Bulletin of Russian State Medical University

ЗАМЕСТИТЕЛЬ ГЛАВНОГО РЕДАКТОРА Александр Эттингер, д. м. н., профессор РЕДАКТОРЫ Валентина Гейдебрехт, к. б. н.; Надежда Тихомирова ТЕХНИЧЕСКИЙ РЕДАКТОР Евгений Лукьянов ПЕРЕВОДЧИКИ Надежда Тихомирова, Вячеслав Витюк ДИЗАЙН И ВЕРСТКА Марины Дорониной DOI выпуска: 10.24075/vrgmu.2025-05 Свидетельство о регистрации средства массовой информации № 012769 от 29 июля 1994 г. ISSN (Print): 2500-1094, ISSN (Online): 2542-1204. Учредитель и издатель — Российский национальный исследовательский медицинский университет имени Н. И. Пирогова (Москва, Россия). Журнал индексируется в научных базах Scopus, Web of Science, Google Scholar, SJR, DOAJ, Scilit, CyberLeninka, Embase, EZB, Lens.org, MITLibaries, OpenAlex, Research4Life, Scholia, Wikidata, ZDB. Журнал распространяется по лицензии Creative Commons Attribution 4.0 International (www.creativecommons.org). Подписано в печать 31.10.2025 Тираж 100 экз. Отпечатано в типографии Print.Formula www.print-formula.ru SJR SCImago Journal & Country Rank 2024: 0,166 Журнал включен в DOAJ Индекс Хирша (h5) журнала по оценке Google Scholar: 11 Журнал включен в Scopus. CiteScore 2024: 0,7 Журнал включен в WoS. JIF 2024: 0,4 Здесь находится открытый архив журнала РЕДАКЦИОННАЯ КОЛЛЕГИЯ В. В. Негребецкий, д. х. н., профессор (Москва, Россия) А. А. Новиков, д. б. н. (Москва, Россия) Ю. П. Пивоваров, д. м. н., академик РАН, профессор (Москва, Россия) Н. В. Полунина, член-корр. РАН, д. м. н., профессор (Москва, Россия) Г. В. Порядин, член-корр. РАН, д. м. н., профессор (Москва, Россия) А. Ю. Разумовский, член-корр. РАН, д. м. н., профессор (Москва, Россия) О. Ю. Реброва, д. м. н. (Москва, Россия) А. С. Рудой, д. м. н., профессор (Минск, Белоруссия) А. К. Рылова, д. м. н., профессор (Москва, Россия) В. Ф. Семиглазов, член-корр. РАН, д. м. н., профессор (Санкт-Петербург, Россия) Н. А. Скоблина, д. м. н., профессор (Москва, Россия) Т. А. Славянская, д. м. н., профессор (Москва, Россия) В. М. Смирнов, д. б. н., профессор (Москва, Россия) А. Спаллоне, д. м. н., профессор (Рим, Италия) В. И. Стародубов, академик РАН, д. м. н., профессор (Москва, Россия) В. А. Степанов, член-корр. РАН, д. б. н., профессор (Томск, Россия) С. В. Сучков, д. м. н., профессор (Москва, Россия) Х. П. Тахчиди, академик РАН, д. м. н., профессор (Москва, Россия) Г. Е. Труфанов, д. м. н., профессор (Санкт-Петербург, Россия) У. Н. Туманова, д. м. н. (Москва, Россия) О. О. Фаворова, д. б. н., профессор (Москва, Россия) М. Л. Филипенко, к. б. н. (Новосибирск, Россия) Р. Н. Хазипов, д. м. н. (Марсель, Франция) М. А. Чундокова, д. м. н., профессор (Москва, Россия) Н. Л. Шимановский, член-корр. РАН, д. м. н., профессор (Москва, Россия) Л. Н. Шишкина, д. б. н. (Новосибирск, Россия) А. И. Щеголев, д. м. н., профессор (Москва, Россия) Р. И. Якубовская, д. б. н., профессор (Москва, Россия) Scimago Journal & Country Rank SJR ПОДАЧА РУКОПИСЕЙ 3 BULLETIN OF RSMU 5, 2025 V ESTNIK.RSMU.PRESS | | Bulletin of RSMU Вестник РГМУ Contents Содержание 5, 2025 Revival of radioimmunoassay for determination of insulin autoantibodies Timofeev AV, Galimov RR, Kolesnikova EA, Artyuhov AS, Skoblov YuS, Taktarov SV Реанимация радиоиммунологического метода определения аутоантител к инсулину А. В. Тимофеев, Р. Р. Галимов, Е. А. Колесникова, А. С. Артюхов, Ю. С. Скоблов, С. В. Тактаров Morphological, immunohistochemistry and molecular analysis of differentiated high-grade carcinoma Makhachev DR, Bulanov DV, Shovkhalov MM, Bekmurziev BZ, Geroev IA, Netsvetova AM, Zhusupova AR, Gubich DS, Manovski AM Морфологический, иммуногистохимический и молекулярный анализ дифференцированной высокозлокачественной карциномы Д. Р. Махачев, Д. В. Буланов, М. М. Шовхалов, Б. З. Бекмурзиев, И. А. Героев, А. М. Нецветова, А. Р. Жусупова, Д. С. Губич, А. М. Мановски Alterations in mitochondrial and lysosomal compartments under chemotherapy-induced senescence Shatalova RO, Shevyrev DV Изменения митохондриального и лизосомного компартментов в условиях химиоиндуцированной сенесцентности Р. О. Шаталова, Д. В. Шевырев Age-related alterations in the immune system of aging mice Matveeva KS, Shevyrev DV Возрастные изменения в иммунной системе стареющих мышей К. С. Матвеева, Д. В. Шевырев ORIGINAL RESEARCH 5 ORIGINAL RESEARCH 12 ORIGINAL RESEARCH 38 CLINICAL CASE 32 ORIGINAL RESEARCH 22 Panel of IFN-I-induced genes in systemic scleroderma: a stratification biomarker potential Shagina IA, Turchaninova MA, Golovina OA, Bufeeva LS, Zhurina TI, Saifullin RF, Myshkin MYu, Mutovina ZYu, Britanova OV Панель IFN-I-индуцируемых генов при системной склеродермии: потенциал биомаркера стратификации И. А. Шагина, М. А. Турчанинова, О. А. Головина, Л. С. Буфеева, Т. И. Журина, Р. Ф. Сайфуллин, М. Ю. Мышкин, З. Ю. Мутовина, О. В. Британова OPINION 44 ORIGINAL RESEARCH 48 ORIGINAL RESEARCH 55 ORIGINAL RESEARCH 65 ORIGINAL RESEARCH 74 Prospects of finding pathologically based therapies for epilepsy associated with brain glioma Ashkhatsava TI, Kalinin VA, Yakunina AV, Poverennova IE Перспективы поиска патогенетически обоснованной терапии эпилепсии, ассоциированной с глиомой головного мозга Т. И. Ашхацава, В. А. Калинин, А. В. Якунина, И. Е. Повереннова Transcriptomic features of FAP+ cells across molecular subtypes of breast cancer Kalinchuk AYu, Patskan IA, Stadelman MM, Grigorieva ES, Tashireva LA Особенности транскриптомного профиля FAP+-клеток в опухолях молочной железы различных молекулярно-биологических подтипов А. Ю. Калинчук, И. А. Пацкан, М. М. Штадельман, Е. С. Григорьева, Л. А. Таширева Optimization of human B cell culture conditions for expansion of activated or differentiated B cells Sokolova SR, Grigorova IL Оптимизация условий культивирования В-клеток человека для экспансии активированных или дифференцированных В-клеток С. Р. Соколова, И. Л. Григорова Comparative analysis of metallic endovascular coil frame designs Chepeleva EV, Kozyr KV, Borodin VP, Khakhalkin VV, Vladimirov SV, Makhmudov MA, Badoian AG, Baranov AA, Krestyaninov OV Сравнительный анализ конструкций металлических каркасов эндоваскулярных спиралей Е. В. Чепелева, К. В. Козырь, В. П. Бородин, В. В. Хахалкин, С. В. Владимиров, М. А. Махмудов, А. Г. Бадоян, А. А. Баранов, О. В. Крестьянинов Cytocompatibility of pressureless sintered porous B4C-ceramics assessed in vitro Chepeleva EV, Kozyr KV, Vaver AA, Khakhalkin VV Цитосовместимость свободноспеченной пористой B4C-керамики при исследовании in vitro Е. В. Чепелева, К. В. Козырь, А. А. Вавер, В. В. Хахалкин 4 ПОДАЧРУ КИСЕ 5, 2025 V ESTNIK.RSMU.PRESS | | ORIGINAL RESEARCH 86 ORIGINAL RESEARCH 94 OPINION 102 Neurophysiological markers of the illusion caused by the mirror visual feedback Mokienko OA, Bobrov PD, Soloveva AA, Isaev MR, Kerechanin YaV, Ratnikova VYu, Kataitsev VA, Shagina ED, Nikishina VB Нейрофизиологические маркеры иллюзии, вызванной зеркальной визуальной обратной связью О. А. Мокиенко, П. Д. Бобров, А. А. Соловьева, М. Р. Исаев, Я. В. Керечанин, В. Ю. Ратникова, В. А. Катайцев, Е. Д. Шагина, В. Б. Никишина Molecular cytogenetic characterization of a rare recombinant chromosome 22 caused by a maternal intrachromosomal insertion Yurchenko DA, Markova ZhG, Petukhova MS, Matyushchenko GN, Shilova NV Молекулярно-цитогенетическая характеристика редкого случая рекомбинантной хромосомы 22 вследствие материнской интрахромосомной инсерции Д. А. Юрченко, Ж. Г. Маркова, М. С. Петухова, Г. Н. Матющенко, Н. В. Шилова The terms "dominant" and "recessive" should be avoided due to gene therapy Gamisonia AM, Rebrikov DV В медицине следует избегать терминов «доминантный» и «рецессивный» из-за развития генной терапии А. М. Гамисония, Д. В. Ребриков ORIGINAL RESEARCH 81 Susceptibility of the nontuberculous mycobacteria circulating in Russia to bedaquiline Smirnova TG, Andreevskaya SN, Larionova EE, Zaytseva AS, Kiseleva EA, Ustinova VV, Chernousova LN, Ergeshov AЕ Чувствительность к бедаквилину нетуберкулезных микобактерий, циркулирующих на территории России Т. Г. Смирнова, С. Н. Андреевская, Е. Е. Ларионова, А. С. Зайцева, Е. А. Киселева, В. В. Устинова, Л. Н. Черноусова, А. Э. Эргешов 5 ORIGINAL RESEARCH IMMUNOLOGY BULLETIN OF RSMU 5, 2025 VESTNIK.RSMU.PRESS DOI: 10.24075/BRSMU.2025.043 | | | Matveeva KS, Shevyrev DV AGE-RELATED ALTERATIONS IN THE IMMUNE SYSTEM OF AGING MICE Accumulation of senescent cells in the tissues is associated with functional impairment and the development of age-related disorders. The key role in this process is played by the senescence-associated secretory phenotype (SASP) contributing to chronic systemic inflammation, which is associated with the increased risk of autoimmune disorders and cancer, as well as the decreased resistance to infections. Normally, the immune system eliminates senescent cells, but the effectiveness of this process decreases with age, including due to the immune system aging. The study aimed to assess age-related alterations in the main lymphocyte and myelocyte populations in the spleen and bone marrow samples of senile mice. The study involved groups of young (n = 8) and elderly (n = 4) С57BL/6 mice. Populations were tested by flow cytometry using the fluorescence-labeled antibodies. The aging phenotype was assessed based on the β-Gal enzyme activity with pre-treatment with bafilomycin А1, ensuring lysosomal alkalinization and allowing one to detect the increased enzyme activity typical for the aging cells (SA-β-Gal). As a result, the significantly increased levels of myeloid populations, CD11c+ B cells, double-negative T cells, along with the decreased levels of the CD8α+ dendritic cells, were reported in elderly mice. Furthermore, aging was associated with the significant increase in the levels of SA-β-Gal-positive cells, especially in the populations of myeloid cells. The data obtained suggest that the age-related alterations are of systemic nature and reflect the so-called myeloid shift, as well as accumulation of pro-inflammatory populations in the myeloid and lymphoid compartments. Keywords: aging, senescence, immune system aging, β-galactosidase, SA-β-Gal, lymphocytes, myelocytes, mice Correspondence should be addressed: Daniil V. Shevyrev Olimpiysky prospekt, 1, Sochi, 354349, Russia; dr.daniil25@mail.ru Sirius University of Science and Technology, Sirius Federal Territory, Krasnodarsky Krai, Russia Received: 27.08.2025 Accepted: 23.09.2025 Published online: 30.09.2025 DOI: 10.24075/brsmu.2025.043 Author contribution: Matveeva KS — experimental procedure, data processing, manuscript formatting, Shevyrev DV — experimental procedure, statistical analysis, manuscript reviewing. Funding: the study was supported by the Russian Science Foundation работа, project No. 24-15-20003 https://rscf.ru/project/24-15-20003/ (date of access: August 19, 2025). Compliance with ethical standards: the study was approved by the Ethics Committee of the Sirius University of Science and Technology (protocol No. 7.1 dated 12 April 2024). Copyright: © 2025 by the authors. Licensee: Pirogov University. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Ключевые слова: старение, сенесцентность, старение иммунной системы, β-галактозидаза, SA-β-Gal, лимфоциты, миелоциты, мыши К. С. Матвеева, Д. В. Шевырев ВОЗРАСТНЫЕ ИЗМЕНЕНИЯ В ИММУННОЙ СИСТЕМЕ СТАРЕЮЩИХ МЫШЕЙ Накопление сенесцентных клеток в тканях связано с функциональным ухудшением и развитием возраст-ассоциированных патологий. Ключевую роль в этом процессе играет сенесцент-ассоциированный секреторный фенотип (SASP), способствующий хроническому вялотекущему системному воспалению, которое ассоциировано с повышенным риском аутоиммунных и онкологических заболеваний, а также снижением устойчивости к инфекциям. В норме иммунная система удаляет сенесцентные клетки, однако с возрастом эффективность этого процесса падает, в том числе по причине старения иммунной системы. Целью исследования было изучить возрастные изменения в основных популяциях лимфоцитов и миелоцитов в образцах селезенки и костного мозга мышей преклонного возраста. Исследование проводили на группах молодых (n = 8) и пожилых (n = 4) мышей линии С57BL/6. Анализ популяций проводили с использованием флуоресцентно-меченых антител методом проточной цитометрии. Фенотип старения оценивали по активности фермента β-Gal с предварительной обработкой бафиломицином А1, который обеспечивает защелачивание лизосом и позволяет выявить повышенную активность фермента, типичную для стареющих клеток (SA-β-Gal). В результате у пожилых мышей было выявлено значимое повышение содержания миелоидных популяций, CD11c+В-клеток, дважды негативных T-лимфоцитов, а также снижение CD8α+ дендритных клеток. Кроме того, при старении значимо возрастало содержание клеток позитивных по SA-β-Gal, особенно в популяциях миелоидных клеток. Полученные данные указывают, что возрастные изменения носят системный характер и отражают так называемый миелоидный сдвиг, а также накопление провоспалительных популяций в миелоидном и лимфоидном компартментах. Для корреспонденции: Даниил Вадимович Шевырев Олимпийский проспект, д. 1, г. Сочи, 354349, Россия; dr.daniil25@mail.ru Научно-технологический университет «Сириус», Федеральная территория «Сириус», Краснодарский край, Россия Статья получена: 27.08.2025 Статья принята к печати: 23.09.2025 Опубликована онлайн: 30.09.2025 DOI: 10.24075/vrgmu.2025.043 Вклад авторов: К. С. Матвеева — проведение экспериментов, обработка данных, оформление рукописи, Д. В. Шевырев — проведение экспериментов, статистический анализ, рецензирование рукописи. Финансирование: данная работа выполнена при поддержке Российского Научного Фонда, проект № 24-15-20003 https://rscf.ru/project/24-15-20003/ (дата доступа 19 августа 2025 г.). Соблюдение этических стандартов: исследование одобрено этическим комитетом Университета «Сириус» (протокол № 7.1 от 12 апреля 2024 г.). Авторские права: © 2025 принадлежат авторам. Лицензиат: РНИМУ им. Н. И. Пирогова. Статья размещена в открытом доступе и распространяется на условиях лицензии Creative Commons Attribution (CC BY) (https://creativecommons.org/licenses/by/4.0/). 6 ПОДАДЧРУКЧПИ ДССУИЕПЙРЧДИ ДЦЦТЧПУПАДН ВЕСТНИК РГМУ 5, 2025 VESTNIK.RSMU.PRESS DOI: 10.24075/VRGMU.2025.043 | | | Cellular senescence is a complex, multifactorial process triggered by diverse stressors, including DNA damage, telomere attrition, retrotransposon activation, oxidative and mechanical stress, as well as adverse physical, chemical, and biological factors [1, 2]. The accumulation of mutations and various types of molecular damage in aging cells increases the risk of neoplastic transformation. Currently, entry into a senescent state is recognized as one of the key tumor-suppressive mechanisms [3, 4]. Senescence is orchestrated through the convergence of several signaling cascades, primarily the p53/p21CIP1 and p16INK4a/RB pathways [5]. These pathways are activated in response to telomere shortening and DNA damage (DNA damage response, DDR), oncogene activation, epigenetic alterations, chromatin architecture disruption, excessive reactive oxygen species (ROS) production due to organelle dysfunction — particularly mitochondrial dysfunction — as well as specific inflammatory and paracrine signals [6, 7]. Stress-induced activation of the NF-κB and mTOR pathways drives senescent cells to secrete a broad spectrum of pro- inflammatory mediators (the senescence-associated secretory phenotype, SASP) and impairs autophagy [8, 9]. Concurrently, upregulation of anti-apoptotic BCL-2 family proteins — BCL-2, BCL-XL, and MCL-1 — inhibits apoptosis [10]. Collectively, these alterations define the hallmark features of senescent cells: irreversible cell cycle arrest, apoptosis resistance, a pro-inflammatory SASP, mitochondrial dysfunction, and proteostasis impairment [11]. A morphofunctional manifestation of the metabolic imbalance and lysosomal dysfunction characteristic of senescent cells is the hypertrophy of the lysosomal compartment and elevated activity of the lysosomal enzyme β-galactosidase (β-Gal) [12]. Consequently, the high concentration of this enzyme within enlarged lysosomes results in detectable enzymatic activity at a suboptimal pH of 6.0, enabling its use as a biomarker for senescent cells – termed senescence-associated β-galactosidase (SA-β-Gal) [13, 14]. Traditionally, chromogenic substrates have been employed to assess β-Gal activity; however, these are incompatible with multiparametric phenotypic analysis of senescent cells by fluorescence-based techniques, including flow cytometry. The recent development of fluorogenic β-Gal substrates has substantially expanded the utility of this marker, allowing simultaneous quantification of SA-β-Gal activity across distinct immune cell populations via flow cytometry [15]. To date, limited data exist regarding age-related changes in SA-β-Gal activity across different immune cell subsets. Evaluating SA-β-Gal activity in lymphoid and myeloid populations from both central (bone marrow) and peripheral (spleen) compartments of the immune system is therefore of considerable interest for understanding immunosenescence. Given that the immune system is continuously exposed throughout life to stressors of varying nature and intensity, distinct lymphoid and myeloid subsets are expected to exhibit heterogeneous trajectories and rates of aging. An increased senescent burden within the immune system exacerbates "inflammaging," heightens the risk of autoimmune and neoplastic disorders, and enhances susceptibility to infections [16]. Consequently, a detailed characterization of age-associated alterations in the immune system provides a critical foundation for developing targeted strategies to restore immune competence in the elderly. Thus, the aim of this study was to perform a comparative analysis of SA-β-Gal activity in major immune cell populations isolated from the spleen and bone marrow of young (3-month-old) and very old (26-month-old) C57BL/6 mice. This approach enabled us to map the distribution of senescent-like cells within the aged immune system and to directly compare central and peripheral immune compartments with respect to their senescent cell content. METHODS Mice The study involved 12 C57BL/6 mice: 8 mice aged 3 months and 4 elderly mice aged 26 months. The animals were kept in the vivarium with the 12-h ligh/dark cycle, unlimited access to water and balanced laboratory feed. Euthanasia compliant with the principles of animal welfare was performed under deep isoflurane anesthesia by cervical dislocation. Appropriate biomaterial was collected immediately after euthanasia. Splenocyte isolation After euthanasia the spleen was retrieved, put it in the glass homogenizer with cold PBS (1% FCS, 0.02% EDTA), and gently grinded with the glass pestle to obtain the homogenous suspension. The resulting cell suspension was twice filtered though the nylon filter (70 μm) with PBS washing. The filtered material was centrifuged for 5 min at 300 g and 8 °C. Precipitate was resuspended in 5 mL of buffer for 2 min to lyse red blood cells. Then it was supplemented with 10 mL of PBS with 1% FBS and centrifuged again. After elimination of supernatant, the cells were resuspended to the desired concentration in the RPMI-1640 complete medium or PBS, depending on the goal. Bone marrow cell isolation Bone marrow cells were isolated from the mouse femur and tibia by washing the bone marrow out of the bone cavity with the PBS solution using a syringe (27 G). The resulting suspension was twice filtered though the 70 μm nylon filter, washed in PBS (0.02% EDTA) by centrifugation for 5 min at 300 g and 8 °C. Then red blood cells were lysed (see above) and resuspended in the complete medium or PBS. The splenocyte and bone marrow cell viability was assessed by the fluorescent method using acridine orange and propidium iodide; the average viability was 98%. SA-β-Gal staining To estimate SA-β-Gal activity in living cells, the SPiDER-βGal vital dye was used (Cellular Senescence Detection Kit, Dojindo Laboratories, Japan), which represents a fluorogenic substrate specific for β-Gal. The bone marrow cells or splenocytes, 2 × 105 cells per well, were incubated in the flat-bottom 96-well plate (NEST Biotechnologies, China), in 200 μL of the RPMI-1640 complete medium supplemented with bafilomycin А1 (Sigma Aldrich, USA) to the final concentration of 100 nM as a lizosome alkalinizing agent, for 1 h in the CO2 incubator at 37°. Then the cells were added the substrate to the final concentration of 1 μmol/L and incubated under the same conditions for 1 h. Then the cells were washed by centrifugation for 5 min at 300 g, 20 °C, stained with the FVS780 dye (BD Biosciences, USA) in accordance with the manufacturer’s protocol to eliminate the dead cells from the further analysis, and antibody-labeled. As a positive control, the cells were simultaneously incubated with the substrate not supplemented with bafilomycin. As a negative control, the cells were incubated with added bafilomycin without the substrate. 7 ORIGINAL RESEARCH IMMUNOLOGY BULLETIN OF RSMU 5, 2025 VESTNIK.RSMU.PRESS DOI: 10.24075/BRSMU.2025.043 | | | Fig. 1. Gating strategies for lymphoid and myeloid populations in spleen and bone marrow samples from mice (A) and quantification of the proportion of SA-β-Gal- positive cells within each analyzed population (B). The histogram on the right shows SPiDER fluorescence intensity in the 488-530/30 nm detection channel for the FMO control (without fluorogenic substrate) and the positive control (Control+, without bafilomycin A1) А B Phenotyping For antibody staining a total of 2 × 105 cells were resuspended in 200 μL of the FACS buffer, added 50 μL of the antibody mixture, mixed thoroughly by pipetting, and incubated at 4 °C for 30 min in the dark. The anti-mouse antibodies used were as follows: TCRβ BB700 (#745846, BD Biosciences, USA), CD19 BV605 (#563148, BD Biosciences, USA), CD11c APC (#550261, BD Biosciences, USA), CD11b BV510 (#562950, BD Biosciences, USA), Ly6G PE (#12-9668-82, ThermoFisher, USA), Ly6C PE-Cy7 (#560593, BD Biosciences, USA), CD4 SB702 (#67-0041-82, ThermoFisher, USA), CD8 SB780 (#78-0081-82, ThermoFisher, USA). Then the cells were twice washed for 5 min at 300 g with the FACS buffer and resuspended in 300 μL. Then these were analyzed using the BD LSRFortessa flow cytometer (BD Biosciences, USA). Statistical analysis Flow cytometry data were analyzed using FlowJo software (version 10.8.1; BD Biosciences, USA). Statistical analyses were performed with GraphPad Prism (version 9.3.1; GraphPad Software, USA). The normality of data distributions was assessed using the Shapiro–Wilk test. Comparisons between young and aged mouse groups were carried out using the non-parametric Mann–Whitney U test. Data are presented as medians with interquartile ranges. RESULTS Multiparameter flow cytometry enabled the assessment of SA-β-Gal activity across ten distinct immune cell populations. These populations were broadly categorized by lineage into lymphoid (T and B cells) and myeloid subsets (conventional dendritic cells, monocytes, macrophages, and granulocytes). The gating strategy is illustrated in Fig. 1A. Within each defined population, the proportion of cells exhibiting elevated SA-β-Gal activity was quantified. The gating threshold was established using fluorescence-minus-one (FMO) controls, in which cells were treated with bafilomycin A1 but without the fluorogenic substrate SPiDER-βGal (Fig. 1B). This control is particularly critical, as bafilomycin A1 itself can alter cellular autofluorescence levels, thereby influencing background signal in the absence of the substrate. The analysis of the data obtained for the mouse spleen revealed a significant increase in the counts of monocytes (8.2% (5.6–12.4) vs. 23.7% (17.3–29.2), p < 0.05), dendritic cells (12.9% (12–14.2) vs. 27.9% (18.2–29.5), p < 0.05), and B cells (53.5% (49.8–57.5) vs. 66.4% (64.9–69.5), p < 0.05) in the group of elderly mice (Fig. 2A). It is interesting to note that the counts of CD11c+ В cells increased significantly with age (0.39% (0.32–0.47) vs. 2.18% (1.33–2.58), p < 0.01). This is a fairly recently described population of B cells associated with aging. Furthermore, despite the increase in the general dendritic cell population, the counts of CD8α+DC significantly decreased (31.9% (29–33) vs. 23.8% (19.3–31.2), p < 0.01). The bone marrow samples also showed the increase in the counts of monocytes (18.2% (14–20) vs. 23.7% (22–28.2), p < 0.05) and decrease in B cell counts (32.9% (29.9–36.3) vs. 26.2% (22.8–27.5), p < 0.05), while the counts of CD11c+ В cells increased (0.03% (0.025–0.048) vs. 0.26% (0.18–0.4), p < 0.01) and that of CD8α+DC decreased (21.6% (19.1–24) vs. 6.5% (4.9–7.1), p < 0.01), like in the splenic samples (Fig. 2B). In the next phase, we assessed the distribution of cells showing the increased SA-β-Gal activity across lymphoid and myeloid populations. Samples of the spleen showed a considerable increase in the counts of SA-β-Gal-positive 8 ПОДАДЧРУКЧПИ ДССУИЕПЙРЧДИ ДЦЦТЧПУПАДН ВЕСТНИК РГМУ 5, 2025 VESTNIK.RSMU.PRESS DOI: 10.24075/VRGMU.2025.043 | | | Fig. 2. Proportions of lymphoid and myeloid populations in spleen (A) and bone marrow (B) samples. Additionally, the frequency of senescent-like cells positive for the SA-β-Gal marker in spleen (C) and bone marrow (D) is shown. Abbreviations: DC — dendritic cells; GrC — granulocytes; Mon — monocytes; Mph — macrophages; DN – double-negative T cells. Group comparisons were performed usin the Mann–Whitney U-test. Data are presented as Me ± interquartile range (IQR); * — p < 0.05, ** — p < 0.01, *** — p < 0.001 А B C D 100 * * * * * * * * * * ** ** ** ** ** *** *** B-cells B-cells B-cells B-cells DC DC DC DC CD8 α+DC CD8 α+DC CD8 α+DC CD8 α+DC GrC GrC GrC GrC Mon Mon Mon Mon Mph Mph Mph Mph CD4 CD4 CD4 CD4 CD8 CD8 CD8 CD8 DN DN DN DN CD11C + B-cells CD11C + B-cells CD11C + B-cells CD11C + B-cells 100 100 Young, n = 6 Young, n = 6 Young, n = 8 Young, n = 8 Spleen Spleen Bone marrow Bone marrow Old, n = 4 Old, n = 4 Old, n = 4 Old, n = 4 100 Cells % of parent BGaL+ cells % of parent BGaL+ cells % of parent Cells % of parent 60 60 60 60 20 20 20 0 20 80 80 80 80 40 40 40 40 3 3 0,3 0,4 2 2 0,2 1 1 0,1 0 0 0,0 granulocytes (7.2% (2.4–15.5) vs. 62.5% (45.8–66.1), p < 0.001), macrophages (23.8% (16.8–29.1) vs. 57.2% (55.1–63.5), p < 0.001), and monocytes (50.5% (43.8–86) vs. 85.1% (77.7–90.2), p < 0.01), as well as CD11c+ В cells (39.2% (35.4–43) vs. 60.5% (57.5–73.3), p < 0.01) in the group of elderly mice (Fig. 2C). The bone marrow showed the age-related increase in the counts of SA-β-Gal-positive macrophages (64.4% (56.8–65.8) vs. 70.9% (66.1–76.5), p < 0.05), CD8α+DC (1.5% (0.96–1.7) vs. 3.25% (2.71–4.26), p < 0.05), double negative lymphocytes (19.5% (15.2–28.3) vs. 41% (30.9–53.3), p < 0.05), and CD11c+ В cells (38.4% (34.2–43.6) vs. 62% (47.8–76.8), p < 0.05) (Fig. 2D). Thus, the study revealed age-related alterations in the major populations of lymphoid and myeloid cells, associated with the larger share of cells showing the increased SA-β-Gal activity. In the bone marrow, as a primary lymphoid organ, these alterations were less prominent. DISCUSSION The findings showed that physiological aging of the immune system is uneven [17] and is accompanied by significant quantitative changes in the populations of B cells, dendritic cells, and monocytes. Furthermore, the cells showing the increased SA-β-Gal activity are accumulated faster in myeloid populations, than in the lymphoid compartment. The increase in B-cell counts in the spleen of elderly mice can reflect the life-long history of antigenic challenges, and accumulation of the age-associated CD11c+ B cells is considered to be associated with aging and the increased risk of autoimmune disorders, as well as the inflammaging phenomenon [18, 19]. These cells with impaired functions, which contribute to acquisition of pro-inflammatory phenotype by macrophages, show the increased counts in various autoimmune disorders and can constitute a large proportion of the mature B-cell population in the elderly body [20]. In contrast, the bone marrow showed the decrease in B-cell counts reflecting the age-related decline in B-cell production, which is likely to negatively affect the immune system capability of responding to new antigenic challenges [21]. Furthermore, despite reduction of the general B-cell population, the CD11c+ B-cell counts were increased, like in the spleen. The age- related increase in the counts of the CD11c+ dendritic cells in the spleen and monocytes in the bone marrow is likely to reflect the so-called “myeloid shift” representing the typical feature of the immune system aging described in detail in the recent reports [22–24]. The detected increase in the share of SA-β- Gal-positive granulocytes, monocytes, and macrophages in the spleen of elderly mice is of special interest. The age-related accumulation of SA-β-Gal-positive cells in myeloid populations is likely to contribute to chronic low-grade inflammation, i.e. inflammaging resulting primarily from production of SASP factors by senescent myeloid cells [25, 26]. The increased counts of SA-β-Gal-positive macrophages, DN T cells, and especially CD11c+ B cells in the bone marrow suggest involvement of the central immune system departments in the aging processes. In this context it should be noted, that the close relationship between the aging macrophages/CD11c+ B cells and hematopoietic stem cells (HSCs) can have a negative effect on the microenvironment in the niches due to SASP production and result in the HSC functional depletion and lymphopoietic potential reduction [27]. This can create a vicious circle, when accumulation of the cells showing signs of senescence in the bone marrow negatively affects hematopoiesis, which, in turn, enhances accumulation of dysfunctional and aging cells [28]. 9 ORIGINAL RESEARCH IMMUNOLOGY BULLETIN OF RSMU 5, 2025 VESTNIK.RSMU.PRESS DOI: 10.24075/BRSMU.2025.043 | | | References 1. Ajoolabady A, Pratico D, Bahijri S, Tuomilehto J, Uversky VN, Ren J. Hallmarks of cellular senescence: biology, mechanisms, regulations. Exp Mol Med. 2025; 57 (7): 1482–91. DOI: 10.1038/s12276-025-01480-7. Epub 2025 Jul 10. PMID: 40634753; PMCID: PMC12322015. 2. Kumari R, Jat P. Mechanisms of Cellular Senescence: Cell Cycle Arrest and Senescence Associated Secretory Phenotype. Front Cell Dev Biol. 2021; 9: 645593. DOI: 10.3389/fcell.2021.645593. PMID: 33855023; PMCID: PMC8039141. 3. Hornsby PJ. Senescence as an anticancer mechanism. J Clin Oncol. 2007; 25 (14): 1852–7. DOI: 10.1200/JCO.2006.10.3101. PMID: 17488983. 4. Baz-Martínez M, Da Silva-Álvarez S, Rodríguez E, Guerra J, El Motiam A, et al. Cell senescence is an antiviral defense mechanism. Sci Rep. 2016; 6: 37007. DOI: 10.1038/srep37007. PMID: 27849057; PMCID: PMC5111111. 5. Prieur A, Besnard E, Babled A, Lemaitre JM. p53 and p16(INK4A) independent induction of senescence by chromatin-dependent alteration of S-phase progression. Nat Commun. 2011; 2: 473. DOI: 10.1038/ncomms1473. PMID: 21915115. 6. Dasgupta N, Arnold R, Equey A, Gandhi A, Adams PD. The role of the dynamic epigenetic landscape in senescence: orchestrating SASP expression. NPJ Aging. 2024; 10 (1): 48. DOI: 10.1038/s41514-024-00172-2. PMID: 39448585; PMCID: PMC11502686. 7. Miwa S, Kashyap S, Chini E, von Zglinicki T. Mitochondrial dysfunction in cell senescence and aging. J Clin Invest. 2022; 132 (13): e158447. DOI: 10.1172/JCI158447. PMID: 35775483; PMCID: PMC9246372. 8. Kwon Y, Kim JW, Jeoung JA, Kim MS, Kang C. Autophagy Is Pro-Senescence When Seen in Close-Up, but Anti- Senescence in Long-Shot. Mol Cells. 2017; 40 (9): 607–12. DOI: 10.14348/molcells.2017.0151. Epub 2017 Sep 20. PMID: 28927262; PMCID: PMC5638768. 9. Tilstra JS, Clauson CL, Niedernhofer LJ, Robbins PD. NF-κB in Aging and Disease. Aging Dis. 2011; 2 (6): 449–65. Epub 2011 Dec 2. PMID: 22396894; PMCID: PMC3295063. 10. Yosef R, Pilpel N, Tokarsky-Amiel R, Biran A, Ovadya Y, Cohen S, et al. Directed elimination of senescent cells by inhibition of BCL-W and BCL-XL. Nat Commun. 2016; 7: 11190. DOI: 10.1038/ncomms11190. PMID: 27048913; PMCID: PMC4823827. 11. Dodig S, Čepelak I, Pavić I. Hallmarks of senescence and aging. Biochem Med (Zagreb). 2019; 29 (3): 030501. DOI: 10.11613/BM.2019.030501. Epub 2019 Aug 5. PMID: 31379458; PMCID: PMC6610675. 12. Debacq-Chainiaux F, Erusalimsky JD, Campisi J, Toussaint O. Protocols to detect senescence-associated beta-galactosidase (SA-betagal) activity, a biomarker of senescent cells in culture and in vivo. Nat Protoc. 2009; 4 (12): 1798–806. DOI: 10.1038/nprot.2009.191. PMID: 20010931. 13. Dimri GP, Lee X, Basile G, Acosta M, Scott G, Roskelley C, et al. A biomarker that identifies senescent human cells in culture and in aging skin in vivo. Proc Natl Acad Sci USA. 1995; 92 (20): 9363–7. DOI: 10.1073/pnas.92.20.9363. PMID: 7568133; PMCID: PMC40985. 14. Yegorov YE, Akimov SS, Hass R, Zelenin AV, Prudovsky IA. Endogenous beta-galactosidase activity in continuously nonproliferating cells. Exp Cell Res. 1998; 243 (1): 207–11. DOI: 10.1006/excr.1998.4169. PMID: 9716464. 15. Doura T, Kamiya M, Obata F, Yamaguchi Y, Hiyama TY, Matsuda T, et al. Detection of LacZ-Positive Cells in Living Tissue with Single-Cell Resolution. Angew Chem Int Ed Engl. 2016 Aug 8;55(33):9620–4. DOI: 10.1002/anie.201603328. Epub 2016 Jul 12. PMID: 27400827. 16. Franceschi C, Garagnani P, Parini P, Giuliani C, Santoro A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat Rev Endocrinol. 2018; 14 (10): 576–90. DOI: 10.1038/s41574-018-0059-4. PMID: 30046148. 17. Liu Z, Liang Q, Ren Y, Guo C, Ge X, Wang L, et al. Immunosenescence: molecular mechanisms and diseases. Signal Transduct Target Ther. 2023; 8 (1): 200. DOI: 10.1038/s41392-023-01451-2. We observed a significant increase in SA-β-Gal activity across multiple lymphoid and myeloid cell populations in aged mice, which – taken together with existing evidence – supports the utility of this marker for investigating immunosenescence. However, certain limitations of our study should be noted. The data obtained are based on the assessment of SA-β-Gal activity as the main cellular senescence marker, but this marker is not absolutely specific and can increase with activation and alteration of metabolism in some types of cells, as well as in the phase of the cell transition to the senescent state [29, 30]. Moreover, we did not assess the functional potential of the studied populations and did not use additional senescence markers, such as p16INK4a, p21CIP1, HMGB1 [5] or SASP components [31], which limits interpretation of the phenomena observed exclusively in the context of the cellular senescence. However, aging is a complex, multifaceted process that is not limited to the cell transition to the senescent state. Thus, the reported quantitative changes in the counts of lymphoid and myeloid subpopulations in the group of elderly mice, along with the changes in SA-β-Gal activity, are likely to reflect the most prominent age-related alterations in the immune system. That is why further comprehensive research is required including transcriptome and proteome assessment and functional tests aimed at investigation of various aspects of the immune system aging. Such an approach will contribute to better understanding of the immune aging mechanisms and the development of strategies aimed at restoring the immune system competence in the elderly. CONCLUSIONS Our findings support the hypothesis of heterogeneous aging across different compartments of the immune system and highlight myeloid skewing as a hallmark feature of immunosenescence. Importantly, age-related alterations were observed not only in the peripheral immune compartment but also in the bone marrow. Specifically, the decline in B-cell frequency reflects age-associated suppression of B-lymphopoiesis, while the marked increase in pro-inflammatory CD11c⁺ B cells and SA-β-Gal-positive double-negative (DN) T lymphocytes, macrophages, and CD8α⁺ dendritic cells indicates active involvement of central immune organs in the aging process [27]. Notably, the accumulation of SA-β-Gal- positive cells occurs in the bone marrow in close proximity to hematopoietic stem cells (HSCs). In this microenvironment, SASP factor secretion may disrupt the functional integrity of HSC niches, impair lymphopoietic potential, and thereby perpetuate a vicious cycle of age-related immune dysfunction [27, 28]. These observations expand current understanding of the dynamic remodeling of the immune system during aging. Consequently, further investigation of age-associated immune alterations — using a multimodal approach that includes SA-β-Gal assessment — holds significant translational potential. Such research could guide the development of targeted strategies for the selective elimination of pro-inflammatory senescent cells, restoration of lymphopoietic capacity, and enhancement of overall immune competence in the elderly. 10 ПОДАДЧРУКЧПИ ДССУИЕПЙРЧДИ ДЦЦТЧПУПАДН ВЕСТНИК РГМУ 5, 2025 VESTNIK.RSMU.PRESS DOI: 10.24075/VRGMU.2025.043 | | | Литература 1. Ajoolabady A, Pratico D, Bahijri S, Tuomilehto J, Uversky VN, Ren J. Hallmarks of cellular senescence: biology, mechanisms, regulations. Exp Mol Med. 2025; 57 (7): 1482–91. DOI: 10.1038/s12276-025-01480-7. Epub 2025 Jul 10. PMID: 40634753; PMCID: PMC12322015. 2. Kumari R, Jat P. Mechanisms of Cellular Senescence: Cell Cycle Arrest and Senescence Associated Secretory Phenotype. Front Cell Dev Biol. 2021; 9: 645593. DOI: 10.3389/fcell.2021.645593. PMID: 33855023; PMCID: PMC8039141. 3. Hornsby PJ. Senescence as an anticancer mechanism. J Clin Oncol. 2007; 25 (14): 1852–7. DOI: 10.1200/JCO.2006.10.3101. PMID: 17488983. 4. Baz-Martínez M, Da Silva-Álvarez S, Rodríguez E, Guerra J, El Motiam A, et al. Cell senescence is an antiviral defense mechanism. Sci Rep. 2016; 6: 37007. DOI: 10.1038/srep37007. PMID: 27849057; PMCID: PMC5111111. 5. Prieur A, Besnard E, Babled A, Lemaitre JM. p53 and p16(INK4A) independent induction of senescence by chromatin-dependent alteration of S-phase progression. Nat Commun. 2011; 2: 473. DOI: 10.1038/ncomms1473. PMID: 21915115. 6. Dasgupta N, Arnold R, Equey A, Gandhi A, Adams PD. The role of the dynamic epigenetic landscape in senescence: orchestrating SASP expression. NPJ Aging. 2024; 10 (1): 48. DOI: 10.1038/s41514-024-00172-2. PMID: 39448585; PMCID: PMC11502686. 7. Miwa S, Kashyap S, Chini E, von Zglinicki T. Mitochondrial dysfunction in cell senescence and aging. J Clin Invest. 2022; 132 (13): e158447. DOI: 10.1172/JCI158447. PMID: 35775483; PMCID: PMC9246372. 8. Kwon Y, Kim JW, Jeoung JA, Kim MS, Kang C. Autophagy Is Pro-Senescence When Seen in Close-Up, but Anti- Senescence in Long-Shot. Mol Cells. 2017; 40 (9): 607–12. DOI: 10.14348/molcells.2017.0151. Epub 2017 Sep 20. PMID: 28927262; PMCID: PMC5638768. 9. Tilstra JS, Clauson CL, Niedernhofer LJ, Robbins PD. NF-κB in Aging and Disease. Aging Dis. 2011; 2 (6): 449–65. Epub 2011 Dec 2. PMID: 22396894; PMCID: PMC3295063. 10. Yosef R, Pilpel N, Tokarsky-Amiel R, Biran A, Ovadya Y, Cohen S, et al. Directed elimination of senescent cells by inhibition of BCL-W and BCL-XL. Nat Commun. 2016; 7: 11190. DOI: 10.1038/ncomms11190. PMID: 27048913; PMCID: PMC4823827. 11. Dodig S, Čepelak I, Pavić I. Hallmarks of senescence and aging. Biochem Med (Zagreb). 2019; 29 (3): 030501. DOI: 10.11613/BM.2019.030501. Epub 2019 Aug 5. PMID: 31379458; PMCID: PMC6610675. 12. Debacq-Chainiaux F, Erusalimsky JD, Campisi J, Toussaint O. Protocols to detect senescence-associated beta-galactosidase (SA-betagal) activity, a biomarker of senescent cells in culture and in vivo. Nat Protoc. 2009; 4 (12): 1798–806. DOI: 10.1038/nprot.2009.191. PMID: 20010931. 13. Dimri GP, Lee X, Basile G, Acosta M, Scott G, Roskelley C, et al. A biomarker that identifies senescent human cells in culture and in aging skin in vivo. Proc Natl Acad Sci USA. 1995; 92 (20): 9363–7. DOI: 10.1073/pnas.92.20.9363. PMID: 7568133; PMCID: PMC40985. 14. Yegorov YE, Akimov SS, Hass R, Zelenin AV, Prudovsky IA. Endogenous beta-galactosidase activity in continuously nonproliferating cells. Exp Cell Res. 1998; 243 (1): 207–11. DOI: 10.1006/excr.1998.4169. PMID: 9716464. 15. Doura T, Kamiya M, Obata F, Yamaguchi Y, Hiyama TY, Matsuda T, et al. Detection of LacZ-Positive Cells in Living Tissue with Single-Cell Resolution. Angew Chem Int Ed Engl. 2016 Aug 8;55(33):9620–4. DOI: 10.1002/anie.201603328. Epub 2016 Jul 12. PMID: 27400827. 16. Franceschi C, Garagnani P, Parini P, Giuliani C, Santoro A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat Rev Endocrinol. 2018; 14 (10): 576–90. DOI: 10.1038/s41574-018-0059-4. PMID: 30046148. 17. Liu Z, Liang Q, Ren Y, Guo C, Ge X, Wang L, et al. Immunosenescence: molecular mechanisms and diseases. Signal Transduct Target Ther. 2023; 8 (1): 200. DOI: 10.1038/s41392-023-01451-2. PMID: 37179335; PMCID: PMC10182360. 18. Frasca D, Diaz A, Romero M, Landin AM, Blomberg BB. Age effects on B cells and humoral immunity in humans. Ageing Res Rev. 2011; 10 (3): 330–5. DOI: 10.1016/j.arr.2010.08.004. Epub 2010 Aug 20. PMID: 20728581; PMCID: PMC3040253. 19. Carey A, Nguyen K, Kandikonda P, Kruglov V, Bradley C, Dahlquist KJV, et al. Age-associated accumulation of B cells promotes macrophage inflammation and inhibits lipolysis in adipose tissue during sepsis. Cell Rep. 2024; 43 (3): 113967. DOI: 10.1016/j.celrep.2024.113967. Epub 2024 Mar 15. 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Cell Mol Life Sci. 2022; 79 (8): 402. DOI: 10.1007/s00018-022-04433-9. PMID: 35798993; PMCID: PMC9263041. 21. Zharhary D. Age-related changes in the capability of the bone marrow to generate B cells. J Immunol. 1988; 141 (6): 1863–9. PMID: 3262642. 22. Pang WW, Price EA, Sahoo D, Beerman I, Maloney WJ, Rossi DJ, et al. Human bone marrow hematopoietic stem cells are increased in frequency and myeloid-biased with age. Proc Natl Acad Sci USA. 2011; 108 (50): 20012–7. DOI: 10.1073/pnas.1116110108. Epub 2011 Nov 28. PMID: 22123971; PMCID: PMC3250139. 23. Yamamoto R, Nakauchi H. In vivo clonal analysis of aging hematopoietic stem cells. Mech Ageing Dev. 2020; 192: 111378. DOI: 10.1016/j.mad.2020.111378. Epub 2020 Oct 3. PMID: 33022333; PMCID: PMC7686268. 24. Ross JB, Myers LM, Noh JJ, Collins MM, Carmody AB, Messer RJ, et al. Depleting myeloid-biased haematopoietic stem cells rejuvenates aged immunity. Nature. 2024; 628 (8006): 162–170. DOI: 10.1038/s41586-024-07238-x. Epub 2024 Mar 27. 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DOI: 10.1146/annurev-pathol-121808-102144. PMID: 20078217; PMCID: PMC4166495. 12 ORIGINAL RESEARCH IMMUNOLOGY BULLETIN OF RSMU 5, 2025 VESTNIK.RSMU.PRESS DOI: 10.24075/BRSMU.2025.047 | | | Shagina IA1,2,5, Turchaninova MA2,5, Golovina OA3, Bufeeva LS2,5, Zhurina TI3, Saifullin RF1,4, Myshkin MYu2,5, Mutovina ZYu3, Britanova OV2,5 PANEL OF IFN-I-INDUCED GENES IN SYSTEMIC SCLERODERMA: A STRATIFICATION BIOMARKER POTENTIAL Systemic scleroderma (SS) remains a disease with a high mortality rate; validated biomarkers for stratification and disease monitoring are still lacking. The study aimed to assess the expression of the developed panel of IFN-I-induced genes (IFI27, IFI44, IFIT3, ISG15, XAF1) in peripheral blood and affected skin of patients with SS. We tested samples of 48 SS patients and 31 healthy donors. Gene expression was analyzed using RT-qPCR (ΔΔCt) method (with normalization to the reference housekeeping gene TBP). The SFRP4 gene expression was used as a marker of skin fibrosis. Expression values of the IFN-I-induced genes were significantly (p < 0.1) increased in both blood and skin of SS patients compared to healthy donors. Comparison between compartments revealed that the expression levels of XAF1, IFI44, IFIT3, ISG15 in the patients’ blood are higher (p < 0.01), than those in skin samples. The IFI27 gene expression, in contrast, is higher in the skin (p < 0.01). The findings show that the test system developed for interferon signature assessment can potentially be used as a noninvasive tool for stratifying SS patients by analysis of RNA from peripheral blood samples, to substantiate the prescription of therapy with the IFN-I receptor blockers. Keywords: systemic scleroderma, SS, Type I interferon, IFN-I-signature, anifrolumab, qRT-PCR, noninvasive diagnosis Correspondence should be addressed: Olga V. Britanova — Miklukho-Maklaya, 16/10, Moscow, 117997, Russia; olbritan@gmail.com; Zinaida Yu. Mutovina — Pekhotnaya, 3, Moscow, 123182, Russia, zmutovina@mail.ru 1 Pirogov Russian National Research Medical University, Moscow, Russia 2 MyLaboratory LLC, Moscow, Russia 3 Moscow City Research Center Hospital No. 52, Moscow, Russia 4 Federal Scientific and Clinical Center of Resuscitation and Rehabilitation, Moscow, Russia 5 Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia Received: 06.09.2025 Accepted: 08.10.2025 Published online: 21.10.2025 DOI: 10.24075/brsmu.2025.047 Compliance with ethical standards: the study was conducted in accordance with the Declaration of Helsinki. The informed consent for biomaterial collection and testing during inpatient assessment settings was obtained from all patients. Author contribution: Mutovina ZYu — concept; Zhurina TI, Saifullin RF — acquisition of rheumatology and medicine data; literature review; Myshkin MYu — data analysis; Bufeeva LS — sample collection, RNA extraction; Shagina IA — RT-qPCR optimization and procedure, primary data analysis, manuscript writing, literature review; Turchaninova MA, Golovina OA, Britanova OV — manuscript writing, literature review. Copyright: © 2025 by the authors. Licensee: Pirogov University. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Funding: the study was supported by the grant from the Moscow Government (R&D project No. 1603-47/23 dated 08.06.2023), sponsored by the Moscow Center for Innovative Technologies in Healthcare. И. А. Шагина1,2,5, М. А. Турчанинова2,5, О. А. Головина3, Л. С. Буфеева2,5, Т. И. Журина3, Р. Ф. Сайфуллин1,4, М. Ю. Мышкин2,5, З. Ю. Мутовина3, О. В. Британова2,5 ПАНЕЛЬ IFN-I-ИНДУЦИРУЕМЫХ ГЕНОВ ПРИ СИСТЕМНОЙ СКЛЕРОДЕРМИИ: ПОТЕНЦИАЛ БИОМАРКЕРА СТРАТИФИКАЦИИ Системная склеродермия (ССД) остается заболеванием с высокой летальностью; валидированных биомаркеров для стратификации и мониторинга недостаточно. Целью работы было изучить экспрессию разработанной панели IFN-I-индуцируемых генов (IFI27, IFI44, IFIT3, ISG15, XAF1) в периферической крови и пораженной коже при ССД. Исследовали образцы 48 пациентов с ССД и 31 здорового донора. Экспрессию генов определяли методом RT-qPCR (ΔΔCt) с нормировкой по референсному гену «домашнего хозяйства»; экспрессию гена SFRP4 использовали как маркер кожного фиброза. Экспрессия IFN-I-индуцируемых генов была достоверно (p < 0,1) повышена как в крови, так и в коже пациентов с ССД относительно образцов здоровых доноров. Сопоставление экспрессии между компартментами показало, что уровни экспрессии генов XAF1, IFI44, IFIT3, ISG15 в крови пациентов выше (p < 0,01), чем в образцах кожи. Экспрессия гена IFI27, напротив, более выражена в коже (p < 0,01). Результаты исследования показывают, что разработанная тест-система оценки интерфероновой сигнатуры потенциально может быть использована как неинвазивный инструмент стратификации пациентов с ССД по анализу РНК, полученной из образца крови, в том числе для обоснования назначения терапии блокаторами рецептора IFN-I. Ключевые слова: системная склеродермия, интерферон I типа, интерфероновая сигнатура, анифролумаб, qRT-PCR, неинвазивная диагностика Для корреспонденции: Ольга Владимировна Британова — ул. Миклухо-Маклая, д. 16/10, 117997, г. Москва, Россия, olbritan@gmail.com; Зинаида Юрьевна Мутовина — ул. Пехотная, д. 3, 123182, г. Москва, Россия, zmutovina@mail.ru 1 Российский национальный исследовательский медицинский университет имени Н. И. Пирогова, Москва, Россия 2 ООО «МайЛаборатори», Москва, Россия 3 Московский городской научно-исследовательский центр Больница № 52, Москва, Россия 4 Федеральный научно-клинический центр реаниматологии и реабилитологии, Москва, Россия 5 Институт биоорганической химии имени М. М. Шемякина и Ю. А. Овчинникова Российской академии наук, Москва, Россия Статья получена: 06.09.2025 Статья принята к печати: 08.10.2025 Опубликована онлайн: 21.10.2025 DOI: 10.24075/vrgmu.2025.047 Соблюдение этических стандартов: исследование проведено в соответствии с требованиями Хельсинкской декларации. От всех пациентов получено добровольное информационное согласие в рамках прохождения обследования в стационаре (на взятие образцов биоматериала и проведение анализов). Вклад авторов: З. Ю. Мутовина — концепция; Т. И. Журина, Р. Ф. Сайфуллин — сбор данных в сфере ревматологии и медицины; анализ литературы М. Ю. Мышкин — анализ данных; Л. С. Буфеева — сбор образцов, выделение РНК; И. А. Шагина — оптимизация и проведение RT-qPCR, первичный анализ данных, подготовка рукописи, анализ литературы; М. А.Турчанинова, О. А. Головина, О. В. Британова — подготовка рукописи, анализ литературы. Авторские права: © 2025 принадлежат авторам. Лицензиат: РНИМУ им. Н. И. Пирогова. Статья размещена в открытом доступе и распространяется на условиях лицензии Creative Commons Attribution (CC BY) (https://creativecommons.org/licenses/by/4.0/). Финансирование: исследование выполнено на средства гранта Правительства Москвы (НИР № 1603-47/23 от 08.06.2023), спонсор — АНО «Московский центр инновационных технологий в здравоохранении». 13 ПОДАДЧРУКЧПИ ДССУИЕПЙРЧДИ ДЦЦТЧПУПАДН ВЕСТНИК РГМУ 5, 2025 VESTNIK.RSMU.PRESS DOI: 10.24075/VRGMU.2025.047 | | | Systemic scleroderma (SS) is a systemic autoimmune disorder characterized by vasculopathy followed by progressive fibrosis of the skin and internal organs manifestations [1] and high mortality rate: 25% of patients die within the first 5 years after making the diagnosis, 37.5% die within the first 10 years [2–5]. The mean life expectancy of SS patients is 16–34 years less than the population average [4]. The disease prevalence varies between 38 and 341 per million population annually, and the disease incidence varies between 8 and 56 individuals per million per year based on the data from different countries [6–11]. The most common cause of death associated with SS in damage to internal organs: the lung, heart, gastrointestinal tract [4, 5]. In addition to these manifestations, in 35% of patients the disease course is complicated by digital ulcers, which, in turn, often leading to gangrene and fingers amputation, which causes severe functional impairment in such patients [12–13]. Although the disease is multisystemic, skin lesion is a distinctive feature that determines the clinical and prognostic stratification. SS is classified based on the extent of skin involvement into the diffuse (dSS) and limited (lSS) forms that have different progression rate, disease course features, and prognosis [1]. The molecular mechanisms that underlie the development of SS are poorly understood. This hinders the selection of targeted therapy selection. Currently, the range of potential treatment options is extremely limited, necessitating the search for new therapies. Although the T cell responses of types Th2 and Th17 play an important role in the SS pathogenesis [15, 16], it has been shown that activation of interferon pathways, especially type I, is more strongly associated with SS, than immune responses of other types [17, 18]. Recent studies have shown similar expression of the genes involved in the interferon cascade in patients with systemic lupus erythematosus and SS [19–21]. Upregulation of the IFN-associated genes is also typical for rheumatoid arthritis, Sjogren's syndrome, and polymyositis [23–29]. Activation of the IFN-I pathway and, as a result, the “interferon” gene signature are observed in blood and skin of a large number of patients with even early-stage SS and, according to some data, are associated with severity of lesions (including pulmonary and skin lesions) [30, 31]. The conclusion about the relationship between the expression of IFN-induced genes and the disease severity was disproven by subsequent research [28, 32]. The up-to-date EULAR guidelines (updated 2023) reflect a shift towards targeted approaches to SS, which increases the importance of the validated stratification biomarkers [33]. Such observations raise the question about the potential effectiveness of the interferon-mediated pathway blocking in some patients with SS. The study aimed to assess the expression of interferon- dependent genes in peripheral blood cells and affected skin areas of SS patients in order to estimate the potential for patient stratification and the prognosis of a promising, but not yet approved therapy with antibodies against interferon receptors in SS [34]. The hypothesis was tested that the IFN-I-signature can be reflected in peripheral blood of patients with SS, the analysis of which can become an alternative to repeated skin biopsy when performing patient stratification and monitoring. METHODS Patients The patients aged 21–77 with the limited or diffuse cutaneous systemic scleroderma form, who were admitted to the Rheumatology Department of the Moscow City Research Center Hospital No. 52 from April 2023 to February 2025 and met the ACR/EULAR2013 criteria, were included in the study [35]. Inclusion criteria: detection of the antinuclear factor in patient’s blood by the indirect immunufluorescence method. When detecting the antinuclear factor, the range of nuclear antibodies was tested by immunoblotting (Table 1). Exclusion criteria: another systemic autoimmune disorder (such as rheumatoid arthritis, idiopathic inflammatory myopathy, systemic lupus erythematosus); signs of infectious disease (upon physical examination). The patients having no specific antibodies or negative immunoblot test results were not excluded from the study, since: 1) according to the ACR/EULAR criteria, the fact of having specific antibodies is not a mandatory criterion for establishing the diagnosis of SS [35]; 2) antibodies against RNA polymerase III, which are not detected in the Russian Federation, are also typical for SS; furthermore, SS can be associated with the antibodies that are outside the spectrum assessed. The screening tests for antinuclear antibodies were performed by enzyme-linked immunoassay (ELISA) with the Multiscan FC semi-automatic ELISA analyzer (Thermo Fisher Scientific Inc., USA) using the ANA-Screen ELISA IgG reagent kit (Euroimmun AG, Germany) in accordance with the manufacturer’s instructions. Confirmatory testing for specific antinuclear antibodies was performed by immunoblotting using the ANA profile 1 IgG reagent kit (Euroimmun AG, Germany) in accordance with the manufacturer’s instructions. The clinical and immunological assessment of patients was conducted that included the following: assessment of disease activity based on EScSG (Table 1) [36], evaluation of the capillaroscopic pattern at the time of examination, and laboratory testing for specific antibodies. All the patients were tested for the presence/absence of damage to possible target organs: skin (Rodnan skin score was assessed), joints (the number of painful and swollen joints was estimated), lung (all the patients underwent chest multislice computed tomography (MSCT)), heart and pulmonary artery (electrocardiography, echocardiography and gastrointestinal catheterization (e.g. barium swallow test in one patient) were performed). Biomaterial selection and RNA extraction Sample collection was performed in the clinic during general patient examination at admission to the hospital. A total of 48 patients with SS were included in the study. In 25 patients, peripheral blood samples only were collected as biomaterial for testing. In another 10 patients, both the affected skin specimens and peripheral blood samples were collected; in 13 patients, the affected skin specimens only were collected. Skin biopsy specimens were collected from the forearm (area with the thickest skin) by incisional biopsy. The healthy skin samples collected from three donors not diagnosed with SS were used as the reference samples, along with the peripheral venous blood samples of 31 healthy donors aged 20–54 years. A total of 20% of the cohort of healthy donors were males. The affected skin samples 4 mm in diameter were placed in the MACS® Tissue Storage Solution (Miltenyi, USA) at +4 °С and transferred to the laboratory for RNA extraction. The resulting samples were ground in liquid nitrogen with RLT lysis buffer added simultaneously. RNA extraction was performed using the HiPure Total RNA Kit (Magen, China) in accordance with the 14 ORIGINAL RESEARCH IMMUNOLOGY BULLETIN OF RSMU 5, 2025 VESTNIK.RSMU.PRESS DOI: 10.24075/BRSMU.2025.047 | | | Table 1. Results of clinical and immunological assessment of SS patients (n = 48) manufacturer’s instructions. RNA concentration was measured using the Qubit 3.0 fluorometer and the reagent kit (Thermo Fisher Scientific, USA). Blood samples (4 mL) were collected into the EDTA-coated tubes (final concentration of 2 mg/mL). After collection, whole blood was stored at 4 °С until mononuclear cells were isolated. Mononuclear cells were isolated from peripheral blood by sedimentation (Ficoll-Paque density gradient centrifugation (density 1.077 g/cm3)) (PanEco, Russia). The resulting cell fraction was placed in the RLT lysis buffer (Qiagen, Germany) and stored at –80 °С until total RNA was extracted. The total RNA was extracted using the HiPure Total RNA Kit (Magen, China) in accordance with the manufacturer’s instructions. The RNA concentration was measured using the Qubit 3.0 fluorometer (USA) and the reagent kit (Thermo Fisher Scientific, USA). The quality of the RNA sample extracted was evaluated by agarose gel electrophoresis. The extracted RNA was frozen and stored at the temperature of –80 °С until the reverse transcription and real-time PCR were launched. RT-PCR and data analysis The one-tube quantitative RT-PCR (reverse transcription PCR) was performed using the One-Tube RT-PCR TaqMan reagent kit (Evrogen, Russia) [37]. The kit contains a ready- to-use master mix comprising a reaction buffer for RT and Indicator Values Standard deviation Average age 61 (21–77) 61.4 Sex Males 5/48 (10%), females 43/48 (90%) 0.31 Disease duration 13.5 years (0.5–47) 13.6 Course acute — 3/48 (6%) subacute — 5/48 (10%) chronic — 40/48 (84%) 0.24 0.31 0.37 Form Limited — 30/48 (62.5%), diffuse — 18/48 (37.5%) 0.48 Organ damage Interstitial lung disease 27/48 (56%) 0.50 – CT pattern for organizing pneumonia 2/48 (4%) 0.20 – CT pattern for nonspecific interstitial pneumonia 25/48 (52%) 0.50 Primary pulmonary hypertension 12/48 (25%) 0.43 Kidney disease 2/48 (4%) 0.20 Heart disease 14/48 (29%) 0.45 – Pericarditis 12/48 (25%) 0.42 – Myocarditis 3/48 (6%) 0.24 Esophageal lesion 34/48 (71%) 0.45 Intestinal lesion 6/48 (12.5%) 0.33 Muscle lesion 9/48 (19%) 0.39 Joint lesion 28/48 (58%) 0.49 Skin manifestations 46/48 (96%) 0.20 – digital ulcers 15/48 (31%) 0.46 Signs of Raynaud’s phenomenon 47/48 (98%) 0.14 Capillaroscopic pattern late — 27/48 (56%), active — 16/48 (33%) early — 3/48 (6%) myopathic — 2/48 (4%) 0.50 0.47 0.24 0.20 Telangiectasia 27/48 (56%) 0.49 Rodnan score, points 7 (0–37) 6.90 Laboratory tests Anti-centromere antibodies (ACAs) 25/48 (52%) 0.50 Anti-Sc-70 antibodies 14/48 (29%) 0.45 Anti-PM/Scl antibodies 1/48 (2%) 0.14 Anti-RNP70 antibodies 1/48 (2%) 0.14 Anti-SSA antibodies 1/48 (2%) 0.14 No specific antibodies 6/48 (12.5%) 0.33 Therapy (at the time of sample collection) Mycophenolate mofetil (MMF) — 14/48 (29%), low-dose glucocorticoids (GCs) — 25/48 (52%), hydroxychloroquine (HC) — 13/48 (27%), rituximab (RTX) — 11/48 (23%), nintedanib — 3/48 (6%), cyclophosphamide (CP) — 1/48 (2%), methotrexate (МТ) — 2/48 (4%) , no therapy — 8/48 (16.7%) Low-intensity immunosuppressive therapy (HC and/or low dose GCs) 18/48 (37.5%) 0.48 High-intensity immunosuppressive therapy (MMF and/or RTX and/or МТ) 22/48 (46%) 0.50 15 ПОДАДЧРУКЧПИ ДССУИЕПЙРЧДИ ДЦЦТЧПУПАДН ВЕСТНИК РГМУ 5, 2025 VESTNIK.RSMU.PRESS DOI: 10.24075/VRGMU.2025.047 | | | PCR, nucleotide triphosphates, and the hot start Taq DNA polymerase. In the first phase, during the first-strand cDNA synthesis, the Taq polymerase was inactivated by monoclonal antibodies; heating at 95 °C prior to PCR ensured the rapid hot start. The modified MMLV reverse transcriptase was provided to the reaction mixture separately. Primers for first-strand cDNA synthesis and subsequent PCR amplification were added to the reaction in a concentration of 0.4 μМ. Real-time PCR product accumulation imaging was performed using the TaqMan type fluorescent probes. The probes were added to the reaction at a concentration of 0.1 μМ. All the oligonucleotides specific for the test and reference genes were selected such that optimal performance was achieved under a single, versatile RT-PCR protocol. Reverse transcription: 55 °C, 15 min, one cycle, without fluorescence acquisition. This was followed by a reverse transcriptase inactivation/polymerase activation step: 95 °C, 1 min, one cycle, without reading. Amplification was carried out for 40 cycles: denaturation — 95 °C, 15 s (without acquisition); annealing — 60 °C, 20 s (with fluorescence acquisition); elongation — 72 °C, 20 s (without acquisition). To analyze the interferon signature, the expression of IFIT1, IFIT3, IFI27, IFI44, ISG15, XAF1 was assessed, that was previously validated in the peripheral blood samples of patients with systemic lupus erythemathosus (SLE), along with that of the TBP reference gene [29, 31]. The same method was used to assess the expression of the SFRP4 marker gene which is a recognized marker associated with fibrosis progression in scleroderma [22]. Assessment of the relative expression of the interferon- dependent genes involved normalization to the expression of the TBP housekeeping gene. Relative expression was determined by the ΔΔCt method with normalization to the reference gene amplified in the same PCR. Relative expression of the test gene was determined based on the amplification effeciency and the difference in cycle thresholds (ΔCt) between the target and reference gene. Statistical data processing The nonparametric methods (appropriate for small sample sizes) were used for statistical processing. The Mann–Whitney test was used to compare gene expression in the donor samples of the experimental (SS patients) and control groups. When comparing gene expression values of the skin and blood samples, the significance of differences between samples was estimated using the paired Wilcoxon signed-rank test. All p-values were further adjusted using the Benjamini–Hochberg (False Discovery Rate (FDR)) procedure. Spearman’s rank correlation coefficient was used to assess correlations between variables. The IFN-I signature value was calculated as the mean of the standardized scores (z-score) of relative expression of five genes (IFIT3, IFI27, IFI44, ISG15, XAF1). The standardized scores for the IFN-I signature (averaged between skin and blood) were calculated for the subgroup of patients from whom paired blood and skin samples were obtained (n = 10). The standardized scores of the IFN-I signature values in SS patients were calculated relative to healthy donors, separately in blood and skin samples). Mean values and the standard deviation were calculated for clinical data (Tables 2 and 3). However, due to the indicators’ limited applicability to small samples (with the unproven hypothesis about the normally distributed values) the median and interquartile range were also calculated. The standard deviation of binary types of data (such as clinical mainfestations) was calculated for the Bernoulli distribution. Work with the tables, data correction, charting, and statistical analysis were accomplished using the R integrated features and supplementary libraries: tidyverse, ggplot2, and corrplot. RESULTS The study included 48 SS patients, mostly females (90%), a mean age 61 years, and mean disease duration 13.5 of years, primarily with the disease chronic course (84%) and limited cutaneous form (62.5%). The leading organ damage and manifestations: interstitial lung disease (56%, mainly NSIP); esophageal (71%) and joint (58%) lesions. were observed. The average skin activity was low (mRSS 7), the Raynaud’s phenomenon was diagnosed in almost all patients (98%), digital ulcers were present in 31%. The results of the clinical and immunological assessment of SS patients are presented in Table 1. The integrated IFN-I signature assessment involved the use of the modified test system conprising five genes (IFI27, XAF1, IFI44, IFIT3, ISG15) using one reference gene instead of two, which had been previously tested in peripheral blood samples of patients with SLE [32, 38]. SFRP4 (secreted frizzled-related protein) was used as a marker gene of the disease, the expression levels of which are associated with skin fibrosis in SS [22]. Significant differences in expression levels of all five test genes between the groups of SS patients and healthy donors were observed in the RNA samples obtained from the skin; significant differences in expression of 4 test genes out of 5 (except IFI27) between the groups of SS patients and healthy donors were reported for blood samples (Fig. 1A, B; Table 2.). The SFRP4 expression levels were higher in the skin samples of patients with SS compared to the skin samples of healthy donors (Fig. 1А). Comparison of expression levels between compartments performed for the paired blood/skin samples (Fig. 2; Table 3) showed that the XAF1, IFI44, IFIT3, ISG15 expression levels in patients’ blood were significantly higher than in skin samples. The IFI27 gene expression, in contrast, was more pronounced in the skin (Fig. 2А, C). The direction of changes was consistent across both sample types, which supports the effectiveness of using both skin biopsy samples and blood samples for the diagnosis and dynamic monitoring (Fig. 2B, D). The IFN-I integral index of patients with SS was significantly higher compared to the reference threshold interval calculated based on the healthy donors’ values in both peripheral blood samples and the affected skin biopsy samples (Fig. 2E, F). A limitation of the skin IFN-I signature index comparison is associated with the small number of samples collected from healthy donors. The integral index based on blood samples showed that the values were above the reference interval in 62% (22 patients). The correlation analysis of clinical parameters and gene expression (Fig. 3; Table 4) has shown that the IFN-I signature genes (IFIT3, IFI27, IFI44, ISG15, XAF1) form a tightly co- expressed module (Rs between 0.52 and 0.87; p < 0.05 for the skin) with the significant positive correlations between genes, while SFRP4 exhibited little association with this module reflecting a distinct fibrosis-associated component. In blood samples, the IFIT3, IFI27, ISG15, XAF1 genes also form a correlation cluster with each other, but the correlation values are lower (Rs between 0.38 and 0.63). The IFIT27 gene 16 ORIGINAL RESEARCH IMMUNOLOGY BULLETIN OF RSMU 5, 2025 VESTNIK.RSMU.PRESS DOI: 10.24075/BRSMU.2025.047 | | | Fig. 1. Comparison of the test gene relative expression levels by RT-qPCR (p-value — based on the Mann–Whitney test) between skin samples in the groups of patients with SS (n = 23) and healthy donors (n = 3) (A), between peripheral blood samples in the groups of patients with SS (n = 35) and healthy donors (n = 31) (B) А B IFIT3 IFIT3 IFI27 IFI27 IFI44 ISG15 XAF1 SFRP4 XAF1 ISG15 IFI44 p = 0.0192 p = 0.0136 p = 4.0e-05 Blood Mean expression Mean expression Skin Control Control Control Control Control Control Control Control Control Control Control Scleroderma Scleroderma Scleroderma Scleroderma Scleroderma Scleroderma Scleroderma Scleroderma Scleroderma Scleroderma Scleroderma 75 20 7.5 6.0 2.5 0.0 1 1 1 0 2 2 2 3 3 3 1.00 1.00 0.75 0.75 0.50 0.50 0.25 0.25 20 20 15 10 10 5 0 0 0 2 4 6 15 10 5 0 50 25 0 p = 0.21 p = 1.2e-06 p = 1.2e-06 p = 1.2e-06 p = 0.0136 p = 0.0136 p = 0.0136 p = 0.0267 is most closely correlated to ISG15 (Rs = 0.77), but it is outside the common correlation cluster (Rs with IFIT3, IFI27, ISG15, XAF1 < 0.15). Clinical indicators of disease severity (activity based on EScSG, progression, diffuse form) significantly correlate with each other. The IFN-I signature genes demonstrate a non-significant correlation with the diffuse disease form: in skin samples, Rs is between 0.23 and 0.34, skin samples show a slight correlation for ISG15 and IFIT27 only (0.21 and 0.27, respectively). Age shows a weak negative association with the expression of the IFN-I-induced genes (Rs between –0.32 and –0.36) in skin samples; the correlations for blood are insignificant (0 to –0.13). Table 2. Gene expression levels in skin samples in the groups of patients with SS (n = 23) and healthy donors (n = 3) (A), in peripheral blood samples in the groups of patients with SS (n = 35) and healthy donors (n = 31) (B) Note: * iqr — interquartile range. А. Skin samples B. Blood samples Gene Group Mean SD Median iqr* Mean SD Median iqr* IFIT3 Control 0.47 0.11 0.46 0.1 3.15 4.87 1.15 1.76 IFIT3 SS 1.36 0.84 1.1 0.72 16.37 21.17 6.35 17.24 IFI27 Control 0.64 0.31 0.78 0.28 0.59 0.39 0.54 0.35 IFI27 SS 3.11 2.02 2.89 2.56 1.91 3.69 0.72 1.07 IFI44 Control 0.31 0.06 0.28 0.06 1.38 0.69 1.15 0.68 IFI44 SS 1.03 0.78 0.78 0.35 4.9 5.98 2.58 1.93 ISG15 Control 0.08 0.02 0.09 0.02 0.65 0.76 0.31 0.71 ISG15 SS 0.3 0.21 0.24 0.15 3.63 5.33 1.48 2.94 XAF1 Control 0.1 0.01 0.1 0.01 0.37 0.57 0.08 0.47 XAF1 SS 0.31 0.22 0.26 0.18 1.39 1.17 1 1.13 SFRP4 Control 0.03 0.01 0.04 0.01 n/a n/a n/a n/a SFRP4 SS 0.36 0.69 0.14 0.28 n/a n/a n/a n/a DISCUSSION Recently, determination of the expression of the IFN-I-stimulated genes by PCR is increasingly used in clinical trials. Various diagnostic test systems for assessment of the IFN signature expression have been developed. Expression levels of the genes IFI44L, IFI44, MX1, MX2, OAS1, OAS2, OAS3, SIGLEC1, IFI35 are most often determined when determining the IFN signature [39]. Russian scientists have also proposed solutions in this area [40, 41]. In particular, one system involves the analysis of three genes (RIG-1, IFIT-1, IFIH-1). However, the expression normalization approach used in these panels seems to be suboptimal: HPRT is used as a reference gene 17 ПОДАДЧРУКЧПИ ДССУИЕПЙРЧДИ ДЦЦТЧПУПАДН ВЕСТНИК РГМУ 5, 2025 VESTNIK.RSMU.PRESS DOI: 10.24075/VRGMU.2025.047 | | | in the first one, but standardization by ΔCt is not described, it is proposed to use GAPDH as a reference gene in the second one, which can result in artifacts due to the presence of numerous pseudogenes in the human genome. In this study, we used integrated assessment of the IFN-I signature using the test system of five genes: IFI27, XAF1, IFI44, IFIT3, ISG15 with normalization to one reference gene. Previously, we tested these genes individually and as a test system in peripheral blood samples from patients with SLE [32, 38]. According to the findings, the SS patients showed the increased expression of the IFN-induced genes compared to healthy donors in both skin and peripheral blood samples. These data confirming involvement of type I interferons in the disease pathogenesis are consistent with the previously reported results for SS [17, 21, 30]. Based on the results of our analysis the expression of IFN-I-induced genes shows a trend toward correlation in blood and affected skin samples. Such a trend has been earlier demonstrated in the large cohort of SS patients using the transcriptome profiling (microarray). It has been shown that the expression of the IFN-associated genes in the skin is consistent with similar changes in peripheral blood [42]. These and other observations suggest the informativeness of assessing IFN-I signature in blood samples of patients with SS [18]. Clinical indicators of the disease severity (activity based on EScSG, progression, diffuse form) significantly correlate with each other, but demonstrate only weak and heterogeneous correlations with the IFN-I signature. Thus, the applicability of Fig. 2. Comparison of interferon signature in blood and skin of patients with SS. А. Comparison of relative expression levels of the test genes IFIT3, IFI27, IFI44, ISG15, XAF1 in the RNA samples obtained from the skin and peripheral blood of SS patients (n = 10) represented as a box-plot. The Wilcoxon signed-rank test was used. B. Pairwise correlation of the IFN-I panel gene expression between blood and skin. C. A bar chart shows the mean log fold change) for the skin and blood. Negative values indicate higher expression in blood, and positive values indicate higher expression in the skin. D. Correlation of the averaged IFN-I signature of blood and skin for paired samples (n = 10), (R = 0.49; p = 0.15). B, D Spearman’s rank correlation coefficient is used (grey areas — 95% CI). E. IFN-I signature values (z-score, standardized against donor values) in skin biopsy samples: red dots — healthy donors (n = 3), blue dots — SS patients (n = 23). F. IFN-I signature values (z-score, standardization by healthy donors) in peripheral blood samples. Red dots — healthy donors (n = 31), blue dots — SS patients (n = 35, including 10 paired skin/blood samples). The dashed line shows the threshold interval А B C D E F IFIT3 IFIT3 IFI27 IFI27 IFI44 IFI44 ISG15 ISG15 XAF1 XAF1 Blood Blood 30 7,5 10.0 7.5 2,0 p = 0.002 p = 0.002 p = 0.0039 p = 0.0059 p = 0.0027 R = 0.032, p = 0.93 R = 0.49, p = 0.15 R = 0.045, p = 0.19 R = 0.87, p = 0.00098 R = 0.28, p = 0.42 R = 0.44, p = 0.21 5.0 1,5 2.5 1,0 0,5 4 2 0 10 2,5 20 5,0 0 3 2 2 3 7.5 0.6 1.00 5.0 0.4 0.75 2.5 0.2 0.50 0.25 0.00 0.0 0.0 1 0 2 1 –1 –2 IFIT3 IFI27 IFI44 ISG15 XAF1 0 1 1 0 0 –1 –1 1 0 0 0 0 0.0 0.0 10 1 2.5 0.5 20 2 2 5.0 1.0 30 3 7.5 1.5 4 4 10.0 2.0 0,0 Mean expression Blood Blood Blood Blood Skin Skin Skin logFC skin/blood Blood Averaged interferon signature Skin Skin Skin Skin 18 ORIGINAL RESEARCH IMMUNOLOGY BULLETIN OF RSMU 5, 2025 VESTNIK.RSMU.PRESS DOI: 10.24075/BRSMU.2025.047 | | | the developed test system for patient stratification and predicting the success of therapy with the IFN-I receptor inhibitors should be assessed independently from the disease severity and form. Since the levels of the assessed IFN-I signature in some SS patients are within the range close to that of controls (Fig. 2), and there is no correlation between gene expression and the disease severity (Fig. 3), it can be assumed that SS patients have various immunological patterns, in addition to the type I interferon activation patterns (by analogy to the patterns reported for SLE [26]). The fact that SS patients have both significantly higher and lower (at the level of the control group) expression levels of the IFN-induced genes suggests the need for patient stratification to predict the response to therapy) with the interferon receptor antibodies with the interferon receptor antibodies. Given the current clinical trials of the IFN-I receptor inhibitor in SS (DAISY; NCT05631227) [37], the elevated IFN-I signature value can potentially be a criterion for patient selection and the tool to monitor the effectiveness of the response to therapy with the interferon receptor blocker (anifrolumab). Similarity of the interferon-associated pathways in SS and SLE [14, 23, 42] together with the available data on the anifrolumab efficacy in SLE [43] suggest that the use of this drug in SS can be highly effective in patients having high interferon signature values. CONCLUSIONS SS patients show the increased expression of interferon- induced genes in both skin and peripheral blood samples compared to healthy donors, which suggests the involvement of type I interferons in the SS pathogenesis. The expression levels of the IFN-I-induced genes show a trend toward correlation in blood samples and affected skin areas. The RT-qPCR panel developed that comprises the IFN-I-induced genes (IFI27, IFI44, IFIT3, ISG15, XAF1) has a potential for stratification of SS patients, as well as for assessment of the efficacy of target therapy with the interferon receptor blockers (in case such therapy is approved for SS). To verify this conclusion it is required to conduct further research focused on the correlation between the decrease in the interferon signature levels and the condition improvement in SS patients treated with the antibodies against the interferon receptor. We recommend using peripheral blood from SS patients for IFN-I- Note: * — interquartile range. Table 3. Expression of the test genes IFIT3, IFI27, IFI44, ISG15, XAF1 in the RNA samples obtained from peripheral blood of the same SS patients (n = 10) Gene Sample Mean valus Standard deviation Median Iqr* IFIT3 Blood 7.48 8.73 5.81 6.17 IFIT3 Skin 1.55 0.91 1.34 0.64 IFI27 Blood 1.34 1.27 0.96 2.07 IFI27 Skin 3.48 2.16 3.07 2.03 IFI44 Blood 3.05 2.57 2.39 1.34 IFI44 Skin 0.95 0.73 0.79 0.32 ISG15 Blood 2.04 1.62 1.57 1.54 ISG15 Skin 0.28 0.15 0.26 0.16 XAF1 Blood 1.15 0.62 0.94 0.97 XAF1 Skin 0.42 0.26 0.35 0.14 Fig. 3. Correlation matrix for expression levels of the IFN-I signature genes (IFI44, IFIT3, ISG15, IFI27, XAF1) in the samples and clinical signs of the group of SS patients for peripheral blood (n = 35) (A) and affected skin samples (n = 23) (B). The SFRP4 gene expression was added as a disease marker. The circle color indicates the correlation sign and strength: blue — positive, red — negative; the more saturated the color, the closer |r| to 1. The circle size is proportional to the Spearman’s rank correlation module (coefficient) А B IFIT3 IFIT3 IFIT3 IFIT3 IFIT3 IFI27 IFI27 IFI27 IFI27 IFI27 IFI44 IFI44 IFI44 IFI44 IFI44 ISG15 ISG15 ISG15 ISG15 ISG15 XAF1 XAF1 XAF1 XAF1 XAF1 0.8 0.8 0.4 0.4 –0.2 –0.2 0.6 0.6 0 0 0.2 0.2 –0.4 –0.4 –0.6 –0.6 –0.8 –0.8 –1 –1 1 1 SFRP4 SFRP4 Age Age Age Age Age Duration Duration Duration Progression Progression Progression Progression Onset type Onset type Onset type Onset type Activity EScSG Activity EScSG Activity EScSG Activity EScSG Diffuse form Diffuse form Diffuse form Diffuse form 19 ПОДАДЧРУКЧПИ ДССУИЕПЙРЧДИ ДЦЦТЧПУПАДН ВЕСТНИК РГМУ 5, 2025 VESTNIK.RSMU.PRESS DOI: 10.24075/VRGMU.2025.047 | | | Table 4. Values of the correlation between the expression of the IFN-I signature genes (IFI44, IFIT3, ISG15, IFI27, XAF1) in the samples and clinical signs of the group of SS patients for blood (n = 35), (4А) and skin (n = 23) samples (4B) 4B IFIT3 IFIT27 IFIT44 ISG15 XAF1 SFRP4 Age Duration Activity (EScSG) Progression Diffuse form Onset type IFIT3 1.00 0.79 0.82 0.80 0.79 0.22 –0.36 0.13 0.01 –0.15 0.23 –0.19 IFIT27 0.79 1.00 0.87 0.74 0.59 0.03 –0.36 –0.09 0.15 –0.09 0.34 –0.08 IFIT44 0.82 0.87 1.00 0.87 0.52 –0.05 –0.51 –0.07 0.13 –0.09 0.28 –0.24 ISG15 0.8 0.74 0.87 1.00 0.54 0.05 –0.57 –0.04 0.21 0.02 0.31 –0.20 XAF1 0.79 0.59 0.52 0.54 1.00 0.22 –0.32 0.16 0.02 –0.19 0.16 –0.12 SFRP4 0.22 0.03 –0.05 0.05 0.22 1.00 0.37 0.22 –0.07 0.13 0.17 –0.15 Age –0.36 –0.36 –0.51 –0.57 –0.32 0.37 1.00 0.34 –0.25 –0.02 –0.19 0.05 Duration 0.13 –0.09 –0.07 –0.04 0.16 0.22 0.34 1.00 –0.01 0.16 –0.39 –0.34 Activity (EScSG) 0.01 0.15 0.13 0.21 0.02 –0.07 –0.25 –0.01 1.00 0.7 0.32 0.35 Progression –0.15 –0.09 –0.09 0.02 –0.19 0.13 –0.02 0.16 0.7 1.00 –0.1 –0.11 Diffuse form 0.23 0.34 0.28 0.31 0.16 0.17 –0.19 –0.39 0.32 –0.1 1.00 0.45 4A IFIT3 IFIT27 IFIT44 ISG15 XAF1 Age Duration Activity (EScSG) Progression Diffuse form Onset type IFIT3 1.00 0.16 0.49 0.63 0.63 0.03 0.01 0.24 0.06 0.08 0.11 IFIT27 0.16 1.00 –0.04 0.77 –0.17 –0.03 0.10 0.06 0.03 0.27 –0.09 IFIT44 0.49 –0.04 1.00 0.38 0.64 –0.13 –0.22 –0.08 –0.08 –0.13 0.24 ISG15 0.63 0.77 0.38 1.00 0.24 0.00 0.00 0.16 0.08 0.21 0.06 XAF1 0.63 –0.17 0.64 0.24 1.00 –0.08 0.04 0.06 –0.13 0.04 0.00 Age 0.03 –0.03 –0.13 0.00 –0.08 1.00 0.31 0.18 0.32 –0.12 –0.16 Duration 0.01 0.10 –0.22 0.00 0.04 0.31 1.00 0.15 0.07 –0.14 –0.36 Activity (EScSG) 0.24 0.06 –0.08 0.16 0.06 0.18 0.15 1.00 0.60 –0.02 0.20 Progression 0.06 0.03 –0.08 0.08 –0.13 0.32 0.07 0.60 1.00 –0.34 0.12 Diffuse form 0.08 0.27 –0.13 0.21 0.04 –0.12 –0.14 –0.02 –0.34 1.00 0.15 Onset type 0.11 –0.09 0.24 0.06 0.00 –0.16 –0.36 0.20 0.12 0.15 1.00 signature analysis by RT-PCR with the proposed gene panel, as this biomaterial is more accessible, less invasive and more reproducible; it also shows the informativeness potentially comparable with that of skin samples. 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