基于质谱技术蛋白质定量方法的研究进展

质谱技术已经成为目前蛋白质鉴定的重要工具。定量分析细胞内蛋白质组的动态变化,是当前研究蛋白质功能、揭示细胞生物机理、寻找疾病蛋白标记物和药物靶标的迫切需要。本文综述了基于质谱技术蛋白质定量的策略、方法和应用等方面近年来的进展,评述了几种蛋白质质谱定量方法的特点和应用潜力。     

Recent Advances in Mass Spectrometry-Based Structural Elucidation Techniques

Mass spectrometry (MS) has become the central technique that is extensively used for the analysis of molecular structures of unknown compounds in the gas phase. It manipulates the molecules by converting them into ions using various ionization sources. With high-resolution MS, accurate molecular weights (MW) of the intact molecular ions can be measured so that they can be assigned a molecular formula with high confidence. Furthermore, the application of tandem MS has enabled detailed structural characterization by breaking the intact molecular ions and protonated or deprotonated molecules into key fragment ions. This approach is not only used for the structural elucidation of small molecules (MW < 2000 Da), but also crucial biopolymers such as proteins and polypeptides; therefore, MS has been extensively used in multiomics studies for revealing the structures and functions of important biomolecules and their interactions with each other. The high sensitivity of MS has enabled the analysis of low-level analytes in complex matrices. It is also a versatile technique that can be coupled with separation techniques, including chromatography and ion mobility, and many other analytical instruments such as NMR. In this review, we aim to focus on the technical advances of MS-based structural elucidation methods over the past five years, and provide an overview of their applications in complex mixture analysis. We hope this review can be of interest for a wide range of audiences who may not have extensive experience in MS-based techniques.     

Mass Spectrometry-Based Metabolomics of Phytocannabinoids from Non-Cannabis Plant Origins

Phytocannabinoids are isoprenylated resorcinyl polyketides produced mostly in glandular trichomes of Cannabis sativa L. These discoveries led to the identification of cannabinoid receptors, which modulate psychotropic and pharmacological reactions and are found primarily in the human central nervous system. As a result of the biogenetic process, aliphatic ketide phytocannabinoids are exclusively found in the cannabis species and have a limited natural distribution, whereas phenethyl-type phytocannabinoids are present in higher plants, liverworts, and fungi. The development of cannabinomics has uncovered evidence of new sources containing various phytocannabinoid derivatives. Phytocannabinoids have been isolated as artifacts from their carboxylated forms (pre-cannabinoids or acidic cannabinoids) from plant sources. In this review, the overview of the phytocannabinoid biosynthesis is presented. Different non-cannabis plant sources are described either from those belonging to the angiosperm species and bryophytes, together with their metabolomic structures. Lastly, we discuss the legal framework for the ingestion of these biological materials which currently receive the attention as a legal high.     

Quantitative Mass Spectrometry-Based Proteomics for Biomarker Development in Ovarian Cancer

Ovarian cancer is the most lethal gynecologic malignancy among women. Approximately 70–80% of patients with advanced ovarian cancer experience relapse within five years and develop platinum-resistance. The short life expectancy of patients with platinum-resistant or platinum-refractory disease underscores the need to develop new and more effective treatment strategies. Early detection is a critical step in mitigating the risk of disease progression from early to an advanced stage disease, and protein biomarkers have an integral role in this process. The best biological diagnostic tool for ovarian cancer will likely be a combination of biomarkers. Targeted proteomics methods, including mass spectrometry-based approaches, have emerged as robust methods that can address the chasm between initial biomarker discovery and the successful verification and validation of these biomarkers enabling their clinical translation due to the robust sensitivity, specificity, and reproducibility of these versatile methods. In this review, we provide background information on the fundamental principles of biomarkers and the need for improved treatment strategies in ovarian cancer. We also provide insight into the ways in which mass spectrometry-based targeted proteomics approaches can provide greatly needed solutions to many of the challenges related to ovarian cancer biomarker development.     

sn-1 Specificity of Lysophosphatidylcholine Acyltransferase-1 Revealed by a Mass Spectrometry-Based Assay

Lysophosphatidylcholine acyltransferase-1 (LPCAT1) plays a critical role in the remodeling of phosphatidylcholines (PCs) in cellular lipidome. However, evidence is scarce regarding its sn-selectivity, viz. the preference of assembling acyl-Coenzyme A (CoA) at the C1 or C2-hydroxyl on a glycerol backbone because of difficulty to quantify the thus-formed PC sn-isomers. We have established a multiplexed assay to measure both sn- and acyl-chain selectivity of LPCAT1 toward a mixture of acyl-CoAs by integrating isomer-resolving tandem mass spectrometry. Our findings reveal that LPCAT1 shows exclusive sn-1 specificity regardless of the identity of acyl-CoAs. We further confirm that elevated PC 18 : 1/16:0 relative to its sn-isomer results from an increased expression of LPCAT1 in human hepatocellular carcinoma (HCC) tissue as compared to normal liver tissue. MS imaging via desorption electrospray ionization of PC 18 : 1/16:0 thus enables visualization of HCC margins in human liver tissue at a molecular level.     

Targeted and Untargeted Mass Spectrometry-Based Metabolomics for Chemical Profiling of Three Coffee Species

While coffee beans have been studied for many years, researchers are showing a growing interest in coffee leaves and by-products, but little information is currently available on coffee species other than Coffea arabica and Coffea canephora. The aim of this work was to perform a targeted and untargeted metabolomics study on Coffea arabica, Coffea canephora and Coffea anthonyi. The application of the recent high-resolution mass spectrometry-based metabolomics tools allowed us to gain a clear overview of the main differences among the coffee species. The results showed that the leaves and fruits of Coffea anthonyi had a different metabolite profile when compared to the two other species. In Coffea anthonyi, caffeine levels were found in lower concentrations while caffeoylquinic acid and mangiferin-related compounds were found in higher concentrations. A large number of specialized metabolites can be found in Coffea anthonyi tissues, making this species a valid candidate for innovative healthcare products made with coffee extracts.     

Predictive Liquid Chromatography of Peptides Based on Hydrophilic Interactions for Mass Spectrometry-Based Proteomics

High-performance liquid chromatography (HPLC) is widely used for separation of complex peptide mixtures before mass spectrometry-based proteome analysis. In this analysis, reversed phase HPLC (RPHPLC) using non-polar stationary phases such as surface-modified silica containing alkyl groups (e.g., C18) is typically employed. Because of the high heterogeneity of proteomic samples, multidimensional separation approaches gained increasing attention recently to tackle this complexity and extremely high range of concentrations. In two-dimensional liquid chromatography, hydrophilic interaction chromatography (HILIC) is often a method of choice for combination with RP-HPLC because it uses reversed-phase type eluents and allows efficient separation of polar peptides. Due to the high degree of orthogonality in this two-dimensional separation space, it is tempting to develop approaches for predicting peptide retention times for HILIC-based separations similar to the ones for RP-HPLC. Recent successful efforts in this area were focused on developing retention coefficient (RC)-based approaches. Herein, we explored the feasibility of using a statistical thermodynamic model for prediction of peptide retention times in HILIC separations and determined the phenomenological parameters of the model for a bare silica column. The performance of the developed model was tested using HPLC-MS analysis of a set of synthetic peptides, as well as a tryptic peptide mixture.     

Mass Spectrometry-Based Peptide Profiling of Haemolymph from Pterostichus melas Exposed to Pendimethalin Herbicide

Pendimethalin-based herbicides are used worldwide for pre-emergence selective control of annual grasses and weeds in croplands. The endurance of herbicides residues in the environment has an impact on the soil biodiversity and fertility, also affecting non-target species, including terrestrial invertebrates. Carabid beetles are known as natural pest control agents in the soil food web of agroecosystems, and feed on invertebrates and weed seeds. Here, a mass spectrometry untargeted profiling of haemolymph is used to investigate Pterostichus melas metabolic response after to pendimethalin-based herbicide exposure. Mass spectrometric data are examined with statistical approaches, such as principal component analysis, for possible correlation with biological effects. Those signals with high correlation are submitted to tandem mass spectrometry to identify the associated biomarker. The time course exposure showed many interesting findings, including a significant downregulation of related to immune and defense peptides (M-lycotoxin-Ls4a, Peptide hormone 1, Paralytic peptide 2, and Serine protease inhibitor 2). Overall, the observed peptide deregulations concur with the general mechanism of uptake and elimination of toxicants reported for Arthropods.     

An Integrated Mass Spectrometry-Based Glycomics-Driven Glycoproteomics Analytical Platform to Functionally Characterize Glycosylation Inhibitors

Cancer progression is linked to aberrant protein glycosylation due to the overexpression of several glycosylation enzymes. These enzymes are underexploited as potential anticancer drug targets and the development of rapid-screening methods and identification of glycosylation inhibitors are highly sought. An integrated bioinformatics and mass spectrometry-based glycomics-driven glycoproteomics analysis pipeline was performed to identify an N-glycan inhibitor against lung cancer cells. Combined network pharmacology and in silico screening approaches were used to identify a potential inhibitor, pictilisib, against several glycosylation-related proteins, such as Alpha1-6FucT, GlcNAcT-V, and Alpha2,6-ST-I. A glycomics assay of lung cancer cells treated with pictilisib showed a significant reduction in the fucosylation and sialylation of N-glycans, with an increase in high mannose-type glycans. Proteomics analysis and in vitro assays also showed significant upregulation of the proteins involved in apoptosis and cell adhesion, and the downregulation of proteins involved in cell cycle regulation, mRNA processing, and protein translation. Site-specific glycoproteomics analysis further showed that glycoproteins with reduced fucosylation and sialylation were involved in apoptosis, cell adhesion, DNA damage repair, and chemical response processes. To determine how the alterations in N-glycosylation impact glycoprotein dynamics, modeling of changes in glycan interactions of the ITGA5–ITGB1 (Integrin alpha 5-Integrin beta-1) complex revealed specific glycosites at the interface of these proteins that, when highly fucosylated and sialylated, such as in untreated A549 cells, form greater hydrogen bonding interactions compared to the high mannose-types in pictilisib-treated A549 cells. This study highlights the use of mass spectrometry to identify a potential glycosylation inhibitor and assessment of its impact on cell surface glycoprotein abundance and protein–protein interaction.     

Analysis of Renal Cell Carcinoma as a First Step for Developing Mass Spectrometry-Based Diagnostics

Immediate diagnosis of human specimen is an essential prerequisites in medical routines. This study aimed to establish a novel cancer diagnostics system based on probe electrospray ionization-mass spectrometry (PESI-MS) combined with statistical data processing. PESI-MS uses a very fine acupuncture needle as a probe for sampling as well as for ionization. To demonstrate the applicability of PESI-MS for cancer diagnosis, we analyzed nine cases of clear cell renal cell carcinoma (ccRCC) by PESI-MS and processed the data by principal components analysis (PCA). Our system successfully delineated the differences in lipid composition between non-cancerous and cancerous regions. In this case, triacylglycerol (TAG) was reproducibly detected in the cancerous tissue of nine different individuals, the result being consistent with well-known profiles of ccRCC. Moreover, this system enabled us to detect the boundaries of cancerous regions based on the expression of TAG. These results strongly suggest that PESI-MS will be applicable to cancer diagnosis, especially when the number of data is augmented.     

A New Strategy of Using O18-Labeled Iodoacetic Acid for Mass Spectrometry-Based Protein Quantitation

A new O(18) labeling protocol is designed to assist quantitation of cysteine-containing proteins using LC/MS. Unlike other O(18) labeling strategies, the labeling is carried out at the intact protein level (prior to its digestion) during reduction/alkylation of cysteine side chains using O(18)-labeled iodoacetic acid (IAA). The latter can be easily prepared by exchanging carboxylic oxygen atoms of commercially available IAA in O(18)-enriched water at low pH. Since incorporation of the O(18) label in the protein occurs at the whole protein, rather than peptide level, the quantitation results are not peptide-dependent. The excellent stability of the label in mild pH conditions provides flexibility and robustness needed of sample processing steps following the labeling. In contrast to generally costly isotope labeling reagents, this approach uses only two relatively inexpensive commercially available reagents (IAA and H(2)O(18)). The feasibility of the new method is demonstrated using an 80?kDa human serum transferrin (hTf) as a model, where linear quantitation is achieved across a dynamic range spanning three orders of magnitude. The new approach can be used in quantitative proteomics applications and is particularly suitable for a variety of tasks in the biopharmaceutical sector, ranging from pharmacokinetic studies to quality control of protein therapeutics.     

SILAC-Pulse Proteolysis: A Mass Spectrometry-Based Method for Discovery and Cross-Validation in Proteome-Wide Studies of Ligand Binding

Reported here is the use of stable isotope labeling with amino acids in cell culture (SILAC) and pulse proteolysis (PP) for detection and quantitation of protein–ligand binding interactions on the proteomic scale. The incorporation of SILAC into PP enables the PP technique to be used for the unbiased detection and quantitation of protein–ligand binding interactions in complex biological mixtures (e.g., cell lysates) without the need for prefractionation. The SILAC-PP technique is demonstrated in two proof-of-principle experiments using proteins in a yeast cell lysate and two test ligands including a well-characterized drug, cyclosporine A (CsA), and a non-hydrolyzable adenosine triphosphate (ATP) analogue, adenylyl imidodiphosphate (AMP-PNP). The well-known tight-binding interaction between CsA and cyclophilin A was successfully detected and quantified in replicate analyses, and a total of 33 proteins from a yeast cell lysate were found to have AMP-PNP-induced stability changes. In control experiments, the method’s false positive rate of protein target discovery was found to be in the range of 2.1% to 3.6%. SILAC-PP and the previously reported stability of protein from rates of oxidation (SPROX) technique both report on the same thermodynamic properties of proteins and protein–ligand complexes. However, they employ different probes and mass spectrometry-based readouts. This creates the opportunity to cross-validate SPROX results with SILAC-PP results, and vice-versa. As part of this work, the SILAC-PP results obtained here were cross-validated with previously reported SPROX results on the same model systems to help differentiate true positives from false positives in the two experiments. Graphical Abstract

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False-Positive Rate Determination of Protein Target Discovery using a Covalent Modification- and Mass Spectrometry-Based Proteomics Platform

Detection and quantitation of protein–ligand binding interactions is important in many areas of biological research. Stability of proteins from rates of oxidation (SPROX) is an energetics-based technique for identifying the proteins targets of ligands in complex biological mixtures. Knowing the false-positive rate of protein target discovery in proteome-wide SPROX experiments is important for the correct interpretation of results. Reported here are the results of a control SPROX experiment in which chemical denaturation data is obtained on the proteins in two samples that originated from the same yeast lysate, as would be done in a typical SPROX experiment except that one sample would be spiked with the test ligand. False-positive rates of 1.2-2.2 % and <0.8 % are calculated for SPROX experiments using Q-TOF and Orbitrap mass spectrometer systems, respectively. Our results indicate that the false-positive rate is largely determined by random errors associated with the mass spectral analysis of the isobaric mass tag (e.g., iTRAQ®) reporter ions used for peptide quantitation. Our results also suggest that technical replicates can be used to effectively eliminate such false positives that result from this random error, as is demonstrated in a SPROX experiment to identify yeast protein targets of the drug, manassantin A. The impact of ion purity in the tandem mass spectral analyses and of background oxidation on the false-positive rate of protein target discovery using SPROX is also discussed.

Mass Spectrometry-Based Identification of Bioactive Bee Pollen Proteins: Evaluation of Allergy Risk after Bee Pollen Supplementation

Bee pollen, because of its high content of nutrients, is a very valuable medicinal and nutritional product. However, since its composition is not completely studied, the consumption of this product may cause adverse effects, including allergic reactions. Therefore, this study aimed to discover and characterize the bioactive proteins of bee pollen collected in Poland, focusing mainly on the allergens. For this purpose, the purified and concentrated pollen aqueous solutions were analyzed using the nanoLC-MALDI-TOF/TOF MS analytical platform. As a result of the experiments, 197 unique proteins derived from green plants (Viridiplantae) and 10 unique proteins derived from bees (Apis spp.) were identified. Among them, potential plant allergens were discovered. Moreover, proteins belonging to the group of hypothetical proteins, whose expression had not been confirmed experimentally before, were detected. Because of the content of bioactive compounds—both beneficial and harmful—there is a critical need to develop guidelines for standardizing bee pollen, especially intended for consumption or therapeutic purposes. This is of particular importance because awareness of the allergen content of bee pollen and other bee products can prevent health- or life-threatening incidents following the ingestion of these increasingly popular “superfoods”.     

Absolute Quantification of Noncoding RNA by Microscale Thermophoresis

Accurate quantification of the copy numbers of noncoding RNA has recently emerged as an urgent problem, with impact on fields such as RNA modification research, tissue differentiation, and others. Herein, we present a hybridization‐based approach that uses microscale thermophoresis (MST) as a very fast and highly precise readout to quantify, for example, single tRNA species with a turnaround time of about one hour. We developed MST to quantify the effect of tRNA toxins and of heat stress and RNA modification on single tRNA species. A comparative analysis also revealed significant differences to RNA‐Seq‐based quantification approaches, strongly suggesting a bias due to tRNA modifications in the latter. Further applications include the quantification of rRNA as well as of polyA levels in cellular RNA.