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Review

Current Methods of Post-Translational Modification Analysis and Their Applications in Blood Cancers

1
Department of Biology, National University of Ireland, W23 F2K8 Maynooth, Ireland
2
Department of Haematology, Mater Misericordiae University Hospital, D07 WKW8 Dublin, Ireland
*
Author to whom correspondence should be addressed.
Cancers 2021, 13(8), 1930; https://doi.org/10.3390/cancers13081930
Submission received: 3 March 2021 / Revised: 4 April 2021 / Accepted: 14 April 2021 / Published: 16 April 2021
(This article belongs to the Special Issue Proteomics in Cancer)

Abstract

:

Simple Summary

This review discusses current techniques used to study post-translational protein modifications (PTMs) and their contribution to blood cancer research. Blood cancer is an umbrella term for cancers that affect blood cells and organs of the hematopoietic system, accounting for approximately 10% of all cancer diagnoses. PTMs are crucial for the normal functioning of cells via the regulation of protein structure, function, and localization. Mass spectrometry and antibody-based techniques are powerful analytical tools used for the detection and quantitation of PTMs such as phosphorylation. The application of these techniques in blood cancer research facilitates the identification of abnormal PTMs that contribute to cancer development and progression by promoting cancer cell growth, survival, and invasion. Many FDA-approved blood cancer treatments exert their anti-cancer effects by targeting protein modifications, thus emphasizing the importance of PTM-focused research in the identification of novel biomarkers and therapeutic targets to improve blood cancer survival outcomes.

Abstract

Post-translational modifications (PTMs) add a layer of complexity to the proteome through the addition of biochemical moieties to specific residues of proteins, altering their structure, function and/or localization. Mass spectrometry (MS)-based techniques are at the forefront of PTM analysis due to their ability to detect large numbers of modified proteins with a high level of sensitivity and specificity. The low stoichiometry of modified peptides means fractionation and enrichment techniques are often performed prior to MS to improve detection yields. Immuno-based techniques remain popular, with improvements in the quality of commercially available modification-specific antibodies facilitating the detection of modified proteins with high affinity. PTM-focused studies on blood cancers have provided information on altered cellular processes, including cell signaling, apoptosis and transcriptional regulation, that contribute to the malignant phenotype. Furthermore, the mechanism of action of many blood cancer therapies, such as kinase inhibitors, involves inhibiting or modulating protein modifications. Continued optimization of protocols and techniques for PTM analysis in blood cancer will undoubtedly lead to novel insights into mechanisms of malignant transformation, proliferation, and survival, in addition to the identification of novel biomarkers and therapeutic targets. This review discusses techniques used for PTM analysis and their applications in blood cancer research.

1. Introduction

The significance of proteome-focused studies in characterizing cellular phenotypes and disease mechanisms, in addition to discovering novel biomarkers and therapeutic targets, has been recognized in recent decades [1]. Proteomics involves the characterization and mapping of the proteome, a highly complex task due to temporal and spatial fluctuations in protein expression depending on the cellular environment. The post-translational modification (PTM) of proteins, a biochemical process that modifies the structure and function of a given protein, further enhances the complexity of the proteome [2]. Over 200 PTMs have been identified, many of which play vital roles in the regulation of various cellular processes such as cell growth, metabolism, differentiation, and apoptosis (Figure 1). Dysregulated PTMs alter the normal functioning of these processes which can contribute to the development and/or progression of disease, thus illustrating the need to identify and define primary PTM events associated with specific diseases such as blood cancers [3].
Hematological malignancies arise from blood or bone marrow cells that acquire genetic abnormalities resulting in uncontrolled proliferation, resisting cell death, and evading the immune response [4]. The incidence of hematological malignancies, which comprises a broad range of cancers including leukemias, lymphomas, multiple myeloma, myeloproliferative neoplasms (MPNs) and myelodysplastic syndromes (MDS), continues to rise, although advances in treatment have led to an increase in the five-year survival rate of many of these cancers [5]. In this review, we provide a comprehensive overview of current analytical techniques employed in the analysis of post-translational modifications and emphasize the contribution of these techniques to our current understanding of disease mechanisms associated with blood cancers. We highlight the central role of PTMs in the development of blood cancer therapeutics and consider the promising prospect of future improvements in the sensitivity and specificity of PTM-focused techniques to expedite our understanding of hematological malignancies and offer novel biological markers and therapeutic targets to augment the current arsenal of therapeutics available for the treatment of blood cancers.

2. Analytical Techniques in Post-Translational Modification Analysis

The most powerful analytical technique applied in proteomic studies is mass spectrometry (MS), which facilitates large-scale, highly specific, quantitative profiling of proteins and post-translationally modified proteins. Other techniques often used to study the proteome include flow cytometry, protein microarrays, Western blotting, and enzyme-linked immunosorbent assays (ELISA) (Figure 2) [1]. Additional experimental steps are often required to optimize proteomic techniques for PTM analysis due to the substoichiometric, transient and labile nature of PTMs which hinders the maintenance and detection of modifications during analysis [2]. To overcome the issue of low stoichiometry, enrichment of the PTM of interest is often performed prior to MS analysis. Modification-specific enrichment techniques such as ion exchange chromatography, immobilized metal ion affinity chromatography and immunoaffinity chromatography, separate modified proteins/peptides from their unmodified counterparts, reduce the complexity of the sample and increase the efficiency and reliability of the analysis (Figure 2) [3].
Highly sensitive mass spectrometers with a high resolution, such as the Fourier transform (FT)-based mass spectrometers, are required for efficient PTM analysis. The “bottom-up” proteomics approach, typically used in PTM analysis, involves the cleavage of proteins in a biological sample by a protease, usually trypsin, prior to MS [6]. Sample preparation techniques, such as filter-aided sample preparation (FASP), separate peptides from undigested material in cell lysates [7]. Most MS-based proteomic protocols couple liquid chromatography with tandem MS (LC–MS/MS) to separate peptides before identification and quantitation by MS/MS. Following separation, the LC eluent is transferred to the ion source of the mass spectrometer where peptides are ionized, often via protonation [8]. High ionization efficiency enhances the sensitivity of the analysis; however, the efficiency of this step can be affected by the chemical nature of peptides being studied [9]. The dominant ionization technologies are matrix-assisted laser desorption/ionization (MALDI), electro-spray ionization (ESI) and atmospheric pressure chemical ionization (APCI) [10]. Following ionization, the peptide precursor ions are fragmented resulting in product ions that are subsequently characterized based on their mass-to-charge (m/z) ratios and relative abundance to produce MS/MS spectra (Figure 2) [9]. Popular fragmentation techniques include collision-induced dissociation (CID) and high-energy collision dissociation (HCD). However, limitations, such as the loss of labile PTMs, mean alternative fragmentation methods, including electron capture/transfer dissociation (ECD/ETD), have been developed and incorporated into MS-based PTM analyses [11]. During CID and HCD, the collision of molecular ions results in cleavage of the amide bond in peptides whereas ETD involves the transfer of an electron to multiply protonated peptides resulting in the cleavage of the peptide backbone [12]. The more recently developed fragmentation methods, EThcD, which combines ETD and HCD, and activated ion ETD (AI-ETD) demonstrate great progress in combatting the challenges presented by conventional fragmentation techniques in PTMomics, such as uncertain site localizations [13,14,15]. The MS/MS spectra obtained during LC–MS/MS are matched to theoretical MS/MS spectra available on databases for peptide and PTM identification. Bioinformatic analysis, using algorithms that predict the probability of a PTM being present such as Ascore, PTMScore, PhosphoRS and Mascot Delta Score, are used to determine the validity of modification sites detected during LC–MS/MS [16].
MS-based quantitation of proteins is typically divided into label-based and label-free techniques. Labeling peptides using differential mass tags, such as tandem mass tag (TMT) or isobaric tag for relative and absolute quantification (iTRAQ), allows for the comparison and multiplexing of multiple samples in a single MS run [17]. These isobaric labeling technologies involve the labelling of target proteins/peptides in different biological samples with tags made up of a reactive group, a mass normalization group, and a reporter group that have an identical overall mass but differ in the mass of the reporter group and mass normalization group. The reactive group facilitates the mass labeling of peptides. Fragmentation during tandem MS cleaves the reporter group, revealing reporter ions of variable molecular weights corresponding to the peptides present in the individual biological samples [18]. The in vitro labeling technique, stable-isotope labeling of amino acids in cell culture (SILAC), involves the labeling of peptides by growing an organism in cultures containing light or heavy stable isotope-containing amino acids (13C, 15N), typically lysine and arginine, which are subsequently metabolically incorporated into proteins. During MS analysis, source samples can be easily differentiated by the isotopic mass shifts between the different isotopically labeled samples [19]. Subsequently, this technique has been adapted to facilitate the labelling of specific PTMs. For example, 13C-glucose and D3-acetate, 13CD3-methionine; and γ-18O4-labeled ATP are added to cell culture media to label acetylation, methylation, and phosphorylation, respectively [20,21,22]. Label-free quantitation (LFQ) is a commonly used method of quantitation which refers to the use of peak intensity analysis and spectral counting for quantitation [23]. The results of qualitative or quantitative studies can undergo downstream statistical analysis to draw conclusions from the data through statistical tests, such as t-tests or ANOVAs, and/or visualize the data by creating networks, plots, and graphs [24].
A substantial effort has been made in recent years to develop specific antibodies for PTM analysis. However, low binding affinity is common among available antibodies due to the minuteness of PTM motifs, similarities in the chemical structure of certain PTMs, poor antigenicity and other difficulties in antibody generation [3]. Pan-PTM-specific antibodies are often applied for immunoaffinity enrichment prior to LC–MS/MS as well as Western blotting, protein microarrays, immunohistochemistry, and flow cytometry. Antibodies against specific modification sites known to play an important biological role are also available for certain modifications including phosphorylation, methylation, and acetylation [25,26,27]. Other PTMs face difficulties in site-specific antibody generation due to the size or transience of the PTM (e.g., ubiquitination); or lack sufficient evidence of site occupancy to invest in antibody generation [28].
Flow cytometry (FCM) is a scientific technique used to measure the physical and biochemical characteristics of cells, including size, internal complexity, and cellular components such as DNA, RNA, and specific proteins on the cell surface or intracellularly. FCM has gained prominence in recent years and is now routinely used in basic research and clinical practice, particularly in the oncology and immunology settings. FCM is an extremely sensitive technique, capable of detecting an extremely small number of cancer cells within a dense cellular network. This technique also provides a quick turnaround time, with the ability to measure a significant number of parameters in thousands of cells in a matter of seconds. The detection of surface antigens by FCM can be used in real time to physically sort cells into specific populations, a process known as cell sorting or fluorescence-activated cell sorting (FACS). This is particularly useful for the analysis of hematological malignancies with immunophenotyping by FCM often used in the clinical diagnosis and subclassification of leukemias and lymphomas. Once the cells of interest are selected, qualitative information may be obtained by measuring fluorescence intensity for specific antigens, for example acute leukemia (CD45), chronic lymphocytic leukemia (CD19/CD5), and Non-Hodgkin’s lymphoma (CD20) [29,30]. FCM-based minimal residual disease (MRD) assays are powerful diagnostic and disease monitoring tools in multiple types of blood cancers. By detecting low-frequency malignant cells (ratio as low as 1:10000 compared to normal cells), this approach is critical in the clinical management of patients [31]. As MRD status appears to correlate with patient outcomes in some hematological malignancies, increasing the sensitivity and robustness of MRD assays analyzed by FCM is currently an active area of research. Ongoing improvements in the quality of PTM site-specific antibodies will strengthen FCM as a promising method for characterization of cellular activities and signal transduction pathways in hematological malignancies in research and clinical settings. When designing a PTM-focused experiment, researchers must consider additional sample preparation steps that may be required to analyze modifications on proteins from specific cellular components, such as the cell surface, extracellular vesicles, and the nucleus. Histone proteins represent a well-known target for PTM-focused analyses as they are extensively modified by various PTMs to facilitate dynamic genomic organization and transcriptional regulation. PTMs found on histones include methylation, acetylation and ubiquitination on lysine residues, phosphorylation on serine, threonine, and tyrosine residues and/or citrullination on arginine residues [32]. Histone PTMs are routinely detected through site-specific antibodies, as demonstrated recently by Xu and co-workers [33] investigating histone H3 lysine 36 dimethylation (H3K36me2) levels in myeloma cells under normoxia or hypoxia conditions using Western blotting [33]. By detecting both the protein and site-specific modification abundance levels using precise antibodies, the degree of histone modification can be quantified. However, in the past, some problems with respect to anti-PTM histone antibody quality surfaced, facilitating the need to develop alternative platforms [34]. Mass spectrometry has proven to be an invaluable research platform in identifying and quantifying histone PTMs. Several histone enrichment methods are available prior to analysis, including chromatin affinity purification with MS (ChAP–MS), an approach that facilitates the isolation of targeted chromatin sections for the identification of histone PTMs [35]. Another isolation method involves the use of acidic or high-salt conditions to extract histones from cell lysates [32]. Due to the high ratio of the basic amino acids, arginine and lysine, in histones, tryptic digestion generates short peptides (2–4 amino acids) that are incompatible with LC–MS/MS analysis. Thus, chemical derivatization, often via propionylation, of lysine residues has been implemented to inhibit cleavage after lysine, resulting in peptides with lengths compatible with LC–MS/MS [36]. Detection of histone PTMs from MS/MS spectra is accomplished by detecting mass shifts corresponding to the PTM of interest, as described in the sections below, to confirm the presence of the modification. Determining the function of histone PTMs often involves investigating interacting partners, in particular the suite of associated enzymes responsible for the regulation of site-specific modifications. Crosslinking mass spectrometry (XL–MS) has emerged as a powerful approach to elucidate protein–protein complexes by mass spectrometry, with applications for structural mechanism discovery involving histone activity [37].

2.1. Phosphorylation

Phosphorylation refers to the addition of a phosphoryl group to an amino acid residue, typically serine, threonine or tyrosine, resulting in the formation of a phosphodiester bond [38]. It is a reversible modification catalyzed by kinases and reversed by phosphatases. Phosphorylation is the most extensively studied PTM due to its major role in many cellular processes including cell signaling, transcriptional regulation, protein synthesis and degradation, metabolic pathways, and many others [39,40,41,42,43]. It is postulated that more than 90% of the human proteome is phosphorylated under certain conditions [38]. Unsurprisingly, abnormal phospho-signaling contributes to the development or exacerbation of a variety of diseases; with phosphoproteomic analysis revealing many biomarkers of disease and therapeutic targets [44]. For example, tyrosine kinase inhibitors (TKIs) are now a major class of therapeutics used in the treatment of many forms of cancer including chronic myeloid leukemia, breast, and lung cancers [45].
Despite widespread research focused on phosphorylation, the identification and quantitation of phosphorylated residues remains a challenge due to substoichiometry, the ability of a protein to be phosphorylated on several residues and difficulties in selective phosphopeptide isolation [46,47]. Conventional electrophoresis-based methods may be employed to analyze phosphoproteins; however, low throughput and reproducibility reduces the robustness of this technique [48]. Two-Dimensional Gel Electrophoresis (2D-PAGE) involves the separation of proteins based on their mass and isoelectric point and can be used as a method of protein separation prior to MS analysis. Different proteoforms are separated and quantitatively compared through differences in staining intensity after visualization using highly specific dyes. The fluorescent dye Pro-Q Diamond is typically used for phosphopeptide analysis [49,50]. Nowadays, the more advanced electrophoresis and antibody-based method, Western blotting, is commonly used for phosphoproteomic analysis. Following electrophoresis, the separated proteins are exposed to phospho-specific antibodies, followed by incubation with a labeled secondary antibody and subsequent detection of the phosphorylated proteins [51].
Protein microarrays are miniaturized immunoassays that allow the identification of phosphorylated proteins through the detection of antibody–antigen interactions. Microarrays effectively analyze thousands of proteins without the need to pre-fractionate or enrich complex samples. The high multiplex of reverse phase protein microarrays (RPPAs) makes them powerful tools as they allow a side-by-side analysis of potentially thousands of proteins and phosphosites present within crude cellular or tissue lysates [52]. Furthermore, RPPAs require small amounts of a sample which is of huge benefit in clinical settings where limited amounts of samples are available [53]. The ability of RPPAs to analyze phosphorylated proteins and map cell signaling pathways has led researchers to examine their potential as a tool for the identification of patient-specific molecular patterns and, thus, patient-specific therapies [54,55]. However, as with all immunoassays, RPPAs are dependent on the availability of highly specific and sensitive antibodies. This is a limiting factor in the analysis of PTMs and the use of low-quality antibodies can lead to false positives and false negatives. Furthermore, interactions between proteins in a complex sample can also result in the masking of epitopes required for antibody binding [52]. Another antibody-based technique, immunohistochemistry, involves the use of phospho-specific antibodies to detect phosphorylated residues in tissue sections which can be visualized by staining. This relatively simple and cost-effective technique is widely used in research and clinical practice for diagnostic and prognostic purposes [56].
Phospho-specific flow cytometry or phospho-flow (to differentiate it from live-cell flow cytometry) demonstrates how FCM can be combined with PTM-specific antibodies for biomarker discovery and/or therapeutic target identification applications [57]. Incorporating phospho-flow techniques in the clinic to characterize phosphorylation events and signal transduction networks associated with disease at the cellular level is a promising approach in precision medicine. As more PTM-specific antibodies become available, the applications of FCM will increase with respect to detecting and quantitating distinctive modifications, with implications in diagnostic and predictive medicine.
MS-based techniques are powerful tools used in the identification and quantitation of phosphorylated proteins. Common methods of phosphopeptide enrichment include strong anion/cation exchange (SAX, SCX), hydrophilic interaction liquid chromatography (HILIC), immobilized metal ion affinity chromatography (IMAC), and metal oxide affinity chromatography (MOAC) [58,59,60,61,62]. Chemical methods such as β-elimination of the phosphoryl group on phosphorylated residues and the linkage of phospho-serine, -tyrosine and -threonine by chemical means, are less commonly used methods of phosphopeptide enrichment [63,64].
Ion exchange chromatography, including SAX, SCX and HILIC, is often used to reduce the complexity of biological samples. SCX is a relatively straightforward and efficient high performance liquid chromatography (HPLC) method used for the enrichment of phosphorylated and acetylated proteins [65]. Following tryptic digestion, most non-phosphorylated peptides have a net charge of +2 at a low pH due to the presence of a lysine or arginine residue at the C-terminal and a free amine group at the N-terminal. Concurrently, at a low pH, the hydroxyl group of phosphorylated residues is negative, resulting in phosphopeptides having a +1 charge. As the SCX column contains a negatively charged stationary phase, phosphorylated peptides (+1) will co-elute at an earlier stage than the unmodified tryptic peptides (+2) [58,65,66]. HILIC is a more powerful alternative to SCX, where separation is based on the hydrophilic nature of phosphorylated peptides. A polar stationary phase, such as TSK gel amide, and a less polar, organic mobile phase such as acetonitrile and water, are used during HILIC. The highly polar phosphopeptides interact with the polar stationary phase and are thus eluted at a later stage than unmodified peptides [48,67]. A variant of HILIC, termed electrostatic repulsion HILIC (ERLIC), utilizes a combination of hydrophilic interactions and electrostatic repulsion in order to separate phosphorylated and un-phosphorylated peptides [68].
Immobilized metal ion affinity chromatography (IMAC) exploits the affinity of phosphopeptides for positively charged metal ions such as Fe3+, Ga3+, and Zr4+. The chosen metal ions, commonly Fe3+, are non-covalently attached to a carrier resin. Sample peptides are incubated with the IMAC resin where the negatively charged phosphopeptides electrostatically interact with the positively charged resin [48,69]. A drawback of this enrichment technique is that non-phosphorylated peptides with many negatively charged residues can interact with the IMAC resin [70]. The conventional IMAC technique has been adapted and improved in recent years. Zhou et al. altered the IMAC technique by using a different resin containing Ti4+ cations [71]. This technique exceeded the performance of IMAC methods using Fe3+ and Zr4+ metal ions. Additionally, the application of dendrimers as IMAC matrices has shown promising results in various studies [72,73,74]. Zou, Jie and Yang [74] introduced a novel method of simultaneous phosphopeptide and N-glycosylation enrichment through the immobilization of Ti4+ cations onto a dendritic polyglycerol coated with chitosan, poly (glycidyl methacrylate) and iminodiacetic acid nanomaterials. The use of this highly sensitive and specific enrichment technique led to the identification of 423 N-glycopeptides and 422 phosphopeptides following LC–MS/MS [74]. A recent study demonstrated the improved performance of Zr-IMAC, Ti-IMAC, and TiO2-MOAC following optimization of the binding solvent used during the enrichment protocol. Following optimization, a comparison of the number of phosphopeptides identified between these techniques, as well as the most commonly used Fe-IMAC, revealed that microparticle-based Zr-IMAC enrichment was superior resulting in a higher phosphoproteome coverage [75]. This study also reinforced the widely documented view that combining enrichment techniques results in more robust PTM-focused MS-based analyses.
Metal oxide affinity chromatography (MOAC) is based on the affinity of the negatively charged phosphate group to metal oxides such as TiO2 or ZrO2. Phosphopeptides bind to the metal TiO2 stationary phase at a low pH and are subsequently separated from unmodified peptides during elution using an alkaline mobile phase [70,76,77]. During PTM analysis, fractionation and enrichment techniques are often combined to reduce sample complexity, enhance selectivity and increase the number of modified peptides identified [78,79,80]. For example, the combination of MOAC and IMAC, known as sequential elution from IMAC (SIMAC), allows for the capture of multi-phosphorylated peptides during the IMAC step followed by the capture of mono-phosphorylated peptides, from the unbound fraction of the first step, on the TiO2 column [81]. Furthermore, SIMAC has been combined with HILIC in an enrichment technique known as TiO2-SIMAC-HILIC (TiSH) to facilitate comprehensive phosphoproteome analysis with limited sample amounts [82].
Protein modifications have been reported to influence ionization efficiency during MS analysis. Using synthetically modified and unmodified peptides, Gropengiesser et al. [83] reported reduced ionization efficiencies of phosphorylated compared to unphosphorylated peptides during MALDI–MS and ESI–MS, with a greater reduction clearly present during MALDI–MS [83]. Although, the suggestion that phosphopeptides have lower ionization efficiencies is disputed by other researchers [84,85]. During phosphoproteomic MS-based analysis, CID fragmentation results in the cleavage of the labile phosphoester bond causing a neutral loss of H3PO4 (98 Da) from phosphorylated serine and threonine residues and the loss of HPO3 (80 Da) from phosphotyrosine residues, which appear as dominant peaks on MS spectra [86]. Extensive neutral loss can complicate phosphopeptide identification and site localization due to the distribution of numerous fragment ions across mass spectra. HCD, or beam-type CID, has been shown to produce less neutral loss peaks through higher-energy collisions [87]. ETD maintains the residue-bound phosphate group enabling conclusive phosphosite localization [69]. The detection of phosphorylation is based on the tryptic peptides demonstrating a mass shift of 80 Da and/or 98 Da, corresponding to the neutral loss of HPO3 and H3PO4, respectively [88]. The quantitation of phosphopeptides may employ label or label-free techniques. Labeling strategies used for phosphopeptide quantification include SILAC, iTRAQ and TMT, as described above [89].

2.2. Ubiquitination

Ubiquitination plays a key role in many cellular processes such as signal transduction, transcriptional regulation, DNA repair, intracellular localization and, most notably, the tagging of proteins for transfer to the proteasome for degradation. Abnormal ubiquitination can lead to enhanced or reduced degradation of specific proteins, hyperactive or inactive cell signaling pathways and an overall altered homeostasis in the cell [2]. A multienzyme cascade constituting ubiquitin-activating (E1), ubiquitin-conjugating (E2) and ubiquitin-ligating (E3) enzymes, results in the covalent attachment of the 76 amino acid protein, ubiquitin (Ub), to a lysine residue on the target protein. Ubiquitination is a highly transient modification with deubiquitination catalyzed by the deubiquitinating enzymes (DUBs) [90].
Within the last decade, promising efforts have been made to enhance current methods of protein ubiquitination analysis. A bottom-up proteomic approach incorporating trypsin is typically used resulting in the cleavage of ubiquitinated proteins after lysine and arginine residues. As Ub is covalently bound to a lysine residue, there is a missed cleavage at this residue followed by a cleavage after the first arginine residue of Ub, leaving two glycine (di-Gly) residues attached to the ubiquitinated peptide. Thus, during MS analysis of ubiquitinated peptides, there will be a mass shift of 114.043Da and a missed cleavage at the lysine residue which can be used to identify and localize ubiquitination sites [91]. The development of highly specific antibodies against the di-Gly remnant has greatly enhanced ubiquitinome research, facilitating immuno-based assays and the use of immunoaffinity purification and immunoprecipitation to enrich ubiquitinated proteins in complex samples prior to MS [92]. Kim et al. identified ~19,000 ubiquitination sites in ~5000 proteins using a di-Gly antibody to enrich for ubiquitinated proteins by immunoprecipitation [93]. The identification of ubiquitination sites can be determined through site-directed mutagenesis, where lysine residues are mutated into arginine, or in some cases cysteine. This inhibits ubiquitination of this residue and the sites are identified based on the exclusion principle. The occasional promiscuous nature of ubiquitin binding is a limiting factor of this method, leading to inaccurate localization of ubiquitination sites [94].
Once again, MS-based techniques are the most powerful tools used to analyze ubiquitination. As with phosphorylation, MS-based analysis of ubiquitination requires prior enrichment of ubiquitinated peptides. Both ESI and MALDI ionization techniques are used for ubiquitinome analysis [95,96]. Regarding fragmentation, ETD was reported to identify a higher number of ubiquitinated peptides when compared CID and HCD fragmentation methods [97]. The quantification and comparison of ubiquitinated peptide levels in different samples can be achieved using labelling technologies. SILAC is commonly used to analyze ubiquitination. However, refined labeling products such as iTRAQ and TMT have become more popular in recent years [18]. A recent study described a highly sensitive, rapid protocol, termed UbiFast, for deep analysis of the ubiquitinome using an on-antibody TMT labelling approach following di-Gly immunoaffinity enrichment of ubiquitinated peptides [98].
Determining whether a protein is mono-ubiquitinated or polyubiquitinated; and which lysine residue is utilized to form polyubiquitinated chains, is a crucial aspect when analyzing ubiquitination as these factors can indicate the functionality of the modification. For example, lysine 48 (K48) linkages target proteins for proteasomal degradation [90,99]. Ubiquitin-Absolute quantification (Ub-AQUA) is a quantitative technique capable of elucidating the specific linkages present on polyubiquitinated chains [100]. AQUA involves the generation of isotopically labeled standard peptides with ubiquitinated and un-ubiquitinated lysine residues. The standards and test sample co-elute during selected reaction monitoring (SRM), and the standards are used to quantify the linkages present within the sample [90,101]. Methods to analyze specific lysine linkages exploit the affinity of ubiquitin for ubiquitin-binding domains (UBDs) and tandem-repeated ubiquitin-binding entities (TUBEs) present on recombinant proteins. Wu et al. generated protein microarrays with bound recombinant UBDs to identify proteins that interact with K63-linked ubiquitin chains [102]. A recently described live-cell, high-throughput, luminescence-based assay incorporates the NanoBiT protein–protein interaction system and TUBEs to detect endogenous ubiquitination [103]. Tagged TUBEs interact with polyubiquitinated chains and can be separated from unmodified proteins and undergo further analysis of the specific linkages during MS [90,104]. Certain branched ubiquitin chains also display specific functions. For example, branched chains containing Lys-11 and Lys-48 linkages play a role in regulating the cell cycle through the degradation of key proteins, such as the cell cycle-regulated kinase, NEK2A [105]. The Ubiquitin Chain Enrichment Middle-down Mass Spectrometry (UbiChEM–MS) method incorporates a non-denaturing, limited trypsinolysis step to produce intact ubiquitin chains containing the di-Gly remnant of the branched chain at conjugated lysine residues allows the analysis of more than one ubiquitin chain present on a single ubiquitin molecule [106,107]. Wang and colleagues recently reviewed the current methods of branched ubiquitin chain detection [108].

2.3. Glycosylation

Glycosylation is the most common PTM, constituting almost half of all protein modifications. It plays a major role in the regulation of cellular processes such as protein structure and transport, cell metabolism and extracellular interactions. Glycosylation involves the attachment of sugar moieties, known as glycans, to target proteins in a process catalyzed by glycosyltransferases. There are various types of glycosylation, the most common being N-glycosylation followed by O-glycosylation. This PTM is one of the most complex PTMs due to the heterogeneity of glycan structures and their glycosylation sites [2,48].
Lectins are carbohydrate-binding proteins with specificities for certain glycan structures, often involved in the process of cell-to-cell interactions. The affinity of a various lectins for specific glycosylated proteins has been exploited and applied for the enrichment of N- and O-glycosylated proteins [109]. Antibodies have also been used in the analysis and enrichment of glycosylated proteins [110]; however, the highly dynamic nature of glycosylation causes difficulties during antibody-based methods. The enhanced stability and low-cost of lectins have made them the preferred tool in glycoprotein analysis [111]. Lectin affinity chromatography involves the separation of glycosylated proteins from non-glycosylated proteins in a complex sample [112]. Multi-lectin affinity columns incorporate lectins with specificities for certain glycoforms. These glycoforms are sequentially eluted, leading to fractions containing lectin-specific glycoproteins such as oligomannose, sialylation and fucosylation [113,114].
Chemistry-based methods have also been applied as enrichment strategies. Hydrazide chemistry is one of the most common methods for the enrichment of glycosylated proteins and involves the oxidation of glycans followed by their conjugation to a solid support through the formation of hydrazide bonds. The N-glycoproteins/peptides are cleaved and released using Peptide-N-Glycosidase F (PNGase F) [115,116]. During O-glycoprotein enrichment by hydrazide chemistry, O-glycoproteins/peptides can be released using hydroxylamine [117]. Boronic acid covalently interacts with cis-diol groups present on glycans and thus can be used to capture glycoproteins/peptides in a complex solution. After the non-glycosylated proteins have been washed through, the glycosylated proteins are released using an acidic solution [115]. HILIC, as described above, has also been applied for the enrichment of glycopeptides [118].
A promising enrichment method combining metabolic labeling and isotopic recording known as isotope targeted glycoproteomics (IsoTaG) has recently been developed [119]. The mass shift caused by the different isotopes allows the glycoproteins to be analyzed and sequenced by MS followed by the use of a specific software to identify glycoprotein structure and localization [115]. Another recently developed method combining chemical and enzymatic reactions known as solid phase extraction of N-linked glycans and glycosite-containing peptides (NGAG) facilitated the discovery of 2044 distinct N-glycosites [120].
Unlike phosphorylation or ubiquitination, the heterogeneity of glycans means there is no constant mass shift during MS analysis. Therefore, techniques to generate a common mass tag for MS analysis of glycoproteins have been developed. Enzymatic methods, such as the use of PNGase F and chemical methods, such as ammonium hydroxide/carbonate-based chemical deglycosylation have been applied to create a common mass tag for N-linked glycosylation [121,122]. β-elimination and the use of zinc finger nuclease (ZFN) to genetically alter cells and simplify the structure of glycans have been applied to create a common mass tag for O-linked glycosylation [123,124]. CID, ETD or HCD fragmentation methods may be used during MS analysis of glycosylation [48]. A combination approach for MS analysis of N-glycoproteins using HCD product ions to trigger ETD MS/MS has also been developed which facilitates targeted analysis of glycoproteins while eliminating the need for pre-enrichment of the complex sample [125].

2.4. Sumoylation

Sumoylation involves the covalent attachment of a small ubiquitin-like modifier (SUMO) to a lysine residue of target proteins through the formation of an isopeptide bond. In humans, there are three SUMO isoforms which are attached to proteins through an enzymatic cascade similar to the process of ubiquitination. The importance of sumoylation in many cellular processes including the cell cycle, transcriptional regulation, and nuclear localization has only recently been discovered [126,127,128]. The substoichiometric nature of sumoylation as well as the presence of high levels of active SUMO-specific proteases (SENPs) in cell lysates makes the analysis of this PTM challenging [129]. Furthermore, sumoylated peptides generated from tryptic digestion leave a large SUMO signature, >2 kDa or > 3 kDa depending on the isoform, resulting in complex MS spectra which hampers detection, sequencing, and identification of sumoylated peptides [130]. To overcome this challenge, various mutant SUMO tags have been created by mutation of specific amino acids into protease-cleavable residues to generate short peptide signatures. Examples of mutant SUMO tags include SUMO-1T95R, SUMO-2T91R and SUMO-3Q87R/Q88N [131,132]. The use of mutant SUMO tags greatly enhances MS resolution, leading to more sumoylation sites being identified. However, the identifications made using these ectopically expressed tags does not equivalate to endogenous sumoylation patterns and mutant SUMO tags are not compatible with the analysis of clinical samples [130]. To enrich for sumoylated peptides, an affinity tag, typically Histidine (His), is often conjugated to SUMO for purification by IMAC [133,134]. Site-directed mutagenesis of a predicted lysine acceptor residue is often used in small-scale studies to detect sumoylation [135,136].
Other non-mutant methods have been developed to detect endogenous sumoylation. These techniques often involve enriching sumoylated peptides by co-immunoprecipitation using anti-SUMO antibodies followed by protein digestion using various proteases such as trypsin, Lys-C, Asp-N and wild-type α-lytic protease (WaLP) [137,138,139]. A method derived to avoid the limitations associated with mutant SUMO peptides, protease-reliant identification of SUMO modification (PRISM), involves the use of His-tagged SUMO, acetylation and SUMO-specific proteases for sumoylation site identification [140]. Furthermore, Hendriks et al. identified 14,869 SUMO2/3 sites in human cells by incorporating a serial digestion workflow using Lys-C and Asp-N and peptide-level immunoprecipitation followed by LC–MS/MS [139].
SUMO-interacting motifs (SIMs) and recombinant SUMO-binding entities (SUBEs) have been adopted for the enrichment and identification of endogenous poly-SUMO proteins [141,142]. SILAC, iTRAQ and LFQ have been used for quantitation of sumoylation [138,143,144]. Various bioinformatic tools such as SUMmOn, SUMOhydro, SumSec, etc., have been introduced for sumoylation site identification [129,145,146,147].

2.5. Acetylation and Methylation

Acetylation and methylation are prominent PTMs that play roles in many cellular processes including cell signaling, metabolic pathways and most notably, DNA-protein interactions. The acetylation of histone proteins is a crucial process that influences the accessibility of DNA to the transcriptional machinery. The transfer of an acetyl group to the α-amino group at the N-terminus of the protein is an irreversible modification, whereas acetylation at a lysine residue is reversible. Acetylation is catalyzed by acetyltransferases and lysine acetylation may be reversed by lysine deacetylases [148]. Methylation mainly occurs on lysine and arginine amino acids. However, other residues such as histidine, proline, and glutamine may also be subject to methylation. Methylation is catalyzed by lysine or arginine methyltransferases and reversed by demethylases [149] Research on acetylation and methylation is often focused on histone modifications which requires specific sample preparation approaches, as described above [22].
Radiolabeling of proteins with radiolabeled acetyl groups from 14C or 3H-acetyl CoA allows the detection of acetylated proteins by autoradiography after gel electrophoresis is performed [150,151]. However, the use of this technique has dropped in recent years due to the extra safety precautions required when using radioactive molecules [148]. Immuno-based techniques are commonly used to detect acetylation with many highly specific and sensitive anti-acetyl lysine antibodies currently available [152]. Several recent studies have expanded on conventional FCM for acetylation analysis using FCM-based techniques including single-cell imaging flow cytometry and mass cytometry (CyTOF) [153,154,155].
Once again, acetylated proteins are enriched prior to LC–MS/MS. Immunoaffinity purification of acetylated peptides using specific antibodies is the most common and effective method of enrichment [22,156,157,158]. Combined fractional diagonal chromatography (COFRADIC) is also a popular technique that specifically enriches N-terminal acetylated proteins through the derivatization of primary amines [159,160]. The relatively new method, stable-isotope protein N-terminal acetylation quantification (SILProNAQ) allows the direct quantitation of N-terminal peptides, although few studies have reported using this technique [161,162]. The previously discussed fractionation technique, SCX is commonly used for acetylation analysis. Tandem MS identifies acetylation by the presence of a 42 Da mass shift on acetylated peptides. The quantitation of acetylated proteins may be carried out using label or label-free quantification. SILAC, iTRAQ and TMT labeling have been applied for the quantification of acetylated proteins [163,164,165].
Typically, methylated proteins are detected by LC–MS/MS. However, the development of effective enrichment strategies and the accurate localization of methylation sites has lacked the same success as other PTMs such as phosphorylation, due to the small size of the methyl group, difficulties assigning the mass shifts for mono-, di- and tri-methylation (14 Da, 28 Da and 42 Da, respectively), and the lack of significant physicochemical changes between methylated and unmethylated lysine and arginine residues [166,167,168]. The previously described enrichment methods HILIC and SCX as well as isoelectric focusing (IEF) have been applied. However, the overall consensus suggests that orthogonal combination of immunoaffinity enrichment and ion exchange chromatography techniques enhances the robustness of methylome analysis [169,170,171]. Specific antibodies have been developed against mono- and di-methylated arginine as well as mono-, di- and tri-methylated lysine, leading to the identification and localization of over 1000 arginine methylation sites and approximately 160 lysine methylation sites in the human HCT116 cell line [172]. Similar to TUBEs for ubiquitination analysis, methyl-binding domains have been exploited for enrichment purposes. The affinity of naturally occurring triple malignant tumor-binding domains (3×MBT) from the protein L3MBTL1, for mono- and di-methylated lysine residues of proteins resulted in the use of these domains for enrichment [173,174]. Recently, Wang et al. [175] described a new chromatography-based method of methylome analysis combining SCX, IMAC and high-pH reversed-phase chromatography which led to the identification of 765 methylation sites [175]. Positive- and negative-mode CID, HCD and ETD fragmentation techniques have been applied for methylation analysis [176]. For quantitation, label-based techniques such as heavy-methyl SILAC and isomethionine methyl-SILAC; or label-free techniques are often used [171,177,178,179].
The techniques described above can be incorporated into various experimental designs to identify and quantify PTMs associated with specific cellular processes and diseases. The bottom-up approach is among the most popular proteomic workflows which can be adapted for the study of specific biological samples and PTMs of interest (Figure 3).

3. PTM Crosstalk

As described, many methods have been developed to analyze individual PTMs. However, multiple PTMs on a target protein can positively or negatively affect the influence of each PTM on the target protein. This crosstalk between PTMs participates in the regulation of many molecular processes [182]. For example, ubiquitination, acetylation, and phosphorylation are involved in the regulation of the tumor-suppressor protein, p53 [183]. To analyze the interplay between PTMs, orthogonal combinations of enrichment and/or fractionation techniques for individual PTMs have been applied. The serial enrichment of different PTMs (SEPTM) method, developed by Mertins et al., [184] involves the sequential enrichment of phosphorylated, ubiquitinated and acetylated proteins before MS analysis. This method typically incorporates IMAC or TiO2 enrichment for phosphorylated proteins, immunoaffinity enrichment using di-Gly antibodies for ubiquitinated proteins and anti-acetyl lysine antibodies for acetylated proteins [184]. Further discussion on PTM crosstalk is beyond the scope of this review. However, many informative reviews can be found on this increasingly important topic [182,185].

4. Application of PTM-Focused Techniques in Blood Cancer Research

Several reviews have been published focusing on specific PTMs and their involvement in carcinogenesis. However, literature surrounding the relationship between PTMs and hematological malignancies is limited [186,187,188]. Both the individual PTMs and associated modifying enzymes play significant roles as biomarkers and therapeutic targets in hematological malignancies [189]. Many signaling pathways regulated by PTMs have demonstrated aberrant activity in blood cancers, such as the Janus Kinase (JAK)/signal transducer and activator of transcription (STAT), phosphatidylinositol-3-kinase (PI3K)/AKT and B-cell receptor (BCR) signaling pathways (Figure 4). Dysregulation of these pathways cause proliferation, migration, survival, and angiogenesis which promote and/or maintain the malignant phenotype of malignant blood cells. Several genetic abnormalities associated with hematological malignancies have also been found to affect protein modifier genes, such as the t(9;22) translocation which results in the oncogenic BCR-ABL fusion kinase (Figure 4) [190]. Already, many FDA-approved therapies for hematological malignancies incorporate the modulation or inhibition of oncogenic protein modifications as part of their mode of action (Table 1, Figure 4). Therefore, the application of efficient analytical techniques in laboratories globally for thorough PTM analysis may aid in the discovery of novel biomarkers and therapeutic targets in hematological malignancies.
MS-based techniques are typically used for large-scale systematic analysis of PTM marks in blood cancers, generating extensive data. Bioinformatic analysis of this data can identify significant changes in the patterns of protein modification between disease states, such as malignant versus normal samples. Further research can then be conducted to determine the importance and implications of specific PTM changes in blood cancers. The technique of choice for validation and small-scale analyses is Western blot analysis using antibodies specific to the PTM being analyzed.

5. Multiple Myeloma

Multiple myeloma (MM) is an incurable hematological malignancy characterized by the uncontrolled proliferation of monoclonal plasma cells in the bone marrow resulting in end-organ damage, renal injury, and the destruction of bone tissue [213]. Despite being considered incurable, the survival of MM patients has dramatically improved in recent decades with treatment consisting of therapeutic agents including corticosteroids, immunomodulatory agents (IMiDs), proteasome inhibitors and monoclonal antibodies (MAbs) [214]. The analysis of PTMs in MM has identified potential biomarkers and therapeutic targets with the application of the techniques described above being central to these findings (Table 2).
PTM analysis in MM has revealed aberrant modifications of major proteins involved in signaling pathways known to play a role in MM carcinogenesis and progression, such as JAK/STAT signaling, PI3K/Akt/mammalian target of rapamycin (mTOR) and Ras/Raf/MEK/Erk. Targeting kinases, phosphatases, E3 ubiquitin ligases, deubiquitinating enzymes (DUBs) and histone deacetylases (HDACs) showing altered activity resulting in aberrant protein modifications in these signaling pathways has demonstrated promising results in the form of identifying novel therapeutic targets to reduce MM growth and targeting resistance mechanisms to current MM therapies, such as bortezomib (Table 2). A large-scale MS/MS-based phosphoproteomic analysis, incorporating TiO2 phosphopeptide enrichment, on a primary MM cell line by Ge et al. identified many novel phosphosites and provided a basis for future phosphorylation-based research in MM [231]. A more recent large-scale MS-based study utilizing phosphoproteomic techniques, such as IMAC phosphopeptide enrichment, and transcriptomic techniques revealed dynamic kinase activity and differential phosphorylation signatures in KRAS- and NRAS-mutated MM. This study supports the need for improved patient stratification, indicating the potential benefit of incorporating the level of kinase activity as well as the mutational status of the patient during therapeutic decision making [217]. Western blot analysis has identified altered phosphorylation of several important regulatory proteins including EZH2, AMPK, p53, STAT3, mTOR and ERK1/2, which promote MM growth and survival, thus identifying several potential therapeutic targets and enhancing our understanding of MM pathogenesis (Table 2). Brown et al. conducted a phospho flow study to detect constitutive and IL-6-induced phosphorylation of key signaling molecules in MM, demonstrating the potential of this technique to detect novel diagnostic and prognostic phosphoproteomic biomarkers [232].
The introduction of proteasome inhibitors, such as bortezomib, revolutionized the treatment of MM and patient survival. Bortezomib exerts its anti-myeloma activity by blocking the proteasomal degradation of ubiquitinated tumor suppressing regulatory proteins, such as p53. Due to the development of drug resistance, targeting other components of the ubiquitin-proteasome system has become an attractive concept. A recent preclinical study illustrated the potential of an E1 ubiquitin activating enzyme (UAE) inhibitor, TAK-243, for the treatment of MM [233]. Approved IMiDs target the ubiquitin proteasome system by modulating the substrate specificity E3 ubiquitin ligases to exert their anti-cancer effects. Lenalidomide targets Cereblon, the substrate recognition subunit of a biologically important Cullin RING E3 ubiquitin ligase complex, resulting in the degradation of two B-cell transcription factors, Ikaros (IKZF1) and Aiolos (IKZF3), that are essential for the survival of MM cells [207]. Inhibition of the DUB, USP1, has also been shown to induce apoptosis in myeloma cells [234]. A recent review by Wirth et al. provides an in-depth discussion on ubiquitination and ubiquitin-like modifications in MM [235].
Altered methylation and acetylation of histones and non-histones have been implicated in MM. Deacetylation of c-MYC and DNMT1 by HDAC3 was found to stabilize these oncogenic proteins and promote MM cell survival [227]. MMSET and EZH2 have histone methyltransferase activity and are associated with MM progression and a poor prognosis [236,237]. Panobinostat is an FDA-approved pan-HDAC inhibitor used in the treatment of relapsed or refractory MM [238]. HDAC inhibitors have been found to improve the efficacy of treatment with proteasome inhibitors by blocking the activation of an alternative non-proteasomal degradation pathway, the aggresome/autophagy pathway via the inhibition of HDAC6-mediated transport of polyubiquitinated proteins to aggresomes for degradation [239,240,241]. Other HDAC inhibitors, ricolinostat and citarinostat, are currently in clinical trials for the treatment of MM [240,242].
The dysregulation of the SUMOylation pathway in MM described by Driscoll et al. [224] has led to further research into the targeting of SUMOylation machinery. Upregulation of the SUMO-conjugating enzyme UBE21 and the SUMO E3 ligase PIAS1 are markers of poor prognosis in MM [224]. Aberrant expression of SENPs has been implicated in the constitutive activation of the oncogenic NF-κB pathway in MM. The overexpression of SENP1 causes enhanced proliferation, whereas the silencing of SENP2 induces bortezomib resistance in MM cells [223,243]. Glycosylation analysis incorporating lectin histochemistry demonstrated that inhibiting sialyation prevents MM cells from entering the bone marrow microenvironment, in which they are often protected from conventional chemotherapy; presenting sialyation as a promising target to improve current MM therapies [226]. As can be seen in Table 1, through the application of PTM-focused analytical techniques, significant progress has been made in elucidating factors involved in MM pathogenesis and their potential as therapeutic targets and biomarkers.

6. Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a highly heterogeneous disease of the blood and bone marrow characterized by the uncontrolled proliferation and altered differentiation of myelogenous progenitor or stem cells. The complexity of AML is illustrated by its classification into six disease subtypes based on distinct differences in genetic make-up, morphology and clinical presentation of AML [244]. Progress in understanding the pathobiology of AML has led to the FDA approval of eight therapies since 2017 [245]. PTM analysis has improved the understanding of altered signaling in AML and identified several PTM marks as potential therapeutic targets (Table 3). Interfering with protein modification in AML using tyrosine kinase inhibitors, hypomethylating agents (HMAs), histone deacetylase (HDAC) inhibitors, as well as ubiquitination and SUMOylation inhibitors, has led to considerable progress in the improvement of treatment for AML.
Mutations in the gene encoding the receptor kinase, FMS-like tyrosine kinase 3 (FLT3), are poor prognostic markers found in over 30% of AML cases [245]. Proteomic analysis has revealed that upon ligand binding, FLT3 initiates a phosphorylation cascade resulting in the activation of proliferative signaling pathways [261]. FLT3-internal tandem duplication (ITD) mutants exert pro-proliferative effects through the constitutive activation of STAT5 signaling [262]. Under normal circumstances, FLT3 undergoes glycosylation in the endoplasmic reticulum (ER) and Golgi apparatus to form a mature 160 kDa protein that localizes at the cell surface. In FLT3-ITD+ AML, there is partial retention of the underglycosylated form of FLT3-ITD in the ER. This 130 kDa autophosphorylated isoform stimulates STAT5 signaling, upregulating the oncogenic kinase PIM-1 resulting in the establishment of a positive feedback loop promoting constitutive STAT5 signaling [257]. Targeting of this phosphorylation cascade has led to the approval of three FLT3 tyrosine kinase inhibitors (TKIs) in recent years for the treatment of AML with FLT3 mutations (Figure 4) [263]. Phase 1 studies of the pan-PIM kinase inhibitor, AZD1208, in patients with relapsed or refractory AML revealed no clinical response when administered as a single agent [264]. However, a recent study found that AZD1208 in combination with the proteasome inhibitor, bortezomib, enhanced bortezomib-induced apoptosis [265]. The inhibition of N-linked glycosylation in AML cells using, 2-deoxy-d-glucose (2-DG), reduced tumor growth. However, further studies are required to translate these results into a competent therapy [266].
Unlike other AML subtypes, curative treatment of acute promyelocytic leukemia (APL) has been hugely successful using a combination of all-trans-retinoic acid (ATRA) and arsenic trioxide (ATO) to restore the ability of leukemic cells to differentiate. The mechanism of action of this treatment involves the SUMOylation, ubiquitination and subsequent proteasomal degradation of the PML/RARα fusion oncoprotein [267]. Recent studies have shown that the ubiquitination and degradation of the cyclin-dependent kinase 2 (CDK2) causes differentiation of AML cells, thus demonstrating the potential of CDK2 inhibitors as differentiation agents in AML [251]. The UAE inhibitor, TAK-243, has demonstrated anti-leukemic activity in preclinical studies, with a phase 1 clinical trial for the treatment of relapsed or refractory AML currently ongoing [253]. Studies have also identified the E3 ubiquitin ligases, TRIAD1 and Cbl-b, as tumor suppressors in AML opening avenues for further investigation of their downstream substrates as potential therapeutic targets [127,252]. Western blot analysis and site-directed mutagenesis of specific lysine residues determined that the sumoylation of the transcription factor, sPRDM16, and insulin-like growth factor 1 receptor (IGF-1R) promote leukemogenesis [254,268].
Analysis of the acetylome and methylome using a combination of MS-based and antibody-based techniques has provided insights into aberrant epigenetic regulation in AML. Compounds have been developed to target commonly overexpressed or hyperactive HDACs, histone methyltransferases and demethylases, with several clinical trials evaluating their efficacy against AML in progress [269,270]. These epigenetic therapies show low levels of toxicity and have proved to be promising agents for the treatment of aging AML where patients are unable to tolerate intensive chemotherapy [269]. Taken together, investigating the role of altered protein modifications on the development and progression of leukemogenesis has contributed to the discovery of novel biomarkers and therapeutics targets in AML.

7. Myeloproliferative Neoplasms

Myeloproliferative neoplasms (MPNs) are a group of disorders derived from hematopoietic and progenitor stem cells (HPSCs). They are associated with enhanced proliferation of myelogenous cells, with specific MPNs often characterized by genetic mutations. Chronic myeloid leukemia (CML) is characterized by the BCR–ABL1 fusion gene mutation whereas polycythemia vera (PV), essential thrombocythemia (ET) and myelofibrosis (MF) are typically characterized by the presence of JAK2, CALR, or MPL gene mutations [271]. These neoplasms develop slowly but have an increased risk of disease progression to the more severe myeloproliferative neoplasm, primary myelofibrosis (PMF), or AML [272]. Myelodysplastic syndromes (MDS) are clonal hematopoietic disorders often characterized by ineffective hematopoiesis, a dysplastic morphology of one or more myelogenous cell types and peripheral cytopenia [273]. Similar to MPNs, these disorders are associated with genetic alterations in a variety of genes such as TET2 and ASXL1, and an increased risk of transformation to AML [274,275]. Few large-scale PTM analyses have been carried out on MPNs and MDS compared to AML and MM. Research is often focused on genomic analysis due to the contribution of genomic abnormalities and somatic mutations to disease progression [275,276]. Despite this, MS-based analysis, protein microarrays and, most commonly, Western blot analysis have been applied in various studies, aiding in the identification of dysregulated modifications and potential therapeutic targets in MPNs and MDS (Table 4).
The JAK2V617F mutation, found in a high proportion of patients with MPNs, results in the phosphorylation of JAK2 in the absence of cytokine stimulation, leading to the constitutive phosphorylation and activation of the downstream target proteins, STAT and ERK, and the development of a pro-proliferative phenotype [272]. The efficacy of the FDA-approved JAK1 and JAK2 inhibitor, ruxolitinib, for the treatment of JAK2V617F-MPNs, mainly MF and PV, has been varied [291]. In comparison, the inhibition of the kinase activity of BCR-ABL in CML by the FDA-approved imatinib, nilotinib, dasatinib, bosutinib and ponatinib, has demonstrated huge success with an increase in complete cytogenetic responses and improved overall survival [292]. Phospho-analysis has identified the upregulation of the programmed death ligand 1 (PD-L1) and programmed death receptor 1 (PD-1) in JAK2V617F-mutant MPN, as well as increased RalA which promotes disease progression in CML [278,280]. A phase I-II clinical trial (ClinicalTrials.gov Identifier: NCT04051307) in PV and ET patients investigating the safety and efficacy of vaccinating with PD-L1 and Aginase1 peptides is currently in the recruitment stage.
The Asrij/OCIAD1 protein prevents MDM2-mediated ubiquitination and degradation of p53, thus promoting hematopoietic stem cell quiescence. The modulation of p53 ubiquitination by Asrii could be used as a base for further research into targeted therapies for MPNs with dysregulated p53 [281]. Using a combination of genomic, transcriptomic and antibody-based proteomic techniques, an MDS-associated germline mutation in the HLTF gene was found to impair polyubiquitination of proliferating cell nuclear antigen (PCNA) causing decreased DNA repair and an accumulation of DNA damage [293]. Limited studies have been conducted on the involvement of SUMOylation in MPNs. One study revealed that the β-catenin antagonist, Chibby 1 (CBY1), is downmodulated in CML due to enhanced SUMOylation, leading to the aberrant activation of β-catenin. This provides an insight into the potential role of SUMOylation in pathogenic molecular pathways contributing to MPNs. Furthermore, the inhibition of the E3 ligase responsible for the enhanced SUMOyation of CBY1 may be a promising avenue for further research [285]. Impaired glycosylation of the colony stimulating factor 3 receptor (CSF3R) and the thrombopoietin receptor (TpoR) resulting in the constituent activation of the JAK/STAT pathway has been identified in MPNs [286,287]. ASXL1, a commonly mutated gene in MDS, was recently found to be part of a complex with O-GlcNAc transferase (OGT) in which OGT stabilizes ASXL1 via O-glycosylation. Disruption of this complex blocks myeloid differentiation via the inhibition of H3K4 methylation, providing insight into a possible mechanism of MDS pathogenesis [294]. HDAC inhibitors and hypomethylating agents are promising therapeutics that have demonstrated efficacy in MPNs when combined with ruxolitinib [295,296]. Mutant JAK2 phosphorylates the arginine methyltransferase, PRMT5, impairing its ability to methylate target proteins resulting in a myeloproliferative phenotype in JAK2V617F-mutant patient samples [297]. Inhibitors of class I and II HDACs, givinostat, panobinostat and vorinostat, have demonstrated clinical efficacy in various MPNs [295]. A recent study demonstrated that the class IV HDAC, HDAC11, rather than HDAC6 stimulates proliferation and oncogenic hematopoiesis in JAK2-driven MPNs and is a promising therapeutic target for future research [289]. A quantitative MS-based analysis identified differentially expressed histone marks as candidate markers of MDS progression in an early-stage MDS and late-stage leukemic cell line, as well as histone marks with potential relevance in resistance to the standard of care hypomethylating agent, decitabine [298]. PTM analysis has identified various potential biomarkers and therapeutic targets in MPNs and MDS as well as specific proteins involved in the modification of proteins, such as JAK2, which are targets of current FDA-approved therapies.

8. Lymphomas

Lymphomas are a heterogenous group of almost 100 distinct B- and T-cell malignancies that develop from the oncogenic transformation of normal lymphoid cells [299]. The majority of PTM-based lymphoma research focuses on phosphorylation, with a limited number of studies focusing on other PTMs (Table 5). Studies incorporating phospho-analysis have identified changes in protein phosphorylation levels in normal compared to lymphoma cells; mechanisms of lymphomagenesis and tumor survival; and modes of action of potential therapies [300,301,302]. Large-scale quantitative phosphoproteomic analysis has given insights into the signaling pathways involved in the development and progression of lymphomas. MS-based analysis of chimeric antigen receptor (CAR)- stimulated phosphorylation events utilizing an IMAC enrichment technique and TMT labeling identified 26,804 unique phosphorylation sites [303]. CAR T cell therapy is FDA approved for the treatment of several large B cell lymphomas [304]. This study compared phosphorylation-mediated signaling pathways in CD28/CD3ζ and 4–1BB/CD3ζ CAR T cells, providing important information for future improvements in CAR design to enhance clinical efficacy [303]. A semiquantitative, label-free, MS-based analysis of B-cell non-Hodgkin lymphomas (NHL) mapped phosphorylation sites and created distinct phosphorylation signatures in different NHLs [305]. A recent focused study on anaplastic large-cell lymphomas (ALCLs) expressing the oncogenic fusion kinase NPM-ALK, identified NPM-ALK targeted phosphorylation sites on nuclear interaction partner of ALK (NIPA) which promote lymphomagenesis when phosphorylated [306]. Dysregulation of B cell receptor (BCR) signaling, through constitutive phosphorylation of downstream targets, contributes to the growth and survival of many B cell lymphomas. TKIs, such as ibrutinib and idelalisib, target kinases involved in BCR signaling [307].
Viral infections can drive lymphomagenesis and disease progression through the activation of signaling pathways by viral oncogenic proteins. Activation of PIK3/AKT signaling and STAT3 via constitutive phosphorylation contribute to the growth and survival of primary effusion lymphoma (PEL) cells, a type of NHL driven by dual infection with Epstein Barr virus (EPV) and Kaposi sarcoma–associated herpesvirus (KSHV) [317,318]. Inhibition of STAT3 and PI3K signaling promotes apoptosis in PEL cells and dual inhibition of PI3K and mTOR kinases inhibits PEL cell proliferation, illustrating STAT3, PI3K and mTOR signaling as potential targets for the treatment of PEL [319]. Subsequently, a study by Mohanty et al. [320] revealed everolimus as a multi-targeted therapy that induces apoptosis and downregulates mTOR, STAT-3 and NF-κB signaling in KSHV+ PEL [320]. Recent kinome profiling of PEL cell lines using kinase inhibitor-conjugated beads to capture kinases followed by quantitative MS revealed Tyro3 as a potential therapeutic target with subsequent analyses revealing that Tyro3 inhibition reduced PEL survival and growth in vitro, and tumor burden in a mouse PEL xenograft model in vivo [321]. KSHV latency-associated nuclear antigen (LANA) was found to prevent degradation of the oncogene c-Myc in PEL cell lines by inhibiting glycogen synthase kinase-3β (GSK-3β)-mediated phosphorylation of the T58 residue of c-Myc [322].
Certain E2 ubiquitin-conjugating enzymes, E3 ubiquitin ligases and DUBs have been suggested to play a role in aberrant BCR and NF-κB signaling in lymphomas and represent potential targets for novel therapies [323]. Components of E3 ligases involved in lymphomagenesis include members of the Inhibitor of APoptosis (IAP) protein family, FBXO10, FBXO11, cereblon and TRIM11. Compounds targeting IAPs, known as Smac mimetics, are being investigated for the treatment of activated B cell-like (ABC) subtype of diffuse large B cell lymphoma (DLBCL) and rituximab-resistant B-cell lymphoma [309,324]. A recent study utilizing Ub-specific antibodies and ubiquitination assays, identified that upon BCR stimulation, FBXO10 acts as the critical component of a negative autoregulatory feedback loop via the ubiquitination and degradation of human germinal center-associated lymphoma (HGAL) protein [325]. Low FBXO10 expression levels and loss-of-function mutations in the FBXO10 gene have been identified in mantle cell lymphoma and DLBCL, respectively [325,326]. Cereblon is a direct target of IMiDs used in the treatment of several types of lymphoma [327]. SILAC-based quantitative MS analysis demonstrated that bromodomain and extraterminal protein family inhibitors downmodulate the E2-conjugating enzymes, UBE2C and UBE2T, promoting the anti-lymphoma activity of PI3K inhibitors [328]. DUBs, ubiquitin carboxyl-terminal hydrolase L5 (UCHL5) and the ubiquitin-specific peptidase 14 (USP14), were detected in the cytoplasm of a high proportion of DLBCL tumor cells [329]. UCHL5 and USP14 inhibitor, b-AP15, induces apoptosis in DLBCL cell lines and mouse xenograft models demonstrating a potential novel mechanism for DLBCL treatment [330]. Human T-cell leukemia virus type 1 (HTLV-1), the etiologic agent of adult T-cell leukemia/lymphoma, encodes several oncogenic proteins that promote ATLL progression, including Tax, a protein whose function and localization is largely regulated by PTMs [331]. A recent study provided evidence that UBE4B, an E3/E4 ubiquitin conjugation factor, is a Tax-binding protein that conjugates Tax with K48- and K63-linked polyubiquitin chains resulting in the downstream activation of NF- κB signaling in Tax+ ATLL [332].
A study, incorporating transcriptomics, immunoblotting and immunohistochemistry (SUMO-1,2,3 antibodies), showed an upregulation of components of the SUMOylation machinery in various Myc-overexpressing lymphomas. Furthermore, the inhibition of SUMOylation in Myc-driven lymphomas resulted in the induction of cell cycle arrest and apoptosis [333]. EBV-associated lymphomas have also shown dysregulated SUMOylation through the EBV latent membrane protein-1 (LMP1)-mediated interactions with SENP2 and Ubc9 [312,334]. Altered SUMOylation presents an avenue for possible therapeutic targeting of the SUMOylation pathway in lymphomas. A small-molecule inhibitor of SUMOylation, TAK-981, is currently being evaluated in clinical trials; in a phase I study in patients with metastatic solid tumors and lymphomas (ClinicalTrials.gov Identifier: NCT03648372) and in a phase Ib/II study in combination with rituximab in patients with relapsed or refractory CD20+ non-Hodgkins lymphoma (NHL) (ClinicalTrials.gov Identifier: NCT04074330).
Altered glycosylation in lymphoma contributes to metastasis and tumor growth [335]. Studies applying lectin histochemistry revealed that the loss of Phaseolus vulgaris lectin (L-PHA)-reactive oligosaccharides and their sialyation on the cell surface is associated with a poor prognosis in Burkitt’s lymphoma and DLBCL [336,337]. Using glycosylation inhibitors, sialyation of surface glycans was shown to weaken cell adhesion to extracellular matrix proteins, such as galectin-1, and promote tumor cell migration and invasiveness in human Burkitt’s lymphoma cell line and a human ALCL cell line [338]. Therefore, cell surface sialyation represents a potential target to prevent lymphoma cell invasion. Somatic mutations insert N-glycosylation sites in the immunoglobulin gene in follicular lymphoma (FL) and other germinal center-associated lymphomas [339]. Lectin binding to the oligosaccharides attached to these sites promotes survival and proliferation of tumor cells [340].
Epigenetics contribute to lymphomagenesis often due to mutations in histone modifier genes resulting in aberrant histone modifications. MS-based and immuno-based analysis has identified altered enzymatic activity of acetyltransferases, HDACs, methyltransferases and demethylases in lymphomas [314,315,316,341]. Inactivating mutations of the histone and non-histone acetyltransferase, CREBBP, have been observed in B- and T-cell lymphomas [342,343]. Using immune-based strategies, CREBBP was identified as a haploinsufficient tumor suppressor in FL and DLBCL [314]. MS-based methods identified PRMT5 as a crucial regulator of GC formation through the methylation of BCL6, a protein whose expression is often dysregulated in GC-derived B-cell lymphomas [316]. Interestingly, BCL6 recruitment of the demethylase, LSD1, is also required for GC formation and enhances BCL6-associated lymphomagenesis [341]. Chromatin immunoprecipitation, MS and immunoblot analysis identified a mechanism of DOT1L-mediated transcriptional regulation in HDAC1-deficient thymic lymphoma via PTM crosstalk between acetylation, methylation, and ubiquitination [315].

9. Conclusions

The adaptation and fine tuning of conventional proteomic techniques for use in the analysis of PTMs have seen significant progress in recent decades. The widespread availability of highly sensitive equipment capable of high-throughput analyses has led to breakthroughs in cancer research and the application of these techniques in blood cancer research is limitless. The analysis of PTMs, thus far, has opened avenues for translational research into the development of novel biomarker signatures and therapeutics to improve patient survival. As can be seen in the tables above, many studies have focused on phosphorylation and ubiquitination analysis. However, recently, the targeting of less common PTMs, such as SUMOylation, has been demonstrated as a promising approach for the treatment of hematological malignancies. Continuous improvements in the analytical power of mass spectrometry with simultaneous optimization of bioinformatic pipelines and enhanced sensitivity and specificity of PTM-specific antibodies will facilitate deeper analysis of obscure modifications that will indefinitely provide meaning to many molecular processes occurring in the cellular environment. In conclusion, the application of specialized proteomic techniques to study PTMs in blood cancer research has been the basis of many fundamental advances in the understanding and treatment of hematological malignancies. Improvements on existing analytical techniques to allow the analysis of protein modifications with extremely low stoichiometry will lead to the identification of novel PTM sites with unique molecular functions. Future applications of the proteomic techniques described in this review will enhance our overall understanding of hematological malignancies, leading to improvements in therapies and, thus, patient survival.

Author Contributions

Conceptualization, P.D., K.D., D.B. and P.O.; writing—original draft preparation, K.D. and P.D.; writing—review and editing, K.D., P.D., D.B. and P.O.; visualization, K.D.; supervision, P.D., D.B. and P.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Aslam, B.; Basit, M.; Nisar, M.A.; Khurshid, M.; Rasool, M.H. Proteomics: Technologies and Their Applications. J. Chromatogr. Sci. 2017, 55, 182–196. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Yakubu, R.R.; Nieves, E.; Weiss, L.M. The Methods Employed in Mass Spectrometric Analysis of Posttranslational Modifications (PTMs) and Protein–Protein Interactions (PPIs). Adv. Exp. Med. Biol. 2019, 1140, 169–198. [Google Scholar] [CrossRef]
  3. Zhao, Y.; Jensen, O.N. Modification-specific proteomics: Strategies for characterization of post-translational modifications using enrichment techniques. Proteomics 2009, 9, 4632–4641. [Google Scholar] [CrossRef] [Green Version]
  4. Rahman, S.; Mansour, M.R. The role of noncoding mutations in blood cancers. Dis. Model. Mech. 2019, 12, dmm041988. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Pulte, D.; Jansen, L.; Brenner, H. Changes in long term survival after diagnosis with common hematologic malignancies in the early 21st century. Blood Cancer J. 2020, 10, 1–8. [Google Scholar] [CrossRef] [PubMed]
  6. Doll, S.; Gnad, F.; Mann, M. The Case for Proteomics and Phospho-Proteomics in Personalized Cancer Medicine. Proteom. Clin. Appl. 2019, 13, e1800113. [Google Scholar] [CrossRef] [PubMed]
  7. Wiśniewski, J.R. Filter Aided Sample Preparation—A tutorial. Anal. Chim. Acta 2019, 1090, 23–30. [Google Scholar] [CrossRef] [PubMed]
  8. Sinha, A.; Mann, M. A beginner’s guide to mass spectrometry–based proteomics. Biochemist 2020, 42, 64–69. [Google Scholar] [CrossRef]
  9. Larsen, M.R.; Trelle, M.B.; Thingholm, T.E.; Jensen, O.N. Analysis of posttranslational modifications of proteins by tandem mass spectrometry. BioTechniques 2006, 40, 790–798. [Google Scholar] [CrossRef]
  10. Siuzdak, G. An introduction to mass spectrometry ionization: An excerpt from The Expanding Role of Mass Spectrometry in Biotechnology, 2nd ed.; MCC Press: San Diego, 2005. J. Lab. Autom. 2004, 9, 50–63. [Google Scholar] [CrossRef] [Green Version]
  11. Fort, K.L.; Cramer, C.N.; Voinov, V.G.; Vasil’Ev, Y.V.; Lopez, N.I.; Beckman, J.S.; Heck, A.J.R. Exploring ECD on a Benchtop Q Exactive Orbitrap Mass Spectrometer. J. Proteome Res. 2017, 17, 926–933. [Google Scholar] [CrossRef] [PubMed]
  12. Penkert, M.; Hauser, A.; Harmel, R.; Fiedler, D.; Hackenberger, C.P.R.; Krause, E. Electron Transfer/Higher Energy Collisional Dissociation of Doubly Charged Peptide Ions: Identification of Labile Protein Phosphorylations. J. Am. Soc. Mass Spectrom. 2019, 30, 1578–1585. [Google Scholar] [CrossRef]
  13. Frese, C.K.; Zhou, H.; Taus, T.; Altelaar, A.F.M.; Mechtler, K.; Heck, A.J.R.; Mohammed, S. Unambiguous Phosphosite Localization using Electron-Transfer/Higher-Energy Collision Dissociation (EThcD). J. Proteome Res. 2013, 12, 1520–1525. [Google Scholar] [CrossRef] [PubMed]
  14. Riley, N.M.; Hebert, A.S.; Dürnberger, G.; Stanek, F.; Mechtler, K.; Westphall, M.S.; Coon, J.J. Phosphoproteomics with Activated Ion Electron Transfer Dissociation. Anal. Chem. 2017, 89, 6367–6376. [Google Scholar] [CrossRef] [Green Version]
  15. Yu, Q.; Wang, B.; Chen, Z.; Urabe, G.; Glover, M.S.; Shi, X.; Guo, L.-W.; Kent, K.C.; Li, L. Electron-Transfer/Higher-Energy Collision Dissociation (EThcD)-Enabled Intact Glycopeptide/Glycoproteome Characterization. J. Am. Soc. Mass Spectrom. 2017, 28, 1751–1764. [Google Scholar] [CrossRef] [PubMed]
  16. Chalkley, R.J.; Clauser, K.R. Modification Site Localization Scoring: Strategies and Performance. Mol. Cell. Proteom. 2012, 11, 3–14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Megger, D.A.; Pott, L.L.; Ahrens, M.; Padden, J.; Bracht, T.; Kuhlmann, K.; Eisenacher, M.; Meyer, H.E.; Sitek, B. Comparison of label-free and label-based strategies for proteome analysis of hepatoma cell lines. Biochim. Biophys. Acta (BBA)Proteins Proteom. 2014, 1844, 967–976. [Google Scholar] [CrossRef]
  18. Anand, S.; Samuel, M.; Ang, C.-S.; Keerthikumar, S.; Mathivanan, S. Label-Based and Label-Free Strategies for Protein Quantitation. Adv. Struct. Saf. Stud. 2016, 1549, 31–43. [Google Scholar] [CrossRef]
  19. Ong, S.-E.; Blagoev, B.; Kratchmarova, I.; Kristensen, D.B.; Steen, H.; Pandey, A.; Mann, M. Stable Isotope Labeling by Amino Acids in Cell Culture, SILAC, as a Simple and Accurate Approach to Expression Proteomics. Mol. Cell. Proteom. 2002, 1, 376–386. [Google Scholar] [CrossRef] [Green Version]
  20. Nemutlu, E.; Zhang, S.; Gupta, A.; Juranic, N.O.; Macura, S.I.; Terzic, A.; Jahangir, A.; Dzeja, P.P. Dynamic phosphometabolomic profiling of human tissues and transgenic models by18O-assisted31P NMR and mass spectrometry. Physiol. Genom. 2012, 44, 386–402. [Google Scholar] [CrossRef] [Green Version]
  21. Liao, H.; Zang, Q.; Lv, Q.; Gao, Y.; Zhao, Z.; He, J.; Zhang, R.; Song, Y.; Chen, Y.; Abliz, Z. Development of methionine methylation profiling and relative quantification in human breast cancer cells based on metabolic stable isotope labeling. Analyst 2019, 144, 3988–3998. [Google Scholar] [CrossRef]
  22. Lund, P.J.; Kori, Y.; Zhao, X.; Sidoli, S.; Yuan, Z.-F.; Garcia, B.A. Isotopic Labeling and Quantitative Proteomics of Acetylation on Histones and Beyond. Breast Cancer 2019, 1977, 43–70. [Google Scholar] [CrossRef]
  23. Filiou, M.D.; Martins-De-Souza, D.; Guest, P.C.; Bahn, S.; Turck, C.W. To label or not to label: Applications of quantitative proteomics in neuroscience research. Proteomics 2012, 12, 736–747. [Google Scholar] [CrossRef]
  24. Refsgaard, J.C.; Munk, S.; Jensen, L.J. Search Databases and Statistics: Pitfalls and Best Practices in Phosphoproteomics. Adv. Struct. Safety Stud. 2016, 1355, 323–339. [Google Scholar] [CrossRef]
  25. Barski, A.; Cuddapah, S.; Cui, K.; Roh, T.-Y.; Schones, D.E.; Wang, Z.; Wei, G.; Chepelev, I.; Zhao, K. High-Resolution Profiling of Histone Methylations in the Human Genome. Cell 2007, 129, 823–837. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Jiang, Y.-Y.; Jiang, Y.; Li, C.-Q.; Zhang, Y.; Dakle, P.; Kaur, H.; Deng, J.-W.; Lin, R.Y.-T.; Han, L.; Xie, J.-J.; et al. TP63, SOX2, and KLF5 Establish a Core Regulatory Circuitry That Controls Epigenetic and Transcription Patterns in Esophageal Squamous Cell Carcinoma Cell Lines. Gastroenterology 2020, 159, 1311–1327.e19. [Google Scholar] [CrossRef]
  27. Kim, S.W.; Hasanuzzaman, M.; Cho, M.; Heo, Y.R.; Ryu, M.-J.; Ha, N.-Y.; Park, H.J.; Park, H.-Y.; Shin, J.-G. Casein Kinase 2 (CK2)-mediated Phosphorylation of Hsp90β as a Novel Mechanism of Rifampin-induced MDR1 Expression. J. Biol. Chem. 2015, 290, 17029–17040. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Van Kruijsbergen, I.; Mulder, M.P.C.; Uckelmann, M.; Van Welsem, T.; De Widt, J.; Spanjaard, A.; Jacobs, H.; El Oualid, F.; Ovaa, H.; Van Leeuwen, F. Strategy for Development of Site-Specific Ubiquitin Antibodies. Front. Chem. 2020, 8, 111. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Keeney, M.; Hedley, B.D.; Chin-Yee, I.H. Flow cytometry-Recognizing unusual populations in leukemia and lymphoma diagnosis. Int. J. Lab. Hematol. 2017, 39, 86–92. [Google Scholar] [CrossRef] [Green Version]
  30. Heo, S.-K.; Noh, E.-K.; Ju, L.J.; Sung, J.Y.; Jeong, Y.K.; Cheon, J.; Koh, S.J.; Min, Y.J.; Choi, Y.; Jo, J.-C. CD45dimCD34+CD38−CD133+ cells have the potential as leukemic stem cells in acute myeloid leukemia. BMC Cancer 2020, 20, 1–10. [Google Scholar] [CrossRef]
  31. Ngai, L.L.; Kelder, A.; Janssen, J.J.W.M.; Ossenkoppele, G.J.; Cloos, J. MRD Tailored Therapy in AML: What We Have Learned So Far. Front. Oncol. 2021, 10, 603636. [Google Scholar] [CrossRef]
  32. Shechter, D.; Dormann, H.L.; Allis, C.D.; Hake, S.B. Extraction, purification and analysis of histones. Nat. Protoc. 2007, 2, 1445–1457. [Google Scholar] [CrossRef]
  33. Xu, Y.; Guo, J.; Liu, J.; Xie, Y.; Li, X.; Jiang, H.; Wang, J.; Peng, Z.; Wang, J.; Wang, S.; et al. Hypoxia-induced CREB cooperates MMSET to modify chromatin and promote DKK1 expression in multiple myeloma. Oncogene 2021, 40, 1231–1241. [Google Scholar] [CrossRef]
  34. Egelhofer, T.A.; Minoda, A.; Klugman, S.; Lee, K.; Kolasinska-Zwierz, P.; Alekseyenko, A.A.; Cheung, M.-S.; Day, D.S.; Gadel, S.; Gorchakov, A.A.; et al. An assessment of histone-modification antibody quality. Nat. Struct. Mol. Biol. 2010, 18, 91–93. [Google Scholar] [CrossRef] [Green Version]
  35. Byrum, S.D.; Taverna, S.D.; Tackett, A.J. Purification of specific chromatin loci for proteomic analysis. Breast Cancer 2014, 1228, 83–92. [Google Scholar] [CrossRef] [Green Version]
  36. Sidoli, S.; Bhanu, N.V.; Karch, K.R.; Wang, X.; Garcia, B.A. Complete Workflow for Analysis of Histone Post-translational Modifications Using Bottom-up Mass Spectrometry: From Histone Extraction to Data Analysis. J. Vis. Exp. 2016, e54112. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Gallego, L.D.; Steger, M.G.; Polyansky, A.A.; Schubert, T.; Zagrovic, B.; Zheng, N.; Clausen, T.; Herzog, F.; Köhler, A. Structural mechanism for the recognition and ubiquitination of a single nucleosome residue by Rad6–Bre1. Proc. Natl. Acad. Sci. USA 2016, 113, 10553–10558. [Google Scholar] [CrossRef] [Green Version]
  38. Ardito, F.; Giuliani, M.; Perrone, D.; Troiano, G.; Muzio, L.L. The crucial role of protein phosphorylation in cell signaling and its use as targeted therapy (Review). Int. J. Mol. Med. 2017, 40, 271–280. [Google Scholar] [CrossRef] [Green Version]
  39. Riedl, T.; Egly, J.-M. Phosphorylation in Transcription: The CTD and More. Gene Expr. 2001, 9, 3–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Hay, N.; Sonenberg, N. Upstream and Downstream of mTOR. Genes Dev. 2004, 18, 1926–1945. [Google Scholar] [CrossRef] [Green Version]
  41. Cuadrado, A.; Nebreda, A.R. Mechanisms and functions of p38 MAPK signalling. Biochem. J. 2010, 429, 403–417. [Google Scholar] [CrossRef] [Green Version]
  42. Papa, S.; Martino, P.L.; Capitanio, G.; Gaballo, A.; De Rasmo, D.; Signorile, A.; Petruzzella, V. The Oxidative Phosphorylation System in Mammalian Mitochondria. Adv. Exp. Med. Biol. 2011, 942, 3–37. [Google Scholar] [CrossRef]
  43. VerPlank, J.J.; Goldberg, A.L. Regulating protein breakdown through proteasome phosphorylation. Biochem. J. 2017, 474, 3355–3371. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Cohen, P. The role of protein phosphorylation in human health and disease. The Sir Hans Krebs Medal Lecture. Eur. J. Biochem. 2001, 268, 5001–5010. [Google Scholar] [CrossRef] [PubMed]
  45. Natoli, C.; Perrucci, B.; Perrotti, F.; Falchi, L.; Iacobelli, S. Tyrosine Kinase Inhibitors. Curr. Cancer Drug Targets 2010, 10, 462–483. [Google Scholar] [CrossRef] [PubMed]
  46. Bodenmiller, B.; Mueller, L.N.; Mueller, M.; Domon, B.; Aebersold, R. Reproducible isolation of distinct, overlapping segments of the phosphoproteome. Nat. Chem. Biol. 2007, 4, 231–237. [Google Scholar] [CrossRef] [PubMed]
  47. Smith, L.E.; Rogowska-Wrzesinska, A. The challenge of detecting modifications on proteins. Essays Biochem. 2020, 64, 135–153. [Google Scholar] [CrossRef]
  48. Ke, M.; Shen, H.; Wang, L.; Luo, S.; Lin, L.; Yang, J.; Tian, R. Identification, Quantification, and Site Localization of Protein Posttranslational Modifications via Mass Spectrometry-Based Proteomics. Adv. Exp. Med. Biol. 2016, 919, 345–382. [Google Scholar] [CrossRef]
  49. Raggiaschi, R.; Gotta, S.; Terstappen, G.C. Phosphoproteome Analysis. Biosci. Rep. 2005, 25, 33–44. [Google Scholar] [CrossRef]
  50. Zavialova, M.; Zgoda, V.; Nikolaev, E. Analysis of contribution of protein phosphorylation in the development of the diseases. Biomeditsinskaya Khimiya 2017, 63, 101–114. [Google Scholar] [CrossRef] [Green Version]
  51. Sawasdikosol, S. Detecting Tyrosine-Phosphorylated Proteins by Western Blot Analysis. Curr. Protoc. Immunol. 2010, 89, 11.3.1–11.3.11. [Google Scholar] [CrossRef]
  52. Zhang, H.; Shi, X.; Pelech, S. Monitoring Protein Kinase Expression and Phosphorylation in Cell Lysates with Antibody Microarrays. Adv. Struct. Saf. Stud. 2016, 1360, 107–122. [Google Scholar] [CrossRef]
  53. Pierobon, M.; Wulfkuhle, J.; Liotta, L.A.; Iii, E.F.P. Utilization of Proteomic Technologies for Precision Oncology Applications. Cancer Treat. Res. 2019, 178, 171–187. [Google Scholar] [CrossRef] [PubMed]
  54. Zhang, H.; Pelech, S. Using protein microarrays to study phosphorylation-mediated signal transduction. Semin. Cell Dev. Biol. 2012, 23, 872–882. [Google Scholar] [CrossRef] [PubMed]
  55. Neradil, J.; Kyr, M.; Polaskova, K.; Kren, L.; Macigova, P.; Skoda, J.; Sterba, J.; Veselska, R. Phospho-Protein Arrays as Effec-tive Tools for Screening Possible Targets for Kinase Inhibitors and Their Use in Precision Pediatric Oncology. Front. Cology 2019, 9, 930. [Google Scholar]
  56. Sukswai, N.; Khoury, J.D. Immunohistochemistry Innovations for Diagnosis and Tissue-Based Biomarker Detection. Curr. Hematol. Malign Rep. 2019, 14, 368–375. [Google Scholar] [CrossRef]
  57. Krutzik, P.O.; Trejo, A.; Schulz, K.R.; Nolan, G.P. Phospho Flow Cytometry Methods for the Analysis of Kinase Signaling in Cell Lines and Primary Human Blood Samples. Methods Mol. Biol. 2010, 699, 179–202. [Google Scholar] [CrossRef]
  58. Beausoleil, S.A.; Jedrychowski, M.; Schwartz, D.; Elias, J.E.; Villén, J.; Li, J.; Cohn, M.A.; Cantley, L.C.; Gygi, S.P. Large-scale characterization of HeLa cell nuclear phosphoproteins. Proc. Natl. Acad. Sci. USA 2004, 101, 12130–12135. [Google Scholar] [CrossRef] [Green Version]
  59. Wolschin, F.; Wienkoop, S.; Weckwerth, W. Enrichment of phosphorylated proteins and peptides from complex mixtures using metal oxide/hydroxide affinity chromatography (MOAC). Proteomics 2005, 5, 4389–4397. [Google Scholar] [CrossRef] [PubMed]
  60. Ruprecht, B.; Koch, H.; Medard, G.; Mundt, M.; Kuster, B.; Lemeer, S. Comprehensive and Reproducible Phosphopeptide Enrichment Using Iron Immobilized Metal Ion Affinity Chromatography (Fe-IMAC) Columns. Mol. Cell. Proteom. 2015, 14, 205–215. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Cantin, G.T.; Shock, T.R.; Park, S.K.; Madhani, H.D.; Yates, J.R. Optimizing TiO2-Based Phosphopeptide Enrichment for Automated Multidimensional Liquid Chromatography Coupled to Tandem Mass Spectrometry. Anal. Chem. 2007, 79, 4666–4673. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Bak, S.; León, I.R.; Jensen, O.N.; Højlund, K. Tissue Specific Phosphorylation of Mitochondrial Proteins Isolated from Rat Liver, Heart Muscle, and Skeletal Muscle. J. Proteome Res. 2013, 12, 4327–4339. [Google Scholar] [CrossRef] [PubMed]
  63. Zhou, H.; Watts, J.D.; Aebersold, R. A systematic approach to the analysis of protein phosphorylation. Nat. Biotechnol. 2001, 19, 375–378. [Google Scholar] [CrossRef]
  64. Jiang, X.; Wang, Y. β-Elimination Coupled with Tandem Mass Spectrometry for the Identification of in Vivo and in Vitro Phosphorylation Sites in Maize Dehydrin DHN1 Protein. Biochemistry 2004, 43, 15567–15576. [Google Scholar] [CrossRef]
  65. Edelmann, M.J. Strong Cation Exchange Chromatography in Analysis of Posttranslational Modifications: Innovations and Perspectives. J. Biomed. Biotechnol. 2011, 2011, 1–7. [Google Scholar] [CrossRef]
  66. Mohammed, S.; Heck, A.J. Strong cation exchange (SCX) based analytical methods for the targeted analysis of protein post-translational modifications. Curr. Opin. Biotechnol. 2011, 22, 9–16. [Google Scholar] [CrossRef] [PubMed]
  67. Boersema, P.J.; Mohammed, S.; Heck, A.J.R. Hydrophilic interaction liquid chromatography (HILIC) in proteomics. Anal. Bioanal. Chem. 2008, 391, 151–159. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Alpert, A.J. Electrostatic Repulsion Hydrophilic Interaction Chromatography for Isocratic Separation of Charged Solutes and Selective Isolation of Phosphopeptides. Anal. Chem. 2008, 80, 62–76. [Google Scholar] [CrossRef] [Green Version]
  69. Bodenmiller, B.; Aebersold, R. Quantitative Analysis of Protein Phosphorylation on a System-Wide Scale by Mass Spectrometry-Based Proteomics. Methods Enzymol. 2010, 470, 317–334. [Google Scholar] [CrossRef]
  70. Leitner, A. Enrichment Strategies in Phosphoproteomics. Methods Mol. Biol. 2016, 1355, 105–121. [Google Scholar] [CrossRef]
  71. Zhou, H.; Ye, M.; Dong, J.; Han, G.; Jiang, X.; Wu, R.; Zou, H. Specific Phosphopeptide Enrichment with Immobilized Titanium Ion Affinity Chromatography Adsorbent for Phosphoproteome Analysis. J. Proteome Res. 2008, 7, 3957–3967. [Google Scholar] [CrossRef] [PubMed]
  72. Iliuk, A.B.; Martin, V.A.; Alicie, B.M.; Geahlen, R.L.; Tao, W.A. In-depth Analyses of Kinase-dependent Tyrosine Phosphoproteomes Based on Metal Ion-functionalized Soluble Nanopolymers. Mol. Cell. Proteom. 2010, 9, 2162–2172. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Jayasundera, K.B.; Iliuk, A.B.; Nguyen, A.; Higgins, R.; Geahlen, R.L.; Tao, W.A. Global Phosphoproteomics of Activated B Cells Using Complementary Metal Ion Functionalized Soluble Nanopolymers. Anal. Chem. 2014, 86, 6363–6371. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Zou, X.; Jie, J.; Yang, B. Single-Step Enrichment of N-Glycopeptides and Phosphopeptides with Novel Multifunctional Ti4+-Immobilized Dendritic Polyglycerol Coated Chitosan Nanomaterials. Anal. Chem. 2017, 89, 7520–7526. [Google Scholar] [CrossRef] [PubMed]
  75. Díez, I.A.; Govender, I.; Naicker, P.; Stoychev, S.; Jordaan, J.; Jensen, O.N. Zirconium(IV)-IMAC Revisited: Improved Performance and Phosphoproteome Coverage by Magnetic Microparticles for Phosphopeptide Affinity Enrichment. J. Proteome Res. 2021, 20, 453–462. [Google Scholar] [CrossRef] [PubMed]
  76. Dunn, J.D.; Reid, G.E.; Bruening, M.L. Techniques for phosphopeptide enrichment prior to analysis by mass spectrometry. Mass Spectrom. Rev. 2009, 29, 29–54. [Google Scholar] [CrossRef]
  77. Engholm-Keller, K.; Larsen, M.R. Titanium dioxide as chemo-affinity chromatographic sorbent of biomolecular compounds—Applications in acidic modification-specific proteomics. J. Proteom. 2011, 75, 317–328. [Google Scholar] [CrossRef] [PubMed]
  78. Carrera, M.; Cañas, B.; Lopez-Ferrer, D. Fast Global Phosphoproteome Profiling of Jurkat T Cells by HIFU-TiO2-SCX-LC-MS/MS. Anal. Chem. 2017, 89, 8853–8862. [Google Scholar] [CrossRef]
  79. Junemann, J.; Just, I.; Gerhard, R.; Pich, A. Quantitative Phosphoproteome Analysis of Clostridioides difficile Toxin B Treated Human Epithelial Cells. Front. Microbiol. 2018, 9, 3083. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  80. Hong, Y.; Zhan, Q.; Zheng, Y.; Pu, C.; Zhao, H.; Lan, M. Hydrophilic phytic acid-functionalized magnetic dendritic mesoporous silica nanospheres with immobilized Ti4+: A dual-purpose affinity material for highly efficient enrichment of glycopeptides/phosphopeptides. Talanta 2019, 197, 77–85. [Google Scholar] [CrossRef]
  81. Thingholm, T.E.; Jensen, O.N.; Robinson, P.J.; Larsen, M.R. SIMAC (Sequential Elution from IMAC), a Phosphoproteomics Strategy for the Rapid Separation of Monophosphorylated from Multiply Phosphorylated Peptides. Mol. Cell. Proteom. 2008, 7, 661–671. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  82. Engholm-Keller, K.; Larsen, M.R. Improving the Phosphoproteome Coverage for Limited Sample Amounts Using TiO2-SIMAC-HILIC (TiSH) Phosphopeptide Enrichment and Fractionation. Adv. Struct. Saf. Stud. 2016, 1355, 161–177. [Google Scholar] [CrossRef]
  83. Gropengiesser, J.; Varadarajan, B.T.; Stephanowitz, H.; Krause, E. The relative influence of phosphorylation and methylation on responsiveness of peptides to MALDI and ESI mass spectrometry. J. Mass Spectrom. 2009, 44, 821–831. [Google Scholar] [CrossRef] [PubMed]
  84. Steen, H.; Jebanathirajah, J.A.; Rush, J.; Morrice, N.; Kirschner, M.W. Phosphorylation Analysis by Mass Spectrometry. Mol. Cell. Proteom. 2006, 5, 172–181. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Wu, Z.; Tiambeng, T.N.; Cai, W.; Chen, B.; Lin, Z.; Gregorich, Z.R.; Ge, Y. Impact of Phosphorylation on the Mass Spectrometry Quantification of Intact Phosphoproteins. Anal. Chem. 2018, 90, 4935–4939. [Google Scholar] [CrossRef] [Green Version]
  86. Silva, A.M.; Vitorino, R.; Domingues, M.M.; Spickett, C.M.; Domingues, P. Post-translational Modifications and Mass Spectrometry Detection. Free. Radic. Biol. Med. 2013, 65, 925–941. [Google Scholar] [CrossRef] [Green Version]
  87. Solari, F.A.; Dell’Aica, M.; Sickmann, A.; Zahedi, R.P. Why phosphoproteomics is still a challenge. Mol. BioSyst. 2015, 11, 1487–1493. [Google Scholar] [CrossRef] [Green Version]
  88. Mann, M.; Ong, S.-E.; Grønborg, M.; Steen, H.; Jensen, O.N.; Pandey, A. Analysis of protein phosphorylation using mass spectrometry: Deciphering the phosphoproteome. Trends Biotechnol. 2002, 20, 261–268. [Google Scholar] [CrossRef]
  89. Tichy, A.; Salovska, B.; Rehulka, P.; Klimentova, J.; Vavrova, J.; Stulik, J.; Hernychova, L. Phosphoproteomics: Searching for a needle in a haystack. J. Proteom. 2011, 74, 2786–2797. [Google Scholar] [CrossRef]
  90. Rose, A.; Mayor, T. Exploring the Rampant Expansion of Ubiquitin Proteomics. Methods Mol. Biol. 2018, 1844, 345–362. [Google Scholar] [CrossRef]
  91. Zhang, Y.; Fonslow, B.R.; Shan, B.; Baek, M.-C.; Yates, J.R. Protein Analysis by Shotgun/Bottom-up Proteomics. Chem. Rev. 2013, 113, 2343–2394. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  92. Fulzele, A.; Bennett, E.J. Ubiquitin diGLY Proteomics as an Approach to Identify and Quantify the Ubiquitin-Modified Proteome. Adv. Struct. Saf. Stud. 2018, 1844, 363–384. [Google Scholar] [CrossRef]
  93. Kim, W.; Bennett, E.J.; Huttlin, E.L.; Guo, A.; Li, J.; Possemato, A.; Sowa, M.E.; Rad, R.; Rush, J.; Comb, M.J.; et al. Systematic and Quantitative Assessment of the Ubiquitin-Modified Proteome. Mol. Cell 2011, 44, 325–340. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  94. Danielsen, J.M.R.; Sylvestersen, K.B.; Bekker-Jensen, S.; Szklarczyk, D.; Poulsen, J.W.; Horn, H.; Jensen, L.J.; Mailand, N.; Nielsen, M.L. Mass Spectrometric Analysis of Lysine Ubiquitylation Reveals Promiscuity at Site Level. Mol. Cell. Proteom. 2011, 10, 110–003590. [Google Scholar] [CrossRef] [Green Version]
  95. Pontrelli, P.; Conserva, F.; Papale, M.; Oranger, A.; Barozzino, M.; Vocino, G.; Rocchetti, M.T.; Gigante, M.; Castellano, G.; Rossini, M.; et al. Lysine 63 ubiquitination is involved in the progression of tubular damage in diabetic nephropathy. FASEB J. 2017, 31, 308–319. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  96. Doellinger, J.; Grossegesse, M.; Nitsche, A.; Lasch, P. DMSO as a mobile phase additive enhances detection of ubiquitination sites by nano-LC-ESI-MS/MS. J. Mass Spectrom. 2018, 53, 183–187. [Google Scholar] [CrossRef]
  97. Porras-Yakushi, T.R.; Sweredoski, M.J.; Hess, S. ETD Outperforms CID and HCD in the Analysis of the Ubiquitylated Proteome. J. Am. Soc. Mass Spectrom. 2015, 26, 1580–1587. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  98. Udeshi, N.D.; Mani, D.C.; Satpathy, S.; Fereshetian, S.; Gasser, J.A.; Svinkina, T.; Olive, M.E.; Ebert, B.L.; Mertins, P.; Carr, S.A. Rapid and deep-scale ubiquitylation profiling for biology and translational research. Nat. Commun. 2020, 11, 1–11. [Google Scholar] [CrossRef] [Green Version]
  99. Yau, R.; Rape, M. The increasing complexity of the ubiquitin code. Nat. Cell Biol. 2016, 18, 579–586. [Google Scholar] [CrossRef]
  100. Harris, L.D.; Le Pen, J.; Scholz, N.; Mieszczanek, J.; Vaughan, N.; Davis, S.; Berridge, G.; Kessler, B.M.; Bienz, M.; Licchesi, J.D. The deubiquitinase TRABID stabilizes the K29/K48-specific E3 ubiquitin ligase HECTD1. J. Biol. Chem. 2021, 296, 100246. [Google Scholar] [CrossRef]
  101. Ordureau, A.; Münch, C.; Harper, J.W. Quantifying Ubiquitin Signaling. Mol. Cell 2015, 58, 660–676. [Google Scholar] [CrossRef] [Green Version]
  102. Wu, X.; Liu, S.; Sagum, C.; Chen, J.; Singh, R.; Chaturvedi, A.; Horton, J.R.; Kashyap, T.R.; Fushman, D.; Cheng, X.; et al. Crosstalk between Lys63- and Lys11-polyubiquitin signaling at DNA damage sites is driven by Cezanne. Genes Dev. 2019, 33, 1702–1717. [Google Scholar] [CrossRef] [PubMed]
  103. Akinjiyan, F.A.; Fazal, A.; Hild, M.; Beckwith, R.E.J.; Ross, N.T.; Paulk, J.; Carbonneau, S. A Novel Luminescence-Based High-Throughput Approach for Cellular Resolution of Protein Ubiquitination Using Tandem Ubiquitin Binding Entities (TUBEs). SLAS Discov. Adv. Life Sci. R D 2020, 25, 350–360. [Google Scholar] [CrossRef] [PubMed]
  104. Hjerpe, R.; Aillet, F.; Lopitz-Otsoa, F.; Lang, V.; England, P.; Rodriguez, M.S. Efficient protection and isolation of ubiquitylated proteins using tandem ubiquitin-binding entities. EMBO Rep. 2009, 10, 1250–1258. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  105. Meyer, H.-J.; Rape, M. Enhanced Protein Degradation by Branched Ubiquitin Chains. Cell 2014, 157, 910–921. [Google Scholar] [CrossRef] [Green Version]
  106. Crowe, S.O.; Rana, A.S.J.B.; Deol, K.K.; Ge, Y.; Strieter, E.R. Ubiquitin Chain Enrichment Middle-Down Mass Spectrometry Enables Characterization of Branched Ubiquitin Chains in Cellulo. Anal. Chem. 2017, 89, 4428–4434. [Google Scholar] [CrossRef] [Green Version]
  107. Valkevich, E.M.; Sanchez, N.A.; Ge, Y.; Strieter, E.R. Middle-Down Mass Spectrometry Enables Characterization of Branched Ubiquitin Chains. Biochemistry 2014, 53, 4979–4989. [Google Scholar] [CrossRef] [PubMed]
  108. Wang, Y.-S.; Wu, K.-P.; Jiang, H.-K.; Kurkute, P.; Chen, R.-H. Branched Ubiquitination: Detection Methods, Biological Functions and Chemical Synthesis. Molecules 2020, 25, 5200. [Google Scholar] [CrossRef]
  109. King, S.L.; Joshi, H.J.; Schjoldager, K.T.; Halim, A.; Madsen, T.D.; Dziegiel, M.H.; Woetmann, A.; Vakhrushev, S.Y.; Wandall, H.H. Characterizing the O-glycosylation landscape of human plasma, platelets, and endothelial cells. Blood Adv. 2017, 1, 429–442. [Google Scholar] [CrossRef] [Green Version]
  110. Anonsen, J.H.; Vik, Å.; Egge-Jacobsen, W.; Koomey, M. An Extended Spectrum of Target Proteins and Modification Sites in the General O-Linked Protein Glycosylation System in Neisseria gonorrhoeae. J. Proteome Res. 2012, 11, 5781–5793. [Google Scholar] [CrossRef] [PubMed]
  111. Dan, X.; Liu, W.; Ng, T.B. Development and Applications of Lectins as Biological Tools in Biomedical Research. Med. Res. Rev. 2015, 36, 221–247. [Google Scholar] [CrossRef] [PubMed]
  112. Durham, M.; Regnier, F.E. Targeted glycoproteomics: Serial lectin affinity chromatography in the selection of O-glycosylation sites on proteins from the human blood proteome. J. Chromatogr. A 2006, 1132, 165–173. [Google Scholar] [CrossRef]
  113. Totten, S.M.; Adusumilli, R.; Kullolli, M.; Tanimoto, C.; Brooks, J.D.; Mallick, P.; Pitteri, S.J. Multi-lectin Affinity Chromatography and Quantitative Proteomic Analysis Reveal Differential Glycoform Levels between Prostate Cancer and Benign Prostatic Hyperplasia Sera. Sci. Rep. 2018, 8, 1–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  114. Totten, S.M.; Kullolli, M.; Pitteri, S.J. Multi-Lectin Affinity Chromatography for Separation, Identification, and Quantitation of Intact Protein Glycoforms in Complex Biological Mixtures. Methods Mol. Biol. 2017, 1550, 99–113. [Google Scholar] [CrossRef] [PubMed]
  115. Xiao, H.; Sun, F.; Suttapitugsakul, S.; Wu, R. Global and site-specific analysis of protein glycosylation in complex biological systems with Mass Spectrometry. Mass Spectrom. Rev. 2019, 38, 356–379. [Google Scholar] [CrossRef]
  116. Bai, H.; Pan, Y.; Qi, L.; Liu, L.; Zhao, X.; Dong, H.; Cheng, X.; Qin, W.; Wang, X. Development a hydrazide-functionalized thermosensitive polymer based homogeneous system for highly efficient N-glycoprotein/glycopeptide enrichment from human plasma exosome. Talanta 2018, 186, 513–520. [Google Scholar] [CrossRef] [PubMed]
  117. Klement, E.; Lipinszki, Z.; Kupihár, Z.; Udvardy, A.; Medzihradszky, K.F. Enrichment of O-GlcNAc Modified Proteins by the Periodate Oxidation−Hydrazide Resin Capture Approach. J. Proteome Res. 2010, 9, 2200–2206. [Google Scholar] [CrossRef] [Green Version]
  118. Calvano, C.D.; Zambonin, C.G.; Jensen, O.N. Assessment of lectin and HILIC based enrichment protocols for characterization of serum glycoproteins by mass spectrometry. J. Proteom. 2008, 71, 304–317. [Google Scholar] [CrossRef]
  119. Woo, C.M.; Iavarone, A.T.; Spiciarich, D.R.; Palaniappan, K.K.; Bertozzi, C.R. Isotope-targeted glycoproteomics (IsoTaG): A mass-independent platform for intact N- and O-glycopeptide discovery and analysis. Nat. Methods 2015, 12, 561–567. [Google Scholar] [CrossRef]
  120. Sun, S.; Shah, P.; Eshghi, S.T.; Yang, W.; Trikannad, N.; Yang, S.; Chen, L.; Aiyetan, P.; Höti, N.; Zhang, Z.; et al. Comprehensive analysis of protein glycosylation by solid-phase extraction of N-linked glycans and glycosite-containing peptides. Nat. Biotechnol. 2016, 34, 84–88. [Google Scholar] [CrossRef] [Green Version]
  121. Triguero, A.; Cabrera, G.; Royle, L.; Harvey, D.J.; Rudd, P.M.; Dwek, R.A.; Bardor, M.; Lerouge, P.; Cremata, J.A. Chemical and enzymatic N-glycan release comparison for N-glycan profiling of monoclonal antibodies expressed in plants. Anal. Biochem. 2010, 400, 173–183. [Google Scholar] [CrossRef] [PubMed]
  122. Stavenhagen, K.; Hinneburg, H.; Thaysen-Andersen, M.; Hartmann, L.; Silva, D.V.; Fuchser, J.; Kaspar, S.; Rapp, E.; Seeberger, P.H.; Kolarich, D. Quantitative mapping of glycoprotein micro-heterogeneity and macro-heterogeneity: An evaluation of mass spectrometry signal strengths using synthetic peptides and glycopeptides. J. Mass Spectrom. 2013, 48, 627–639. [Google Scholar] [CrossRef]
  123. Wells, L.; Vosseller, K.; Cole, R.N.; Cronshaw, J.M.; Matunis, M.J.; Hart, G.W. Mapping Sites of O-GlcNAc Modification Using Affinity Tags for Serine and Threonine Post-translational Modifications. Mol. Cell. Proteom. 2002, 1, 791–804. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  124. Steentoft, C.; Vakhrushev, S.Y.; Vester-Christensen, M.B.; Schjoldager, K.T.-B.G.; Kong, Y.; Bennett, E.P.; Mandel, U.; Wandall, H.H.; Levery, S.B.; Clausen, H. Mining the O-glycoproteome using zinc-finger nuclease–glycoengineered SimpleCell lines. Nat. Methods 2011, 8, 977–982. [Google Scholar] [CrossRef] [PubMed]
  125. Singh, C.; Zampronio, C.G.; Creese, A.J.; Cooper, H.J. Higher Energy Collision Dissociation (HCD) Product Ion-Triggered Electron Transfer Dissociation (ETD) Mass Spectrometry for the Analysis of N-Linked Glycoproteins. J. Proteome Res. 2012, 11, 4517–4525. [Google Scholar] [CrossRef]
  126. Eifler, K.; Vertegaal, A.C. SUMOylation-Mediated Regulation of Cell Cycle Progression and Cancer. Trends Biochem. Sci. 2015, 40, 779–793. [Google Scholar] [CrossRef] [Green Version]
  127. Park, I.S.; Han, Y.G.; Chung, H.J.; Jung, Y.W.; Kim, Y.; Kim, H. SUMOylation regulates nuclear localization and stability of TRAIP/RNF206. Biochem. Biophys. Res. Commun. 2016, 470, 881–887. [Google Scholar] [CrossRef]
  128. Rosonina, E.; Akhter, A.; Dou, Y.; Babu, J.; Theivakadadcham, V.S.S. Regulation of transcription factors by sumoylation. Transcription 2017, 8, 220–231. [Google Scholar] [CrossRef] [Green Version]
  129. Zhang, Y.; Li, Y.; Tang, B.; Zhang, C.-Y. The strategies for identification and quantification of SUMOylation. Chem. Commun. 2017, 53, 6989–6998. [Google Scholar] [CrossRef]
  130. Sheng, Z.; Wang, X.; Ma, Y.; Zhang, D.; Yang, Y.; Zhang, P.; Zhu, H.; Xu, N.; Liang, S. MS-based strategies for identification of protein SUMOylation modification. Electrophoresis 2019, 40, 2877–2887. [Google Scholar] [CrossRef] [PubMed]
  131. Impens, F.; Radoshevich, L.; Cossart, P.; Ribet, D. Mapping of SUMO sites and analysis of SUMOylation changes induced by external stimuli. Proc. Natl. Acad. Sci. USA 2014, 111, 12432–12437. [Google Scholar] [CrossRef] [Green Version]
  132. Lamoliatte, F.; Caron, D.; Durette, C.; Mahrouche, L.; Maroui, M.A.; Caron-Lizotte, O.; Bonneil, E.; Chelbi-Alix, M.K.; Thibault, P. Large-scale analysis of lysine SUMOylation by SUMO remnant immunoaffinity profiling. Nat. Commun. 2014, 5, 5409. [Google Scholar] [CrossRef] [Green Version]
  133. Tatham, M.H.; Rodriguez, M.S.; Xirodimas, D.P.; Hay, R.T. Detection of protein SUMOylation in vivo. Nat. Protoc. 2009, 4, 1363–1371. [Google Scholar] [CrossRef] [PubMed]
  134. Galisson, F.; Mahrouche, L.; Courcelles, M.; Bonneil, E.; Meloche, S.; Chelbi-Alix, M.K.; Thibault, P. A Novel Proteomics Approach to Identify SUMOylated Proteins and Their Modification Sites in Human Cells. Mol. Cell. Proteom. 2011, 10, S1–S15. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  135. Oh, S.-M.; Liu, Z.; Okada, M.; Jang, S.-W.; Liu, X.; Chan, C.-B.; Luo, H.; Ye, K. Ebp1 sumoylation, regulated by TLS/FUS E3 ligase, is required for its anti-proliferative activity. Oncogene 2009, 29, 1017–1030. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  136. Zubiete-Franco, I.; García-Rodríguez, J.L.; Lopitz-Otsoa, F.; Serrano-Macia, M.; Simon, J.; Fernández-Tussy, P.; Barbier-Torres, L.; Fernández-Ramos, D.; Gutiérrez-De-Juan, V.; De Davalillo, S.L.; et al. SUMOylation regulates LKB1 localization and its oncogenic activity in liver cancer. EBioMedicine 2019, 40, 406–421. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  137. Becker, J.; Barysch, S.V.; Karaca, S.; Dittner, C.; Hsiao, H.-H.; Diaz, M.B.; Herzig, S.; Urlaub, H.; Melchior, F. Detecting endogenous SUMO targets in mammalian cells and tissues. Nat. Struct. Mol. Biol. 2013, 20, 525–531. [Google Scholar] [CrossRef]
  138. Lumpkin, R.J.; Gu, H.; Zhu, Y.; Leonard, M.; Ahmad, A.S.; Clauser, K.R.; Meyer, J.G.; Bennett, E.J.; Komives, E.A. Site-specific identification and quantitation of endogenous SUMO modifications under native conditions. Nat. Commun. 2017, 8, 1–11. [Google Scholar] [CrossRef] [Green Version]
  139. Hendriks, I.A.; Lyon, D.; Su, D.; Skotte, N.H.; Daniel, J.A.; Jensen, L.J.; Nielsen, M.L. Site-specific characterization of endogenous SUMOylation across species and organs. Nat. Commun. 2018, 9, 1–17. [Google Scholar] [CrossRef] [Green Version]
  140. Hendriks, I.A.; D’Souza, R.C.; Chang, J.-G.; Mann, M.; Vertegaal, A.C.O. System-wide identification of wild-type SUMO-2 conjugation sites. Nat. Commun. 2015, 6, 7289. [Google Scholar] [CrossRef] [PubMed]
  141. Bruderer, R.; Tatham, M.H.; Plechanovová, A.; Matic, I.; Garg, A.K.; Hay, R.T. Purification and identification of endogenous polySUMO conjugates. EMBO Rep. 2011, 12, 142–148. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  142. Lopitz-Otsoa, F.; Delgado, T.C.; Lachiondo-Ortega, S.; Azkargorta, M.; Elortza, F.; Rodríguez, M.S.; Martínez-Chantar, M.L. SUMO-Binding Entities (SUBEs) as Tools for the Enrichment, Isolation, Identification, and Characterization of the SUMO Proteome in Liver Cancer. J. Vis. Exp. 2019, e60098. [Google Scholar] [CrossRef] [PubMed]
  143. Hendriks, I.A.; D’Souza, R.C.J.; Yang, B.; Verlaan-de Vries, M.; Mann, M.; Vertegaal, A.C. Uncovering global SUMOylation signaling networks in a site-specific manner. Nat. Struct. Mol. Biol. 2014, 21, 927–936. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  144. Handu, M.; Kaduskar, B.; Ravindranathan, R.; Soory, A.; Giri, R.; Elango, V.B.; Gowda, H.; Ratnaparkhi, G.S. SUMO-Enriched Proteome for Drosophila Innate Immune Response. G3: Genes|Genomes|Genetics 2015, 5, 2137–2154. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  145. A Pedrioli, P.G.; Raught, B.; Zhang, X.-D.; Rogers, R.; Aitchison, J.; Matunis, M.; Aebersold, R. Automated identification of SUMOylation sites using mass spectrometry and SUMmOn pattern recognition software. Nat. Chem. Biol. 2006, 3, 533–539. [Google Scholar] [CrossRef] [PubMed]
  146. Chen, Y.-Z.; Chen, Z.; Gong, Y.-A.; Ying, G. SUMOhydro: A Novel Method for the Prediction of Sumoylation Sites Based on Hydrophobic Properties. PLoS ONE 2012, 7, e39195. [Google Scholar] [CrossRef] [Green Version]
  147. Dehzangi, A.; López, Y.; Taherzadeh, G.; Sharma, A.; Tsunoda, T. SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure. Molecules 2018, 23, 3260. [Google Scholar] [CrossRef] [Green Version]
  148. Diallo, I.; Seve, M.; Cunin, V.; Minassian, F.; Poisson, J.-F.; Michelland, S.; Bourgoin-Voillard, S. Current trends in protein acetylation analysis. Expert Rev. Proteom. 2019, 16, 139–159. [Google Scholar] [CrossRef]
  149. Murn, J.; Shi, Y. The winding path of protein methylation research: Milestones and new frontiers. Nat. Rev. Mol. Cell Biol. 2017, 18, 517–527. [Google Scholar] [CrossRef]
  150. Chen, J.; Chen, L.-F. Methods to detect NF-κB acetylation and methylation. Breast Cancer 2015, 1280, 395–409. [Google Scholar] [CrossRef] [Green Version]
  151. Schreiber, K.J.; Lewis, J.D. Protein Acetylation in Pathogen Virulence and Host Defense: In Vitro Detection of Protein Acetylation by Radiolabeled Acetyl Coenzyme A. Breast Cancer 2019, 1991, 23–32. [Google Scholar] [CrossRef]
  152. Hatakeyama, D.; Shoji, M.; Yamayoshi, S.; Yoh, R.; Ohmi, N.; Takenaka, S.; Saitoh, A.; Arakaki, Y.; Masuda, A.; Komatsu, T.; et al. Influenza A virus nucleoprotein is acetylated by histone acetyltransferases PCAF and GCN5. J. Biol. Chem. 2018, 293, 7126–7138. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  153. Ronzoni, S.; Faretta, M.; Ballarini, M.; Pelicci, P.; Minucci, S. New method to detect histone acetylation levels by flow cytometry. Cytom. Part A 2005, 66, 52–61. [Google Scholar] [CrossRef] [Green Version]
  154. Yucel, N.; Wang, Y.X.; Mai, T.; Porpiglia, E.; Lund, P.J.; Markov, G.; Garcia, B.A.; Bendall, S.C.; Angelo, M.; Blau, H.M. Glucose Metabolism Drives Histone Acetylation Landscape Transitions that Dictate Muscle Stem Cell Function. Cell Rep. 2019, 27, 3939–3955.e6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  155. Uitrakul, S.; Hutton, C.; Veal, G.J.; Jamieson, D. A novel imaging flow cytometry method for the detection of histone H4 acetylation in myeloid cells. Eur. J. Clin. Investig. 2019, 49, e13115. [Google Scholar] [CrossRef] [PubMed]
  156. Guo, J.; Wang, C.; Han, Y.; Liu, Z.; Wu, T.; Liu, Y.; Tan, Y.; Cai, X.; Cao, Y.; Wang, B.; et al. Identification of Lysine Acetylation in Mycobacterium abscessus Using LC–MS/MS after Immunoprecipitation. J. Proteome Res. 2016, 15, 2567–2578. [Google Scholar] [CrossRef] [PubMed]
  157. Schilling, B.; Meyer, J.G.; Wei, L.; Ott, M.; Verdin, E. High-Resolution Mass Spectrometry to Identify and Quantify Acetylation Protein Targets. Methods Mol. Biol. 2019, 1983, 3–16. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  158. Zhu, L.; Fung, S.-Y.; Xie, G.; Wong, L.-Y.R.; Jin, D.-Y.; Cai, Z. Identification of Lysine Acetylation Sites on MERS-CoV Replicase pp1ab. Mol. Cell. Proteom. 2020, 19, 1303–1309. [Google Scholar] [CrossRef] [PubMed]
  159. Gevaert, K.; Goethals, M.; Martens, L.; Van Damme, J.; Staes, A.; Thomas, G.R.; Vandekerckhove, J. Exploring proteomes and analyzing protein processing by mass spectrometric identification of sorted N-terminal peptides. Nat. Biotechnol. 2003, 21, 566–569. [Google Scholar] [CrossRef] [PubMed]
  160. Van Damme, P.; Kalvik, T.V.; Starheim, K.K.; Jonckheere, V.; Myklebust, L.M.; Menschaert, G.; Varhaug, J.E.; Gevaert, K.; Arnesen, T. A Role for Human N-alpha Acetyltransferase 30 (Naa30) in Maintaining Mitochondrial Integrity. Mol. Cell. Proteom. 2016, 15, 3361–3372. [Google Scholar] [CrossRef] [Green Version]
  161. Dinh, T.V.; Bienvenut, W.V.; Linster, E.; Feldman-Salit, A.; Jung, V.A.; Meinnel, T.; Hell, R.; Giglione, C.; Wirtz, M. Molecular identification and functional characterization of the first Nα-acetyltransferase in plastids by global acetylome profiling. Proteomics 2015, 15, 2426–2435. [Google Scholar] [CrossRef] [Green Version]
  162. Bienvenut, W.V.; Giglione, C.; Meinnel, T. SILProNAQ: A Convenient Approach for Proteome-Wide Analysis of Protein N-Termini and N-Terminal Acetylation Quantitation. Breast Cancer 2017, 1574, 17–34. [Google Scholar] [CrossRef]
  163. Romanick, S.S.; Ulrich, C.; Schlauch, K.; Hostler, A.; Payne, J.; Woolsey, R.; Quilici, D.; Feng, Y.; Ferguson, B.S. Obesity-mediated regulation of cardiac protein acetylation: Parallel analysis of total and acetylated proteins via TMT-tagged mass spectrometry. Biosci. Rep. 2018, 38. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  164. Zhang, H.; Liu, T.; Zhang, Z.; Payne, S.H.; Zhang, B.; McDermott, J.E.; Zhou, J.-Y.; Petyuk, V.A.; Chen, L.; Ray, D.; et al. Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer. Cell 2016, 166, 755–765. [Google Scholar] [CrossRef] [Green Version]
  165. Zhang, T.; Meng, J.; Liu, X.; Peng, X.; Cheng, Z.; Zhang, F.; Zhang, X. ING5 differentially regulates protein lysine acetylation and promotes p300 autoacetylation. Oncotarget 2017, 9, 1617–1629. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  166. Gehrig, P.M.; Hunziker, P.E.; Zahariev, S.; Pongor, S. Fragmentation pathways of NG-methylated and unmodified arginine residues in peptides studied by ESI-MS/MS and MALDI-MS. J. Am. Soc. Mass Spectrom. 2004, 15, 142–149. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  167. Jung, S.Y.; Li, Y.; Wang, Y.; Chen, Y.; Zhao, A.Y.; Qin, J. Complications in the Assignment of 14 and 28 Da Mass Shift Detected by Mass Spectrometry as in Vivo Methylation from Endogenous Proteins. Anal. Chem. 2008, 80, 1721–1729. [Google Scholar] [CrossRef] [PubMed]
  168. Afjehi-Sadat, L.; Garcia, B.A. Comprehending dynamic protein methylation with mass spectrometry. Curr. Opin. Chem. Biol. 2013, 17, 12–19. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  169. Uhlmann, T.; Geoghegan, V.L.; Thomas, B.; Ridlova, G.; Trudgian, D.C.; Acuto, O. A Method for Large-scale Identification of Protein Arginine Methylation. Mol. Cell. Proteom. 2012, 11, 1489–1499. [Google Scholar] [CrossRef] [Green Version]
  170. Wang, Q.; Wang, K.; Ye, M. Strategies for large-scale analysis of non-histone protein methylation by LC-MS/MS. Analyst 2017, 142, 3536–3548. [Google Scholar] [CrossRef]
  171. Hartel, N.G.; Chew, B.; Qin, J.; Xu, J.; Graham, N.A. Deep Protein Methylation Profiling by Combined Chemical and Immunoaffinity Approaches Reveals Novel PRMT1 Targets. Mol. Cell. Proteom. 2019, 18, 2149–2164. [Google Scholar] [CrossRef] [PubMed]
  172. Guo, A.; Gu, H.; Zhou, J.; Mulhern, D.; Wang, Y.; Lee, K.A.; Yang, V.; Aguiar, M.; Kornhauser, J.; Jia, X.; et al. Immunoaffinity Enrichment and Mass Spectrometry Analysis of Protein Methylation. Mol. Cell. Proteom. 2014, 13, 372–387. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  173. Carlson, S.M.; E Moore, K.; Green, E.M.; Martín, G.M.; Gozani, O. Proteome-wide enrichment of proteins modified by lysine methylation. Nat. Protoc. 2013, 9, 37–50. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  174. Yu, R.; Wu, H.; Ismail, H.; Du, S.; Cao, J.; Wang, J.; Ward, T.; Yang, F.; Gui, P.; Ali, M.; et al. Methylation of PLK1 by SET7/9 ensures accurate kinetochore–microtubule dynamics. J. Mol. Cell Biol. 2020, 12, 462–476. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  175. Wang, Q.; Liu, Z.; Wang, K.; Wang, Y.; Ye, M. A new chromatographic approach to analyze methylproteome with enhanced lysine methylation identification performance. Anal. Chim. Acta 2019, 1068, 111–119. [Google Scholar] [CrossRef]
  176. Katsanovskaja, K.; Driver, T.; Pipkorn, R.; Edelson-Averbukh, M. Negative Ion Mode Collision-Induced Dissociation for Analysis of Protein Arginine Methylation. J. Am. Soc. Mass Spectrom. 2019, 30, 1229–1241. [Google Scholar] [CrossRef]
  177. Ong, S.-E.; Mittler, G.; Mann, M. Identifying and quantifying in vivo methylation sites by heavy methyl SILAC. Nat. Chem. Biol. 2004, 1, 119–126. [Google Scholar] [CrossRef]
  178. Geoghegan, V.; Guo, A.; Trudgian, D.; Thomas, B.; Acuto, O. Comprehensive identification of arginine methylation in primary T cells reveals regulatory roles in cell signalling. Nat. Commun. 2015, 6, 6758. [Google Scholar] [CrossRef]
  179. Wang, K.; Ye, M. Enrichment of Methylated Peptides Using an Antibody-free Approach for Global Methylproteomics Analysis. Curr. Protoc. Protein Sci. 2018, 91, 14.18.1–14.18.14. [Google Scholar] [CrossRef]
  180. Zheng, Y.; Fornelli, L.; Compton, P.D.; Sharma, S.; Canterbury, J.; Mullen, C.; Zabrouskov, V.; Fellers, R.T.; Thomas, P.M.; Licht, J.D.; et al. Unabridged Analysis of Human Histone H3 by Differential Top-Down Mass Spectrometry Reveals Hypermethylated Proteoforms from MMSET/NSD2 Overexpression. Mol. Cell. Proteom. 2016, 15, 776–790. [Google Scholar] [CrossRef] [Green Version]
  181. Cristobal, A.; Marino, F.; Post, H.; Toorn, H.W.P.V.D.; Mohammed, S.; Heck, A.J.R. Toward an Optimized Workflow for Middle-Down Proteomics. Anal. Chem. 2017, 89, 3318–3325. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  182. Wu, Z.; Huang, R.; Yuan, L. Crosstalk of intracellular post-translational modifications in cancer. Arch. Biochem. Biophys. 2019, 676, 108138. [Google Scholar] [CrossRef] [PubMed]
  183. Meek, D.W. Regulation of the p53 response and its relationship to cancer1. Biochem. J. 2015, 469, 325–346. [Google Scholar] [CrossRef] [PubMed]
  184. Mertins, P.; Qiao, J.W.; Patel, J.; Udeshi, N.D.; Clauser, K.R.; Mani, D.R.; Burgess, M.W.; Gillette, M.A.; Jaffe, J.D.; Carr, S.A. Integrated proteomic analysis of post-translational modifications by serial enrichment. Nat. Methods 2013, 10, 634–637. [Google Scholar] [CrossRef] [PubMed]
  185. Vu, L.D.; Gevaert, K.; De Smet, I. Protein Language: Post-Translational Modifications Talking to Each Other. Trends Plant Sci. 2018, 23, 1068–1080. [Google Scholar] [CrossRef]
  186. Singh, V.; Ram, M.; Kumar, R.; Prasad, R.; Roy, B.K.; Singh, K.K. Phosphorylation: Implications in Cancer. Protein J. 2017, 36, 1–6. [Google Scholar] [CrossRef]
  187. Han, Z.-J.; Feng, Y.-H.; Gu, B.-H.; Li, Y.-M.; Chen, H. The post-translational modification, SUMOylation, and cancer (Review). Int. J. Oncol. 2018, 52, 1081–1094. [Google Scholar] [CrossRef] [Green Version]
  188. Mansour, M.A. Ubiquitination: Friend and foe in cancer. Int. J. Biochem. Cell Biol. 2018, 101, 80–93. [Google Scholar] [CrossRef] [PubMed]
  189. Abramson, H.N. The Multiple Myeloma Drug Pipeline—2018: A Review of Small Molecules and Their Therapeutic Targets. Clin. Lymphoma Myeloma Leuk. 2018, 18, 611–627. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  190. Salesse, S.; Verfaillie, C.M. BCR/ABL: From molecular mechanisms of leukemia induction to treatment of chronic myelogenous leukemia. Oncogene 2002, 21, 8547–8559. [Google Scholar] [CrossRef] [Green Version]
  191. Raedler, L.A. Jakafi (Ruxolitinib): First FDA-Approved Medication for the Treatment of Patients with Polycythemia Vera. Am. Health Drug Benefits 2015, 8, 75–79. [Google Scholar]
  192. Verstovsek, S.; Gotlib, J.; Mesa, R.A.; Vannucchi, A.M.; Kiladjian, J.-J.; Cervantes, F.; Harrison, C.N.; Paquette, R.; Sun, W.; Naim, A.; et al. Long-term survival in patients treated with ruxolitinib for myelofibrosis: COMFORT-I and -II pooled analyses. J. Hematol. Oncol. 2017, 10, 1–6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  193. Levis, M. Midostaurin approved for FLT3-mutated AML. Blood 2017, 129, 3403–3406. [Google Scholar] [CrossRef]
  194. Stone, R.M.; Manley, P.W.; Larson, R.A.; Capdeville, R. Midostaurin: Its odyssey from discovery to approval for treating acute myeloid leukemia and advanced systemic mastocytosis. Blood Adv. 2018, 2, 444–453. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  195. Dhillon, S. Gilteritinib: First Global Approval. Drugs 2019, 79, 331–339. [Google Scholar] [CrossRef]
  196. Waller, C.F. Imatinib Mesylate. Methods Mol. Biol. 2018, 212, 1–27. [Google Scholar]
  197. Lindauer, M.; Hochhaus, A. Dasatinib. Methods Mol. Biol. 2018, 212, 29–68. [Google Scholar]
  198. Sacha, T.; Saglio, G. Nilotinib in the treatment of chronic myeloid leukemia. Futur. Oncol. 2019, 15, 953–965. [Google Scholar] [CrossRef]
  199. Cortes, J.E.; Gambacorti-Passerini, C.; Deininger, M.W.; Mauro, M.J.; Chuah, C.; Kim, D.-W.; Dyagil, I.; Glushko, N.; Milojkovic, D.; Le Coutre, P.; et al. Bosutinib Versus Imatinib for Newly Diagnosed Chronic Myeloid Leukemia: Results From the Randomized BFORE Trial. J. Clin. Oncol. 2018, 36, 231–237. [Google Scholar] [CrossRef] [PubMed]
  200. Massaro, F.; Molica, M.; Breccia, M.; Massaro, M.M.A.M.B.F. Ponatinib: A Review of Efficacy and Safety. Curr. Cancer Drug Targets 2018, 18, 847–856. [Google Scholar] [CrossRef] [PubMed]
  201. Charalambous, A.; Schwarzbich, M.-A.; Witzens-Harig, M. Ibrutinib. Methods Mol. Biol. 2018, 212, 133–168. [Google Scholar]
  202. Fruman, D.A.; Chiu, H.; Hopkins, B.D.; Bagrodia, S.; Cantley, L.C.; Abraham, R.T. The PI3K Pathway in Human Disease. Cell 2017, 170, 605–635. [Google Scholar] [CrossRef] [Green Version]
  203. Robak, P.; Robak, T. Bortezomib for the Treatment of Hematologic Malignancies: 15 Years Later. Drugs R D 2019, 19, 73–92. [Google Scholar] [CrossRef] [Green Version]
  204. Groen, K.; Van De Donk, N.W.C.J.; Stege, C.; Zweegman, S.; Nijhof, I. Carfilzomib for relapsed and refractory multiple myeloma. Cancer Manag. Res. 2019, ume 11, 2663–2675. [Google Scholar] [CrossRef] [Green Version]
  205. Richardson, P.G.; Zweegman, S.; O’Donnell, E.K.; Laubach, J.P.; Raje, N.; Voorhees, P.; Ferrari, R.H.; Skacel, T.; Kumar, S.K.; Lonial, S. Ixazomib for the treatment of multiple myeloma. Expert Opin. Pharmacother. 2018, 19, 1949–1968. [Google Scholar] [CrossRef] [PubMed]
  206. Tomita, A.; Kiyoi, H.; Naoe, T. Mechanisms of action and resistance to all-trans retinoic acid (ATRA) and arsenic trioxide (As2O3) in acute promyelocytic leukemia. Int. J. Hematol. 2013, 97, 717–725. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  207. Fink, E.C.; Ebert, B.L. The novel mechanism of lenalidomide activity. Blood 2015, 126, 2366–2369. [Google Scholar] [CrossRef] [Green Version]
  208. Zhu, Y.X.; Kortuem, K.M.; Stewart, A.K. Molecular mechanism of action of immune-modulatory drugs thalidomide, lenalidomide and pomalidomide in multiple myeloma. Leuk. Lymphoma 2013, 54, 683–687. [Google Scholar] [CrossRef]
  209. Sivaraj, D.; Green, M.M.; Gasparetto, C. Panobinostat for the management of multiple myeloma. Futur. Oncol. 2017, 13, 477–488. [Google Scholar] [CrossRef] [PubMed]
  210. Bubna, A.K. Vorinostat—An Overview. Indian J. Derm. 2015, 60, 419. [Google Scholar]
  211. Rashidi, A.; Cashen, A.F. Belinostat for the treatment of relapsed or refractory peripheral T-cell lymphoma. Futur. Oncol. 2015, 11, 1659–1664. [Google Scholar] [CrossRef] [PubMed]
  212. Smolewski, P.; Robak, T. The discovery and development of romidepsin for the treatment of T-cell lymphoma. Expert Opin. Drug Discov. 2017, 12, 1–15. [Google Scholar] [CrossRef]
  213. D’Agostino, M.; Bertamini, L.; Oliva, S.; Boccadoro, M.; Gay, F. Pursuing a Curative Approach in Multiple Myeloma: A Review of New Therapeutic Strategies. Cancers 2019, 11, 2015. [Google Scholar] [CrossRef] [Green Version]
  214. Rajkumar, S.V. Multiple myeloma: Every year a new standard? Hematol. Oncol. 2019, 37, 62–65. [Google Scholar] [CrossRef] [Green Version]
  215. De Veirman, K.; Menu, E.; Maes, K.; De Beule, N.; De Smedt, E.; Maes, A.; Vlummens, P.; Fostier, K.; Kassambara, A.; Moreaux, J.; et al. Myeloid-derived suppressor cells induce multiple myeloma cell survival by activating the AMPK pathway. Cancer Lett. 2019, 442, 233–241. [Google Scholar] [CrossRef]
  216. Chong, P.S.; Zhou, J.; Lim, J.S.; Hee, Y.T.; Chooi, J.-Y.; Chung, T.-H.; Tan, Z.T.; Zeng, Q.; Waller, D.D.; Sebag, M.; et al. IL6 Promotes a STAT3-PRL3 Feedforward Loop via SHP2 Repression in Multiple Myeloma. Cancer Res. 2019, 79, 4679–4688. [Google Scholar] [CrossRef]
  217. Lin, Y.-H.T.; Way, G.P.; Barwick, B.G.; Mariano, M.C.; Marcoulis, M.; Ferguson, I.D.; Driessen, C.; Boise, L.H.; Greene, C.S.; Wiita, A.P. Integrated phosphoproteomics and transcriptional classifiers reveal hidden RAS signaling dynamics in multiple myeloma. Blood Adv. 2019, 3, 3214–3227. [Google Scholar] [CrossRef] [Green Version]
  218. Lam, C.; Ferguson, I.D.; Mariano, M.C.; Lin, Y.-H.T.; Murnane, M.; Liu, H.; Smith, G.A.; Wong, S.W.; Taunton, J.; Liu, J.O.; et al. Repurposing tofacitinib as an anti-myeloma therapeutic to reverse growth-promoting effects of the bone marrow microenvironment. Haematologica 2018, 103, 1218–1228. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  219. Breitkopf, S.B.; Yuan, M.; Helenius, K.P.; Lyssiotis, C.A.; Asara, J.M. Triomics Analysis of Imatinib-Treated Myeloma Cells Connects Kinase Inhibition to RNA Processing and Decreased Lipid Biosynthesis. Anal. Chem. 2015, 87, 10995–11006. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  220. Franqui-Machin, R.; Hao, M.; Bai, H.; Gu, Z.; Zhan, X.; Habelhah, H.; Jethava, Y.; Qiu, L.; Frech, I.; Tricot, G.; et al. Destabilizing NEK2 overcomes resistance to proteasome inhibition in multiple myeloma. J. Clin. Investig. 2018, 128, 2877–2893. [Google Scholar] [CrossRef] [PubMed]
  221. Zhou, L.; Jiang, H.; Du, J.; Li, L.; Li, R.; Lu, J.; Fu, W.; Hou, J. USP15 inhibits multiple myeloma cell apoptosis through activating a feedback loop with the transcription factor NF-κBp65. Exp. Mol. Med. 2018, 50, 1–12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  222. Jia, H.; Liu, C.; Ge, F.; Xiao, C.; Lu, C.; Wang, T.; He, Q.-Y. Identification of ubiquitinated proteins from human multiple myeloma U266 cells by proteomics. Biomed. Environ. Sci. 2011, 24, 422–430. [Google Scholar]
  223. Xie, H.; Gu, Y.; Wang, W.; Wang, X.; Ye, X.; Xin, C.; Lu, M.; Reddy, B.A.; Shu, P. Silencing of SENP2 in Multiple Myeloma Induces Bortezomib Resistance by Activating NF-κB Through the Modulation of IκBα Sumoylation. Sci. Rep. 2020, 10, 766. [Google Scholar] [CrossRef]
  224. Driscoll, J.J.; Pelluru, D.; Lefkimmiatis, K.; Fulciniti, M.; Prabhala, R.H.; Greipp, P.R.; Barlogie, B.; Tai, Y.-T.; Anderson, K.C.; Shaughnessy, J.D.; et al. The sumoylation pathway is dysregulated in multiple myeloma and is associated with adverse patient outcome. Blood 2010, 115, 2827–2834. [Google Scholar] [CrossRef] [Green Version]
  225. Zhang, Z.; Westhrin, M.; Bondt, A.; Wuhrer, M.; Standal, T.; Holst, S. Serum protein N-glycosylation changes in multiple myeloma. Biochim. Biophys. Acta (BBA) Gen. Subj. 2019, 1863, 960–970. [Google Scholar] [CrossRef]
  226. Natoni, A.; Farrell, M.L.; Harris, S.; Falank, C.; Kirkham-McCarthy, L.; Macauley, M.S.; Reagan, M.R.; Dwyer, M.O. Sialyltransferase inhibition leads to inhibition of tumor cell interactions with E-selectin, VCAM1, and MADCAM1, and improves survival in a human multiple myeloma mouse model. Haematologica 2019, 105, 457–467. [Google Scholar] [CrossRef]
  227. Harada, T.; Ohguchi, H.; Grondin, Y.; Kikuchi, S.; Sagawa, M.; Tai, Y.-T.; Mazitschek, R.; Hideshima, T.; Anderson, K.C. HDAC3 regulates DNMT1 expression in multiple myeloma: Therapeutic implications. Leukemia 2017, 31, 2670–2677. [Google Scholar] [CrossRef] [Green Version]
  228. Tang, S.; Ma, D.; Cheng, B.; Fang, Q.; Kuang, X.; Yu, K.; Wang, W.; Hu, B.; Wang, J.; Kuang, X. Crucial role of HO-1/IRF4-dependent apoptosis induced by panobinostat and lenalidomide in multiple myeloma. Exp. Cell Res. 2018, 363, 196–207. [Google Scholar] [CrossRef]
  229. Ohguchi, H.; Harada, T.; Sagawa, M.; Kikuchi, S.; Tai, Y.-T.; Richardson, P.G.; Hideshima, T.; Anderson, K.C. KDM6B modulates MAPK pathway mediating multiple myeloma cell growth and survival. Leukemia 2017, 31, 2661–2669. [Google Scholar] [CrossRef] [Green Version]
  230. Gao, J.; Liao, R.; Yu, Y.; Zhai, H.; Wang, Y.; Sack, R.; Peters, A.H.F.M.; Chen, J.; Wu, H.; Huang, Z.; et al. Absolute Quantification of Histone PTM Marks by MRM-Based LC-MS/MS. Anal. Chem. 2014, 86, 9679–9686. [Google Scholar] [CrossRef]
  231. Ge, F.; Xiao, C.-L.; Yin, X.-F.; Lu, C.-H.; Zeng, H.-L.; He, Q.-Y. Phosphoproteomic analysis of primary human multiple myeloma cells. J. Proteom. 2010, 73, 1381–1390. [Google Scholar] [CrossRef]
  232. Brown, R.; Yang, S.; Weatherburn, C.; Gibson, J.; Ho, P.J.; Suen, H.; Hart, D.; Joshua, D. Phospho-flow detection of constitutive and cytokine-induced pSTAT3/5, pAKT and pERK expression highlights novel prognostic biomarkers for patients with multiple myeloma. Leukemia 2014, 29, 483–490. [Google Scholar] [CrossRef] [PubMed]
  233. Zhuang, J.; Shirazi, F.; Singh, R.K.; Kuiatse, I.; Wang, H.; Lee, H.C.; Berkova, Z.; Berger, A.; Hyer, M.; Chattopadhyay, N.; et al. Ubiquitin-activating enzyme inhibition induces an unfolded protein response and overcomes drug resistance in myeloma. Blood 2019, 133, 1572–1584. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  234. Das, D.S.; Das, A.; Ray, A.; Song, Y.; Samur, M.K.; Munshi, N.C.; Chauhan, D.; Anderson, K.C. Blockade of Deubiquitylating Enzyme USP1 Inhibits DNA Repair and Triggers Apoptosis in Multiple Myeloma Cells. Clin. Cancer Res. 2017, 23, 4280–4289. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  235. Wirth, M.; Schick, M.; Keller, U.; Krönke, J. Ubiquitination and Ubiquitin-Like Modifications in Multiple Myeloma: Biology and Therapy. Cancers 2020, 12, 3764. [Google Scholar] [CrossRef] [PubMed]
  236. Keats, J.J.; Reiman, T.; Maxwell, C.A.; Taylor, B.J.; Larratt, L.M.; Mant, M.J.; Belch, A.R.; Pilarski, L.M. In multiple myeloma, t(4;14)(p16;q32) is an adverse prognostic factor irrespective of FGFR3 expression. Blood 2003, 101, 1520–1529. [Google Scholar] [CrossRef] [PubMed]
  237. Pawlyn, C.; Bright, M.D.; Buros, A.F.; Stein, C.K.; Walters, Z.; I Aronson, L.; Mirabella, F.; Jones, J.R.; Kaiser, M.F.; A Walker, B.; et al. Overexpression of EZH2 in multiple myeloma is associated with poor prognosis and dysregulation of cell cycle control. Blood Cancer J. 2017, 7, e549. [Google Scholar] [CrossRef] [PubMed]
  238. Yee, A.J.; Raje, N.S. Panobinostat and Multiple Myeloma in 2018. Oncologist 2018, 23, 516–517. [Google Scholar] [CrossRef] [Green Version]
  239. Hideshima, T.; Qi, J.; Paranal, R.M.; Tang, W.; Greenberg, E.; West, N.; Colling, M.E.; Estiu, G.; Mazitschek, R.; Perry, J.A.; et al. Discovery of selective small-molecule HDAC6 inhibitor for overcoming proteasome inhibitor resistance in multiple myeloma. Proc. Natl. Acad. Sci. USA 2016, 113, 13162–13167. [Google Scholar] [CrossRef] [Green Version]
  240. Vogl, D.T.; Raje, N.; Jagannath, S.; Richardson, P.; Hari, P.; Orlowski, R.; Supko, J.G.; Tamang, D.; Yang, M.; Jones, S.S.; et al. Ricolinostat, the First Selective Histone Deacetylase 6 Inhibitor, in Combination with Bortezomib and Dexamethasone for Relapsed or Refractory Multiple Myeloma. Clin. Cancer Res. 2017, 23, 3307–3315. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  241. Seval, G.C.; Beksac, M. A comparative safety review of histone deacetylase inhibitors for the treatment of myeloma. Expert Opin. Drug Saf. 2019, 18, 563–571. [Google Scholar] [CrossRef]
  242. Bae, J.; Hideshima, T.; Tai, Y.-T.; Song, Y.; Richardson, P.; Raje, N.; Munshi, N.C.; Anderson, K.C. Histone deacetylase (HDAC) inhibitor ACY241 enhances anti-tumor activities of antigen-specific central memory cytotoxic T lymphocytes against multiple myeloma and solid tumors. Leukemia 2018, 32, 1932–1947. [Google Scholar] [CrossRef]
  243. Xu, J.; Sun, H.-Y.; Xiao, F.-J.; Wang, H.; Yang, Y.; Wang, L.; Gao, C.-J.; Guo, Z.-K.; Wu, C.-T.; Wang, L.-S. SENP1 inhibition induces apoptosis and growth arrest of multiple myeloma cells through modulation of NF-κB signaling. Biochem. Biophys. Res. Commun. 2015, 460, 409–415. [Google Scholar] [CrossRef]
  244. De Kouchkovsky, I.; Abdul-Hay, M. Acute myeloid leukemia: A comprehensive review and 2016 update. Blood Cancer J. 2016, 6, e441. [Google Scholar] [CrossRef]
  245. Short, N.J.; Konopleva, M.; Kadia, T.M.; Borthakur, G.; Ravandi, F.; Dinardo, C.D.; Daver, N. Advances in the Treatment of Acute Myeloid Leukemia: New Drugs and New Challenges. Cancer Discov. 2020, 10, 506–525. [Google Scholar] [CrossRef] [Green Version]
  246. Takami, M.; Katayama, K.; Noguchi, K.; Sugimoto, Y. Protein kinase C alpha-mediated phosphorylation of PIM-1L promotes the survival and proliferation of acute myeloid leukemia cells. Biochem. Biophys. Res. Commun. 2018, 503, 1364–1371. [Google Scholar] [CrossRef]
  247. Brown, F.C.; Still, E.; Koche, R.P.; Yim, C.Y.; Takao, S.; Cifani, P.; Reed, C.; Gunasekera, S.; Ficarro, S.B.; Romanienko, P.; et al. MEF2C Phosphorylation Is Required for Chemotherapy Resistance in Acute Myeloid Leukemia. Cancer Discov. 2018, 8, 478–497. [Google Scholar] [CrossRef] [Green Version]
  248. Nepstad, I.; Hatfield, K.J.; Grønningsæter, I.S.; Aasebø, E.; Hernandez-Valladares, M.; Hagen, K.M.; Rye, K.P.; Berven, F.S.; Selheim, F.; Reikvam, H.; et al. Effects of insulin and pathway inhibitors on the PI3K-Akt-mTOR phosphorylation profile in acute myeloid leukemia cells. Signal Transduct. Target. Ther. 2019, 4, 1–12. [Google Scholar] [CrossRef] [Green Version]
  249. Mori, M.; Kaneko, N.; Ueno, Y.; Yamada, M.; Tanaka, R.; Saito, R.; Shimada, I.; Mori, K.; Kuromitsu, S. Gilteritinib, a FLT3/AXL inhibitor, shows antileukemic activity in mouse models of FLT3 mutated acute myeloid leukemia. Investig. New Drugs 2017, 35, 556–565. [Google Scholar] [CrossRef] [Green Version]
  250. Huang, X.; Cao, M.; Wu, S.; Wang, L.; Hu, J.; Mehran, R.J.; Roth, J.A.; Swisher, S.G.; Wang, R.-Y.; Kantarjian, H.M.; et al. Anti-leukemia activity of NSC-743380 in SULT1A1-expressing acute myeloid leukemia cells is associated with inhibitions of cFLIP expression and PI3K/AKT/mTOR activities. Oncotarget 2017, 8, 102150–102160. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  251. Ying, M.; Shao, X.; Jing, H.; Liu, Y.; Qi, X.; Cao, J.; Chen, Y.; Xiang, S.; Song, H.; Hu, R.; et al. Ubiquitin-dependent degradation of CDK2 drives the therapeutic differentiation of AML by targeting PRDX2. Blood 2018, 131, 2698–2711. [Google Scholar] [CrossRef]
  252. Wang, H.; Bei, L.; Shah, C.A.; Huang, W.; Platanias, L.C.; Eklund, E.A. The E3 ubiquitin ligase Triad1 influences development of Mll-Ell-induced acute myeloid leukemia. Oncogene 2018, 37, 2532–2544. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  253. Barghout, S.H.; Patel, P.S.; Wang, X.; Xu, G.W.; Kavanagh, S.; Halgas, O.; Zarabi, S.F.; Gronda, M.; Hurren, R.; Jeyaraju, D.V.; et al. Preclinical evaluation of the selective small-molecule UBA1 inhibitor, TAK-243, in acute myeloid leukemia. Leukemia 2019, 33, 37–51. [Google Scholar] [CrossRef]
  254. Dong, S.; Chen, J. SUMOylation of sPRDM16 promotes the progression of acute myeloid leukemia. BMC Cancer 2015, 15, 893. [Google Scholar] [CrossRef]
  255. Zhou, P.; Chen, X.; Li, M.; Tan, J.; Zhang, Y.; Yuan, W.; Zhou, J.; Wang, G. 2-D08 as a SUMOylation inhibitor induced ROS accumulation mediates apoptosis of acute myeloid leukemia cells possibly through the deSUMOylation of NOX2. Biochem. Biophys. Res. Commun. 2019, 513, 1063–1069. [Google Scholar] [CrossRef]
  256. Reiter, K.; Polzer, H.; Krupka, C.; Maiser, A.; Vick, B.; Rothenberg-Thurley, M.; Metzeler, K.H.; Dörfel, D.; Salih, H.R.; Jung, G.; et al. Tyrosine kinase inhibition increases the cell surface localization of FLT3-ITD and enhances FLT3-directed immunotherapy of acute myeloid leukemia. Leukemia 2018, 32, 313–322. [Google Scholar] [CrossRef] [Green Version]
  257. Natarajan, K.; Xie, Y.; Burcu, M.; Linn, D.E.; Qiu, Y.; Baer, M.R. Pim-1 Kinase Phosphorylates and Stabilizes 130 kDa FLT3 and Promotes Aberrant STAT5 Signaling in Acute Myeloid Leukemia with FLT3 Internal Tandem Duplication. PLoS ONE 2013, 8, e74653. [Google Scholar] [CrossRef] [PubMed]
  258. Zhu, X.; Liu, X.; Cheng, Z.; Zhu, J.; Xu, L.; Wang, F.; Qi, W.; Yan, J.; Liu, N.; Sun, Z.; et al. Quantitative Analysis of Global Proteome and Lysine Acetylome Reveal the Differential Impacts of VPA and SAHA on HL60 Cells. Sci. Rep. 2016, 6, 19926. [Google Scholar] [CrossRef] [Green Version]
  259. Tu, H.-J.; Lin, Y.-J.; Chao, M.-W.; Sung, T.-Y.; Wu, Y.-W.; Chen, Y.-Y.; Lin, M.-H.; Liou, J.-P.; Pan, S.-L.; Yang, C.-R. The anticancer effects of MPT0G211, a novel HDAC6 inhibitor, combined with chemotherapeutic agents in human acute leukemia cells. Clin. Epigenetics 2018, 10, 1–13. [Google Scholar] [CrossRef] [Green Version]
  260. He, X.; Zhu, Y.; Lin, Y.-C.; Li, M.; Du, J.; Dong, H.; Sun, J.; Zhu, L.; Wang, H.; Ding, Z.; et al. PRMT1-mediated FLT3 arginine methylation promotes maintenance of FLT3-ITD+ acute myeloid leukemia. Blood 2019, 134, 548–560. [Google Scholar] [CrossRef]
  261. Dosil, M.; Wang, S.; Lemischka, I.R. Mitogenic signalling and substrate specificity of the Flk2/Flt3 receptor tyrosine kinase in fibroblasts and interleukin 3-dependent hematopoietic cells. Mol. Cell. Biol. 1993, 13, 6572–6585. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  262. Larrosa-Garcia, M.; Baer, M.R. FLT3 Inhibitors in Acute Myeloid Leukemia: Current Status and Future Directions. Mol. Cancer Ther. 2017, 16, 991–1001. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  263. Kiyoi, H.; Kawashima, N.; Ishikawa, Y. FLT3mutations in acute myeloid leukemia: Therapeutic paradigm beyond inhibitor development. Cancer Sci. 2020, 111, 312–322. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  264. Cortes, J.; Tamura, K.; DeAngelo, D.J.; De Bono, J.; Lorente, D.; Minden, M.; Uy, G.L.; Kantarjian, H.; Chen, L.S.; Gandhi, V.; et al. Phase I studies of AZD1208, a proviral integration Moloney virus kinase inhibitor in solid and haematological cancers. Br. J. Cancer 2018, 118, 1425–1433. [Google Scholar] [CrossRef]
  265. Nogami, A.; Okada, K.; Ishida, S.; Akiyama, H.; Umezawa, Y.; Miura, O. Inhibition of the STAT5/Pim Kinase Axis Enhances Cytotoxic Effects of Proteasome Inhibitors on FLT3-ITD–Positive AML Cells by Cooperatively Inhibiting the mTORC1/4EBP1/S6K/Mcl-1 Pathway. Transl. Oncol. 2019, 12, 336–349. [Google Scholar] [CrossRef] [PubMed]
  266. Larrue, C.; Saland, E.; Vergez, F.; Serhan, N.; Delabesse, E.; De Mas, V.M.-; Hospital, M.-A.; Tamburini, J.; Manenti, S.; Sarry, J.E.; et al. Antileukemic Activity of 2-Deoxy-d-Glucose through Inhibition of N-Linked Glycosylation in Acute Myeloid Leukemia with FLT3-ITD or c-KIT Mutations. Mol. Cancer Ther. 2015, 14, 2364–2373. [Google Scholar] [CrossRef] [Green Version]
  267. Boulanger, M.; Paolillo, R.; Piechaczyk, M.; Bossis, G. The SUMO Pathway in Hematomalignancies and Their Response to Therapies. Int. J. Mol. Sci. 2019, 20, 3895. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  268. Zhang, J.; Huang, F.-F.; Wu, D.-S.; Li, W.-J.; Zhan, H.-E.; Peng, M.-Y.; Fang, P.; Cao, P.-F.; Zhang, M.-M.; Zeng, H.; et al. SUMOylation of insulin-like growth factor 1 receptor, promotes proliferation in acute myeloid leukemia. Cancer Lett. 2015, 357, 297–306. [Google Scholar] [CrossRef]
  269. Castelli, G.; Pelosi, E.; Testa, U. Targeting histone methyltransferase and demethylase in acute myeloid leukemia therapy. OncoTargets Ther. 2017, ume 11, 131–155. [Google Scholar] [CrossRef] [Green Version]
  270. Xu, Q.-Y.; Yu, L. Epigenetic therapies in acute myeloid leukemia: The role of hypomethylating agents, histone deacetylase inhibitors and the combination of hypomethylating agents with histone deacetylase inhibitors. Chin. Med J. 2020, 133, 699–715. [Google Scholar] [CrossRef]
  271. Palumbo, G.A.; Stella, S.; Pennisi, M.S.; Pirosa, C.; Fermo, E.; Fabris, S.; Cattaneo, D.; Iurlo, A. The Role of New Technologies in Myeloproliferative Neoplasms. Front. Oncol. 2019, 9, 321. [Google Scholar] [CrossRef] [Green Version]
  272. O’Sullivan, J.; Mead, A.J. Heterogeneity in myeloproliferative neoplasms: Causes and consequences. Adv. Biol. Regul. 2019, 71, 55–68. [Google Scholar] [CrossRef] [PubMed]
  273. Valent, P. ICUS, IDUS, CHIP and CCUS: Diagnostic Criteria, Separation from MDS and Clinical Implications. Pathobiology 2019, 86, 30–38. [Google Scholar] [CrossRef] [PubMed]
  274. Chen, J.; Kao, Y.-R.; Sun, D.; Todorova, T.I.; Reynolds, D.; Narayanagari, S.-R.; Montagna, C.; Will, B.; Verma, A.; Steidl, A.U. Myelodysplastic syndrome progression to acute myeloid leukemia at the stem cell level. Nat. Med. 2019, 25, 103–110. [Google Scholar] [CrossRef] [PubMed]
  275. Haferlach, T. The Molecular Pathology of Myelodysplastic Syndrome. Pathobiology 2018, 86, 24–29. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  276. Zoi, K.; Cross, N.C. Genomics of Myeloproliferative Neoplasms. J. Clin. Oncol. 2017, 35, 947–954. [Google Scholar] [CrossRef]
  277. Podszywalow-Bartnicka, P.; Cmoch, A.; Wolczyk, M.; Bugajski, L.; Tkaczyk, M.; Dadlez, M.; Nieborowska-Skorska, M.; Koromilas, A.E.; Skorski, T.; Piwocka, K. Increased phosphorylation of eIF2α in chronic myeloid leukemia cells stimulates secretion of matrix modifying enzymes. Oncotarget 2016, 7, 79706–79721. [Google Scholar] [CrossRef] [Green Version]
  278. Prestipino, A.; Emhardt, A.J.; Aumann, K.; O’Sullivan, D.; Gorantla, S.P.; Duquesne, S.; Melchinger, W.; Braun, L.; Vuckovic, S.; Boerries, M.; et al. Oncogenic JAK2V617Fcauses PD-L1 expression, mediating immune escape in myeloproliferative neoplasms. Sci. Transl. Med. 2018, 10, eaam7729. [Google Scholar] [CrossRef] [Green Version]
  279. Hu, M.; Xu, C.; Yang, C.; Zuo, H.; Chen, C.; Zhang, D.; Shi, G.; Wang, W.; Shi, J.; Zhang, T. Discovery and evaluation of ZT55, a novel highly-selective tyrosine kinase inhibitor of JAK2V617F against myeloproliferative neoplasms. J. Exp. Clin. Cancer Res. 2019, 38, 1–12. [Google Scholar] [CrossRef]
  280. Gu, C.; Feng, M.; Yin, Z.; Luo, X.; Yang, J.; Li, Y.; Li, T.; Wang, R.; Fei, J. RalA, a GTPase targeted by miR-181a, promotes transformation and progression by activating the Ras-related signaling pathway in chronic myelogenous leukemia. Oncotarget 2016, 7, 20561–20573. [Google Scholar] [CrossRef] [Green Version]
  281. Sinha, S.; Dwivedi, T.R.; Yengkhom, R.; Bheemsetty, V.A.; Abe, T.; Kiyonari, H.; VijayRaghavan, K.; Inamdar, M.S. Asrij/OCIAD1 suppresses CSN5-mediated p53 degradation and maintains mouse hematopoietic stem cell quiescence. Blood 2019, 133, 2385–2400. [Google Scholar] [CrossRef]
  282. Park, C.S.; Lewis, A.H.; Chen, T.J.; Bridges, C.S.; Shen, Y.; Suppipat, K.; Puppi, M.; Tomolonis, J.A.; Pang, P.D.; Mistretta, T.-A.; et al. A KLF4-DYRK2–mediated pathway regulating self-renewal in CML stem cells. Blood 2019, 134, 1960–1972. [Google Scholar] [CrossRef] [PubMed]
  283. Bigenzahn, J.W.; Collu, G.M.; Kartnig, F.; Pieraks, M.; Vladimer, G.I.; Heinz, L.X.; Sedlyarov, V.; Schischlik, F.; Fauster, A.; Rebsamen, M.; et al. LZTR1 is a regulator of RAS ubiquitination and signaling. Science 2018, 362, 1171–1177. [Google Scholar] [CrossRef] [PubMed]
  284. Tvorogov, D.; Thomas, D.; Liau, N.P.D.; Dottore, M.; Barry, E.F.; Lathi, M.; Kan, W.L.; Hercus, T.R.; Stomski, F.; Hughes, T.P.; et al. Accumulation of JAK activation loop phosphorylation is linked to type I JAK inhibitor withdrawal syndrome in myelofibrosis. Sci. Adv. 2018, 4, eaat3834. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  285. Mancini, M.; Leo, E.; Takemaru, K.-I.; Campi, V.; Castagnetti, F.; Soverini, S.; De Benedittis, C.; Rosti, G.; Cavo, M.; Santucci, M.A.; et al. 14-3-3 Binding and Sumoylation Concur to the Down-Modulation of β-catenin Antagonist chibby 1 in Chronic Myeloid Leukemia. PLoS ONE 2015, 10, e0131074. [Google Scholar] [CrossRef] [Green Version]
  286. Spiciarich, D.R.; Oh, S.T.; Foley, A.; Hughes, S.B.; Mauro, M.J.; Abdel-Wahab, O.; Press, R.D.; Viner, R.; Thompson, S.L.; Chen, Q.; et al. A Novel Germline Variant in CSF3R Reduces N-Glycosylation and Exerts Potent Oncogenic Effects in Leukemia. Cancer Res. 2018, 78, 6762–6770. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  287. Pecquet, C.; Chachoua, I.; Roy, A.; Balligand, T.; Vertenoeil, G.; Leroy, E.; Albu, R.-I.; Defour, J.-P.; Nivarthi, H.; Hug, E.; et al. Calreticulin mutants as oncogenic rogue chaperones for TpoR and traffic-defective pathogenic TpoR mutants. Blood 2019, 133, 2669–2681. [Google Scholar] [CrossRef]
  288. Sun, J.; He, X.; Zhu, Y.; Ding, Z.; Dong, H.; Feng, Y.; Du, J.; Wang, H.; Wu, X.; Zhang, L.; et al. SIRT1 Activation Disrupts Maintenance of Myelodysplastic Syndrome Stem and Progenitor Cells by Restoring TET2 Function. Cell Stem Cell 2018, 23, 355–369.e9. [Google Scholar] [CrossRef] [Green Version]
  289. Yue, L.; Sharma, V.; Horvat, N.P.; Akuffo, A.A.; Beatty, M.S.; Murdun, C.; Colin, C.; Billington, J.M.R.; Goodheart, W.E.; Sahakian, E.; et al. HDAC11 deficiency disrupts oncogene-induced hematopoiesis in myeloproliferative neoplasms. Blood 2020, 135, 191–207. [Google Scholar] [CrossRef]
  290. Peeken, J.C.; Jutzi, J.S.; Wehrle, J.; Koellerer, C.; Staehle, H.F.; Becker, H.; Schoenwandt, E.; Seeger, T.S.; Schanne, D.H.; Gothwal, M.; et al. Epigenetic regulation of NFE2 overexpression in myeloproliferative neoplasms. Blood 2018, 131, 2065–2073. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  291. Greenfield, G.; McPherson, S.; Mills, K.; McMullin, M.F. The ruxolitinib effect: Understanding how molecular pathogenesis and epigenetic dysregulation impact therapeutic efficacy in myeloproliferative neoplasms. J. Transl. Med. 2018, 16, 360. [Google Scholar] [CrossRef] [PubMed]
  292. Jabbour, E.; Kantarjian, H. Chronic myeloid leukemia: 2018 update on diagnosis, therapy and monitoring. Am. J. Hematol. 2018, 93, 442–459. [Google Scholar] [CrossRef] [Green Version]
  293. Takaoka, K.; Kawazu, M.; Koya, J.; Yoshimi, A.; Masamoto, Y.; Maki, H.; Toya, T.; Kobayashi, T.; Nannya, Y.; Arai, S.; et al. A germline HLTF mutation in familial MDS induces DNA damage accumulation through impaired PCNA polyubiquitination. Leukemia 2019, 33, 1773–1782. [Google Scholar] [CrossRef] [PubMed]
  294. Inoue, D.; Fujino, T.; Sheridan, P.; Zhang, Y.-Z.; Nagase, R.; Horikawa, S.; Li, Z.; Matsui, H.; Kanai, A.; Saika, M.; et al. A novel ASXL1– OGT axis plays roles in H3K4 methylation and tumor suppression in myeloid malignancies. Leukemia 2018, 32, 1327–1337. [Google Scholar] [CrossRef] [PubMed]
  295. Bose, P.; Verstovsek, S. Developmental Therapeutics in Myeloproliferative Neoplasms. Clin. Lymphoma Myeloma Leuk. 2017, 17, S43–S52. [Google Scholar] [CrossRef]
  296. Mascarenhas, J.; Marcellino, B.; Lu, M.; Kremyanskaya, M.; Fabris, F.; Sandy, L.; Mehrotra, M.; Houldsworth, J.; Najfeld, V.; El Jamal, S.; et al. A phase I study of panobinostat and ruxolitinib in patients with primary myelofibrosis (PMF) and post--polycythemia vera/essential thrombocythemia myelofibrosis (post--PV/ET MF). Leuk. Res. 2020, 88, 106272. [Google Scholar] [CrossRef]
  297. Liu, F.; Zhao, X.; Perna, F.; Wang, L.; Koppikar, P.; Abdel-Wahab, O.; Harr, M.W.; Levine, R.L.; Xu, H.; Tefferi, A.; et al. JAK2V617F-Mediated Phosphorylation of PRMT5 Downregulates Its Methyltransferase Activity and Promotes Myeloproliferation. Cancer Cell 2011, 19, 283–294. [Google Scholar] [CrossRef] [Green Version]
  298. Zhang, C.; Suo, J.; Katayama, H.; Wei, Y.; Garcia-Manero, G.; Hanash, S. Quantitative proteomic analysis of histone modifications in decitabine sensitive and resistant leukemia cell lines. Clin. Proteom. 2016, 13, 1–11. [Google Scholar] [CrossRef] [Green Version]
  299. Swerdlow, S.H.; Campo, E.; Pileri, S.A.; Harris, N.L.; Stein, H.; Siebert, R.; Advani, R.; Ghielmini, M.; Salles, G.A.; Zelenetz, A.D.; et al. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood 2016, 127, 2375–2390. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  300. Song, T.L.; Nairismägi, M.-L.; Laurensia, Y.; Lim, J.-Q.; Tan, J.; Li, Z.-M.; Pang, W.-L.; Kizhakeyil, A.; Wijaya, G.-C.; Huang, D.-C.; et al. Oncogenic activation of the STAT3 pathway drives PD-L1 expression in natural killer/T-cell lymphoma. Blood 2018, 132, 1146–1158. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  301. Tarantelli, C.; Gaudio, E.; Arribas, A.J.; Kwee, I.; Hillmann, P.; Rinaldi, A.; Cascione, L.; Spriano, F.; Bernasconi, E.; Guidetti, F.; et al. PQR309 Is a Novel Dual PI3K/mTOR Inhibitor with Preclinical Antitumor Activity in Lymphomas as a Single Agent and in Combination Therapy. Clin. Cancer Res. 2017, 24, 120–129. [Google Scholar] [CrossRef] [Green Version]
  302. Murga-Zamalloa, C.; Rolland, D.C.; Polk, A.; Wolfe, A.; Dewar, H.; Chowdhury, P.; Önder, Ö.; Dewar, R.; Brown, N.A.; Bailey, N.G.; et al. Colony-Stimulating Factor 1 Receptor (CSF1R) Activates AKT/mTOR Signaling and Promotes T-Cell Lymphoma Viability. Clin. Cancer Res. 2019, 26, 690–703. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  303. Salter, A.I.; Ivey, R.G.; Kennedy, J.J.; Voillet, V.; Rajan, A.; Alderman, E.J.; Voytovich, U.J.; Lin, C.; Sommermeyer, D.; Liu, L.; et al. Phosphoproteomic analysis of chimeric antigen receptor signaling reveals kinetic and quantitative differences that affect cell function. Sci. Signal. 2018, 11, eaat6753. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  304. Chung, C. Current targeted therapies in lymphomas. Am. J. Health Pharm. 2019, 76, 1825–1834. [Google Scholar] [CrossRef] [PubMed]
  305. Rolland, D.; Basrur, V.; Conlon, K.; Wolfe, T.; Fermin, D.; Nesvizhskii, A.I.; Lim, M.S.; Elenitoba-Johnson, K.S. Global Phosphoproteomic Profiling Reveals Distinct Signatures in B-Cell Non-Hodgkin Lymphomas. Am. J. Pathol. 2014, 184, 1331–1342. [Google Scholar] [CrossRef] [Green Version]
  306. Gengenbacher, A.; Müller-Rudorf, A.; Poggio, T.; Gräßel, L.; Dumit, V.I.; Kreutmair, S.; Lippert, L.J.; Duyster, J.; Illert, A.L. Proteomic Phosphosite Analysis Identified Crucial NPM-ALK-Mediated NIPA Serine and Threonine Residues. Int. J. Mol. Sci. 2019, 20, 4060. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  307. Valla, K.; Flowers, C.R.; Koff, J.L. Targeting the B cell receptor pathway in non-Hodgkin lymphoma. Expert Opin. Investig. Drugs 2018, 27, 513–522. [Google Scholar] [CrossRef]
  308. Saint-Germain, E.; Mignacca, L.; Huot, G.; Acevedo, M.; Moineau-Vallée, K.; Calabrese, V.; Bourdeau, V.; Rowell, M.-C.; Ilangumaran, S.; Lessard, F.; et al. Phosphorylation of SOCS1 Inhibits the SOCS1–p53 Tumor Suppressor Axis. Cancer Res. 2019, 79, 3306–3319. [Google Scholar] [CrossRef]
  309. Yang, Y.; Kelly, P.; Shaffer, A.L.; Schmitz, R.; Yoo, H.M.; Liu, X.; Huang, D.W.; Webster, D.; Young, R.M.; Nakagawa, M.; et al. Targeting Non-proteolytic Protein Ubiquitination for the Treatment of Diffuse Large B Cell Lymphoma. Cancer Cell 2016, 29, 494–507. [Google Scholar] [CrossRef] [Green Version]
  310. Hou, Y.; Ding, M.; Wang, C.; Yang, X.; Ye, T.; Yu, H. TRIM11 promotes lymphomas by activating the β-catenin signaling and Axin1 ubiquitination degradation. Exp. Cell Res. 2020, 387, 111750. [Google Scholar] [CrossRef]
  311. Vishwamitra, D.; Curry, C.V.; Shi, P.; Alkan, S.; Amin, H.M. SUMOylation Confers Posttranslational Stability on NPM-ALK Oncogenic Protein. Neoplasia 2015, 17, 742–754. [Google Scholar] [CrossRef] [Green Version]
  312. Salahuddin, S.; Fath, E.K.; Biel, N.; Ray, A.; Moss, C.R.; Patel, A.; Patel, S.; Hilding, L.; Varn, M.; Ross, T.; et al. Epstein-Barr Virus Latent Membrane Protein-1 Induces the Expression of SUMO-1 and SUMO-2/3 in LMP1-positive Lymphomas and Cells. Sci. Rep. 2019, 9, 208. [Google Scholar] [CrossRef]
  313. Liu, X.; Zhang, Y.; Han, Y.; Lu, W.; Yang, J.; Tian, J.; Sun, P.; Yu, T.; Hu, Y.; Zhang, H.; et al. Overexpression of GLT1D1 induces immunosuppression through glycosylation of PD-L1 and predicts poor prognosis in B-cell lymphoma. Mol. Oncol. 2020, 14, 1028–1044. [Google Scholar] [CrossRef] [PubMed]
  314. Zhang, J.; Vlasevska, S.; Wells, V.A.; Nataraj, S.; Holmes, A.B.; Duval, R.; Meyer, S.N.; Mo, T.; Basso, K.; Brindle, P.K.; et al. The CREBBP Acetyltransferase Is a Haploinsufficient Tumor Suppressor in B-cell Lymphoma. Cancer Discov. 2017, 7, 322–337. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  315. Vlaming, H.; McLean, C.M.; Korthout, T.; Alemdehy, M.F.; Hendriks, S.; Lancini, C.; Palit, S.; Klarenbeek, S.; Kwesi-Maliepaard, E.M.; Molenaar, T.M.; et al. Conserved crosstalk between histone deacetylation and H3K79 methylation generates DOT1L-dose dependency in HDAC1-deficient thymic lymphoma. EMBO J. 2019, 38, e101564. [Google Scholar] [CrossRef]
  316. Lu, X.; Fernando, T.M.; Lossos, C.; Yusufova, N.; Liu, F.; Fontán, L.; Durant, M.; Geng, H.; Melnick, J.; Luo, Y.; et al. PRMT5 interacts with the BCL6 oncoprotein and is required for germinal center formation and lymphoma cell survival. Blood 2018, 132, 2026–2039. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  317. Aoki, Y.; Feldman, G.M.; Tosato, G. Inhibition of STAT3 signaling induces apoptosis and decreases survivin expression in primary effusion lymphoma. Blood 2003, 101, 1535–1542. [Google Scholar] [CrossRef]
  318. Uddin, S.; Hussain, A.R.; Al-Hussein, K.A.; Manogaran, P.S.; Wickrema, A.; Gutierrez, M.I.; Bhatia, K.G. Inhibition of Phosphatidylinositol 3′-Kinase/AKT Signaling Promotes Apoptosis of Primary Effusion Lymphoma Cells. Clin. Cancer Res. 2005, 11, 3102–3108. [Google Scholar] [CrossRef] [Green Version]
  319. Bhatt, A.P.; Bhende, P.M.; Sin, S.-H.; Roy, D.; Dittmer, D.P.; Damania, B. Dual inhibition of PI3K and mTOR inhibits autocrine and paracrine proliferative loops in PI3K/Akt/mTOR-addicted lymphomas. Blood 2010, 115, 4455–4463. [Google Scholar] [CrossRef]
  320. Mohanty, S.; Kumar, S.S.; Das, P.; Sahu, S.K.; Choudhuri, T. Multi-targeted therapy of everolimus in Kaposi’s sarcoma associated herpes virus infected primary effusion lymphoma. Apoptosis 2017, 22, 1098–1115. [Google Scholar] [CrossRef]
  321. Wong, J.P.; Stuhlmiller, T.J.; Giffin, L.C.; Lin, C.; Bigi, R.; Zhao, J.; Zhang, W.; Cruz, A.G.B.; Park, S.I.; Earp, H.S.; et al. Kinome profiling of non-Hodgkin lymphoma identifies Tyro3 as a therapeutic target in primary effusion lymphoma. Proc. Natl. Acad. Sci. USA 2019, 116, 16541–16550. [Google Scholar] [CrossRef] [Green Version]
  322. Bubman, D.; Guasparri, I.; Cesarman, E. Deregulation of c-Myc in primary effusion lymphoma by Kaposi’s sarcoma herpesvirus latency-associated nuclear antigen. Oncogene 2007, 26, 4979–4986. [Google Scholar] [CrossRef] [Green Version]
  323. Yang, Y.; Staudt, L.M. Protein ubiquitination in lymphoid malignancies. Immunol. Rev. 2014, 263, 240–256. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  324. Runckel, K.; Barth, M.J.; Mavis, C.; Gu, J.J.; Hernandez-Ilizaliturri, F.J. The SMAC mimetic LCL-161 displays antitumor activity in preclinical models of rituximab-resistant B-cell lymphoma. Blood Adv. 2018, 2, 3516–3525. [Google Scholar] [CrossRef] [PubMed]
  325. Guo, F.; Luo, Y.; Jiang, X.; Lu, X.; Roberti, D.; Lossos, C.; Kunkalla, K.; Magistri, M.; Rui, L.; Verdun, R.; et al. Recent BCR stimulation induces a negative autoregulatory loop via FBXO10 mediated degradation of HGAL. Leukemia 2019, 34, 553–566. [Google Scholar] [CrossRef] [PubMed]
  326. Li, Y.; Bouchlaka, M.N.; Wolff, J.; Grindle, K.M.; Lu, L.; Qian, S.; Zhong, X.; Pflum, N.; Jobin, P.; Kahl, B.S.; et al. FBXO10 deficiency and BTK activation upregulate BCL2 expression in mantle cell lymphoma. Oncogene 2016, 35, 6223–6234. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  327. Gribben, J.G.; Fowler, N.; Morschhauser, F. Mechanisms of Action of Lenalidomide in B-Cell Non-Hodgkin Lymphoma. J. Clin. Oncol. 2015, 33, 2803–2811. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  328. Derenzini, E.; Mondello, P.; Erazo, T.; Portelinha, A.; Liu, Y.; Scallion, M.; Asgari, Z.; Philip, J.; Hilden, P.; Valli, D.; et al. BET Inhibition-Induced GSK3β Feedback Enhances Lymphoma Vulnerability to PI3K Inhibitors. Cell Rep. 2018, 24, 2155–2166. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  329. Delforoush, M.; Berglund, M.; Edqvist, P.; Sundström, C.; Gullbo, J.; Enblad, G. Expression of possible targets for new proteasome inhibitors in diffuse large B-cell lymphoma. Eur. J. Haematol. 2016, 98, 52–56. [Google Scholar] [CrossRef] [Green Version]
  330. Jiang, L.; Sun, Y.; Wang, J.; He, Q.; Chen, X.; Lan, X.; Chen, J.; Dou, Q.P.; Shi, X.; Liu, J. Proteasomal cysteine deubiquitinase inhibitor b-AP15 suppresses migration and induces apoptosis in diffuse large B cell lymphoma. J. Exp. Clin. Cancer Res. 2019, 38, 453. [Google Scholar] [CrossRef]
  331. Mohanty, S.; Harhaj, E.W. Mechanisms of Oncogenesis by HTLV-1 Tax. Pathog. 2020, 9, 543. [Google Scholar] [CrossRef]
  332. Mohanty, S.; Han, T.; Choi, Y.B.; Lavorgna, A.; Zhang, J.; Harhaj, E.W. The E3/E4 ubiquitin conjugation factor UBE4B interacts with and ubiquitinates the HTLV-1 Tax oncoprotein to promote NF-κB activation. PLoS Pathog. 2020, 16, e1008504. [Google Scholar] [CrossRef]
  333. Hoellein, A.; Fallahi, M.; Schoeffmann, S.; Steidle, S.; Schaub, F.X.; Rudelius, M.; Laitinen, I.; Nilsson, L.; Goga, A.; Peschel, C.; et al. Myc-induced SUMOylation is a therapeutic vulnerability for B-cell lymphoma. Blood 2014, 124, 2081–2090. [Google Scholar] [CrossRef] [Green Version]
  334. Selby, T.L.; Biel, N.; Varn, M.; Patel, S.; Patel, A.; Hilding, L.; Ray, A.; Ross, T.; Cramblet, W.T.; Moss, C.R.; et al. The Epstein-Barr Virus Oncoprotein, LMP1, Regulates the Function of SENP2, a SUMO-protease. Sci. Rep. 2019, 9, 1–16. [Google Scholar] [CrossRef]
  335. Suzuki, O. Glycosylation in lymphoma: Biology and glycotherapy. Pathol. Int. 2019, 69, 441–449. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  336. Suzuki, O.; Nozawa, Y.; Kawaguchi, T.; Abe, M. Alpha-2,6-sialylation of L-PHA reactive oligosaccharides and expression of N-acetylglucosaminyltransferase V in human diffuse large B cell lymphoma. Oncol. Rep. 2003, 10, 1759–1764. [Google Scholar] [CrossRef]
  337. Suzuki, O.; Nozawa, Y.; Abe, M. Loss of L-PHA-, PNA-, or ConA-reactive oligosaccharides is associated with a poor progno-sis in human Burkitt’s lymphoma. Oncol. Rep. 2007, 17, 775–779. [Google Scholar] [PubMed] [Green Version]
  338. Suzuki, O.; Abe, M.; Hashimoto, Y. Sialylation and glycosylation modulate cell adhesion and invasion to extracellular matrix in human malignant lymphoma: Dependency on integrin and the Rho GTPase family. Int. J. Oncol. 2015, 47, 2091–2099. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  339. Hollander, N.; Haimovich, J. Altered N-Linked Glycosylation in Follicular Lymphoma and Chronic Lymphocytic Leukemia: Involvement in Pathogenesis and Potential Therapeutic Targeting. Front. Immunol. 2017, 8, 912. [Google Scholar] [CrossRef]
  340. Linley, A.; Krysov, S.; Ponzoni, M.; Johnson, P.W.; Packham, G.; Stevenson, F.K. Lectin binding to surface Ig variable regions provides a universal persistent activating signal for follicular lymphoma cells. Blood 2015, 126, 1902–1910. [Google Scholar] [CrossRef] [Green Version]
  341. Hatzi, K.; Geng, H.; Doane, A.S.; Meydan, C.; Lariviere, R.; Cardenas, M.; Duy, C.; Shen, H.; Vidal, M.N.C.; Baslan, T.; et al. Histone demethylase LSD1 is required for germinal center formation and BCL6-driven lymphomagenesis. Nat. Immunol. 2019, 20, 86–96. [Google Scholar] [CrossRef] [PubMed]
  342. Ji, M.-M.; Huang, Y.-H.; Huang, J.-Y.; Wang, Z.-F.; Fu, D.; Liu, H.; Liu, F.; Leboeuf, C.; Wang, L.; Ye, J.; et al. Histone modifier gene mutations in peripheral T-cell lymphoma not otherwise specified. Haematologica 2018, 103, 679–687. [Google Scholar] [CrossRef] [PubMed]
  343. Lue, J.K.; Prabhu, S.A.; Liu, Y.; Gonzalez, Y.; Verma, A.; Mundi, P.S.; Abshiru, N.; Camarillo, J.M.; Mehta, S.; Chen, E.I.; et al. Precision Targeting with EZH2 and HDAC Inhibitors in Epigenetically Dysregulated Lymphomas. Clin. Cancer Res. 2019, 25, 5271–5283. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Figure 1. Post-translational modifications (PTMs) within the mammalian cell. This figure illustrates some of the most well-known PTMs and their functions within the cell. PTMs are found throughout the cell from the plasma membrane to the nucleosomes present within the nucleus. PTMs play crucial roles in almost all cellular processes including the cell cycle, degradation, apoptosis, cell signaling, transcription, etc. Different proteins modified by the same PTM will not always yield the same response, demonstrating the diverse functions of PTMs within the cell. JAK, Janus kinase; STAT, signal transducer and activator of transcription; P, phosphate group; G, glycosyl group; Ub, ubiquitin; SUMO, small ubiquitin-like modifier; Ac, acetyl group; Me, methyl group; Val, valine; Met, methionine; Cys, cysteine; Tyr, tyrosine; Glu, glutamic acid; Lys, lysine.
Figure 1. Post-translational modifications (PTMs) within the mammalian cell. This figure illustrates some of the most well-known PTMs and their functions within the cell. PTMs are found throughout the cell from the plasma membrane to the nucleosomes present within the nucleus. PTMs play crucial roles in almost all cellular processes including the cell cycle, degradation, apoptosis, cell signaling, transcription, etc. Different proteins modified by the same PTM will not always yield the same response, demonstrating the diverse functions of PTMs within the cell. JAK, Janus kinase; STAT, signal transducer and activator of transcription; P, phosphate group; G, glycosyl group; Ub, ubiquitin; SUMO, small ubiquitin-like modifier; Ac, acetyl group; Me, methyl group; Val, valine; Met, methionine; Cys, cysteine; Tyr, tyrosine; Glu, glutamic acid; Lys, lysine.
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Figure 2. Schematic workflow illustrating analytical techniques used in the analysis of post-translational modifications. Proteomic techniques used for PTM analysis can be divided into antibody-based analysis or mass spectrometry (MS)-based analysis. Protein microarrays, immunohistochemistry and Western blot analysis demonstrate widespread popularity as efficient tools for antibody-based PTM analysis. MS-based analysis is a powerful technique in the study of PTMs. This figure outlines the steps involved in the preparation of crude samples for MS and the transformation of MS data into interpretable results.
Figure 2. Schematic workflow illustrating analytical techniques used in the analysis of post-translational modifications. Proteomic techniques used for PTM analysis can be divided into antibody-based analysis or mass spectrometry (MS)-based analysis. Protein microarrays, immunohistochemistry and Western blot analysis demonstrate widespread popularity as efficient tools for antibody-based PTM analysis. MS-based analysis is a powerful technique in the study of PTMs. This figure outlines the steps involved in the preparation of crude samples for MS and the transformation of MS data into interpretable results.
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Figure 3. Bottom-up proteomics workflow for PTM analysis and subsequent validation. Bottom-up proteomic analyses are often applied for PTM-focused studies. This workflow can be adapted based on the PTM of interest through the selection of PTM-specific enrichment techniques. The examples provided in the figure (red font) highlight potential adaptations of this approach for blood cancer research. Middle-down and top-down proteomic approaches for PTM analysis are gaining popularity and may also present as useful techniques for future studies in blood cancer research [180,181]. FASP, filter-aided sample preparation; TMT, tandem mass tag; SCX, strong cation exchange chromatography; IMAC, immobilized metal ion affinity chromatography; MOAC, metal oxide affinity chromatography; TUBEs, tandem ubiquitin-binding entities, UBDs, ubiquitin-binding domains; SUBEs, SUMO-binding entities; HILIC, hydrophilic interaction liquid chromatography; COFRADIC, combined fractional diagonal chromatography. Created using BioRender.com.
Figure 3. Bottom-up proteomics workflow for PTM analysis and subsequent validation. Bottom-up proteomic analyses are often applied for PTM-focused studies. This workflow can be adapted based on the PTM of interest through the selection of PTM-specific enrichment techniques. The examples provided in the figure (red font) highlight potential adaptations of this approach for blood cancer research. Middle-down and top-down proteomic approaches for PTM analysis are gaining popularity and may also present as useful techniques for future studies in blood cancer research [180,181]. FASP, filter-aided sample preparation; TMT, tandem mass tag; SCX, strong cation exchange chromatography; IMAC, immobilized metal ion affinity chromatography; MOAC, metal oxide affinity chromatography; TUBEs, tandem ubiquitin-binding entities, UBDs, ubiquitin-binding domains; SUBEs, SUMO-binding entities; HILIC, hydrophilic interaction liquid chromatography; COFRADIC, combined fractional diagonal chromatography. Created using BioRender.com.
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Figure 4. Targeting of protein modifications by clinically used blood cancer therapeutics. Ruxolitinib, midostaurin, gilteritinib, imatinib, dasatinib, nilotinib, bosutinib, ponatinib, ibrutinib and idelalisib are kinase inhibitors that inhibit the phosphorylation and activation of key proteins of oncogenic signaling pathways, promoting growth arrest and apoptosis in cancer cells. ATRA therapy for the treatment of APL induces SUMOylation-dependent polyubiquitination and proteasomal degradation of the fusion oncoprotein PML-RARA. The immunomodulatory drugs, lenalidomide, thalidomide and pomalidomide bind Cereblon, the substrate adaptor of the CRL4CRBN E3 ubiquitin ligase, modulating its substrate specificity. Bortezomib, carfilzomib and ixazomib target the 26S proteasome, blocking the degradation of polyubiquitinated regulatory proteins and inducing the unfolded protein response resulting in apoptosis. Panobinostat, vorinostat, belinostat and romidepsin are HDAC inhibitors that block the deacetylation activity of HDACs and exert their anti-tumor activity through modulating the acetylation status of a variety of histone and non-histone proteins. JAK2V617F, Janus kinase 2 V617F; STAT, signal transducer and activator of transcription; SHC, Src homology and Collagen; GRB2, growth factor receptor-bound protein 2; SOS, Son of sevenless; RAF, RAF proto-oncogene serine/threonine-protein kinase; MEK, Mitogen-activated protein kinase kinase; ERK, extracellular signal-regulated kinase; FLT3-ITD, fms like tyrosine kinase 3 – internal tandem duplications; PI3K, phosphatidylinositol 3-kinase; MDM2, mouse double minute 2 homolog; IKK, IκB kinase; Bad, BCL2 associated agonist of cell death; NF-κB, Nuclear factor kappa B; Bcl-xL, B-cell lymphoma-extra large; GAB2, GRB2 associated binding protein 2; BCR-ABL, breakpoint cluster region-proto-oncogene tyrosine-protein kinase; CBL, E3 ubiquitin-protein ligase CBL; CRK, Proto-oncogene c-Crk; CRKL, Crk-like protein; CAS, Crk-associated substrate; FAK, focal adhesion kinase; BCR, B-cell receptor; Lyn, tyrosine-protein kinase Lyn; SYK, tyrosine-protein kinase SYK; BTK, Bruton’s tyrosine kinase; BLNK, B-cell linker protein; PLC, phospholipase C; DAG, diacyl glycerol; IP3, inositol 1,4,5-trisphosphate; NFAT, nuclear factor of activated T-cells; PML-RARA, promyelocytic leukemia/retinoic acid receptor alpha; ATRA, all-trans retinoic acid; Ubc9, SUMO-conjugating enzyme UBC9; RNF4, ring finger protein 4; CUL4A, cullin 4A; DDB1, DNA damage binding protein 1; CRBN, cereblon; ROC1, regulator of cullins-1; Len, lenalidomide; JNK, c-Jun N-terminal kinase (JNK); IκB, inhibitor of nuclear factor kappa B; HIF-1α, hypoxia-inducible factor 1-alpha; HSP90, heat shock protein 90; P, phosphorylation; S, sumoylation; Ub, ubiquitination; Ac, acetylation.
Figure 4. Targeting of protein modifications by clinically used blood cancer therapeutics. Ruxolitinib, midostaurin, gilteritinib, imatinib, dasatinib, nilotinib, bosutinib, ponatinib, ibrutinib and idelalisib are kinase inhibitors that inhibit the phosphorylation and activation of key proteins of oncogenic signaling pathways, promoting growth arrest and apoptosis in cancer cells. ATRA therapy for the treatment of APL induces SUMOylation-dependent polyubiquitination and proteasomal degradation of the fusion oncoprotein PML-RARA. The immunomodulatory drugs, lenalidomide, thalidomide and pomalidomide bind Cereblon, the substrate adaptor of the CRL4CRBN E3 ubiquitin ligase, modulating its substrate specificity. Bortezomib, carfilzomib and ixazomib target the 26S proteasome, blocking the degradation of polyubiquitinated regulatory proteins and inducing the unfolded protein response resulting in apoptosis. Panobinostat, vorinostat, belinostat and romidepsin are HDAC inhibitors that block the deacetylation activity of HDACs and exert their anti-tumor activity through modulating the acetylation status of a variety of histone and non-histone proteins. JAK2V617F, Janus kinase 2 V617F; STAT, signal transducer and activator of transcription; SHC, Src homology and Collagen; GRB2, growth factor receptor-bound protein 2; SOS, Son of sevenless; RAF, RAF proto-oncogene serine/threonine-protein kinase; MEK, Mitogen-activated protein kinase kinase; ERK, extracellular signal-regulated kinase; FLT3-ITD, fms like tyrosine kinase 3 – internal tandem duplications; PI3K, phosphatidylinositol 3-kinase; MDM2, mouse double minute 2 homolog; IKK, IκB kinase; Bad, BCL2 associated agonist of cell death; NF-κB, Nuclear factor kappa B; Bcl-xL, B-cell lymphoma-extra large; GAB2, GRB2 associated binding protein 2; BCR-ABL, breakpoint cluster region-proto-oncogene tyrosine-protein kinase; CBL, E3 ubiquitin-protein ligase CBL; CRK, Proto-oncogene c-Crk; CRKL, Crk-like protein; CAS, Crk-associated substrate; FAK, focal adhesion kinase; BCR, B-cell receptor; Lyn, tyrosine-protein kinase Lyn; SYK, tyrosine-protein kinase SYK; BTK, Bruton’s tyrosine kinase; BLNK, B-cell linker protein; PLC, phospholipase C; DAG, diacyl glycerol; IP3, inositol 1,4,5-trisphosphate; NFAT, nuclear factor of activated T-cells; PML-RARA, promyelocytic leukemia/retinoic acid receptor alpha; ATRA, all-trans retinoic acid; Ubc9, SUMO-conjugating enzyme UBC9; RNF4, ring finger protein 4; CUL4A, cullin 4A; DDB1, DNA damage binding protein 1; CRBN, cereblon; ROC1, regulator of cullins-1; Len, lenalidomide; JNK, c-Jun N-terminal kinase (JNK); IκB, inhibitor of nuclear factor kappa B; HIF-1α, hypoxia-inducible factor 1-alpha; HSP90, heat shock protein 90; P, phosphorylation; S, sumoylation; Ub, ubiquitination; Ac, acetylation.
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Table 1. Current FDA-approved therapeutics for the treatment of various blood cancers that influence protein modifications.
Table 1. Current FDA-approved therapeutics for the treatment of various blood cancers that influence protein modifications.
Type of TherapeuticDrugPTM AffectedType of Blood CancerMechanism of ActionReferences
Kinase Inhibitors (KIs)Ruxolitinib (JAKAFI®)PhosphorylationMyelofibrosis
Polycythemia Vera
JAK2 inhibitor[191,192]
Midostaurin (RYDAPT®)PhosphorylationFLT3-mutant AML,
Advanced systemic mastocytosis (AdvSM)
FLT3 inhibitor in AML.
KIT inhibitor in AdvSM.
[193,194]
Gilteritinib (XOSPATA®)PhosphorylationFLT3-mutant Acute myeloid leukemia (AML)FLT3, ALK inhibitor[195]
Imatinib (GLEEVEC®)PhosphorylationPh+ Chronic myeloid leukemia (CML), Ph+ Acute lymphoblastic leukemia (ALL),
Myelodysplastic/ myeloproliferative diseases (MDS/MPD), Aggressive systemic mastocytosis (ASM), Chronic eosinophilic leukemia (CEL)
BCR-ABL inhibitor[196]
Dasatinib (SPRYCEL®)PhosphorylationPh+ CML, Ph+ ALLBCR-ABL, SRC inhibitor[197]
Nilotinib (TASIGNA®)PhosphorylationPh+ CMLBCR-ABL inhibitor[198]
Bosutinib (BOSULIF®)PhosphorylationPh+ CMLBCR-ABL and SRC inhibitor[199]
Ponatinib (ICLUSIG®)PhosphorylationCML, Ph+ ALLBCR-ABL inhibitor[200]
Ibrutinib (IMBRUVICA®)PhosphorylationMantle cell lymphoma (MCL), Chronic lymphocytic leukaemia (CLL)
Small lymphocytic lymphoma (SLL)
Waldenström’s macroglobulinemia (WM)
Marginal zone lymphoma (MZL)
BTK inhibitor[201]
Idelalisib (ZYDELIG®)PhosphorylationCLL, SLL
Follicular lymphoma (FL)
Phosphatidylinositol 3-kinase delta (PI3Kδ) inhibitor[202]
Proteasome Inhibitors (PIs)Bortezomib (VELCADE®)UbiquitinationMCL, Multiple myeloma
(MM)
26S proteasome inhibitor[203]
Carfilzomib (KYPROLIS®)UbiquitinationMM26S proteasome inhibitor[204]
Ixazomib (NINLARO®)UbiquitinationMM26S proteasome inhibitor[205]
Differentiation TherapyAll-trans retinoic acid (ATRA) (VESANOID®) and arsenic trioxide (TRISENOX®)Sumoylation
Ubiquitination
Acute promyelocytic leukemia (APL)Sumoylation-dependent degradation of the fusion oncoprotein PML-RARα.[206]
Immunomodulatory Drugs (IMiDs)Lenalidomide (REVLIMID®)UbiquitinationMM, MDS, MCL, FL, MZLModulation of CRL4CRBN E3 ubiquitin ligase activity.[207]
Thalidomide (THALOMID®)UbiquitinationMMModulation of CRL4CRBN E3 ubiquitin ligase activity.[208]
Pomalidomide (POMALYST®)UbiquitinationMMModulation of CRL4CRBN E3 ubiquitin ligase activity.[208]
Histone Deacetylase Inhibitors (HDACi)Panobinostat (FARYDAK®)AcetylationMMPan-HDAC inhibitor[209]
Vorinostat (ZOLINZA®)AcetylationCutaneous T cell lymphoma (CTCL)Class I, II HDAC inhibitor[210]
Belinostat (BELEODAQ®)AcetylationPeripheral T cell lymphoma (PTCL)Pan-HDAC inhibitor[211]
Romidepsin (ISTODAX®)AcetylationCTCL, PTCLClass I HDAC inhibitor[212]
Abbreviations: JAK2, Janus kinase 2; KIT, proto-oncogene c-Kit; ALK, anaplastic lymphoma kinase; Ph+, Philadelphia chromosome positive; BCR-ABL, breakpoint cluster region-proto-oncogene tyrosine-protein kinase; SRC, proto-oncogene c-Src; BTK, Bruton’s tyrosine kinase; PML-RARα, promyelocytic leukemia/retinoic acid receptor alpha; CRL4CRBN, CRL4/Cereblon E3 ubiquitin ligase complex; HDAC, histone deacetylase.
Table 2. Recent studies utilizing proteomic techniques to analyze PTMs in multiple myeloma.
Table 2. Recent studies utilizing proteomic techniques to analyze PTMs in multiple myeloma.
PTM AnalyzedProteomic TechniqueMain FindingReference
PhosphorylationWestern blot analysisMyeloid-derived suppressor cells (MDSCs) drive enhanced phosphorylation of AMPK, promoting MM cell survival[215]
PhosphorylationWestern blot analysisPRL-3 aberrantly phosphorylates STAT3 through SHP-2 repression, leading to constant activation of STAT3[216]
PhosphorylationTrypsin digestion, IMAC phosphopeptide enrichment, LC–MS/MS, Western blot analysisElucidated signaling dynamics in MM to aid precision medicine[217]
PhosphorylationTrypsin digestion, IMAC phosphopeptide enrichment, LC–MS/MS, Western blot analysisBone marrow stromal cells stimulate the activation, through phosphorylation, of JAK/STAT signaling. Tofacitinib reverses BMSC-mediated proliferation of MM cells[218]
PhosphorylationSILAC labeling, trypsin digestion, SCX chromatography, IMAC phosphopeptide enrichment, phosphotyrosine immunoprecipitation, LC–MS/MSMM cells treated with imatinib show inhibition of kinase activity due to RNA processing and a decrease in lipid biosynthesis[219]
UbiquitinationHuman influenza hemagglutinin (HA)-tagged ubiquitin, immunoprecipitation, Western blot analysisDestabilization of NEK2, via its ubiquitination, reduces MM cell growth and overcomes resistance to proteasome inhibitors[220]
UbiquitinationImmunoprecipitation, immunoblot analysisUSP15 inhibits ubiquitination and degradation of NF-κBp65 which in turn promotes USP15 expression resulting in a feedback loop enhancing MM survival[221]
UbiquitinationImmunoprecipitation, SDS-PAGE, trypsin digestion, LC–MS/MSIdentification of 73 ubiquitination sites on 52 ubiquitinated proteins in human MM U266 cells[222]
SUMOylationImmunoprecipitation, Western blot analysisDownregulation of SENP2 increases IκBα sumoylation which activates NF-κB, leading to bortezomib resistance[223]
SUMOylationCell culture, transfection, SDS-PAGE, Western blot analysis, chemiluminescence, co-immunoprecipitationIdentification of a sumoylation signature in MM that is associated with adverse clinical outcome[224]
N-glycosylationHILIC-solid phase extraction, MALDI–TOF–MSAnalysis of serum protein N-glycosylation in MM revealed a correlation between N-glycosylation marks and ISS stages[225]
Glycosylation (Sialylation)Lectin histochemistry, lectins: Sambucus Nigra (SNA), Peanut Agglutinin (PNA), Maackia Amurensis Lectin II (MALII)Inhibition of sialylation prevents MM cell interactions with E-selectin, MADCAM1 and VCAM1 restricting the access of tumor cells to the protective BM microenvironment[226]
AcetylationSite directed mutagenesis, SDS-PAGE, immunoblot analysisInhibition of HDAC3 and DNMT1 reduces survival of MM cells[227]
AcetylationSDS-PAGE, immunoblot analysisFDA-approved panobinostat increases acetylation of H3K9 resulting in IRF4 inhibition and MM cell apoptosis[228]
MethylationSDS-PAGE, immunoblot analysisKDM6B, independent of demethylase activity, upregulates MAPK signaling, leading to MM survival and proliferation[229]
Methylation
Acetylation
Isotopic labeling, -Multiple reactionmonitoring based LC–MS/MS, label-free quantificationQuantification of histone PTM marks in MM cell line[230]
Abbreviations: AMPK, 5’ adenosine monophosphate-activated protein kinase; MM, multiple myeloma; PRL-3, phosphatase of regenerating liver 3; STAT3, signal transducer and activator of transcription 3; SHP-2, SH2 domain-containing protein tyrosine phosphatase-2; IMAC, immobilized metal ion affinity chromatography; LC-MS/MS, liquid chromatography- tandem mass spectrometry; BMSC, bone marrow stromal cells; NEK2, NIMA related kinase 2; SILAC, stable isotopic labelling of amino acids in cell culture; SCX, strong cation exchange chromatography; USP15, Ubiquitin carboxyl-terminal hydrolase 15; NF-κBp65, transcription factor p65; SDS-PAGE, sodium dodecyl sulfate polyacrylamide gel electrophoresis; SENP2, SUMO specific protease 2; IκBα, inhibitor of nuclear factor kappa B, alpha; HILIC, hydrophilic interaction liquid chromatography; MALDI–TOF–MS, matrix assisted laser desorption ionization-time of flight mass spectrometry; MADCAM1, mucosal addressin cell adhesion molecule 1; VCAM1, vascular cell adhesion molecule 1; HDAC3, histone deacetylase 3; DNMT1, DNA methyltransferase 1; H3K9, histone 3 lysine 9; IRF4, interferon regulatory factor 4; KDM6B, lysine demethylase 6B; MAPK, mitogen-activated protein kinase.
Table 3. Recent studies utilizing proteomic techniques to analyze PTMs in acute myeloid leukemia.
Table 3. Recent studies utilizing proteomic techniques to analyze PTMs in acute myeloid leukemia.
PTM AnalyzedProteomic TechniqueMain FindingReference
PhosphorylationImmunoprecipitation, Western blot analysisPhosphorylation of the oncogenic kinase PIM-1L by PKCα stimulates proliferation and growth of AML cells[246]
PhosphorylationTrypsin digestion, iTRAQ labeling, Fe3+-IMAC, RP-SAX-RP, LC–MS, Western blot analysisResistance to chemotherapy in AML requires the phosphorylation of transcription factor, MEF2C[247]
PhosphorylationSILAC, filter-aided sample preparation (FASP), LC–MSAnalysis of the impact of insulin and specific inhibitors on the phosphorylation of PI3K-Akt-mTOR pathway components in AML cells[248]
PhosphorylationWestern blot analysisGilteritinib reduces phosphorylation of FLT3 and its downstream targets, improving the survival of FLT3-mutated AML mouse models[249]
PhosphorylationReverse-phase protein microarray (RPPA), Western blot analysisSTAT3 inhibitor, NSC-743380, induces apoptosis in SULT1A1-expressing AML cells by inhibiting the activity of the PI3K/AKT/mTOR pathway[250]
UbiquitinationSite-directed mutagenesis, immunoprecipitation, Western blot analysisUbiquitination and degradation of CDK2 results in the differentiation of AML cells through the activation of PRDX2[251]
Ubiquitination
Phosphorylation
Western blot analysisE3 ubiquitin ligase, TRIAD1, suppresses leukemogenesis in 11q23-AML[252]
UbiquitinationImmunoblot analysis, immunohistochemistryInhibitor of E1 ubiquitin-activating enzyme, Uba1, TAK-243 selectively decreases growth and survival of AML cells.[253]
SUMOylationSite-directed mutagenesis, immunoprecipitation, Western blot analysisSUMOylation of sPRDM16, promotes AML progression and inhibits differentiation[254]
SUMOylationWestern blot analysesSUMOylation inhibitor, 2-D08, inhibits AML cell viability through ROS accumulation-mediated apoptosis[255]
Phosphorylation
Glycosylation
Western blot analysisTyrosine kinase inhibition increases surface expression of FLT3 via increased glycosylation, demonstrating therapeutic potential in combination with FLT3-directed therapy[256]
Phosphorylation
Ubiquitination
Glycosylation
Immunoprecipitation, Western blot analysisSerine/threonine kinase PIM-1 stabilizes unglycosylated FLT3-ITD, promoting the activation of STAT5 signaling[257]
AcetylationSILAC labeling, HPLC fractionation, immunoaffinity enrichment, LC–MS/MSHDAC inhibitors have differential impacts on the lysine acetylome in AML cells[258]
Acetylation
Phosphorylation
Immunoprecipitation, Western blot analysisCombination of novel HDAC inhibitor, MPT0G211, with current chemotherapeutics has anti-proliferative effects on human AML cells[259]
MethylationMutagenesis, Western blot analysisMethylation of FLT3-ITD by PRMT1 supports the persistence of FLT3-ITD+ AML cells[260]
Abbreviations: PIM-1L, serine/threonine-protein kinase pim-1, isoform 2; PKCα, protein kinase C alpha; iTRAQ, isobaric tags for relative and absolute quantitation; IMAC, immobilized metal ion affinity chromatography; RP-SAX-RP, reversed phase-strong anion exchange-reversed phase; MEF2C, myocyte enhancer factor 2C; SILAC, stable isotopic labelling of amino acids in cell culture; PI3K, phosphatidylinositol 3-kinase; mTOR, mammalian target of rapamycin; FLT3, FMS-like tyrosine kinase 3; STAT, signal transducer and activator of transcription; SULT1A1, sulfotransferase 1A1; CDK2, cyclin-dependent kinase 2; PRDX2, peroxiredoxin 2; TRIAD1, two RING fingers and DRIL1; sPRDM16, short form PR domain containing 16; ROS, reactive oxygen species; HPLC, high performance liquid chromatography; HDAC, histone deacetylase; PRMT1, protein arginine N-methyltransferase 1.
Table 4. Recent studies utilizing proteomic techniques to analyze PTMs in myeloproliferative neoplasms.
Table 4. Recent studies utilizing proteomic techniques to analyze PTMs in myeloproliferative neoplasms.
PTM AnalyzedProteomic TechniqueMain FindingReference
PhosphorylationSILAC, FASP, in-gel isoelectric focusing, HPLC fractionation, LC–MS/MS, Western blot analysisEnhanced phosphorylation of eukaryotic initiation factor 2 α subunit (eIF2α) increases the secretion of extracellular enzymes, promoting the invasiveness of CML cells[277]
PhosphorylationWestern blot analysisMPNs with JAK2V617F mutation evade the immune system through upregulated PD-L1 expression [278]
PhosphorylationWestern blot analysisSelective inhibitor of mutant JAK2 (V617F), ZT55, inhibits proliferation and induces apoptosis in HEL cell line[279]
PhosphorylationWestern blot analysis, phospho-specific protein microarray analysisIncreased RalA, a small GTPase, promotes malignant transformation and progression in CML through the activation of Ras signaling.[280]
UbiquitinationImmunoblot analysisThe stem cell protein, Asrij/OCIAD1, prevents the degradation of p53, thus mediating hematopoietic stem cell quiescence and preventing uncontrolled proliferation[281]
Phosphorylation
Ubiquitination
Immunoprecipitation, immunoblot analysisKLF4 promotes leukemogenesis through the repression of DYRK2, which mediates the activation of p53 and the degradation of c-Myc in CML-like disease in mice[282]
Phosphorylation
Ubiquitination
Immunoprecipitation, Western blot analysis, TUBE-based ubiquitin-binding assayThe loss of LZTR1 function, a mediator of Ras ubiquitination and MAPK signaling contributes to TKI resistance in BCR-ABL CML cells.[283]
Phosphorylation
Ubiquitination
Immunoprecipitation, Western blot analysis, TUBE-based ubiquitin-binding assayType I JAK inhibitor, but not a type II inhibitor, mediates pathogenic withdrawal signaling through the accumulation of phosphorylated JAK2 by preventing dephosphorylation and ubiquitination.[284]
Phosphorylation
SUMOylation
Immunoprecipitation, Western blot analysisDown-modulation of the β-catenin antagonist, CBY1, in CML is induced by 14-3-3 binding and enhanced SUMOylation followed by proteasomal degradation[285]
Phosphorylation
Glycosylation
Immunoprecipitation, Western blot analysis, TMT labeling, N-glycan permethylation, MALDI–TOF–MSMutations in CSF3R prevents N-glycosylation, promoting ligand-independent activation of the JAK/STAT pathway[286]
Phosphorylation
Glycosylation
Immunoprecipitation, Western blot analysis, size-exclusion chromatography, MS-based analysisMutant calreticulin (CALR) acts as a rogue chaperone thrombopoietin receptor (TpoR), immature TpoR and mutant TpoR resulting in cytokine-independent activation and constituent JAK/STAT activation[287]
AcetylationWestern blot analysis, immunoprecipitation, SDS-PAGE, in-gel digestion, LC–MS/MSActivation of deacetylase Sirtuin 1 (SIRT1) restores Tet methylcytosine dioxygenase 2 (TET2) activity, disrupting the maintenance of MDS HSPCs.[288]
AcetylationImmunoprecipitation, Western blot analysis, LC–MS/MSInhibition of HDAC11 downregulates the JAK/STAT pathway and induces apoptosis in MPLW515L-MPN mouse model[289]
Phosphorylation
Methylation
Immunoprecipitation, Western blot analysisOverexpression of the histone demethylase, JMJD1C, in MPNs prevents H3K9me2 and HP1α-mediated repression of NFE2 resulting in a positive feedback loop[290]
Abbreviations: SILAC, stable isotopic labelling of amino acids in cell culture; FASP, filter aided sample preparation; HPLC, high performance liquid chromatography; CML, chronic myeloid leukemia; JAK, Janus kinase; PD-L1, programmed death-ligand 1; HEL, human erythroleukemia; RalA, Ras-related protein Ral-A; OCIAD1, ovarian cancer immunoreactive antigen domain containing 1; KLF4, krüppel-like factor 4; DYRK2, dual specificity tyrosine phosphorylation regulated kinase 2; LZTR1, leucine zipper like transcription regulator 1; TKI, tyrosine kinase inhibitor; TUBE, tandem ubiquitin binding entities; BCR-ABL, breakpoint cluster region-proto-oncogene tyrosine-protein kinase; CBY1, protein chibby homolog 1; MALDI–TOF–MS, matrix assisted laser desorption ionization-time of flight mass spectrometry; CSF3R, colony stimulating factor 3 receptor; SDS-PAGE, sodium dodecyl sulfate polyacrylamide gel electrophoresis; HSPCs, hematopoietic stem/progenitor cells; HDAC11, histone deacetylase 11; MPL, myeloproliferative leukemia virus; JMJD1C, Jumonji domain-containing protein 1C; H3K9me2, histone 3 lysine 9 dimethylation; HP1α, heterochromatin protein 1; NFE2, nuclear factor erythroid 2.
Table 5. Recent studies utilizing proteomic techniques to analyze PTMs in lymphomas.
Table 5. Recent studies utilizing proteomic techniques to analyze PTMs in lymphomas.
PTM AnalyzedProteomic TechniqueMain FindingReference
PhosphorylationImmunohistochemistry, Western blot analysisIncreased STAT3 phosphorylation promotes PD-L1 expression in NKTL[300]
PhosphorylationWestern blot analysis, PhosphoFlow cytometry, reverse phase protein microarray, TiO2 enrichment, LC–MS/MSThe dual PI3K/mTOR inhibitor, PQR309, demonstrates anti-cancer activity alone and in combination with other therapies in preclinical lymphoma models[301]
PhosphorylationImmunohistochemistry, Western blot analysis, LC–MS/MSPhosphorylation of SOCS1 by Src family kinases inhibits SOCS1-p53-mediated senescence[308]
PhosphorylationTrypsin digestion, TiO2 enrichment, tyrosine-phosphorylated peptide immunoprecipitation, LC–MS/MS, Western blot analysisCSF1R expression is altered in many T cell lymphomas. CSF1R activation by CSF1 leads to phosphorylation and activation of downstream PI3K/AKT/mTOR signaling, thus promoting proliferation and survival[302]
UbiquitinationSite-directed mutagenesis, immunoprecipitation, Western blot analysis, TUBE assayThe ubiquitin ligases cIAP1 and cIAP2 promote oncogenic BCR signaling and represent the potential of SMAC mimetics for the treatment of ABC DLBCL[309]
UbiquitinationWestern blot analysisEnhanced expression of TRIM11 activates β-catenin signaling via Axin1 ubiquitination and degradation, thus promoting lymphomagenesis[310]
SUMOylationImmunoprecipitation, Western blot analysis, in vitro SUMOylation assay, site-directed mutagenesisDysregulated SUMOylation in NPM-ALK+ T-cell lymphoma stabilizes the NPM-ALK fusion protein resulting in its accumulation promoting carcinogenesis[311]
SUMOylationWestern blot analysisEpstein-Barr Virus LMP1 enhances SUMOylation through interaction with the SUMO E2-conjugating enzyme, Ubc9, in LMP1-positive lymphomas[312]
GlycosylationWestern blot analysis, glycoprotein deglycosylation assay, GC-MSOverexpression of the glycosyltransferase, GLT1D1, is a poor prognostic marker that enhances PD-L1 glycosylation promoting tumor growth in B cell NHL[313]
AcetylationWestern blot analysisThe tumor suppressing acetyltransferase, CREBBP, is haploinsufficient in GC-derived B cell NHL[314]
Acetylation
Methylation
Ubiquitination
Western blot analysis, nanoLC-MRM, MS/MSHDAC1-deficient thymic lymphomas show increased H3K79 methylation and demonstrate sensitivity to a DOT1L inhibitor[315]
MethylationImmunoprecipitation, Western blot analysis, in vitro methyltransferase assay, SDS-PAGE, trypsin digestion, LC–MS/MSPRMT5-mediated regulation of BCL6, via methylation, is required for germinal center formation. Dual inhibition of PRMT5 and BCL6 suppresses DLBCL proliferation[316]
Abbreviations: STAT, signal transducer and activator of transcription; PD-L1, programmed death-ligand 1; NKTL, natural killer T cell lymphoma; PI3K, phosphatidylinositol 3-kinase; mTOR, mammalian target of rapamycin; SOCS, suppressor of cytokine signalling; CSF1R, colony stimulating factor 1 receptor; CSF, colony stimulating factor; cIAP, cellular inhibitor of apoptosis protein; BCR, B cell receptor; SMAC, second mitochondria-derived activator of caspase; ABC DLBCL, activated B-cell diffuse large B cell lymphoma; TRIM11, tripartite motif containing 11; NPM-ALK, nucleophosmin-anaplastic lymphoma kinase; LMP1, latent membrane protein 1; GC/MS, gas chromatography–mass spectrometry; GLT1D1, glycosyltransferase 1 domain-containing protein 1; NHL, non-Hodgkin’s lymphoma; CREBBP, CREB binding protein; GC, germinal center; HDAC, histone deacetylase; H3K79, histone 3 lysine 79; DOT1L, disruptor of telomeric silencing 1-like; MRM, multiple reaction monitoring; PRMT5, protein arginine methyltransferase 5; BCL6, B cell lymphoma 6; SDS-PAGE, sodium dodecyl sulfate polyacrylamide gel electrophoresis.
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Dunphy, K.; Dowling, P.; Bazou, D.; O’Gorman, P. Current Methods of Post-Translational Modification Analysis and Their Applications in Blood Cancers. Cancers 2021, 13, 1930. https://doi.org/10.3390/cancers13081930

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Dunphy K, Dowling P, Bazou D, O’Gorman P. Current Methods of Post-Translational Modification Analysis and Their Applications in Blood Cancers. Cancers. 2021; 13(8):1930. https://doi.org/10.3390/cancers13081930

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Dunphy, Katie, Paul Dowling, Despina Bazou, and Peter O’Gorman. 2021. "Current Methods of Post-Translational Modification Analysis and Their Applications in Blood Cancers" Cancers 13, no. 8: 1930. https://doi.org/10.3390/cancers13081930

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