPD, such as the one found on the AURKA.
While we were unable to validate any novel FBXW7 phosphodegron features, our interdisciplinary effort demonstrates the difficulty of discovering FBXW7 substrates with sequence-based approaches. Given the range of phosphodegrons FBXW7 can bind, it is difficult to model for FBXW7 binding, as well as to validate from peptide-binding arrays. Nevertheless, the wealth of peptide-binding assay data, along with our BioID dataset, may be a useful resource for future studies of the FBXW7 phosphodegron and associated substrates. For one, suspected substrates of FBXW7 can be searched within our dataset for two supporting lines of evidence and a putative phosphodegron. Attempts to predict peptide binding by FBXW7 can be improved by shifting away from manual assignment of binding to systematic assignment based on intensity values. To improve success rates in downstream validation experiments, peptide-binding assays can be performed with peptides in solution by cleaving the synthesized peptides from the membranes with trifluoroacetic acid, followed by high-throughput screening via fluorescent polarization assays.
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Appendices
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Table S1: A curated overview of validated FBXW7 substrates and phosphodegrons. Residues that conform to the CPD are bolded in phosphodegron sequences. 1 Multiple degrons are necessary for binding; 2 Interaction results in non-proteolytic consequences; (!) Phosphodegron deviates significantly from CPD by missing +1 proline or inversion of charge at +4 residue.
Symbol Protein name Phosphodegron Reference
AURKA Aurora kinase A APLGTVYRELQ (!) (Kwon et al. 2012)
BRAF Serine/threonine-protein kinase B-raf GLSATPPASLP (de la Cova and
Greenwald 2012)
AUKRB Aurora kinase B Unidentified/unverified (Teng et al. 2012)
1 CCDC6 Coiled-coil domain-containing protein PYTPSPSSSRP (S359) (Zhao et al. 2012) 6 ITRPSPRRSNS (S413) FKRPTPPPSPN (T427)
CCNE1 G1/S-specific cyclin-E1 SGLLTPPQSGK (T380) (Welcker et al. 2013) SLIPTPDKEDD (T62)
1 CDX2 Homeobox protein CDX-2 DSAQSPGPSWP (S60) (Kumar et al. 2016) PEPLSPVSSLQ (S283)
CEBPA CCAAT/enhancer-binding protein PGHPTPPPTPV (Bengoechea-Alonso
alpha and Ericsson 2010)
CEBPD CCAAT/enhancer-binding protein delta AGQPTPPTSPE (Balamurugan et al.
2013)
CREB3L1/2 Cyclic AMP-responsive element- QMPPTPPSSHG (Yumimoto et al. 2013) binding protein 3-like protein 1/2
CSF3R Granulocyte colony-stimulating factor Unidentified/unverified (Lochab et al. 2013) receptor
DEK 1 Protein DEK EGTPTQPASEK (T15) (!) (Babaei-Jadidi et al.
VERLTMQVSSL (T77) (!) 2011)
EBNA1BP2 Probably rRNA-processing protein MDTPPLSDS (Welcker et al. 2011) 2 EBP2 (continued)
114
EGLN2 Egl nine homolog 2 QVPVSQPPTPT (!) (Takada et al. 2017)
EZH2 Histone-lysine N-methyltransferase PPECTPNIDGP (Jin et al. 2017) EZH2
FAAP20 Fanconi anemia core complex- GHLESPARSLP (Wang et al. 2016) associated protein 20
FOXM1 Forkhead box protein M1 EEWPSPAPSFK (Chen et al. 2016)
HIF1A Hypoxia-inducible factor 1-alpha IQDQTPSPSDG (Cassavaugh et al.
2011)
JUN Transcription factor AP-1 MPGETPPLSPI (Nateri et al. 2004)
JUNB Transcription factor jun-B SRDATPPVSPI (Pérez-Benavente et al.
2013)
KLF13 Krueppel-like factor 13 AAPPSPAWSEP (Kim et al. 2012)
1 KLF2 Krueppel-like factor 2 PPPDTPPLSPD (T173) (Wang et al. 2013) RGLLTPPASPL (T244)
KLF5 1 Krueppel-like factor 5 QLLNTPDLDMP (T234) (Liu et al. 2010) YFPPSPPSSEP (S303) (Zhao et al. 2010) LQNLTPPPSYA (S323)
LT-AG 2 Large T antigen (SV40) KKPPTPPPEPE (Welcker and Clurman
2005)
MCL1 Induced myeloid leukemia cell DAIMSPEEELD (S121) (Wertz et al. 2011) differentiation protein Mcl-1 STDGSLPSTPP (S159) (!)
MED13 Mediator of RNA polymerase II SVTLTPPTSPE (Davis et al. 2013) transcription subunit 13
MED13L Mediator of RNA polymerase II GMPLTPPTSPE (Davis et al. 2013) transcription subunit 13-like
MTOR Serine-threonine-protein kinase mTOR SRLLTPSIHLI (!) (Mao et al. 2008)
(continued)
115
MYB Transcriptional activator Myb Unidentified/unverified (Kanei-Ishii et al. 2008)
MYC Myc proto-oncogene protein ELLPTPPLSPS (Yada et al. 2004)
NCOA3 Nuclear receptor coactivator 3 AGVHSPMASSG (Wu et al. 2007)
NF1 Neurofibromin ESLLTPTSPYP (Tan et al. 2011)
NFE2L1 Endoplasmic reticulum membrane FLLFSPEVESL (Biswas et al. 2011) sensor NFE2L1
NFKB2 Nuclear factor NF-kappa-B p100 CPLPSPPTSDS (Busino et al. 2012) subunit
NOTCH1 Neurogenic locus notch homolog HPFLTPSPESP (Fryer et al. 2004) protein 1
NR1D1 Nuclear receptor subfamily 1 group D PQQLTPPRSPS (Zhao et al. 2016) member 1
NR3C1 Glucocorticoid receptor PDVSSPPSSSS (Malyukova et al. 2013)
NS5B Nonstructural protein 5B (HCV) XXXLTPPHSAR (Chen et al. 2016)
PA2G4 Proliferation-associated protein 2G4 VEASSSGVSVL (!) (Wang et al. 2017)
PPARGC1A Peroxisome proliferator-activated SLPLTPESPND (T263) (Olson et al. 2008) 1 receptor gamma coactivator 1-alpha TAGLTPPTTPP (T295)
PSEN1 Presenilin-1 Unidentified/unverified (Li et al. 2002)
SETD3 Histone-lysine N-methyltransferase ALHFTEPPISA (!) (Cheng et al. 2017) setd3
SOX10 Transcription factor SOX-10 HGPPTPPTTPK (Lv et al. 2015)
SOX9 Transcription factor SOX-9 QGPPTPPTTDL (Hong et al. 2016)
SREBF1 Sterol regulatory element-binding EDTLTPPPSDA (Sundqvist et al. 2005) protein 1
(continued)
116
TGIF1 Homeobox protein TGIF1 GLFNTPPPTPP (Bengoechea-Alonso
and Ericsson 2010)
TOP2A DNA topoisomerase 2-alpha KTKTSPKLSNK (Chen et al. 2011)
TP63 Tumor protein 63 Unidentified/unverified (Galli et al. 2010)
2 XRCC4 DNA repair protein XRCC4 LRNSSPEDLFD (Zhang et al. 2016)
117
Table S2: List of primers used for the generation of FlagBirA-tagged or 3xHA-tagged expression vectors. Expression vectors were also generated for the following genes, but are not presented in this thesis: FAM60A, CHAF1A, LIN9, LIN54, LIN52, RBL1, HMGCR.
Gene NCBI sequence Primer direction Restriction enzyme Primer sequence
E2F4 BC033180.1 F AscI aaaaggcgcgcctatggcggaggccgggccaca
R NotI aaaagcggccgctcagaggttgagaacaggca
E2F5 NM_001951.3 F AscI aaaaggcgcgcctatggcggcggcagagcccgc
R XhoI aaaactcgagctaataatttagtatctgga
Nucleoplasmic FBXW7 BC117244 F AscI aaaaggcgcgcctagtagtattgtggacctgcccgtt
R NotI aaaagcggccgcaagttggaccatggttctgaggtc
Cytoplasmic FBXW7 F AscI aaaaggcgcgccttattgtcagagactgccaagcagc
R NotI aaaagcggccgcaagttggaccatggttctgaggtc
Nucleolar FBXW7 F AscI aaaaggcgcgcctccatggcttggttcctgttgatct
R NotI aaaagcggccgcagcattagcatcattgcccaaggc
FOXM1 NM_202002.2 F AscI aaaaggcgcgcctatgaaaactagcccccgtcg
R NotI aaaagcggccgcctactgtagctcaggaataa
LIN37 BC009071.1 F AscI aaaaggcgcgcctatgttccctgtgaaggtgaa
R NotI aaaagcggccgctcactgtcgttcgtacatct
MBIP NM_016586.2 F AscI aaaaggcgcgcctatggctgctgccacggagct
R NotI aaaagcggccgctcatggaaggtggtgggttg
MYBL2 BC007585 F AscI aaaaggcgcgcctatgtctcggcggacgcgctg
R NotI aaaagcggccgctcaggacaagatgagggtcc
NCOA6 BC136272.1 F AscI aaaaggcgcgcctatggttttggatgaccttcc
R NotI aaaagcggccgcttacttggattttcttcgct
PAXIP1 NM_007349.3 F AscI tataggcgcgccaatgtcggaccaggcgcccaaa
R NotI aaaagcggccgctcagttaaacttatatgattc
SUPT3H NM_003599.3 F AscI aaaaggcgcgcctatgaataatacggcagctag
R NotI aaaagcggccgctcagcaggctagaaaagcca
TAF5L NM_014409.3 F AscI aaaaggcgcgcctatgaaacgagtgcgtaccga
R NotI aaaagcggccgcttaatgttcctgattttctt
(continued)
118
TAF6L NM_006473.3 F AscI aaaaggcgcgcctatgtcagagcgagaagagcg
R XhoI aaaactcgagtcagagcggcaagtacagcg
TAF6L S589A F To be used with TAF6L R cctccgtacgggcctgccccggcctcgcgctac
R To be used with TAF6L F gtagcgcgaggccggggcaggcccgtacggagg
YEATS2 NM_001351370.1 F AscI aaaaggcgcgcctatgtctggaatcaagcgaac
R NotI aaaagcggccgctcactggtcctcattcaata
119
Table S3: List of antibodies used in immunoblotting (IB) and immunofluorescence microscopy (IF). Buffer systems were either phosphate-buffered saline (PBS) or Tris-buffered saline (TBS), depending on the antibody. All antibodies were diluted in their respective blocking buffers. For IB, antibodies were incubated with membranes with gentle rocking.
Antibody Manufacturer Buffer Dilution Incubation conditions
Anti-HA tag antibody - ChIP Abcam PBS 1:6000 (IB) 1h RT, 3x 30m wash grade
HRP donkey anti-rabbit IgG Biotium TBS 1:8000 (IB) 1h RT, 3x 20m wash (H+L), highly cross-adsorbed
Goat anti-mouse IgG (H+L)- Bio-Rad PBS 1:8000 (IB) 1h RT, 3x 20m wash HRP conjugate
Monoclonal ANTI-FLAG Sigma TBS 1:6000 (IB), 4°C overnight, 3x M2 1:1000 (IF) 30m wash (IB), 3x 2m wash (IF)
Anti-Fibrillarin antibody - Abcam PBS 1:1000 (IF) 1h RT, 3x 2m wash Nucleolar Marker
Goat anti-mouse IgG (H+L), Invitrogen PBS 1:1000 (IF) 1h RT, 3x 2m wash Alexa Fluor 488
Goat anti-mouse IgG (H+L) Invitrogen PBS 1:1000 (IF) 1h RT, 3x 2m wash cross-adsorbed secondary antibody, Texas Red-X
120
HIR, high intensity repeat; MIR, medium intensity repeat; LIR, low intensity repeat. low intensity LIR, mediumrepeat; intensity MIR, repeat; high HIR, intensity plotted. and are ranked 3.2) Figure (from phosphodegrons validated and peptides Repeated S1: Figure
Binding Binding
intensities in separate peptide inseparate intensities
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binding assays are generally reproducible. generally are assays binding
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