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Zhang X, Lv X, Qian D, Li J, Qian Y, Wang J, Zhu Y, Zhou J, Ma H. Quantification of peptide components in cinobufacini injection and toad skin by ultra-high-performance liquid chromatography/triple quadrupole mass spectrometry. J Sep Sci 2023; 46:e2201048. [PMID: 37155296 DOI: 10.1002/jssc.202201048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 04/21/2023] [Accepted: 05/03/2023] [Indexed: 05/10/2023]
Abstract
Cinobufacini injection is commonly used in the clinical treatment of tumors and hepatitis B, but the quality is uneven. Currently, the main focus of its quality assessment is on steroids and alkaloids. Based on a previous study, we screened four peptides with high reproducibility, responsiveness, and specificity. This research was the first to develop an ultra-high-performance liquid chromatography/triple quadrupole mass spectrometry approach for evaluating the quality of cinobufacini preparations from the peptide perspective. In this study, we have identified 230 peptides in cinobufacini injection by Q-Exactive mass spectrometry, which contains species-specific peptides. Then, we used ultra-high-performance liquid chromatography/triple quadrupole mass spectrometry to establish a quantitative method for species-specific peptides and carried out method validation. The result revealed that four peptides were linear in a specific range, and had great reproducibility, accuracy, and stability. Eventually, we evaluated the quality of eight batches of cinobufacini injections and 26 batches of toad skins using the total content of target peptides as the criterion. The outcomes demonstrated that the quality of cinobufacini injection is generally stable and the toad skin from Shandong is of the best quality. In conclusion, the quantitative approach that focuses on peptides will offer innovative perspectives on assessing the quality of cinobufacini preparations.
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Affiliation(s)
- Xinwen Zhang
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, P. R. China
| | - Xiang Lv
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, P. R. China
| | - Dong Qian
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, P. R. China
| | - Junxian Li
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, P. R. China
| | - Yi Qian
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, P. R. China
| | - Jiaojiao Wang
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, P. R. China
| | - Yuyu Zhu
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, P. R. China
| | - Jing Zhou
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, P. R. China
- Animal-Derived Chinese Medicine and Functional Peptides International Collaboration Joint Laboratory, Nanjing University of Chinese Medicine, Nanjing, P. R. China
| | - Hongyue Ma
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, P. R. China
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Starodubtseva NL, Tokareva AO, Rodionov VV, Brzhozovskiy AG, Bugrova AE, Chagovets VV, Kometova VV, Kukaev EN, Soares NC, Kovalev GI, Kononikhin AS, Frankevich VE, Nikolaev EN, Sukhikh GT. Integrating Proteomics and Lipidomics for Evaluating the Risk of Breast Cancer Progression: A Pilot Study. Biomedicines 2023; 11:1786. [PMID: 37509426 PMCID: PMC10376786 DOI: 10.3390/biomedicines11071786] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/16/2023] [Accepted: 06/19/2023] [Indexed: 07/30/2023] Open
Abstract
Metastasis is a serious and often life-threatening condition, representing the leading cause of death among women with breast cancer (BC). Although the current clinical classification of BC is well-established, the addition of minimally invasive laboratory tests based on peripheral blood biomarkers that reflect pathological changes in the body is of utmost importance. In the current study, the serum proteome and lipidome profiles for 50 BC patients with (25) and without (25) metastasis were studied. Targeted proteomic analysis for concertation measurements of 125 proteins in the serum was performed via liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) using the BAK 125 kit (MRM Proteomics Inc., Victoria, BC, Canada). Untargeted label-free lipidomic analysis was performed using liquid chromatography coupled to tandem mass-spectrometry (LC-MS/MS), in both positive and negative ion modes. Finally, 87 serum proteins and 295 lipids were quantified and showed a moderate correlation with tumor grade, histological and biological subtypes, and the number of lymph node metastases. Two highly accurate classifiers that enabled distinguishing between metastatic and non-metastatic BC were developed based on proteomic (accuracy 90%) and lipidomic (accuracy 80%) features. The best classifier (91% sensitivity, 89% specificity, AUC = 0.92) for BC metastasis diagnostics was based on logistic regression and the serum levels of 11 proteins: alpha-2-macroglobulin, coagulation factor XII, adiponectin, leucine-rich alpha-2-glycoprotein, alpha-2-HS-glycoprotein, Ig mu chain C region, apolipoprotein C-IV, carbonic anhydrase 1, apolipoprotein A-II, apolipoprotein C-II and alpha-1-acid glycoprotein 1.
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Affiliation(s)
- Natalia L Starodubtseva
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- Department of Chemical Physics, Moscow Institute of Physics and Technology, 141700 Moscow, Russia
| | - Alisa O Tokareva
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
| | - Valeriy V Rodionov
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
| | - Alexander G Brzhozovskiy
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- Laboratory of Omics Technologies and Big Data for Personalized Medicine and Health, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Anna E Bugrova
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- Emanuel Institute of Biochemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Vitaliy V Chagovets
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
| | - Vlada V Kometova
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
| | - Evgenii N Kukaev
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Nelson C Soares
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates
| | - Grigoriy I Kovalev
- Laboratory of Omics Technologies and Big Data for Personalized Medicine and Health, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Alexey S Kononikhin
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- Laboratory of Omics Technologies and Big Data for Personalized Medicine and Health, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Vladimir E Frankevich
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
- Laboratory of Translational Medicine, Siberian State Medical University, 634050 Tomsk, Russia
| | - Evgeny N Nikolaev
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Gennady T Sukhikh
- V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997 Moscow, Russia
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Kononikhin AS, Brzhozovskiy AG, Bugrova AE, Chebotareva NV, Zakharova NV, Semenov S, Vinogradov A, Indeykina MI, Moiseev S, Larina IM, Nikolaev EN. Targeted MRM Quantification of Urinary Proteins in Chronic Kidney Disease Caused by Glomerulopathies. Molecules 2023; 28:molecules28083323. [PMID: 37110557 PMCID: PMC10142111 DOI: 10.3390/molecules28083323] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/28/2023] [Accepted: 04/07/2023] [Indexed: 04/29/2023] Open
Abstract
Glomerulopathies with nephrotic syndrome that are resistant to therapy often progress to end-stage chronic kidney disease (CKD) and require timely and accurate diagnosis. Targeted quantitative urine proteome analysis by mass spectrometry (MS) with multiple-reaction monitoring (MRM) is a promising tool for early CKD diagnostics that could replace the invasive biopsy procedure. However, there are few studies regarding the development of highly multiplexed MRM assays for urine proteome analysis, and the two MRM assays for urine proteomics described so far demonstrate very low consistency. Thus, the further development of targeted urine proteome assays for CKD is actual task. Herein, a BAK270 MRM assay previously validated for blood plasma protein analysis was adapted for urine-targeted proteomics. Because proteinuria associated with renal impairment is usually associated with an increased diversity of plasma proteins being present in urine, the use of this panel was appropriate. Another advantage of the BAK270 MRM assay is that it includes 35 potential CKD markers described previously. Targeted LC-MRM MS analysis was performed for 69 urine samples from 46 CKD patients and 23 healthy controls, revealing 138 proteins that were found in ≥2/3 of the samples from at least one of the groups. The results obtained confirm 31 previously proposed CKD markers. Combination of MRM analysis with machine learning for data processing was performed. As a result, a highly accurate classifier was developed (AUC = 0.99) that enables distinguishing between mild and severe glomerulopathies based on the assessment of only three urine proteins (GPX3, PLMN, and A1AT or SHBG).
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Affiliation(s)
- Alexey S Kononikhin
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, Bld. 1, 121205 Moscow, Russia
| | - Alexander G Brzhozovskiy
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, Bld. 1, 121205 Moscow, Russia
- Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology of the Ministry of Health, 117997 Moscow, Russia
| | - Anna E Bugrova
- Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology of the Ministry of Health, 117997 Moscow, Russia
- Emanuel Institute for Biochemical Physics, Russian Academy of Science, Kosygina Str. 4, 119334 Moscow, Russia
| | - Natalia V Chebotareva
- Nephrology Department, Sechenov First Moscow State Medical University, Trubezkaya 8, 119048 Moscow, Russia
- Department of Internal Medicine, Lomonosov Moscow State University, GSP-1, Leninskie Gory, 119991 Moscow, Russia
| | - Natalia V Zakharova
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, Bld. 1, 121205 Moscow, Russia
- Emanuel Institute for Biochemical Physics, Russian Academy of Science, Kosygina Str. 4, 119334 Moscow, Russia
| | - Savva Semenov
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, Bld. 1, 121205 Moscow, Russia
- Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia
| | - Anatoliy Vinogradov
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, Bld. 1, 121205 Moscow, Russia
- Department of Internal Medicine, Lomonosov Moscow State University, GSP-1, Leninskie Gory, 119991 Moscow, Russia
| | - Maria I Indeykina
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, Bld. 1, 121205 Moscow, Russia
- Emanuel Institute for Biochemical Physics, Russian Academy of Science, Kosygina Str. 4, 119334 Moscow, Russia
| | - Sergey Moiseev
- Nephrology Department, Sechenov First Moscow State Medical University, Trubezkaya 8, 119048 Moscow, Russia
| | - Irina M Larina
- Institute of Biomedical Problems, Russian Federation State Scientific Research Center, Russian Academy of Sciences, Khoroshevskoe Shosse 76A, 123007 Moscow, Russia
| | - Evgeny N Nikolaev
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, Bld. 1, 121205 Moscow, Russia
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Kononikhin AS, Zakharova NV, Semenov SD, Bugrova AE, Brzhozovskiy AG, Indeykina MI, Fedorova YB, Kolykhalov IV, Strelnikova PA, Ikonnikova AY, Gryadunov DA, Gavrilova SI, Nikolaev EN. Prognosis of Alzheimer's Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning. Int J Mol Sci 2022; 23:7907. [PMID: 35887259 PMCID: PMC9318764 DOI: 10.3390/ijms23147907] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/14/2022] [Accepted: 07/16/2022] [Indexed: 12/16/2022] Open
Abstract
Early recognition of the risk of Alzheimer's disease (AD) onset is a global challenge that requires the development of reliable and affordable screening methods for wide-scale application. Proteomic studies of blood plasma are of particular relevance; however, the currently proposed differentiating markers are poorly consistent. The targeted quantitative multiple reaction monitoring (MRM) assay of the reported candidate biomarkers (CBs) can contribute to the creation of a consistent marker panel. An MRM-MS analysis of 149 nondepleted EDTA-plasma samples (MHRC, Russia) of patients with AD (n = 47), mild cognitive impairment (MCI, n = 36), vascular dementia (n = 8), frontotemporal dementia (n = 15), and an elderly control group (n = 43) was performed using the BAK 125 kit (MRM Proteomics Inc., Canada). Statistical analysis revealed a significant decrease in the levels of afamin, apolipoprotein E, biotinidase, and serum paraoxonase/arylesterase 1 associated with AD. Different training algorithms for machine learning were performed to identify the protein panels and build corresponding classifiers for the AD prognosis. Machine learning revealed 31 proteins that are important for AD differentiation and mostly include reported earlier CBs. The best-performing classifiers reached 80% accuracy, 79.4% sensitivity and 83.6% specificity and were able to assess the risk of developing AD over the next 3 years for patients with MCI. Overall, this study demonstrates the high potential of the MRM approach combined with machine learning to confirm the significance of previously identified CBs and to propose consistent protein marker panels.
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Affiliation(s)
- Alexey S. Kononikhin
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia; (A.E.B.); (A.G.B.); (M.I.I.)
| | - Natalia V. Zakharova
- Emanuel Institute for Biochemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia; (N.V.Z.); (P.A.S.)
| | - Savva D. Semenov
- Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia;
| | - Anna E. Bugrova
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia; (A.E.B.); (A.G.B.); (M.I.I.)
- Emanuel Institute for Biochemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia; (N.V.Z.); (P.A.S.)
| | - Alexander G. Brzhozovskiy
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia; (A.E.B.); (A.G.B.); (M.I.I.)
| | - Maria I. Indeykina
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia; (A.E.B.); (A.G.B.); (M.I.I.)
- Emanuel Institute for Biochemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia; (N.V.Z.); (P.A.S.)
| | - Yana B. Fedorova
- Mental Health Research Center, 115522 Moscow, Russia; (Y.B.F.); (I.V.K.); (S.I.G.)
| | - Igor V. Kolykhalov
- Mental Health Research Center, 115522 Moscow, Russia; (Y.B.F.); (I.V.K.); (S.I.G.)
| | - Polina A. Strelnikova
- Emanuel Institute for Biochemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia; (N.V.Z.); (P.A.S.)
- Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia;
| | - Anna Yu. Ikonnikova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (A.Y.I.); (D.A.G.)
| | - Dmitry A. Gryadunov
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (A.Y.I.); (D.A.G.)
| | | | - Evgeny N. Nikolaev
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia; (A.E.B.); (A.G.B.); (M.I.I.)
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