Mass Spectrometry-based Proteomic Technologies Research Articles

Mass Spectrometry-Based Proteomics for Classification and Treatment Optimisation of Triple Negative Breast Cancer.

Triple-negative breast cancer (TNBC) presents a significant medical challenge due to its highly invasive nature, high rate of metastasis, and lack of drug-targetable receptors, which together lead to poor prognosis and limited treatment options. The traditional treatment guidelines for early TNBC are based on a multimodal approach integrating chemotherapy, surgery, and radiation and are associated with low overall survival and high relapse rates. Therefore, the approach to treating early TNBC has shifted towards neoadjuvant treatment (NAC), given to the patient before surgery and which aims to reduce tumour size, reduce the risk of recurrence, and improve the pathological complete response (pCR) rate. However, recent studies have shown that NAC is associated with only 30% of patients achieving pCR. Thus, novel predictive biomarkers are essential if treatment decisions are to be optimised and chemotherapy toxicities minimised. Given the heterogeneity of TNBC, mass spectrometry-based proteomics technologies offer valuable tools for the discovery of targetable biomarkers for prognosis and prediction of toxicity. These biomarkers can serve as critical targets for therapeutic intervention. This review aims to provide a comprehensive overview of TNBC diagnosis and treatment, highlighting the need for a new approach. Specifically, it highlights how mass spectrometry-based can address key unmet clinical needs by identifying novel protein biomarkers to distinguish and early prognostication between TNBC patient groups who are being treated with NAC. By integrating proteomic insights, we anticipate enhanced treatment personalisation, improved clinical outcomes, and ultimately, increased survival rates for TNBC patients.

Characterization of the plasma proteome from healthy adult dogs.

Bloodwork is a widely used diagnostic tool in veterinary medicine, as diagnosis and therapeutic interventions often rely on blood biomarkers. However, biomarkers available in veterinary medicine often lack sensitivity or specificity. Mass spectrometry-based proteomics technology has been extensively used in the analysis of biological fluids. It offers excellent potential for a more comprehensive characterization of the plasma proteome in veterinary medicine. In this study, we aimed to identify and quantify plasma proteins in a cohort of healthy dogs and compare two techniques for depleting high-abundance plasma proteins to enable the detection of lower-abundance proteins via label-free quantification liquid chromatography-mass spectrometry. We utilized surplus lithium-heparin plasma from 30 healthy dogs, subdivided into five groups of pooled plasma from 6 randomly selected individuals each. Firstly, we used a commercial kit to deplete high-abundance plasma proteins. Secondly, we employed an in-house method to remove albumin using Blue-Sepharose. Among all the samples, some of the most abundant proteins identified were apolipoprotein A and B, albumin, alpha-2-macroglobulin, fibrinogen beta chain, fibronectin, complement C3, serotransferrin, and coagulation factor V. However, neither of the depletion techniques achieved significant depletion of highly abundant proteins. Despite this limitation, we could detect and quantify many clinically relevant proteins. Determining the healthy canine proteome is a crucial first step in establishing a reference proteome for canine plasma. After enrichment, this reference proteome can later be utilized to identify protein markers associated with different diseases, thereby contributing to the diagnosis and prognosis of various pathologies.

TransGCN: a semi-supervised graph convolution network-based framework to infer protein translocations in spatio-temporal proteomics.

Protein subcellular localization (PSL) is very important in order to understand its functions, and its movement between subcellular niches within cells plays fundamental roles in biological process regulation. Mass spectrometry-based spatio-temporal proteomics technologies can help provide new insights of protein translocation, but bring the challenge in identifying reliable protein translocation events due to the noise interference and insufficient data mining. We propose a semi-supervised graph convolution network (GCN)-based framework termed TransGCN that infers protein translocation events from spatio-temporal proteomics. Based on expanded multiple distance features and joint graph representations of proteins, TransGCN utilizes the semi-supervised GCN to enable effective knowledge transfer from proteins with known PSLs for predicting protein localization and translocation. Our results demonstrate that TransGCN outperforms current state-of-the-art methods in identifying protein translocations, especially in coping with batch effects. It also exhibited excellent predictive accuracy in PSL prediction. TransGCN is freely available on GitHub at https://github.com/XuejiangGuo/TransGCN.

Plasma proteome profiling reveals metabolic and immunologic differences between Anorexia Nervosa subtypes

Aims/hypothesisAnorexia Nervosa (AN) is a severe psychiatric disorder of an unknown etiology with a crude mortality rate of about 5 % per decade, making it one of the deadliest of all psychiatric illnesses. AN is broadly classified into two main subtypes, restricting and binge/purging disorder. Despite extensive research efforts during several decades, the underlying pathophysiology of AN remains poorly understood. In this study, we aimed to identify novel protein biomarkers for AN by performing a proteomics analysis of fasting plasma samples from 78 females with AN (57 restrictive and 21 binge/purge type) and 70 healthy controls. MethodsUsing state-of-the-art mass spectrometry-based proteomics technology in conjunction with an advanced bioinformatics pipeline, we quantify >500 plasma proteins. ResultsDifferential expression analysis and correlation of proteomics data with clinical variables led to identification of a panel of novel protein biomarkers with potential pathophysiological significance for AN. Our findings demonstrate evidence of a humoral immune system response, altered lipid metabolism and potential alteration of plasma cells in AN patients. Additionally, we stratified AN patients based on the quantified proteins and suggest a potential autoimmune nature in the restrictive subtype of AN. Conclusions/interpretationIn summary, on top of biomarkers of AN subtypes, this study provides a comprehensive map of plasma proteins that constitute a resource for further studies of the pathophysiology of AN.

Mass spectrometry–based proteomics technology in pancreatic cancer research

Pancreatic ductal adenocarcinoma (PDAC) has become a significant health concern with increasing incidence and mortality rates over the past few decades. Researchers have turned their attention to cutting-edge mass spectrometry (MS) technology due to its high-throughput and accurate detection capacity, which plays a vital role in understanding the mechanisms and discovering biomarkers for pancreatic diseases. In this review, we comprehensively investigate various methodologies of quantitative and qualitative proteomics MS technologies, alongside bioinformatical platforms employed in pancreatic cancer research. The integration of these optimized approaches provides novel insights into the molecular mechanisms underlying tumorigenesis and disease progression, ultimately facilitating the discovery of potential diagnostic, prognostic biomarkers, and therapeutic targets. The robust MS-based strategy shows promise in paving the way for early diagnosis and personalized medicine for pancreatic cancer patients.

Potential relevance of salivary legumain for the clinical diagnostic of hand, foot, and mouth disease.

The fight against hand, foot, and mouth disease (HFMD) remains an arduous challenge without existing point-of-care (POC) diagnostic platforms for accurate diagnosis and prompt case quarantine. Hence, the purpose of this salivary biomarker discovery study is to set the fundamentals for the realization of POC diagnostics for HFMD. Whole salivary proteome profiling was performed on the saliva obtained from children with HFMD and healthy children, using a reductive dimethylation chemical labeling method coupled with high-resolution mass spectrometry-based quantitative proteomics technology. We identified 19 upregulated (fold change = 1.5-5.8) and 51 downregulated proteins (fold change = 0.1-0.6) in the saliva samples of HFMD patients in comparison to that of healthy volunteers. Four upregulated protein candidates were selected for dot blot-based validation assay, based on novelty as biomarkers and exclusions in oral diseases and cancers. Salivary legumain was validated in the Singapore (n = 43 healthy, 28 HFMD cases) and Taiwan (n = 60 healthy, 47 HFMD cases) cohorts with an area under the receiver operating characteristic curve of 0.7583 and 0.8028, respectively. This study demonstrates the feasibility of a broad-spectrum HFMD POC diagnostic test based on legumain, a virus-specific host systemic signature, in saliva.

Determining DNA-Protein Binding Affinities and Specificities from Crude Lysates Using a Combined SILAC/TMT Labeling Strategy.

In recent years, quantitative mass spectrometry-based interaction proteomics technology has proven very useful in identifying specific DNA-protein interactions using single pull-downs from crude lysates. Here, we applied a SILAC/TMT-based higher-order multiplexing approach to develop an interaction proteomics workflow called Protein-nucleic acid Affinity and Specificity quantification by MAss spectrometry in Nuclear extracts or PASMAN. In PASMAN, DNA pull-downs using a concentration range of specific and control DNA baits are performed in SILAC-labeled nuclear extracts. MS1-based quantification to determine specific DNA-protein interactions is then combined with sequential TMT-based quantification of fragmented SILAC peptides, allowing the generation of Hill-like curves and determination of apparent binding affinities. We benchmarked PASMAN using the SP/KLF motif and further applied it to gain insights into two CGCG-containing consensus DNA motifs. These motifs are recognized by two BEN domain-containing proteins, BANP and BEND3, which we find to interact with these motifs with distinct affinities. Finally, we profiled the BEND3 proximal proteome, revealing the NuRD complex as the major BEND3 proximal protein complex in vivo. In summary, PASMAN represents, to our knowledge, the first higher-order multiplexing-based interaction proteomics method that can be used to decipher specific DNA-protein interactions and their apparent affinities in various biological and pathological contexts.

Proteomic characterization of post-translational modifications in drug discovery.

Protein post-translational modifications (PTMs), which areusually enzymatically catalyzed, are major regulators of protein activity and involved in almost all celluar processes. Dysregulation of PTMs is associated with various types of diseases. Therefore, PTM regulatory enzymes representas an attractive and important class of targets in drug research and development. Inhibitors against kinases, methyltransferases, deacetyltransferases, ubiquitin ligases have achieved remarkable success in clinical application. Mass spectrometry-based proteomics technologies serve as a powerful approach for system-wide characterization of PTMs, which facilitates the identification of drug targets, elucidation of the mechanisms of action of drugs, and discovery of biomakers in personalized therapy. In this review, we summarize recent advances of proteomics-based studies on PTM targeting drugs and discuss how proteomics strategies facilicate drug target identification, mechanism elucidation, and new therapy development in precision medicine.

Enrichment and proteomic identification of Cryptosporidium parvum oocyst wall

BackgroundCryptosporidium parvum is a zoonotic parasitic protozoan that can infect a variety of animals and humans and is transmitted between hosts via oocysts. The oocyst wall provides strong protection against hostile environmental factors; however, research is limited concerning the oocyst wall at the proteomic level.MethodsA comprehensive analysis of the proteome of oocyst wall of C. parvum was performed using label-free qualitative high-performance liquid chromatography (HPLC) fractionation and mass spectrometry-based qualitative proteomics technologies. Among the identified proteins, a surface protein (CpSP1) encoded by the C. parvum cgd7_5140 (Cpcgd7_5140) gene was predicted to be located on the surface of the oocyst wall. We preliminarily characterized the sequence and subcellular localization of CpSP1.ResultsA total of 798 proteins were identified, accounting for about 20% of the CryptoDB proteome. By using bioinformatic analysis, functional annotation and subcellular localization of the identified proteins were examined for better understanding of the characteristics of the oocyst wall. To verify the localization of CpSP1, an indirect immunofluorescent antibody assay demonstrated that the protein was localized on the surface of the oocyst wall, illustrating the potential usage as a marker for C. parvum detection in vitro.ConclusionThe results provide a global framework about the proteomic composition of the Cryptosporidium oocyst wall, thereby providing a theoretical basis for further study of Cryptosporidium oocyst wall formation as well as the selection of targets for Cryptosporidium detection.Graphical

Quantitative Proteomics Reveals Metabolic Reprogramming in Host Cells Induced by Trophozoites and Intermediate Subunit of Gal/GalNAc Lectins from Entamoeba histolytica.

ABSTRACTEntamoeba histolytica is an intestinal protozoan parasite with remarkable ability to kill and phagocytose host cells, causing amoebic colitis and extraintestinal abscesses. The intermediate subunit (Igl) of galactose (Gal)- and N-acetyl-d-galactosamine (GalNAc)-specific lectins is considered an important surface antigen involved in the pathogenesis of E. histolytica. Here, we applied mass spectrometry-based quantitative proteomics technology to analyze the protein expression profile changes occurring in host Caco2 cells incubated with E. histolytica trophozoites or stimulated by purified native Igl protein. The expression levels of 1,490 and 489 proteins were significantly altered in the E. histolytica-treated and Igl-treated groups, respectively, among 6,875 proteins totally identified. Intriguingly, central carbon metabolism of host cells was suppressed in both E. histolytica-treated and Igl-treated groups, with evidence of decreased expression levels of several key enzymes, including pyruvate kinase muscle type 2, presenting a Warburg-like effect in host cells. Besides, Igl had potential physical interactions with central carbon metabolism enzymes and the proteolytic degradation family members proteasome subunit alpha and beta, which may be responsible for the degradation of key enzymes in carbon metabolism. These results provided a novel perspective on the pathogenic mechanism of E. histolytica and compelling evidence supporting the important role of Igl in the virulence of E. histolytica.IMPORTANCE Metabolic reprogramming is considered a hallmark of some infectious diseases. However, in amoebiasis, a neglected tropical disease caused by protozoan parasite E. histolytica, metabolic changes in host cells have yet to be proven. In this study, advanced data-independent acquisition mass spectrometry-based quantitative proteomics was applied to investigate the overall host cellular metabolic changes as high-throughput proteomics could measure molecular changes in a cell or tissue with high efficiency. Enrichment analysis of differentially expressed proteins showed biological processes and cellular pathways related to amoeba infection and Igl cytotoxicity. Specifically, central carbon metabolism of host cells was dramatically suppressed in both E. histolytica-treated and Igl-treated groups, indicating the occurrence of a Warburg-like effect induced by trophozoites or Igl from E. histolytica. Distinct differences in ubiquitin-mediated proteolysis, rapamycin (mTOR) signaling pathway, autophagy, endocytosis, and tight junctions provided novel perspectives on the pathogenic mechanism of E. histolytica.

Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling.

Post-translational modifications (PTMs) regulate complex biological processes through the modulation of protein activity, stability, and localization. Insights into the specific modification type and localization within a protein sequence can help ascertain functional significance. Computational models are increasingly demonstrated to offer a low-cost, high-throughput method for comprehensive PTM predictions. Algorithms are optimized using existing experimental PTM data, thus accurate prediction performance relies on the creation of robust datasets. Herein, advancements in mass spectrometry-based proteomics technologies to maximize PTM coverage are reviewed. Further, requisite experimental validation approaches for PTM predictions are explored to ensure that follow-up mechanistic studies are focused on accurate modification sites.

QPTMplants: an integrative database of quantitative post-translational modifications in plants.

As a crucial molecular mechanism, post-translational modifications (PTMs) play critical roles in a wide range of biological processes in plants. Recent advances in mass spectrometry-based proteomic technologies have greatly accelerated the profiling and quantification of plant PTM events. Although several databases have been constructed to store plant PTM data, a resource including more plant species and more PTM types with quantitative dynamics still remains to be developed. In this paper, we present an integrative database of quantitative PTMs in plants named qPTMplants (http://qptmplants.omicsbio.info), which hosts 1 242 365 experimentally identified PTM events for 429 821 nonredundant sites on 123 551 proteins under 583 conditions for 23 PTM types in 43 plant species from 293 published studies, with 620 509 quantification events for 136 700 PTM sites on 55 361 proteins under 354 conditions. Moreover, the experimental details, such as conditions, samples, instruments and methods, were manually curated, while a variety of annotations, including the sequence and structural characteristics, were integrated into qPTMplants. Then, various search and browse functions were implemented to access the qPTMplants data in a user-friendly manner. Overall, we anticipate that the qPTMplants database will be a valuable resource for further research on PTMs in plants.

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics.

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. However, deriving biological insights from such valuable datasets is challenging. Here we introduce a systems biology-based software JUMPn, and its associated protocol to organize the proteome into protein co-expression clusters across samples and protein-protein interaction (PPI) networks connected by modules (e.g., protein complexes). Using the R/Shiny platform, the JUMPn software streamlines the analysis of co-expression clustering, pathway enrichment, and PPI module detection, with integrated data visualization and a user-friendly interface. The main steps of the protocol include installation of the JUMPn software, the definition of differentially expressed proteins or the (dys)regulated proteome, determination of meaningful co-expression clusters and PPI modules, and result visualization. While the protocol is demonstrated using an isobaric labeling-based proteome profile, JUMPn is generally applicable to a wide range of quantitative datasets (e.g., label-free proteomics). The JUMPn software and protocol thus provide a powerful tool to facilitate biological interpretation in quantitative proteomics.

Prospects of Using High-Throughput Proteomics to Underpin the Discovery of Animal Host-Nematode Interactions.

Parasitic nematodes impose a significant public health burden, and cause major economic losses to agriculture worldwide. Due to the widespread of anthelmintic resistance and lack of effective vaccines for most nematode species, there is an urgent need to discover novel therapeutic and vaccine targets, informed through an understanding of host–parasite interactions. Proteomics, underpinned by genomics, enables the global characterisation proteins expressed in a particular cell type, tissue and organism, and provides a key to insights at the host–parasite interface using advanced high-throughput mass spectrometry-based proteomic technologies. Here, we (i) review current mass-spectrometry-based proteomic methods, with an emphasis on a high-throughput ‘bottom-up’ approach; (ii) summarise recent progress in the proteomics of parasitic nematodes of animals, with a focus on molecules inferred to be involved in host–parasite interactions; and (iii) discuss future research directions that could enhance our knowledge and understanding of the molecular interplay between nematodes and host animals, in order to work toward new, improved methods for the treatment, diagnosis and control of nematodiases.

Phosphoproteomics Reveals the AMPK Substrate Network in Response to DNA Damage and Histone Acetylation

AMP-activated protein kinase (AMPK) is a conserved energy sensor that plays roles in diverse biological processes via phosphorylating various substrates. Emerging studies have demonstrated the regulatory roles of AMPK in DNA repair, but the underlying mechanisms remain to be fully understood. Herein, using mass spectrometry-based proteomic technologies, we systematically investigate the regulatory network of AMPK in DNA damage response (DDR). Our system-wide phosphoproteome study uncovers a variety of newly-identified potential substrates involved in diverse biological processes, whereas our system-wide histone modification analysis reveals a link between AMPK and histone acetylation. Together with these findings, we discover that AMPK promotes apoptosis by phosphorylating apoptosis-stimulating of p53 protein 2 (ASPP2) in an irradiation (IR)-dependent manner and regulates histone acetylation by phosphorylating histone deacetylase 9 (HDAC9) in an IR-independent manner. Besides, we reveal that disrupting the histone acetylation by the bromodomain BRD4 inhibitor JQ-1 enhances the sensitivity of AMPK-deficient cells to IR. Therefore, our study has provided a resource to investigate the interplay between phosphorylation and histone acetylation underlying the regulatory network of AMPK, which could be beneficial to understand the exact role of AMPK in DDR.

Temporal Proteomic Profiling of SH-SY5Y Differentiation with Retinoic Acid Using FAIMS and Real-Time Searching.

The SH-SY5Y cell line is often used as a surrogate for neurons in cell-based studies. This cell line is frequently differentiated with all-trans retinoic acid (ATRA) over a 7-day period, which confers neuron-like properties to the cells. However, no analysis of proteome remodeling has followed the progress of this transition. Here, we quantitatively profiled over 9400 proteins across a 7-day treatment with retinoic acid using state-of-the-art mass spectrometry-based proteomics technologies, including FAIMS, real-time database searching, and TMTpro16 sample multiplexing. Gene ontology analysis revealed that categories with the highest increases in protein abundance were related to the plasma membrane/extracellular space. To showcase our data set, we surveyed the protein abundance profiles linked to neurofilament bundle assembly, neuron projections, and neuronal cell body formation. These proteins exhibited increases in abundance level, yet we observed multiple patterns among the queried proteins. The data presented represent a rich resource for investigating temporal protein abundance changes in SH-SY5Y cells differentiated with retinoic acid. Moreover, the sample preparation and data acquisition strategies used here can be readily applied to any analogous cell line differentiation analysis.

A Critical Review of Proteomic Studies in Gestational Diabetes Mellitus.

Gestational diabetes mellitus is a progressive and complex pregnancy complication, which threatens both maternal and fetal health. It is urgent to screen for specific biomarkers for early diagnosis and precise treatment, as well as to identify key moleculars to better understand the pathogenic mechanisms. In the present review, we comprehensively summarized recent studies of gestational diabetes using mass spectrometry-based proteomic technologies. Focused on the entire experimental design and proteomic results, we showed that these studies have covered a broad range of research contents in terms of sampling time, sample types, and outcome associations. Although most of the studies only stayed in the stage of initial discovery, several proteins were further verified to be efficient for disease diagnosis. Functional analysis of all the combined significant proteins also showed that a small number of proteins are known to be involved in the regulation of insulin or indirect signaling pathways. However, many factors such as diagnostic criteria, sample processing, proteomic method, and statistical method can greatly affect the identification of reproducible and reliable protein candidates. Thus, we further provided constructive suggestions and recommendations for carrying out proteomic or follow-up studies of gestational diabetes or other pregnancy complications in the future.

Identification of B cell antigenome in Mycobacterium bovis by immunoproteomic analysis

Bovine tuberculosis (bTB) is a common zoonosis prevalent in many countries with grave economic consequences. Most developed and developing countries have implemented the test-and-slaughter policy to protect public health and reduce economic losses in the cattle industry. The official diagnosis of bTB is based on assays dependent on cell-mediated immunity (CMI). CMI-based diagnosis demonstrates diagnostic incapability at late stages of infection, which could be overcome by diagnosis based on humoral immunity (HI). Therefore, there is an urgent need to identify and define the B cell antigenome of Mycobacterium bovis. In this study, the B cell antigenome of culture filtrate proteins (CFP) was defined by mass spectrometry-based proteomics technology. Four spots were detected on 2-dimensional gel electrophoresis (2-DE) against M. bovis-positive serum in an immunoblotting experiment. Twenty-one proteins were identified in four spots by proteomic tools, such as Mb2900, Mb2898, Mb0448, Mb3834c, Mb1918c, Mb0134c, Mb0358 and Mb1868c, which are known B cell antigens, including 13 new proteins, i.e. Mb3751, Mb2006c, Mb3276c, Mb2244, Mb1164c, Mb2553c, Mb2946c, Mb1849c, Mb1511c, Mb1034c, Mb2616c, Mb0854c and Mb2267. These new proteins identified by 2-DE and immunoblotting were the B cell antigens used in developing serological diagnostic methods based on HI to bTB.

MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion.

Mass spectrometry-based proteomics technologies are prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues. Still today, most mass spectrometry-based proteomics approaches use a data-dependent acquisition strategy, which favors the collection of mass spectra from proteins of higher abundance. Since the computational identification of proteins from proteomics data is typically performed after mass spectrometry analysis, large numbers of mass spectra are typically redundantly acquired from the same abundant proteins, and little to no mass spectra are acquired for proteins of lower abundance. We therefore propose a novel supervised learning algorithm, MealTime-MS, that identifies proteins in real-time as mass spectrometry data are acquired and prevents further data collection from confidently identified proteins to ultimately free mass spectrometry resources to improve the identification sensitivity of low abundance proteins. We use real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysate to show that our approach can identify 92.1% of the proteins detected in the experiment using 66.2% of the MS2 spectra. We also demonstrate that our approach outperforms a previously proposed method, is sufficiently fast for real-time mass spectrometry analysis, and is flexible. Finally, MealTime-MS' efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples.

Proteome-wide identification of arginine methylation in colorectal cancer tissues from patients

BackgroundProtein arginine methylation reaction is catalyzed by protein arginine methyltransferase (PRMT) and the modification is implicated in various diseases including cancer. Currently, thousands of arginine methylation sites have been identified using high-resolution mass spectrometry-based proteomics technology. However, identification of arginine methylation using clinical samples at proteome level has not been reported yet. The objective of the present study was to identify, monomethyl-arginine (MMA) and asymmetric dimethyl-arginine (ADMA) sites in colorectal cancer (CRC) tissues at proteome level.MethodsPooled CRC tissue samples from 10 patients with stage II and III were digested by trypsin and these digests were further processed and lyophilized. Using monomethyl- or asymmetric dimethyl arginine (MMA or ADMA, respectively) motif kits, methylarginine-containing peptides were enriched and subsequently analyzed by high-resolution LC-MS/MS. DLD1 and HCT116 colon cancer cells were treated with type I PRMTs inhibitor (MS023) alone or combined with SN-38, and the effect of the drugs on CRC cell proliferation and apoptosis was measured by water-soluble tetrazolium salt (WST-1) assay and FACS analysis, respectively.ResultsIn the present study, 455 MMA sites of 272 proteins and 314 ADMA sites of 155 proteins were identified from CRC tissues acquired from patients. In addition, 216 methylation sites and 75 substrates for PRMTs were newly identified. These results reveal the significant presence of MMA and ADMA sites on nucleic acid binding proteins and protein complexes involved in transcription. To investigate the effect of protein arginine methylation in CRC proliferation and apoptosis, MS023 was treated to two CRC cell lines. After 48 h treatment with various concentrations of MS023, CRC cell proliferation was significantly suppressed, with concomitant apoptosis induction. Furthermore, MS023 treatment significantly enhanced the inhibitory effect of SN-38 on CRC cell proliferation.ConclusionThis work reports the first comprehensive analysis of arginine methylation with clinical sample and suggests that type I PRMTs are potential therapeutic targets for drug discovery in CRC.