Comparative Proteomics of Dying and Surviving Cancer Cells Improves the Identification of Drug Targets and Sheds Light on Cell Life/Death Decisions

On the utility of ultrafast MS1-only proteomics in drug target discovery studies based on thermal proteome profiling method

Advances in high-throughput high-resolution mass spectrometry and the development of thermal proteome profiling approach (TPP) have made it possible to accelerate a drug target search. Since its introduction in 2014, TPP quickly became a method of choice in chemical proteomics for identifying drug-to-protein interactions on a proteome-wide scale and mapping the pathways of these interactions, thus further elucidating the unknown mechanisms of action of a drug under study. However, the current TPP implementations based on tandem mass spectrometry (MS/MS), associated with employing lengthy peptide separation protocols and expensive labeling techniques for sample multiplexing, limit the scaling of this approach for the ever growing variety of drug-to-proteomes. A variety of ultrafast proteomics methods have been developed in the last couple of years. Among them, DirectMS1 provides MS/MS-free quantitative proteome-wide analysis in 5-min time scale, thus opening the way for sample-hungry applications, such as TPP. In this work, we demonstrate the first implementation of the TPP approach using the ultrafast proteome-wide analysis based on DirectMS1. Using a drug topotecan, which is a known topoisomerase I (TOP1) inhibitor, the feasibility of the method for identifying drug targets at the whole proteome level was demonstrated for an ovarian cancer cell line.

On the Feasibility of Using an Ultra-Fast DirectMS1 Method of Proteome-Wide Analysis for Searching Drug Targets in Chemical Proteomics

Protein quantitation in tissue cells or physiological fluids based on liquid chromatography/mass spectrometry is one of the key sources of information on the mechanisms of cell functioning during chemotherapeutic treatment. Information on significant changes in protein expression upon treatment can be obtained by chemical proteomics and requires analysis of the cellular proteomes, as well as development of experimental and bioinformatic methods for identification of the drug targets. Low throughput of whole proteome analysis based on liquid chromatography and tandem mass spectrometry is one of the main factors limiting the scale of these studies. The method of direct mass spectrometric identification of proteins, DirectMS1, is one of the approaches developed in recent years allowing ultrafast proteome-wide analyses employing minute-scale gradients for separation of proteolytic mixtures. Aim of this work was evaluation of both possibilities and limitations of the method for identification of drug targets at the level of whole proteome and for revealing cellular processes activated by the treatment. Particularly, the available literature data on chemical proteomics obtained earlier for a large set of onco-pharmaceuticals using multiplex quantitative proteome profiling were analyzed. The results obtained were further compared with the proteome-wide data acquired by the DirectMS1 method using ultrashort separation gradients to evaluate efficiency of the method in identifying known drug targets. Using ovarian cancer cell line A2780 as an example, a whole-proteome comparison of two cell lysis techniques was performed, including the freeze-thaw lysis commonly employed in chemical proteomics and the one based on ultrasonication for cell disruption, which is the widely accepted as a standard in proteomic studies. Also, the proteome-wide profiling was performed using ultrafast DirectMS1 method for A2780 cell line treated with lonidamine, followed by gene ontology analyses to evaluate capabilities of the method in revealing regulation of proteins in the cellular processes associated with drug treatment.

Proteomics-Compatible Fourier Transform Isotopic Ratio Mass Spectrometry of Polypeptides

Measuring the relative abundances of heavy stable isotopes of the elements C, H, N, and O in proteins is of interest in environmental science, archeology, zoology, medicine, and other fields. The isotopic abundance measurements of the fine structure of immonium ions with ultrahigh resolution mass spectrometry obtained in gas-phase fragmentation of polypeptides have previously uncovered anomalous deuterium enrichment in (hydroxy)proline of bone collagen in marine mammals. Here, we provide a detailed description and validation of this approach and demonstrate per mil-range precision of isotopic ratio measurements in aliphatic residues from proteins and cell lysates. The analysis consists of proteomics-type experiment demanding sub-microgram amounts of a protein sample and providing concomitantly protein sequence data allowing one to verify sample purity and establish its identity. A novel software tool protein amino acid-resolved isotopic ratio mass spectrometry (PAIR-MS) is presented for extracting isotopic ratio data from the raw data files acquired on an Orbitrap mass spectrometer.

Quantitative Proteomics Based on iTRAQ Reveal that Nitidine Chloride Induces Apoptosis by Activating JNK/c-Jun Signaling in Hepatocellular Carcinoma Cells

The aim of the present study was to investigate the cytotoxic effects and underlying molecular mechanisms of nitidine chloride (NC) in hepatocellular carcinoma cells via quantitative proteomics. MTT assays were used to detect the inhibitory effects of NC in Bel-7402 liver cancer cells, and the number of apoptotic cells was measured by flow cytometry. Quantitative proteomics technology based on iTRAQ was used to discover differential expressed proteins after NC treatment, and bioinformatic techniques were further used to screen potential targets of NC. Molecular docking was applied to evaluate the docking activity of NC with possible upstream proteins, and their expression was detected at the mRNA and protein levels by quantitative reverse transcription PCR and western blotting. NC inhibited the proliferation of Bel-7402 cells after 24 h of treatment and stimulated apoptosis in vitro. The proteomics experiment showed that NC triggers mitochondrial damage in HCC cells and transcription factor AP-1 (c-Jun) may be a potential target of NC (fold change = 4.36 ± 0.23). Molecular docking results revealed the highest docking score of NC with c-Jun N-terminal kinase (JNK), one of the upstream proteins of c-Jun. Moreover, the mRNA and protein expression of c-Jun and JNK were significantly increased after NC treatment (p < 0.05). These findings indicate that NC significantly induced mitochondrial damage in HCC cells, and induced apoptosis by activating JNK/c-Jun signaling.

Single-Cell Chemical Proteomics (SCCP) Interrogates the Timing and Heterogeneity of Cancer Cell Commitment to Death

Chemical proteomics studies the effects of drugs upon a cellular proteome. Due to the complexity and diversity of tumors, the response of cancer cells to drugs is also heterogeneous, and thus, proteome analysis at the single-cell level is needed. Here, we demonstrate that single-cell proteomics techniques have become quantitative enough to tackle the drug effects on target proteins, enabling single-cell chemical proteomics (SCCP). Using SCCP, we studied here the time-resolved response of individual adenocarcinoma A549 cells to anticancer drugs methotrexate, camptothecin, and tomudex, revealing the early emergence of cellular subpopulations committed and uncommitted to death. As a novel and useful approach to exploring the heterogeneous response to drugs of cancer cells, SCCP may prove to be a breakthrough application for single-cell proteomics.

Validating Amino Acid Variants in Proteogenomics Using Sequence Coverage by Multiple Reads

Mass spectrometry-based proteome analysis implies matching the mass spectra of proteolytic peptides to amino acid sequences predicted from genomic sequences. Reliability of peptide variant identification in proteogenomic studies is often lacking. We propose a way to interpret shotgun proteomics results, specifically in the data-dependent acquisition mode, as protein sequence coverage by multiple reads as it is done in nucleic acid sequencing for calling of single nucleotide variants. Multiple reads for each sequence position could be provided by overlapping distinct peptides, thus confirming the presence of certain amino acid residues in the overlapping stretch with a lower false discovery rate. Overlapping distinct peptides originate from miscleaved tryptic peptides in combination with their properly cleaved counterparts and from peptides generated by multiple proteases after the same specimen is subject to parallel digestion and analyzed separately. We illustrate this approach using publicly available multiprotease data sets and our own data generated for the HEK-293 cell line digests obtained using trypsin, LysC, and GluC proteases. Totally, up to 30% of the whole proteome was covered by tryptic peptides with up to 7% covered twofold and more. The proteogenomic analysis of the HEK-293 cell line revealed 36 single amino acid variants, seven of which were supported by multiple reads.

Isobaric Tags for Relative and Absolute Quantitation Identification of Blood Proteins Relevant to Paroxetine Response in Patients With Major Depressive Disorder

OBJECTIVES

Isobaric tags for relative and absolute quantitation (iTRAQ) is a proteomic investigation that could be utilized for rapid identification and quantification of proteins, which we would use to identify differentially expressed proteins in treatment responsive patients with major depressive disorder (MDD).

METHODS

Six treatment responsive patients of MDD were recruited, and their peripheral blood mononuclear cell (PBMC) were collected before and after 4 weeks of paroxetine treatment. iTRAQ and Mascot search engine were used to detect differentially expressed proteins, which were then validated by Western blot.

RESULTS

Two thousand one hundred and fifty three proteins were screened, and seven proteins showed differences of more than two-fold and 62 proteins with a differences of less than two-fold. Six proteins with commercially available antibodies were identified, and were validated by Western blot in 10 paroxetine responsive MDD patients. Putative hydroxypyruvate isomerase (HYI), eukaryotic translation initiation factor 4H (eIF4H), and RNA binding motif 8A (RBM8A) had statistically significant differences before and after treatment in the validation. Data are available via ProteomeXchange with identifier PXD028947.

CONCLUSIONS

By using iTRAQ and Western blot, we were able to identify HYI, eIF4H, and RAM8a to be the potential predictors of paroxetine treatment response in patients with MDD. This finding could help establish future individualized medicine.

Comprehensive chemical proteomics analyses reveal that the new TRi-1 and TRi-2 compounds are more specific thioredoxin reductase 1 inhibitors than auranofin

Anticancer drugs that target cellular antioxidant systems have recently attracted much attention. Auranofin (AF) is currently evaluated in several clinical trials as an anticancer agent that targets the cytosolic and mitochondrial forms of the selenoprotein thioredoxin reductase, TXNRD1 and TXNRD2. Recently, two novel TXNRD1 inhibitors (TRi-1 and TRi-2) have been developed that showed anticancer efficacy comparable to AF, but with lower mitochondrial toxicity. However, the cellular action mechanisms of these drugs have not yet been thoroughly studied. Here we used several proteomics approaches to determine the effects of AF, TRi-1 and TRi-2 when used at IC50 concentrations with the mouse B16 melanoma and LLC lung adenocarcinoma cells, as these are often used for preclinical mouse models in evaluation of anticancer drugs. The results demonstrate that TRi-1 and TRi-2 are more specific TXNRD1 inhibitors than AF and reveal additional AF-specific effects on the cellular proteome. Interestingly, AF triggered stronger Nrf2-driven antioxidant responses than the other two compounds. Furthermore, AF affected several additional proteins, including GSK3A, GSK3B, MCMBP and EEFSEC, implicating additional effects on glycogen metabolism, cellular differentiation, inflammatory pathways, DNA replication and selenoprotein synthesis processes. Our proteomics data provide a resource for researchers interested in the multidimensional analysis of proteome changes associated with oxidative stress in general, and the effects of TXNRD1 inhibitors and AF protein targets in particular.

The role of proteomics in the multiplexed analysis of gene alterations in human cancer

INTRODUCTION

Proteomics has played a pivotal role in identifying proteins perturbed in disease conditions when compared with healthy samples. Study of dysregulated proteins aids in identifying diagnostic markers and potential therapeutic targets. Cancer is an outcome of interplay of several such disarrayed proteins and molecular pathways which perturb cellular homeostasis, resulting in transformation. In this review, we discuss various facets of proteomic approaches, including tools and technological advancements, aiding in understanding differentially expressed molecules and signaling mechanisms.

AREAS COVERED

In this review, we have taken the approach of documenting the different methods of proteomic studies, ranging from labeling techniques, data analysis methods, and the nature of molecule detected. We summarize each technique and provide a glimpse of cancer research carried out using them, highlighting the advantages and drawbacks in comparison with others. Literature search using online resources, such as PubMed and Google Scholar were carried out for this approach.

EXPERT OPINION

Technological advancements in proteomics studies have come a long way from the study of two-dimensional mapping of proteins separated on gels in the early 1970s. Higher precision in molecular identification and quantification (high throughput), and greater number of samples analyzed have been the focus of researchers.

Comprehensive chemical proteomics for target deconvolution of the redox active drug auranofin

Chemical proteomics encompasses novel drug target deconvolution methods in which compound modification is not required. Herein we use Thermal Proteome Profiling, Functional Identification of Target by Expression Proteomics and multiplexed redox proteomics for deconvolution of auranofin targets to aid elucidation of its mechanisms of action. Auranofin (Ridaura®) was approved for treatment of rheumatoid arthritis in 1985. Because several clinical trials are currently ongoing to repurpose auranofin for cancer therapy, comprehensive characterization of its targets and effects in cancer cells is important. Together, our chemical proteomics tools confirmed thioredoxin reductase 1 (TXNRD1, EC:1.8.1.9) as a main auranofin target, with perturbation of oxidoreductase pathways as the top mechanism of drug action. Additional indirect targets included NFKB2 and CHORDC1. Our comprehensive data can be used as a proteomic signature resource for further analyses of the effects of auranofin. Here we also assessed the orthogonality and complementarity of different chemical proteomics methods that can furnish invaluable mechanistic information and thus the approach can facilitate drug discovery efforts in general.

ProTargetMiner as a proteome signature library of anticancer molecules for functional discovery

Deconvolution of targets and action mechanisms of anticancer compounds is fundamental in drug development. Here, we report on ProTargetMiner as a publicly available expandable proteome signature library of anticancer molecules in cancer cell lines. Based on 287 A549 adenocarcinoma proteomes affected by 56 compounds, the main dataset contains 7,328 proteins and 1,307,859 refined protein-drug pairs. These proteomic signatures cluster by compound targets and action mechanisms. The targets and mechanistic proteins are deconvoluted by partial least square modeling, provided through the website http://protargetminer.genexplain.com. For 9 molecules representing the most diverse mechanisms and the common cancer cell lines MCF-7, RKO and A549, deep proteome datasets are obtained. Combining data from the three cell lines highlights common drug targets and cell-specific differences. The database can be easily extended and merged with new compound signatures. ProTargetMiner serves as a chemical proteomics resource for the cancer research community, and can become a valuable tool in drug discovery.

Anticancer drugs often have widespread effects on the cellular proteome. Here, the authors generate a proteome signature library of drug-treated cancer cell lines and develop a software tool to deconvolute drug targets and gain insights into their mechanisms of action.

MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

Mining cancer biology through bioinformatic analysis of proteomic data

Introduction: Discovery proteomics for cancer research generates complex datasets of diagnostic, prognostic, and therapeutic significance in human cancer. With the advent of high-resolution mass spectrometers, able to identify thousands of proteins in complex biological samples, only the application of bioinformatics can lead to the interpretation of data which can be relevant for cancer research. Areas covered: Here, we give an overview of the current bioinformatic tools used in cancer proteomics. Moreover, we describe their applications in cancer proteomics studies of cell lines, serum, and tissues, highlighting recent results and critically evaluating their outcomes. Expert opinion: The use of bioinformatic tools is a fundamental step in order to manage the large amount of proteins (from hundreds to thousands) that can be identified and quantified in a cancer biological samples by proteomics. To handle this challenge and obtain useful data for translational medicine, it is important the combined use of different bioinformatic tools. Moreover, a particular attention to the global experimental design, and the integration of multidisciplinary skills are essential for best setting of tool parameters and best interpretation of bioinformatics output.

Macrophage-Neisseria gonorrhoeae Interactions: A Better Understanding of Pathogen Mechanisms of Immunomodulation

Neisseria gonorrhoeae is a significant health problem worldwide due to multi-drug resistance issues and absence of an effective vaccine. Patients infected with N. gonorrhoeae have not shown a better immune response in successive infections. This might be explained by the fact that N. gonorrhoeae possesses several mechanisms to evade the innate and adaptative immune responses at different levels. Macrophages are a key cellular component in the innate immune response against microorganisms. The current information suggests that gonococcus can hijack the host response by mechanisms that involve the control of macrophages activity. In this mini review, we intend to condense the recent knowledge on the macrophage–N. gonorrhoeae interactions with a focus on strategies developed by gonococcus to evade or to exploit immune response to establish a successful infection. Finally, we discuss the opportunities and challenges of therapeutics for controlling immune manipulation by N. gonorrhoeae.

Dynamic Proteomics Reveals High Plasticity of Cellular Proteome: Growth-Related and Drug-Induced Changes in Cancer Cells are Comparable

In chemical proteomics, the changes occurring in cellular proteomes upon drug treatment are used to identify the drug targets and the mechanism of action. However, proteomes of cultured cells undergo also natural alteration associated with changes in the media, attaining a degree of confluence as well as due to cell division and cell metabolism. These changes are implicitly assumed to be smaller in magnitude than the drug-induced changes that ultimately lead to cell demise. In this study, it is shown that growth-related proteome changes in the untreated control group are comparable in magnitude to drug-induced changes over the course of 48 h treatment. In two well-characterized cancer cell lines, growth-related effects assessed with deep proteomics analysis (10 481 proteins quantified with at least two peptides) show common trends, the steady downregulation of cell division processes, and the upregulation of metabolism-related pathways. The magnitude of these variations, which are present even before reaching 100% confluence reveals unexpectedly high plasticity of the cellular proteome. This finding reinforces the need, generally accepted in theory but not always followed in practice, to use a time-matched control when measuring drug-induced proteome changes.

Discovery of the Consistently Well-Performed Analysis Chain for SWATH-MS Based Pharmacoproteomic Quantification

Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) has emerged as one of the most popular techniques for label-free proteome quantification in current pharmacoproteomic research. It provides more comprehensive detection and more accurate quantitation of proteins comparing with the traditional techniques. The performance of SWATH-MS is highly susceptible to the selection of processing method. Till now, ≥27 methods (transformation, normalization, and missing-value imputation) are sequentially applied to construct numerous analysis chains for SWATH-MS, but it is still not clear which analysis chain gives the optimal quantification performance. Herein, the performances of 560 analysis chains for quantifying pharmacoproteomic data were comprehensively assessed. Firstly, the most complete set of the publicly available SWATH-MS based pharmacoproteomic data were collected by comprehensive literature review. Secondly, substantial variations among the performances of various analysis chains were observed, and the consistently well-performed analysis chains (CWPACs) across various datasets were for the first time generalized. Finally, the log and power transformations sequentially followed by the total ion current normalization were discovered as one of the best performed analysis chains for the quantification of SWATH-MS based pharmacoproteomic data. In sum, the CWPACs identified here provided important guidance to the quantification of proteomic data and could therefore facilitate the cutting-edge research in any pharmacoproteomic studies requiring SWATH-MS technique.