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Медицинская иммунология Medical Immunology (Russia)/
2021, Т. 23, № Z Оригинальные статьи Meditsinskaya Immunologiya
стр. 275-292 ^ , . . . . 2021, Vol. 23, No 2, pp. 275-292
© 2021, СПбРО РААКИ Original OVÍlCleS © 2021, SPb RAACI
ПРОТЕОМНОЕ ПРОФИЛИРОВАНИЕ МОНОЦИТОПОДОБНЫХ КЛЕТОК ЛИНИИ THP-1 И ПРОДУЦИРУЕМЫХ ИМИ МИКРОВЕЗИКУЛ С ПОМОЩЬЮ MALDI-МАСС-СПЕКТРОМЕТРИИ
Кореневский А.В.1, Милютина Ю.П.1, Березкина М.Э.1, Александрова Е.П.1, Балабас О.А.2, Маркова К.Л.1, Сельков С.А.1, Соколов Д.И.1
1ФГБНУ«Научно-исследовательский институт акушерства, гинекологии и репродуктологии имени Д.О. Отта», Санкт-Петербург, Россия
2 ФГБОУВО «Санкт-Петербургский государственный университет», Санкт-Петербург, Россия
Резюме. Отделяющиеся от плазматической мембраны клетки экстраклеточные везикулы принимают активное участие в межклеточной коммуникации, транспортируя широкий спектр молекул, среди которых важное функциональное значение придается белкам, липидам, нуклеиновым кислотам и са-харам. Одним из важных этапов в понимании дистантной коммуникации клеток и механизмов ее регуляции является изучение протеома различных экстраклеточных везикул, в том числе микровезикул и экзосом. Синтезируемые моноцитами провоспалительные цитокины и отдельные компоненты системы комплемента играют ключевую роль в осуществлении их специфических функций. Целью данного исследования явилось изучение протеомного состава моноцитоподобных клеток линии THP-1 и продуцируемых ими микровезикул. В результате MALDI-масс-спектрометрического анализа элек-трофоретических белковых фракций лизата клеток и микровезикул идентифицировано 107 белков, выполняющих различные функции. Среди 19 функциональных групп наибольшие по численности группы образуют белки-регуляторы транскрипции и белки c неизвестными функциями, домены. Наименьшие по численности функциональные группы представлены белками-регуляторами клеточной дифференцировки и морфогенеза, белками иммунного ответа и воспаления, рецепторами и их регуляторами, транспортными белками и белками-регуляторами транспорта, белками-регуляторами клеточной адгезии и процессинга белков, белками убиквитин-протеасомной системы деградации белков, белками внутриклеточной сигнализации, белками-регуляторами аутофагоцитоза и экзоци-тоза, белками структуры хроматина, белками-регуляторами гемостаза, гормонами. Промежуточное
Адрес для переписки:
Кореневский Андрей Валентинович
ФГБНУ «Научно-исследовательский институт
акушерства, гинекологии и репродуктологии
имени Д.О. Отта»
199034, Россия, Санкт-Петербург,
Менделеевская линия, 3.
Тел.: 8(812) 328-98-91, 323-75-45.
Факс: 8(812) 323-75-45.
E-mail: a.korenevsky@yandex.ru
Образец цитирования:
А.В. Кореневский, Ю.П. Милютина, М.Э. Березкина, Е.П. Александрова, О.А. Балабас, К.Л. Маркова, С.А. Сельков, Д.И. Соколов «Протеомное профилирование моноцитоподобных клеток линии THP-1 и продуцируемых ими микровезикул с помощью MALDI-масс-спектрометрии» // Медицинская иммунология, 2021. Т. 23, № 2. С. 275-292. doi: 10.15789/1563-0625-MTM-2141 © Кореневский А.В. и соавт., 2021
Address for correspondence:
Korenevsky Andrey V.
D. Ott Institute of Obstetrics, Gynecology and Reproductology
199034, Russian Federation, St. Petersburg,
Mendeleyevskaya line, 3.
Phone: 7(812) 328-98-91, 323-75-45.
Fax: 7(812) 323-75-45.
E-mail: a.korenevsky@yandex.ru
For citation:
A.V. Korenevsky, Yu.P. Milyutina, M.E. Berezkina,
E.P. Alexandrova, O.A. Balabas, K.L. Markova, S.A. Selkov,
D.I. Sokolov "MALDI-TOFmassspectrometricprotein
profiling of THP-1 cells and their microvesicles", Medical
Immunology (Russia)/Meditsinskaya Immunologiya, 2021,
Vol. 23, no. 2, pp. 275-292.
doi: 10.15789/1563-0625-MTM-2141
DOI: 10.15789/1563-0625-MTM-2141
положение занимают цитокины и факторы роста, ферменты, белки цитоскелета, структурные и моторные белки, белки-регуляторы трансляции, транскрипции и процессинга РНК. С помощью последующего кластерного анализа (DAVID Functional Annotation Clustering) идентифицированы наиболее широко представленные группы белков, распределенных по молекулярной функции, биологическому процессу и по положению в клетке. Отдельно в микровезикулах идентифицированы среди прочих белковых молекул белки иммунного ответа и воспаления, цитокины и факторы роста, белки внутриклеточной сигнализации, белки-регуляторы клеточной дифференцировки и морфогенеза, белки-регуляторы клеточной адгезии. Полученные данные о частичном протеоме моноцитоподобных клеток линии THP-1 и продуцируемых ими микровезикул расширяют имеющиеся представления о дистантной коммуникации клеток и указывают на новые механизмы взаимодействия моноцитов/макрофагов и их микроокружения.
Ключевые слова: иммунный ответ, моноциты, макрофаги, микровезикулы, воспаление, протеомный анализ, MALDI-масс-спектрометрия
MALDI-TOF MASS SPECTROMETRIC PROTEIN PROFILING OF THP-1 CELLS AND THEIR MICROVESICLES
Korenevsky A.V.a, Milyutina Yu.P.a, Berezkina M.E.a, Alexandrova E.P.a, Balabas O.A.b, Markova K.L.a, Selkov S.A.a, Sokolov D.I.a
a D. Ott Institute of Obstetrics, Gynecology and Reproductology, St. Petersburg, Russian Federation b St. Petersburg State University, St. Petersburg, Russian Federation
Abstract. Extracellular vesicles that are shed from the plasma membranes take an active part in intercellular communication, transporting a wide range of molecules, including proteins, lipids, nucleic acids and carbohydrates, being of great functional importance. One of the steps to better understanding of distant communications of cells and their regulatory mechanisms is a proteomic study of various extracellular vesicles, including microvesicles and exosomes. Pro-inflammatory cytokines produced by monocytes and individual complement system components play a key role in their specific functioning. The aim of this work was to study proteomic composition of THP-1 monocyte-like cells and their microvesicles. The MALDI-mass spectrometric analysis of electrophoretic protein fractions of cell lysates and microvesicles allowed for identifying 107 proteins that perform various functions. Among 19 determined functional groups, the largest ones comprise transcription regulators and proteins with unknown functions. The smallest functional groups include regulators of cell differentiation and development, proteins participating in immune response and inflammation, cellular receptors and their regulators, transporter and transport regulatory proteins, as well as cell proteins mediating adhesion and matrix structures, processing regulators, proteins of ubiquitin-proteasome system, intracellular signaling, autophagy and exocytosis regulators, chromatin structural proteins, hemostatic regulators, and peptide hormones. An intermediate position is occupied by cytokines and growth factors, enzymes, cytoskeleton and motor proteins, as well as RNA processing and translation regulators. The subsequent DAVID Functional Annotation Clustering analysis allowed for identifying the most common groups distributed by their molecular function, biological processes, and cellular component. Separately, in the microvesicles derived from THP-1 monocyte-like cells, proteins of the immune response and inflammation, cytokines and growth factors, intra-cellular signaling proteins, cell differentiation regulators and developmental proteins, as well as cell adhesion and matrix proteins were identified among other protein molecules. The data obtained on the partial proteome of THP-1 monocyte-like cells and their microvesicles extend the existing knowledge on distant communications between the cells and suggest new mechanisms of interaction between monocytes/macrophages and their microenvironment.
Keywords: immune response, monocytes, macrophages, microvesicles, inflammation, proteomics, MALDI-TOF mass spectrometry
This work was performed in the Department of Immunology and Cell Interactions, D. Ott Institute of Obstetrics, Gynecology, and Reproductology (St. Petersburg, Russia), within the institutional state assignment framework (R&D State Registration No. AAAA-A19-119021290116-1), using the equipment of Chemical Analysis and Materials Research Centre, St. Petersburg State University (St. Petersburg, Russia).
Introduction
Microvesicles (MVs) are subcellular structures that are shed from the plasma membrane and may participate in intercellular communication. MVs transfer a variety of proteins, nucleic acids, lipids, and sugars from cell to cell [23] and are involved in the regulation of numerous biological processes, including angiogenesis, placentation, regeneration, and malignancy [32].
Of particular interest among various MV sources are monocytes, which are the most active phagocytes of peripheral blood. They carry out antitumor, antiviral, antimicrobial, antifungal and antiparasitic immunity, as well as participate in the specific immune response [27, 35]. There is evidence of the ability of monocytes to produce MVs, since those with the CD14 phenotype (LPS-R) have been found in peripheral blood plasma [34, 37].
The activity of exosomes derived from inactivated and activated THP-1 cells against THP-1 differentiated macrophages and THP-1 undifferentiated monocytes, as well as other types of cells, has been shown in a number of studies devoted to the isolation and description of various types of extracellular MVs produced by the THP-1 monocyte-like cell line. According to the authors, this activity was due to the activation of ERK1/2 and p38 kinases and the increased secretion of proinflammatory cytokines (TNFa, IL-8, IL-12) [10, 33], as well as the presence of such monocyte effector molecules in those exosomes as chitinase-3-like protein 1, acidic mammalian chitinase, C-C motif chemokine 5, interleukin 4-induced 1, vimentin, cell division control protein 42 homolog, RhoC, Rap1-GTP-interacting adaptor molecule, integrin-linked kinase [33], thyroid hormone receptor-associated protein 3, HLA-DRA, deoxynucleoside triphosphate triphosphohydrolase SAMHD1, STAT1, STAT2, interferon-induced protein with tetratricopeptide repeats 1, ubiquitin-like protein ISG15, interferon induced protein 44 like, and other proteins [40]. Other researchers have shown a similar activity of MVs produced by infected macrophages against intact macrophages in vitro and in vivo [39], as well as an activity of MVs derived from infected THP-1 monocyte-like cells against intact THP-1 cells [14]. The authors of these studies consider promising the use of proteomic technologies for
elucidating the mechanisms of interaction of immune cells with their microenvironment in response to infection and are considering the possibility of using extracellular MVs as an alternative to existing therapeutic drug delivery systems.
Previously, using various modifications of gel elec-trophoresis and mass spectrometry, there was shown that the MVs produced by the THP-1 monocyte-like cell line contain cytoskeleton proteins, cell adhesion receptors, signaling molecules, heat shock proteins, protein biosynthesis and energy metabolism enzymes, components of the ubiquitin-proteasome system, nuclear proteins [4], as well as proteins involved in MV formation, vesicular transport, and the immune response [4, 14]. Given the incompleteness of available information on the proteome of the source cells [12, 17, 18] and extracellular MVs produced by them, this study was aimed at expanding the existing knowledge of the proteomic profile of THP-1 cells and their MVs. To undertake this, direct MALDI-TOF mass spectrometry assay was used for identification of tryptic peptides in gel strips obtained after the one-dimensional gel electrophoresis analysis. Data on protein profiling of MVs produced by monocytes/ macrophages will allow for assessing previously unknown mechanisms of interaction between these cells and their microenvironment under physiological and inflammatory conditions.
Materials and methods
Cells and cell culture
The cells of the THP-1 monocyte-like cell line (American Tissue Culture Collection, USA) obtained from the peripheral blood of a 1-year-old human male with acute monocytic leukemia were cultured in a suspension culture in accordance with the manufacturer's recommendations at a concentration of 0.71.0 x 106 cells/ml using the complete cell culture medium based on RPMI-1640 (Sigma-Aldrich Chem. Co., USA) containing 10% fetal calf serum (Invitro-gen, USA). The medium was inactivated at 56 °С for 30 min, depleted of its own MVs using membrane filters with a pore diameter of 0.1 |im, and supplemented with 2 mM L-glutamine, 100 IU/ml penicillin, and 100 |g/ml streptomycin (Sigma-Aldrich Chem. Co., USA). The cells were cultured using standard cell culture procedures under the damp atmosphere at 37 °С and 5% CO2. Using the trypan blue solution, the cell vitality was evaluated, which was not less than 96%.
Isolation of biomaterial
One day before the isolation of MVs in the flasks containing the cell culture, the culture medium was completely replaced with a dilution required to achieve a concentration of 1.0 x 106 cells/ml. The next day, the cell vitality was evaluated, after which the culture media from the flasks were centrifuged at 200 g (22 °С, 10 min) to separate the cells.
Because of no single standard protocol available for the isolation and characterization of MVs, a variety of methodological approaches are currently used to obtain MV fractions with a proper degree of purity and enrichment [22]. Therefore, the MVs separated from THP-1 cells were isolated by the modified step-wise centrifugation method in Hanks's solution without Ca2+ and Mg2+ (Sigma-Aldrich Chem. Co., USA), for which the supernatants were sequentially centrifuged at 500 g (4 °C, 10 min) and 9 900 g (4 °C, 10 min). After the second centrifugation, the pellet was washed twice with cold phosphate buffer solution (PBS; Sigma-Aldrich Chem. Co., USA) and was recentri-fuged at 19 800 g (4 °C, 20 min). The supernatant was discarded, with the pellet washed several times with cold PBS, each time precipitating the MVs by cen-trifugation at 19 800 g (4 °C, 20 min). The purified pellet was resuspended in MilliQ deionized water, the protease inhibitor mixture (cOmplete, EDTA-free; Roche Diagnostics GmbH, Germany) being added at the concentration specified by the manufacturer, and was then stored at -80 °C until being analyzed. This protocol allows for isolating MVs with a diameter of 100-200 nm with sufficient purity and minimal loss-
250 kDa 150 kDa
100 kDa 75 kDa
50 kDa 37 kDa
25 kDa 15 kDa 10 kDa
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Figure 1. One-dimensional gel electrophoregrams showing 35 excised segments: L. Ladder; 1. Lysate of THP-1 cells; 2. Lysate of microvesicles produced by THP-1 cells (Coomassie G250 staining, 40 ^g protein load in the both lysates)
es of the biomaterial, while the MVs are sequentially separated from coarse particles of cellular debris and large apoptotic bodies, as well as from exosomes [21].
Laser correlation analysis of microvesicles
The granulometric analysis of the MVs was performed by the dynamic light scattering method described in [20]. The MV diameter was calculated using Zetasizer Software 7.11 (Malvern Instruments, UK). The dimensions of the isolated MVs were shown to lie in the 170-410 nm range, which corresponds to the diameter of ectosomes (100-1000 nm), with the peak size amounting to 195 nm. The granulometric data obtained by us were consistent with the results of other researchers who evaluated the size of MVs produced by a number of cells [38].
Biomaterial preparation
The frozen cells and their MVs were thawed and subjected to repeated "freeze-thaw" cycles five times, and were then intensively homogenized in a glass ho-mogenizer for 5 min. The debris was removed by cen-trifugation at 16 000 g (4 °C, 10 min), with the supernatant collected for further investigation.
Spectrophotometric analysis
The analysis of protein content in the cell and MV lysates was performed through the Bradford protein assay using the NanoDrop One spectrophotometer and NanoDrop One Viewer software (Thermo Scientific, USA).
One-dimensional gel electrophoresis analysis
Cell and MV proteins (the protein content was 40 |g in the both lysates) were fractionated in the Laemmli SDS electrophoresis system in 10% poly-acrylamide gel under denaturing conditions in accordance with the manufacturer's protocol (Bio-Rad Laboratories, USA). Fractionated proteins in the gel were visualized by Coomassie G250 staining, after which 35 stained segments, an equal amount for the cells and their MVs, were excised from the gel (Figure 1).
MALDI-TOF mass spectrometric analysis
To remove the dye and SDS, the excised gel strips were crushed and washed three times in 50% aceto-nitrile (Sigma-Aldrich Chem. Co., USA) in 30 mM Tris buffer solution (pH 8.2) within 15 min at room temperature. After discarding the solution, the pieces of gel were dehydrated by incubation for 10 min in acetonitrile, and then, after removing the latter, the samples were dried for 40 min at 4 °C.
Thereafter, 10 |l of modified bovine trypsin solution (Promega, USA) in 50 mM ammonium bicarbonate with a concentration of 20 ng/ml were added to the dried samples and incubated for 1 h on ice until the gel was completely rehydrated. After that, the excess trypsin solution was removed, and 50 |l of 30 mM Tris buffer solution (pH 8.2) were added to the samples, and those were incubated for 16-18 h at 37 °C. Mixtures of tryptic peptides were extracted three times
L
2
from the gel with 50% acetonitrile solution in 30 mM Tris buffer solution (pH 8.2) containing 0.1% formic acid (Sigma-Aldrich Chem. Co., USA) in an ultrasonic bath for 20 min. The peptides in the resulting solutions were dried up in the air at 4 ^ and frozen at -80 ^ until being analyzed.
On the day of the analysis, the dried-up mixtures of tryptic peptides were dissolved in 50 |l of 50% ace-tonitrile-water solution (Sigma-Aldrich Chem. Co., USA) containing 0.1% trifluoroacetic acid (Sigma-Aldrich Chem. Co., USA). The contents of the tubes were thoroughly mixed on a Vortex shaker until being completely dissolved. The solutions were then applied to standard steel target plates for MALDI analysis based on the following protocol: 2 x 0.5 |l of the matrix solution and 5 x 0.5 |l of a protein sample solution (in order to concentrate it on the substrate as much as possible). 2,5-dihydroxybenzoic acid at a concentration of 10 mg/ml in 10 mM sodium chloride (Sigma-Aldrich Chem. Co., USA) was used as the matrix. The mixtures were dried up in the air.
MALDI-TOF mass spectra of tryptic peptides were acquired on an Axima Resonance MALDI mass spectrometer (Shimadzu/Kratos Analytical Ltd., UK) in the range of200-3000 m/z with mass accuracy of all measurements within 0.01 m/z unit, selecting the laser power which is optimal for achieving good results. The measurements were carried out in the positive ion mode.
Proteins were searched against the UniProt/Swis-sProt database (https://www.uniprot.org) and the NCBI database (https://www.ncbi.nlm.nih.gov) with a taxonomic restriction for the species Homo sapiens using the Mascot search engine (www.matrixscience. com) by peptide mass fingerprinting. Parallel search was performed using the database of inverted and random amino acid sequences (decoy). After the peptides were identified, they were checked for their compliance with their actual positions on the gel.
Functional analysis
The identified proteins were divided into groups depending on molecular function, cellular component, and biological process. It should be noted that such a division is largely arbitrary, since many of the established proteins show multiple functions in the cell, and in most cases, this division corresponds to the division principle accepted in the SwissProt and NCBI databases. The functions of the proteins and their localizations in the cells were also determined using the GeneGO database with the algorithms of the DAVID Bioinformatics Resources 6.8 (https://david.ncifcrf.gov).
Results
The total protein content in THP-1 cells and their MVs was found to amount to 82.4±5.86 |g/106 cells and 0.15±0.036 |g/106 source cells, respectively.
These data subsequently allowed for calculating the protein load of the gel in order to obtain valid results.
The MALDI-TOF mass spectrometric analysis determined a total of 107 proteins that have a variety of functions (Table 1, Table 2).
The subsequent manual functional analysis showed that among 19 determined functional groups, the largest (> 15% of the total) ones comprise transcription regulators (20 entries) and proteins with unknown functions (19 entries). The smallest (< 5% of the total) functional groups include cell differentiation regulators and developmental proteins (5 entries), proteins of the immune response and inflammation (4 entries), receptors and receptor regulators (4 entries), transport proteins and transport regulatory proteins (4 entries), cell adhesion and matrix proteins (3 entries), protein processing regulators (3 entries), ubiquitin-proteasome system proteins (3 entries), in-tracellular signaling proteins (3 entries), autophagy regulators (2 entries), exocytosis regulators (1 entry), chromatin structural proteins (1 entry), hemostatic regulators (1 entry), and hormones (1 entry). An intermediate position (5-15% of the total) is occupied by cytokines and growth factors (10 entries), enzymes (9 entries), cytoskeleton and motor proteins (8 entries), and RNA processing and translation regulators (6 entries).
The identified entries were also distributed into functional groups by the DAVID GO analysis algorithm, with the proteins showing different functions in the cell appearing simultaneously in several groups. The subsequent cluster analysis (DAVID Functional Annotation Clustering) allowed for combining similar functional groups under one broader concept. Thus, the most common groups were obtained, being distributed by molecular function, biological process, and cellular component (Figure 2).
The analysis of the identified proteins distributed by molecular function showed that the bulk of the clusters is involved in sequence-specific DNA (8.5% of the total) and cytoskeletal protein (7.5% of the total) binding, as well as growth factor activity (4.7% of the total), while the minor components altogether account for only 7.5% of the total number of the identified proteins and include clusters involved in myosin heavy chain binding, transmembrane receptor protein serine/threonine kinase binding, BMP receptor binding, and NADP-retinol dehydrogenase activity (Figure 2A).
The distribution of proteins by biological process showed that the most representative (6.6-8.5% of the total) clusters are involved in positive regulation of transcription and transcription from RNA poly-merase II promoter, while clusters involved in positive chemotaxis, adult locomotory behavior, anterior/ posterior pattern specification, positive regulation of dopamine secretion, organ induction, and cellular re-
TABLE 1. MALDI-TOF MASS SPECTROMETRIC PROTEOME PROFILING OF THP-1 CELLS (p < 0.05)
Protein UniProtKB / NCBI(*) entries Gene MW, kDa pI Number of peptides (% AAC)
Cytoskeleton and motor proteins
Actin-binding LIM protein 2 isoform 1 Q6H8Q1 ABLIM2 67.8 8.29 11 (10)
Coronin-1A P31146 CORO1A 51.0 6.25 7 (6)
Coronin-1B Q9BR76 CORO1B 54.2 5.61 7 (4)
Dynein light chain 2, cytoplasmic Q96FJ2 DYNLL2 10.3 6.81 7 (14)
RNA processing regulators
tRNA (cytosine(38)-C(5))-methyltransferase isoform a O14717 TRDMT1 44.6 8.78 6 (5)
Protein processing regulators
Beta-1,3- galactosyltransferase 2 O43825 B3GALT2 49.2 9.50 10 (6)
Glycoprotein endo-alpha-1,2-mannosidase Q5SRI9 / NP_078917(*) MANEA 53.6 9.14 11 (7)
UDP-N-acetyl-alpha-D-galactosamine polypeptide N-acetylgalactosaminyl-transferase 13 isoform b X5DRI3 / AHW56697(*) GALNT13 7.4 9.39 6 (19)
Enzymes
Iduronate 2-sulfatase 060597/ AAC05984(*) IDS 19.5 8.80 6 (11)
Iduronate 2-sulfatase (Hunter syndrome) isoform CRA_a EAW61280(*) IDS 19.5 9.21 6 (11)
Retinol dehydrogenase 13 isoform 1 Q8NBN7 RDH13 35.9 8.23 6 (7)
Very-long-chain (3R)-3-hydroxyacyl-CoA dehydratase 3 isoform 1 Q9P035 HACD3 43.1 9.04 6 (4)
Receptors. Receptor regulators
Olfactory receptor 1L6 Q8NGR2 OR1L6 39.5 9.60 6 (6)
Prostate and testis expressed protein 4 P0C8F1 PATE4 11.4 8.97 6 (22)
Hormones
Prolactin-releasing peptide P81277 PRLH 9.6 11.66 6 (13)
Proteins of the immune response and inflammation
C-type lectin domain family 2 member B Q92478 CLEC2B 17.3 9.02 6 (13)
Protein phosphatase 1B isoform beta-1 O75688 PPM1B 52.6 4.95 8 (5)
Protein phosphatase 1 regulatory subunit 14B Q96C90 PPP1R14B 15.9 4.75 5 (10)
Таблица 1 (продолжение) Table 1 (continued)
Protein UniProtKB / NCBI(*) entries Gene MW, kDa pI Number of peptides (% AAC)
Cytokines. Growth factors
Bone morphogenetic protein 8A Q7Z5Y6 BMP8A 44.8 9.06 6 (2)
Bone morphogenetic protein 8B isoform 1 P34820 BMP8B 44.7 8.76 6 (3)
Stromal cell-derived factor 1 isoform alpha precursor P48061 CXCL12 10.1 9.72 5 (16)
Stromal cell-derived factor 1 isoform beta P48061 CXCL12 10.7 9.93 5 (16)
Stromal cell-derived factor 1 isoform theta P48061/ ABC69273(*) CXCL12 11.4 9.67 5 (15)
Exocytosis regulators
Synaptotagmin-8 isoform 4 Q8NBV8 SYT8 44.1 9.65 7 (6)
Transcription factors
Cyclin-dependent kinase 9 isoform 1 P50750 CDK9 42.8 8.97 9 (8)
Flt3-interacting zinc finger protein 1 Q96SL8 FIZ1 52.0 8.59 8 (3)
Homeobox protein Hox-A7 P31268 / NP_008827(*) HOXA7 25.3 5.26 6 (6)
Homeobox protein Hox-D12 isoform 1 P35452 HOXD12 29.0 9.82 6 (6)
Pirin 000625 PIR 32.1 6.42 11 (7)
Putative transcription factor ovo-like protein 3 000110/ AAB51180(*) OVOL3 24.3 10.08 9 (11)
Putative transcription factor ovo-like protein 3 isoform X2 XP_011525553(*) OVOL3 13.2 9.77 6 (13)
Putative zinc finger protein 840 A6NDX5 ZNF840P 83.2 9.69 13 (3)
Transcription initiation factor TFIID subunit 8 isoform 1 Q7Z7C8 TAF8 34.2 6.03 6 (5)
Zinc finger and SCAN domain-containing protein 9 isoform 2 O15535/ NP_001186408(*) ZSCAN9 51.6 8.07 9 (8)
Zinc finger protein GLIS2 Q9BZE0 GLIS2 55.7 9.08 6 (4)
Cell differentiation regulators. Developmental proteins
Neuronatin isoform alpha Q16517 NNAT 9.2 10.17 4 (18)
Outer dense fiber protein 3 isoform 1 Q96PU9 ODF3 27.7 9.90 5 (9)
Vexin isoform 1 Q8TAG6 VXN 22.6 10.05 9 (6)
Таблица 1 (окончание) Table 1 (continued)
UniProtKB / NCBI(*) entries Number of
Protein Gene MW, kDa pI peptides (% AAC)
Cell adhesion and matrix proteins
Sperm acrosome membrane-associated protein 3 isoform CRA_b Q8IXA5 / EAW80218(*) SPACA3 15.1 8.93 5 (12)
Proteins with unknown functions. Domains
C2 domain-containing protein 2, partial Q9Y426/ CAB43307(*) C2CD2 19.8 5.97 6 (8)
Capsid scaffold protein A0A126LB05/ AMD82185(*) U53.5 27.6 7.06 7 (6)
cDNA FLJ39825 fis, clone SPLEN2012175, highly similar to Nicotinamide riboside kinase 1 B3KUG3/ BAG53425(*) N/A 21.7 5.44 6 (7)
JHDM1D protein A0JNV9/ AAI27008(*) JHDM1D 9.8 11.46 7 (16)
Nicotinamide riboside kinase 1 isoform 3 Q5W125 / NP_001317607(*) NMRK1 23.8 5.33 6 (6)
Nuclear pore complex-interacting protein family member B7 O75200 NPIPB7 47.7 10.35 6 (3)
Outcome predictor in acute leukemia 1, partial Q1EG69/ AAV68560(*) OPAL1 3.3 11.53 5 (33)
Putative uncharacterized protein B3GALT5-AS1 isoform 1 P59052 B3GALT5-AS1 15.7 9.21 4 (4)
Testis-expressed protein 50 A0A1B0GTY4 TEX50 20.8 9.36 6 (8)
Note. Abbreviations: AAC, amino acid coverage; MW, not available. molecular weight; pI, isoelectric point; N/A, not applicable or
TABLE 2. MALDI-TOF MASS SPECTROMETRIC PROTEOME PROFILING OF MICROVESICLES PRODUCED BY THP-1 CELLS
(p < 0.05)
UniProtKB / NCBI(*) entries Number of
Protein Gene MW, kDa pI peptides (% AAC)
Cytoskeleton and motor proteins
Costars family protein ABRACL Q9P1F3 ABRACL 9.1 5.86 5 (17)
Myosin regulatory light chain 12A P19105 MYL12A 19.8 4.67 7 (10)
Myosin regulatory light chain 12B O14950 MYL12B 19.8 4.71 7 (10)
Myosin regulatory light polypeptide 9 isoform 1 P24844 MYL9 19.8 4.80 7 (10)
RNA processing and translation regulators
cDNA FLJ35275 fis, clone PROST2006282, weakly similar to Translation initiation factor IF-2 Q8NAJ1 / BAC03923(*) N/A 27.4 11.90 10 (10)
Таблица 2 (продолжение) Table 2 (continued)
Protein UniProtKB / NCBI(*) entries Gene MW, kDa pI Number of peptides (% AAC)
28S ribosomal protein S11 isoform 1, mitochondrial P82912 MRPS11 20.6 10.82 7 (11)
39S ribosomal protein L12, mitochondrial P52815 MRPL12 21.3 9.04 7 (7)
Mitochondrial assembly of ribosomal large subunit protein 1 Q96EH3 MALSU1 26.2 5.32 7 (7)
Ribonuclease P protein subunit p20 O75817 POP7 15.6 9.09 8 (14)
Ubiquitin-proteasome system proteins
RING finger protein 175 isoform X3 XP_011530183(*) RNF175 33.9 9.16 7 (5)
RING finger protein 175 isoform CRA_a, partial EAX04950(*) RNF175 34.1 9.15 7 (5)
UBX domain containing 5 isoform CRA_d EAX07827(*) UBXN5 21.5 8.42 7 (12)
Enzymes
Acyl-coenzyme A thioesterase 13 isoform 1 Q9NPJ3 ACOT13 15.0 9.23 8 (16)
cDNA FLJ61119, highly similar to Developmentally-regulated GTP-binding protein 2 B4DIG2 / BAG58474(*) N/A 15.5 9.27 8 (24)
Cytosolic 5'-nucleotidase 1A Q9BXI3 NT5C1A 41.0 6.11 7 (6)
Dehydrogenase/ reductase SDR family member 4 isoform 2 Q9BTZ2/ NP_001269916(*) DHRS4 20.3 10.32 11 (20)
Valacyclovir hydrolase isoform 1 Q86WA6 BPHL 32.5 9.20 6 (7)
Receptors. Receptor regulators
Oestrogen receptor, partial Q13262/ AAB35900(*) 7 ER 4.6 6.45 6 (13)
Paired immunoglobulin-like type 2 receptor beta isoform 3 AAG17224(*) PILRB 29.6 9.84 6 (11)
Proteins of the immune response and inflammation
TLR4 interactor with leucine rich repeats Q7L0X0 TRIL 88.7 9.70 21 (3)
Cytokines. Growth factors
Astrocyte-derived trophic factor 2 AAB33494(*) GDNF 14.7 9.30 9 (16)
Fibroblast growth factor 10 015520 FGF10 23.4 9.61 7 (9)
Fibroblast growth factor 10, partial 015520/ CAG46489(*) FGF10 23.4 9.67 7 (9)
Таблица 2 (продолжение) Table 2 (continued)
Protein UniProtKB / NCBI(*) entries Gene MW, kDa pI Number of peptides (% AAC)
Fibroblast growth factor 10, partial Q8NFI9 / AAM46926(*) FGF10 19.2 9.94 7 (12)
Glial cell line-derived neurotrophic factor isoform 1 P39905 GDNF 23.7 9.26 15 (9)
Autophagy regulators
Microtubule-associated proteins 1A/ 1B light chain 3B Q9GZQ8 MAP1LC3B 14.7 8.89 5 (11)
Microtubule-associated proteins 1A/ 1B light chain 3 beta 2 A6NCE7 MAP1LC3B2 14.6 8.74 5 (11)
Intercellular signaling proteins
BTB/ POZ domain-containing protein KCTD20 isoform 2 Q7Z5Y7/ NP_001273508(*) KCTD20 28.9 5.25 8 (7)
Casein kinase II subunit alpha isoform 1 P68400 CSNK2A1 45.1 7.29 7 (4)
Guanylyl cyclase-activating protein 3 isoform 1 O95843 GUCA1C 23.8 4.95 7 (8)
Transport proteins. Transport regulatory proteins
ATP synthase H+ transporting mitochondrial F1 complex delta subunit isoform CRA_a EAW69531(*) ATP5F1D 14.7 11.83 7 (14)
Biogenesis of lysosome-related organelles complex 1 subunit 6 isoform 1 Q9UL45 BLOC1S6 19.7 6.01 7 (11)
Sesquipedalian-1 isoform 1 Q8N4B1 PHETA1 27.2 9.18 9 (6)
Vacuolar protein sorting-associated protein VTA1 homolog isoform 1 Q9NP79 VTA1 33.9 5.87 5 (3)
Hemostatic regulators
Endothelin-2 isoform 2 preproprotein NP_001289198(*) EDN2 16.5 10.19 7 (11)
Transcription factors
cDNA FLJ38903 fis, clone NT2NE2001252, highly similar to Homeobox protein Hox-B8 Q8N8T3/ BAC04730(*) N/A 27.7 8.18 7 (7)
GA-binding protein subunit beta-1 isoform 1 Q06547 GABPB1 42.5 4.77 6 (5)
Homeobox protein BarH-like 2 Q9UMQ3 BARX2 31.2 8.65 5 (4)
Homeobox protein Hox-B8 P17481 / NP_076921(*) HOXB8 27.6 8.48 7 (7)
Homeobox protein Hox-C8 P31273 HOXC8 27.7 6.57 6 (5)
Таблица 2 (окончание) Table 2 (continued)
Protein UniProtKB / NCBI(*) entries Gene MW, kDa pI Number of peptides (% AAC)
Homeobox protein MOX-1 isoform 1 P50221 MEOX1 28.0 7.79 6 (5)
PAXIP1-associated glutamate-rich protein 1 Q9BTK6 PAGR1 27.7 4.40 6 (4)
THAP domain-containing protein 8 Q8NA92 THAP8 30.1 10.24 8 (6)
ZZ-type zinc finger-containing protein 3, partial Q8IYH5 / BAB84945(*) ZZZ3 58.1 4.96 11 (6)
Chromatin structural proteins
Histone H4 P62805 H4C1 11.4 11.36 11 (22)
Cell differentiation regulators. Developmental proteins
Netrin-4 isoform 1 Q9HB63 NTN4 70.0 8.44 10 (4)
Netrin-4, partial Q9HB63/ BAB14964(*) NTN4 37.9 8.40 13 (9)
Cell adhesion and matrix proteins
LIM and senescent cell antigen-like-containing domain protein 2 isoform 2 Q7Z4I7 / NP_060450(*) LIMS2 41.5 8.71 18 (6)
LIM and senescent cell antigen-like-containing domain protein 2 isoform X2 XP_011509755(*) LIMS2 36.6 8.92 18 (7)
Proteins with unknown functions. Domains
cDNA FLJ75546 A8K1F9/ BAF82563(*) N/A 13.3 10.88 5 (11)
Chromosome 11 open reading frame 58 isoform CRA_c E9PRZ9/ EAW68457(*) C11orf58 10.6 4.09 5 (15)
Divergent protein kinase domain 1C isoform 1 Q0P6D2 DIPK1C 46.4 6.38 6 (4)
Leucine-rich repeat and IQ domain-containing protein 4 A6NIV6 LRRIQ4 63.9 8.43 8 (6)
Protein CEI isoform 1 Q86SI9 C5orf38 15.1 11.42 7 (9)
Small acidic protein O00193 SMAP 20.3 4.57 6 (8)
Testis-expressed protein 50 A0A1B0GTY4 TEX50 20.8 9.36 5 (6)
Testis-expressed protein 51 A0A1B0GUA7 TEX51 18.8 7.59 5 (7)
Testis-expressed protein 51 isoform X12 XP_011510580(*) TEX51 18.6 6.43 5 (8)
Uncharacterized protein C5orf47 Q569G3 C5orf47 19.2 10.48 6 (11)
Uncharacterized protein DKFZp762B162 Q69YQ6 / CAH10612(*) DKFZp762B162 18.5 6.51 12 (12)
Note. As for Table 1.
А
sequence-specific DNA binding cytoskeletal protein binding growth factor activity
myosin heavy chain binding
transmembrane receptor protein serine/ threonine kinase binding
BMP receptor binding NADP-retinol dehydrogenase activity
GO Molecular function
B
о 1 2 3 4
GO Biological process
1о
positive regulation of transcription from RNA... transcription from RNA polymerase II promoter anterior/posterior pattern specification adult locomotory behavior positive chemotaxis cellular response to nitrogen starvation organ induction positive regulation of dopamine secretion
1о
C
GO Cellular component
actincytoskeleton sperm part myosin complex actomyosin actin filament bundle contractile actin filament bundle stress fiber
8 9
1о
5
6
7
8
9
о
1
2
3
4
5
6
7
8
9
о
1
2
3
4
5
6
7
Figure 2. Most common clusters of proteins obtained from lysates of THP-1 cells and their microvesicles: A, molecular function; B, biological process; C, cellular component (percentage of the total number of identified proteins; * p < 0.05, ** p < 0.005; DAVID 6.8)
sponse to nitrogen starvation are minor components, which account for only 14.2% of the total number of the identified proteins (Figure 2B).
In contrast, the functional groups distributed by cellular component were found to be more uniform. Approximately equal proportions (2.8-3.8% of the total) were obtained for the clusters associated with sperm part, myosin complex, actomyosin, actin filament bundle and contractile actin filament bundle, and stress fiber. At the same time, the most representative cluster, which accounts for only 5.7% of the total number of the identified proteins, was formed by proteins associated with the actin cytoskeleton as a whole (Figure 2C).
Among 16 functional groups of proteins identified separately in the MVs, the largest (> 15% of the total) ones are represented by proteins with unknown functions (11 entries) and transcription regulators (9 entries). The smallest (< 5% of the total) functional groups comprise receptors and receptor regulators (2 entries), autophagy regulators (2 entries), cell differentiation regulators and developmental proteins (2 entries), cell adhesion and matrix proteins (2 entries), proteins of the immune response and inflammation (1 entry), hemostatic regulators (1 entry), and chromatin structural proteins (1 entry). An intermediate position (5-15% of the total) is occupied by RNA processing and translation regulators (5 entries), enzymes (5 entries), cytokines and growth factors (5 entries), cytoskeleton and motor proteins (4 entries), transport proteins and transport regulatory proteins (4 entries), ubiquitin-proteasome system proteins (3 entries), and intracellular signaling proteins (3 entries) (Table 2).
Discussion
THP-1 cells used by us allow for in vitro analyzing the main biochemical processes that occur in mono-cytes in vivo, including those leading to spontaneous secretion of their MVs into the extracellular space. The data obtained using MALDI-TOF mass spec-trometry indicate that the studied cells and their MVs are characterized by a wide range of proteins with various functions and properties, providing the possibility of multilateral regulation of cell metabolism.
In recent years, international research teams have analyzed the proteome of the THP-1 monocyte-like cell line [12, 17, 18]. However, the most detailed study only mentioned approximately 5 400 proteins, which is significantly less than the number of proteins contained in any eukaryotic cell. That strongly indicates the incompleteness of the monocyte-like cell proteome described to date. Nevertheless, the main functional groups of proteins presented in the above works matched those found in our study (Table 1). However, from the list of proteins presented in the work [4] devoted to the protein profiling of the MVs derived from
the THP-1 monocyte-like cell line, only 1 entry (his-tone matched those found by us in these MVs (Table 2). Meanwhile, the remaining 59 entries of the proteins identified by us in the studied MVs give new information on the proteome of the MVs produced by monocyte-like cells.
In concordance with the biogenesis pathways of the studied MVs, their proteome constitutes a set of protein molecules of cellular origin. Proteomes of extracellular MVs produced by platelets, mature lymphocytes, endotheliocytes, mast and some other types of cells are currently studied in relative detail, and data from these studies are presented by many authors, in particular [8, 11, 26]. Thanks to these works, it is now known that MVs derived from blood and vascular cells contain both non-specific proteins characteristic of any type of cells and specific proteins involved in the functioning of a particular cell. Proteins specific for cells of a certain type are, for example, a T cell receptor expressed primarily on T lymphocytes, platelet P-selectin, and other proteins capable of being involved in the immune response [2]. Regardless of their cellular origin, MV proteins are most often involved in the very formation of vesicles. Undoubtedly, such proteins are tetraspanins (CD9, CD63, CD81, and CD82), heat shock proteins (HSP70, HSP90), cytoskeletal elements, enzymes of various metabolic pathways, adhesion molecules, receptors, as well as proteins of the main histocompatibility complex (MHC) [36].
In the MVs studied by us, proteins belonging to the above classes were also found, in particular cyto-skeleton and motor proteins, enzymes, cell adhesion and matrix proteins, and receptors. As well, as common proteins should be classified protein processing regulators and ubiquitin-proteasome system proteins identified in this study. Besides, we found some specific proteins, in particular those involved in the implementation of defense mechanisms, such as proteins of the immune response and inflammation, cytokines and growth factors, as well as their receptors.
Among the proteins identified by us in the studied MVs, cytokines, receptors, and regulatory proteins (Table 3) should be especially distinguished for two reasons. First, most of them have been found in monocytes/macrophages isolated from both tissues or peripheral blood and the transplantable monocyte-like cells. Secondly, the expression of such proteins in the MVs can have a certain signaling or regulatory function in relation to the microenvironment.
For example, fibroblast growth factor 10 (FGF10) identified by us in the studied MVs, which was not previously found there by other researchers, can exert multiple effects on cells of the microenvironment, activating the ERK signaling cascade and thus affecting the proliferation, survival and motility of various types of cells, as well as taking part in the activation of T cells, proliferation, migration and differentiation of
TABLE 3. SEVERAL FUNCTIONS OF PROTEINS OF MICROVESICLES PRODUCED BY THP-1 CELLS
Protein Function (according to the SwissProt/UniProt and NCBI databases) Presence in monocytes/ macrophages or THP-1 cells [references]
Fibroblast growth factor 10 (FGF10) a growth factor; plays an important role in the regulation of embryonic development, cell proliferation and cell differentiation; required for normal branching morphogenesis; may play a role in wound healing; activates ERK1/2 cascades [15] (FGF10 gene expression)
Glial cell line-derived neurotrophic factor (GDNF), same as Astrocyte-derived trophic factor (ATF) a neurotrophic factor; enhances survival and morphological differentiation of dopaminergic neurons and increases their high-affinity dopamine uptake; negatively regulates extrinsic apoptotic signaling pathway in absence of ligand; positively regulates cell differentiation and cell population proliferation; binds various TGF-beta receptors leading to recruitment and activation of SMAD family transcription factors that regulate gene expression [7]
TLR4 interactor with leucine rich repeats a component of the TLR4 signaling complex; mediates the innate immune response to bacterial lipopolysaccharide leading to cytokine secretion [6]
Oestrogen receptor an estrogen receptor, a ligand-activated transcription factor composed of several domains important for hormone binding, DNA binding, and activation of transcription; recruited to the NF-kappa-B response element of the CCL2 and IL8 promoters and can displace CREBBP; present with NF-kappa-B components RELA/p65 and NFKB1/p50 on ERE sequences [30]
Paired immunoglobulin-like type 2 receptor beta paired receptors; consist of highly related activating and inhibitory receptors and are widely involved in the regulation of the immune system; thought to act as a cellular signaling activating receptor that associates with ITAM-bearing adapter molecules on the cell surface [31]
LIM and senescent cell antigen-like-containing domain protein 2 an adapter protein in a cytoplasmic complex linking beta-integrins to the actin cytoskeleton; bridges the complex to cell surface receptor tyrosine kinases and growth factor receptors; plays a role in modulating cell spreading and migration [31]
BTB/POZ domain-containing protein KCTD20 an intracellular signaling protein; promotes the phosphorylation of AKT family members [19]
Casein kinase II subunit alpha an intracellular signaling protein; regulates numerous cellular processes, such as cell cycle progression, apoptosis and transcription, as well as viral infection; required for p53/TP53-mediated apoptosis; phosphorylates the caspases CASP9 and CASP2 and the apoptotic regulator NOL3 (phosphorylation protects CASP9 from cleavage and activation by CASP8, and inhibits the dimerization of CASP2 and activation of CASP8); phosphorylates and regulates numerous transcription factors including NF-kappa-B, STAT1, CREB1, IRF1, IRF2, ATF1, SRF, MAX, JUN, FOS, MYC, and MYB; during viral infection, phosphorylates various proteins involved in the viral life cycles of EBV, HSV, HBV, HCV, HIV, CMV, and HPV; regulates Wnt signaling by phosphorylating CTNNB1 and the transcription factor LEF1 [29]
Endothelin-2 isoform 2 preproprotein a member of the endothelin protein family of secretory vasoconstrictive peptides; is processed to a short mature form which functions as a ligand for the endothelin receptors that initiate intracellular signaling events; is involved in a wide range of biological processes, such as hypertension and ovulation; regulates growth in several cell types and may also affect differentiation, inflammation, and angiogenesis [3]
endothelial cells during angiogenesis. FGF10 can also regulate synaptic plasticity and phosphorylation of the transcription factor p53, as well as activate granzyme B cleavage of the FGFR1 receptor [28]. The FGF10 gene expression was shown in tumor associated macrophages [15]. Moreover, the role of FGF10 in the development of chorionic villi was elucidated, and, as currently established, the factor is expressed by both decidual cells and the cytotrophoblast [1]. The data obtained by us on FGF10 being present in the studied MVs may become a reason for further studies, in particular, of placental macrophages, which will possibly expand existing knowledge of their role in cell communication in the uteroplacental contact area.
Glial cell line-derived neurotrophic factor, also identified by us in the studied MVs, is able to protect from degeneration dopaminergic neurons in the substantia nigra of the midbrain and the terminals of tyrosine hydroxylase-positive axons in the striatum [7]. It can be assumed that TLR4 interactor with leucine rich repeats (a component of Toll-like receptor 4), which is also widely present in the brain and other organs and tissues, when transferred to micro-environmental cells (in particular, peripheral blood mononuclear cells and glial cells), will increase their production of cytokines in response to bacterial infection [6]. Similarly, LIM and senescent cell antigenlike-containing domain protein 2 (an adapter protein in a cytoplasmic complex linking beta-integrins to the actin cytoskeleton) is able to modulate the process of cell migration during tumor development [31].
Furthermore, it was found that the studied MVs contain receptors and their regulators, namely the estrogen receptor and paired immunoglobulin-like type 2 receptor beta. Taking into account the fact that these MVs are able to transmit their receptors to the membranes of recipient cells [25], we can assume the ability of those cells to respond to signals that were previously inaccessible to them, provided that the cells have ready intracellular signal transduction pathways for these receptors [30, 31].
The specific effects of monocytes/macrophages on the surrounding cells and tissues can also be contributed to by the broad-spectrum signaling molecules identified by us, such as casein kinase II subunit alpha, which regulates cell survival at different levels — it promotes DNA repair, affects the NF-kB, Wnt, PI3K/ACT and JAK-STAT signaling cascades, interacts with chaperones, activates antiapoptotic proteins and inactivates proapoptotic ones, including caspa-ses [29], as well as endothelin-2 isoform 2 prepropro-tein, which plays a key role in blood vessel homeo-stasis [3], and BTB/POZ domain-containing protein KCTD20, which activates AKT, a key enzyme of the PI3K/AKT signaling pathway involved in the regulation of cell proliferation, growth and survival [19].
The molecular mechanisms of MV formation suggest that MVs should include components of the ac-tin network adjacent to the cell membrane and other cytoskeletal elements [9]. The data obtained by us allow suggesting that the studied MVs may have an ectosomal nature. Thus, we identified proteins of the actin-myosin system and one regulator of the cyto-skeleton dynamics, namely myosin and costars family protein ABRACL, that are involved in MV formation and budding from the cell membrane [24, 32]. Various isoforms of cytoskeletal proteins have not been found in exosomes yet according to the available literature [5, 13].
The performed functional and cluster annotation analyzes altogether revealed the predominant molecular functions of the proteins identified in the studied MVs, and showed possible participation of these proteins in biological processes (Figure 2). It was found that the dominant clusters characterizing the molecular function of the identified proteins are associated with sequence-specific DNA and cytoskeletal protein binding, as well as growth factor activity. The association of the proteins with the actin cytoskeleton is also indicated by the distribution of functional groups by the localization of the identified proteins in the cell and their belonging to a variety of cellular parts. Some of these actin-binding proteins are actively involved in the reorganizing of actin filaments in response to the effects of various growth factors, cytokines and chemoattractants and may play a key role in the development of a number of pathologies in the human body [16]. At the same time, the distribution of the proteins by biological process showed that the most representative clusters comprise proteins involved in the regulation of gene transcription, while the groups of proteins responsible for positive chemotaxis, the spatial organization of differentiating cells, and a cellular response were minor components. The results obtained by us are consistent with the biological nature of the studied MVs and are in line with the data collected by other researchers [12, 17, 18].
To date, a number of good proteomic research strategies have been described that are based on a combination of different approaches and techniques. Unfortunately, protein identification is a rather complex multi-stage process and does not always lead to reproducible results. The scheme proposed in this study can lead to good results with a qualitative description of the protein profile of the MVs released from THP-1 cells. However, the search for the target protein using this approach poses significant difficulties, mainly due to the extremely low yield of the total protein. For a more detailed analysis of the MV proteome using mass spectrometric methods, it would be advisable to include in the study protocol additional preparation steps at the stage of the isolation of the MVs, such as preserving accumulation, ultracentrifugation and im-
munoprecipitation. It is also worth noting that the data obtained by us in this study should also be further verified using the immunoblotting method, in both THP-1 cells and macrophages isolated from a variety of tissues, as well as at different stages of macrophage differentiation.
Conclusion
Summarizing the data obtained, we can conclude that MVs produced by the THP-1 monocyte-like cell line, along with common proteins, also contain pro-
teins of the immune response and inflammation, cytokines and growth factors, with the help of which these MVs can contribute to the specific effects of monocytes/macrophages on the surrounding cells. Our data on the proteome of the studied MVs will expand the existing knowledge of distant communication of cells and indicate new mechanisms of interaction between monocytes/macrophages and their microenvironment. The presented results will be useful for further proteomic studies of MVs produced by cells involved in an immune response under physiological and inflammatory conditions.
References
1. Anteby E.Y., Natanson-Yaron S., Hamani Y., Sciaki Y., Goldman-Wohl D., Greenfield C., Ariel I., Yagel S. Fibroblast growth factor-10 and fibroblast growth factor receptors 1-4: expression and peptide localization in human decidua and placenta. Eur. J. Obstet. Gynecol. Reprod. Biol., 2005, Vol. 119, no. 1, pp. 27-35.
2. Baig S., Kothandaraman N., Manikandan J., Rong L., Ee K.H., Hill J., Lai C.W., Tan W.Y., Yeoh F., Kale A., Su L.L., Biswas A., Vasoo S., Choolani M. Proteomic analysis of human placental syncytiotrophoblast microvesicles in preeclampsia. Clin. Proteomics, 2014, Vol. 11, no. 1, 40. doi: 10.1186/1559-0275-11-40.
3. Battistini B., Chailler P., D'Orléans-Juste P., Brière N., Sirois P. Growth regulatory properties of endothelins. Peptides, 1993, Vol. 14, no. 2, pp. 385-399.
4. Bernimoulin M., Waters E.K., Foy M., Steele B.M., Sullivan M., Falet H., Walsh M.T., Barteneva N., Geng J.G., Hartwig J.H., Maguire P.B., Wagner D.D. Differential stimulation of monocytic cells results in distinct populations of microparticles. J. Thromb. Haemost., 2009, Vol. 7, no. 6, pp. 1019-1028.
5. Burbano C., Rojas M., Vasquez G., Castano D. Microparticles that form immune complexes as modulatory structures in autoimmune responses. Mediators Inflamm., 2015, Vol. 2015, 267590. doi: 10.1155/2015/267590.
6. Carpenter S., Carlson T., Dellacasagrande J., Garcia A., Gibbons S., Hertzog P., Lyons A., Lin L.L., Lynch M., Monie T., Murphy C., Seidl K.J., Wells C., Dunne A., O'Neill L.A. TRIL, a functional component of the TLR4 signaling complex, highly expressed in brain. J. Immunol., 2009, Vol. 183, no. 6, pp. 3989-3995.
7. Chen C., Li X., Ge G., Liu J., Biju K.C., Laing S.D., Qian Y., Ballard C., He Z., Masliah E., Clark R.A., O'Connor J.C., Li S. GDNF-expressing macrophages mitigate loss of dopamine neurons and improve Parkinsonian symptoms in MitoPark mice. Sci. Rep., 2018, Vol. 8, no. 1, 5460. doi: 10.1038/s41598-018-23795-4.
8. Cianciaruso C., Beltraminelli T., Duval F., Nassiri S., Hamelin R., Mozes A., Gallart-Ayala H., Ceada Torres G., Torchia B., Ries C.H., Ivanisevic J., de Palma M. Molecular profiling and functional analysis of macrophage-derived tumor extracellular vesicles. Cell Rep., 2019, Vol. 27, no. 10, pp. 3062-3080.e11.
9. Colombo M., Raposo G., Thery C. Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles. Annu. Rev. Cell Dev. Biol., 2014, Vol. 30, pp. 255-289.
10. Cronemberger-Andrade A., Aragâo-França L., de Araujo C.F., Rocha V.J., Borges-Silva Mda C., Figueira C.P., Oliveira P.R., de Freitas L.A., Veras P.S., Pontes-de-Carvalho L. Extracellular vesicles from Leishmania-infected macrophages confer an anti-infection cytokine-production profile to naïve macrophages. PLoS Negl. Trop. Dis., 2014, Vol. 8, no. 9, e3161. doi: 10.1371/journal.pntd.0003161.
11. Graner M.W. Roles of extracellular vesicles in high-grade gliomas: tiny particles with outsized influence. Annu. Rev. Genomics Hum. Genet., 2019, Vol. 20, pp. 331-357.
12. Gui L., Zhang Q., Cai Y., Deng X., Zhang Y., Li C., Guo Q., He X., Huang J. Effects of let-7e on LPS-stimulated THP-1 cells assessed by iTRAQ proteomic analysis. Proteomics Clin. Appl., 2018, Vol. 12, no. 5, e1700012. doi: 10.1002/prca.201700012.
13. Haraszti R.A., Didiot M.C., Sapp E., Leszyk J., Shaffer S.A., Rockwell H.E., Gao F., Narain N.R., DiFiglia M., Kiebish M.A., Aronin N., Khvorova A. High-resolution proteomic and lipidomic analysis of exosomes and microvesicles from different cell sources. J. Extracell. Vesicles, 2016, Vol. 5, no. 1, 32570. doi: 10.3402/jev.v5.32570.
14. Hare N.J., Chan B., Chan E., Kaufman K.L., Britton W.J., Saunders B.M. Microparticles released from Mycobacterium tuberculosis-infected human macrophages contain increased levels of the type I interferon inducible proteins including ISG15. Proteomics, 2015, Vol. 15, no. 17, pp. 3020-3029.
15. Hegab A.E., Ozaki M., Kagawa S., Hamamoto J., Yasuda H., Naoki K., Soejima K., Yin Y., Kinoshita T., Yaguchi T., Kawakami Y., Ornitz D.M., Betsuyaku T. Tumor associated macrophages support the growth of FGF9-induced lung adenocarcinoma by multiple mechanisms. Lung Cancer, 2018, Vol. 119, pp. 25-35.
16. Izdebska M., Zielinska W., Grzanka D., Gagat M. The role of actin dynamics and actin-binding proteins expression in epithelial-to-mesenchymal transition and its association with cancer progression and evaluation of possible therapeutic targets. Biomed. Res. Int., 2018, Vol. 2018, 4578373. doi: 10.1155/2018/4578373.
17. Kaewseekhao B., Naranbhai V., Roytrakul S., Namwat W., Paemanee A., Lulitanond V., Chaiprasert A., Faksri K. Comparative proteomics of activated THP-1 cells infected with Mycobacterium tuberculosis identifies putative clearance biomarkers for tuberculosis treatment. PLoS ONE, 2015, Vol. 10, no. 7, e0134168. doi: 10.1371/ journal.pone.0134168.
18. Kang J.H., Kim H.T., Choi M.S., Lee W.H., Huh T.L., Park Y.B., Moon B.J., Kwon O.S. Proteome analysis of human monocytic THP-1 cells primed with oxidized low-density lipoproteins. Proteomics, 2006, Vol. 6, no. 4, pp. 1261-1273.
19. Kok Y.L., Vongrad V., Shilaih M., Di Giallonardo F., Kuster H., Kouyos R., Günthard H.F., Metzner K.J. Monocyte-derived macrophages exhibit distinct and more restricted HIV-1 integration site repertoire than CD4(+) T cells. Sci. Rep., 2016, Vol. 6, 24157. doi: 10.1038/srep24157.
20. Korenevskii A.V., Milyutina Y.P., Zhdanova A.A., Pyatygina K.M., Sokolov D.I., Sel'kov S.A. Mass-spectrometric analysis of proteome of microvesicles produced by NK-92 natural killer cells. Bull. Exp. Biol. Med.,
2018, Vol. 165, no. 4, pp. 564-571.
21. Kowal J., Arras G., Colombo M., Jouve M., Morath J.P., Primdal-Bengtson B., Dingli F., Loew D., Tkach M., Thery C. Proteomic comparison defines novel markers to characterize heterogeneous populations of extracellular vesicle subtypes. Proc. Natl. Acad. Sci. USA, 2016, Vol. 113, no. 8, pp. E968-E977.
22. Li P., Kaslan M., Lee S.H., Yao J., Gao Z. Progress in exosome isolation techniques. Theranostics, 2017, Vol. 7, no. 3, pp. 789-804.
23. Maas S.L.N., Breakefield X.O., Weaver A.M. Extracellular vesicles: unique intercellular delivery vehicles. Trends Cell. Biol., 2017, Vol. 27, no. 3, pp. 172-188.
24. Malloci M., Perdomo L., Veerasamy M., Andriantsitohaina R., Simard G., Martinez M.C. Extracellular vesicles: mechanisms in human health and disease. Antioxid. Redox Signal., 2019, Vol. 30, no. 6, pp. 813-856.
25. Markova K.L., Mikhailova V.A., Korenevsky A.V., Milyutina Y.P., Rodygina V.V., Aleksandrova E.P., Markov A.S., Balabas O.A., Selkov S.A., Sokolov D.I. Microvesicles produced by natural killer cells of the NK-92 cell line affect the phenotype and functions of endothelial cells of the EA.Hy926 cell line. Medical Immunology (Russia), 2020, Vol. 22, no. 2, pp. 249-268. doi: 10.15789/1563-0625-MPB-1877.
26. Miguet L., Pacaud K., Felden C., Hugel B., Martinez M.C., Freyssinet J.M., Herbrecht R., Potier N., van Dorsselaer A., Mauvieux L. Proteomic analysis of malignant lymphocyte membrane microparticles using double ionization coverage optimization. Proteomics, 2006, Vol. 6, no. 1, pp. 153-171.
27. Olingy C.E., Dinh H.Q., Hedrick C.C. Monocyte heterogeneity and functions in cancer. J. Leukoc. Biol.,
2019, Vol. 106, no. 2, pp. 309-322.
28. Ornitz D.M., Itoh N. The fibroblast growth factor signaling pathway. Wiley Interdiscip. Rev. Dev. Biol., 2015, Vol. 4, no. 3, pp. 215-266.
29. Palevski D., Levin-Kotler L.P., Kain D., Naftali-Shani N., Landa N., Ben-Mordechai T., Konfino T., Holbova R., Molotski N., Rosin-Arbesfeld R., Lang R.A., Leor J. Loss of macrophage Wnt secretion improves remodeling and function after myocardial infarction in mice. J. Am. Heart Assoc., 2017, Vol. 6, no. 1, e004387. doi: 10.1161/JAHA.116.004387.
30. Pelekanou V., Kampa M., Kiagiadaki F., Deli A., Theodoropoulos P., Agrogiannis G., Patsouris E., Tsapis A., Castanas E., Notas G. Estrogen anti-inflammatory activity on human monocytes is mediated through cross-talk between estrogen receptor ERa36 and GPR30/GPER1. J. Leukoc. Biol., 2016, Vol. 99, no. 2, pp. 333-347.
31. Poczobutt J.M., De S., Yadav V.K., Nguyen T.T., Li H., Sippel T.R., Weiser-Evans M.C., Nemenoff R.A. Expression profiling of macrophages reveals multiple populations with distinct biological roles in an immunocompetent orthotopic model of lung cancer. J. Immunol., 2016, Vol. 196, no. 6, pp. 2847-2859.
32. Raposo G., Stoorvogel W. Extracellular vesicles: exosomes, microvesicles, and friends. J. Cell Biol., 2013, Vol. 200, no. 4, pp. 373-383.
33. Reales-Calderón J.A., Vaz C., Monteoliva L., Molero G., Gil C. Candida albicans modifies the protein composition and size distribution of THP-1 macrophage-derived extracellular vesicles. J. Proteome Res., 2017, Vol. 16, no. 1, pp. 87-105.
34. Sáenz-Cuesta M., Irizar H., Castillo-Triviño T., Muñoz-Culla M., Osorio-Querejeta I., Prada A., Sepúlveda L., López-Mato M.P., López de Munain A., Comabella M., Villar L.M., Olascoaga J., Otaegui D. Circulating microparticles reflect treatment effects and clinical status in multiple sclerosis. Biomark. Med., 2014, Vol. 8, no. 5, pp. 653-661.
35. Shi C., Pamer E.G. Monocyte recruitment during infection and inflammation. Nat. Rev. Immunol., 2011, Vol. 11, no. 11, pp. 762-774.
36. Thery C., Zitvogel L., Amigorena S. Exosomes: composition, biogenesis and function. Nat. Rev. Immunol., 2002, Vol. 2, no. 8, pp. 569-579.
37. Tokés-Füzesi M., Ruzsics I., Rideg O., Kustán P., Kovács G.L., Molnár T. Role of microparticles derived from monocytes, endothelial cells and platelets in the exacerbation of COPD. Int. J. Chron. Obstruct. Pulmon. Dis., 2018, Vol. 13, pp. 3749-3757.
38. van der Pol E., Coumans F.A., Grootemaat A.E., Gardiner C., Sargent I.L., Harrison P., Sturk A., van Leeuwen T.G., Nieuwland R. Particle size distribution of exosomes and microvesicles determined by transmission electron microscopy, flow cytometry, nanoparticle tracking analysis, and resistive pulse sensing. J. Thromb. Haemost., 2014, Vol. 12, no. 7, pp. 1182-1192.
39. Walters S.B., Kieckbusch J., Nagalingam G., Swain A., Latham S.L., Grau G.E., Britton W.J., Combes V., Saunders B.M. Microparticles from mycobacteria-infected macrophages promote inflammation and cellular migration. J. Immunol. 2013, Vol. 190, no. 2, pp. 669-677.
40. Yao Z., Jia X., Megger D.A., Chen J., Liu Y., Li J., Sitek B., Yuan Z. Label-free proteomic analysis of exosomes secreted from THP-1-derived macrophages treated with IFN-a identifies antiviral proteins enriched in exosomes. J. Proteome Res., 2019, Vol. 18, no. 3, pp. 855-864.
Авторы:
Кореневский А.В. — д.б.н., ведущий научный сотрудник, руководитель группы протеомной иммунорегуляции отдела иммунологии и межклеточных взаимодействий ФГБНУ«Научно-исследовательский институт акушерства, гинекологии и репродуктологии имени Д.О. Отта», Санкт-Петербург, Россия Милютина Ю.П. — к.б.н., старший научный сотрудник группы протеомной иммунорегуляции отдела иммунологии и межклеточных взаимодействий ФГБНУ «Научно-исследовательский институт акушерства, гинекологии и репродуктологии имени Д.О. Отта», Санкт-Петербург, Россия
Березкина М.Э. — лаборант-исследователь лаборатории межклеточных взаимодействий отдела иммунологии и межклеточных взаимодействий ФГБНУ «Научно-исследовательский институт акушерства, гинекологии и репродуктологии имени Д.О. Отта», Санкт-Петербург, Россия
Александрова Е.П. — студент лаборатории межклеточных взаимодействий отдела иммунологии и межклеточных взаимодействий ФГБНУ «Научно-исследовательский институт акушерства, гинекологии и репродуктологии имени Д.О. Отта», Санкт-Петербург, Россия
Балабас О.А. — ведущий специалист ресурсного центра «Методы анализа состава вещества» ФГБОУВО «Санкт-Петербургский государственный университет», Санкт-Петербург, Россия
Маркова К.Л. — младший научный сотрудник лаборатории межклеточных взаимодействий отдела иммунологии и межклеточных взаимодействий ФГБНУ «Научно-исследовательский институт акушерства, гинекологии и репродуктологии имени Д.О. Отта», Санкт-Петербург, Россия
Сельков С.А. — д.м.н., профессор, заслуженный деятель науки РФ, руководитель отдела иммунологии и межклеточных взаимодействий ФГБНУ «Научно-исследовательский институт акушерства, гинекологии и репродуктологии имени Д.О. Отта», Санкт-Петербург, Россия
Соколов Д.И. — д.б.н., доцент, заведующий лабораторией межклеточных взаимодействий Отдела иммунологии и межклеточных взаимодействий ФГБНУ «Научно-исследовательский институт акушерства, гинекологии и репродуктологии имени Д.О. Отта», Санкт-Петербург, Россия
Поступила 14.10.2020 Принята к печати 09.01.2021
Authors:
Korenevsky A.V., PhD, MD (Biology), Leading Research Associate, Head, Proteomic Immunoregulation Group, Department of Immunology and Cell Interactions, D. Ott Institute of Obstetrics, Gynecology and Reproductology, St. Petersburg, Russian Federation
Milyutina Yu.P., PhD (Biology), Senior Research Associate, Proteomic Immunoregulation Group, Department of Immunology and Cell Interactions, D. Ott Institute of Obstetrics, Gynecology and Reproductology, St. Petersburg, Russian Federation
Berezkina M.E., Research Assistant, Laboratory of Cell Interactions, Department of Immunology and Cell Interactions, D. Ott Institute of Obstetrics, Gynecology and Reproductology, St. Petersburg, Russian Federation
Alexandrova E.P., Student, Laboratory of Cell Interactions, Department of Immunology and Cell Interactions, D. Ott Institute of Obstetrics, Gynecology and Reproductology, St. Petersburg, Russian Federation
Balabas O.A., Leading Specialist, Chemical Analysis and Materials Research Centre, St. Petersburg State University, St. Petersburg State University, St. Petersburg, Russian Federation
Markova K.L., Junior Research Associate, Laboratory of Cell Interactions, Department of Immunology and Cell Interactions, D. Ott Institute of Obstetrics, Gynecology and Reproductology, St. Petersburg, Russian Federation
Selkov S.A., PhD, MD (Medicine), Professor, Honored Scientist of the Russian Federation, Head, Department of Immunology and Cell Interactions, D. Ott Institute of Obstetrics, Gynecology and Reproductology, St. Petersburg, Russian Federation
Sokolov D.I., PhD, MD (Biology), Head, Laboratory of Cell Interactions, Department of Immunology and Cell Interactions, D. Ott Institute of Obstetrics, Gynecology and Reproductology, St. Petersburg, Russian Federation
Received 14.10.2020 Accepted 09.01.2021
