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Towards the Baikal Open Laboratory in Astroparticle Physics [article]

Pavel Bezyazeekov, Igor Bychkov, Nikolay Budnev, Daria Chernykh, Yulia Kazarina, Dmitriy Kostunin, Alexander Kryukov, Roman Monkhoev, Alexey Shigarov, Dmitriy Shipilov
2021 arXiv   pre-print
The open science framework defined in the German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI) has triggered educational and outreach activities at the Irkutsk State University (ISU), which is actively participated in the two major astroparticle facilities in the region: TAIGA observatory and Baikal-GVD neutrino telescope. We describe the ideas grew out of this unique environment and propose a new open science laboratory based on education and outreach as well as on the development
more » ... and testing new methods and techniques for the multimessenger astronomy.
arXiv:1906.10594v2 fatcat:zhlqf5r5zvfnhfj3wlcrwcpasi

Integrated Mobile Robotic Platform Model

Mikhail V. Chugunov, Irina N. Polunina, Alexander G. Divin, Aleksandra A. Generalova, Artem A. Nikulin, Dmitriy S. Bychkov
2021 Engineering Technologies and Systems  
Introduction. The "Smart Agroˮ committee of Research and Education Center "Engineering of the Future" has identified a number of tasks relevant for improving the efficiency of precision, soil-protecting and conservation agriculture. One of these tasks is the development of a digital multi-agent system, which provides a number of services for agricultural enterprises, developers and manufacturers of agricultural machinery. The purpose of the present study is to model an autonomous mobile robotic
more » ... platform, including the development of software and hardware for trajectory control. Materials and Methods. To solve the problem, there are used modern CAx systems and their applications, the methods of 3D and full-body modeling, and the method of numerical solution of problems in solid mechanics. To expand and improve the standard functionality of CAx-systems (SolidWorks) in the software implementation of trajectory control algorithms, the methods and technologies of programming using API SolidWorks, VisualStudio C++ (MFC, ATL, COM) are used, and to build physical full-scale models ‒ Arduino and fischertechnik platforms. Results. The result of the study is a software and hardware module of trajectory control for an integrated (physical and virtual) model of a mobile robotic platform, which can be provided to the consumer as a service for technology autonomation. For the developed integrated model, control algorithms for various types of trajectories were tested. Discussion and Conclusion. The developed integrated software and hardware model of trajectory control can be used by developers and manufacturers of agricultural machinery, and directly by agro-enterprises for implementing typical technological processes. A feature of the implementation is an open hardware and software interface that provides the integration of mobile robotic platforms based on a digital multi-agent system.
doi:10.15507/2658-4123.031.202104.609-627 fatcat:s46vtkz2izfkplyo74vwvgh3ou

Deep learning based tissue analysis predicts outcome in colorectal cancer

Dmitrii Bychkov, Nina Linder, Riku Turkki, Stig Nordling, Panu E. Kovanen, Clare Verrill, Margarita Walliander, Mikael Lundin, Caj Haglund, Johan Lundin
2018 Scientific Reports  
Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue
more » ... icroarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79-3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28-2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30-2.15; AUC 0.57) in the stratification into low-and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer. Reincarnation of artificial neural networks in the form of deep learning 1-3 has improved the accuracy of several pattern recognition tasks, such as classification of objects, scenes and various other entities in digital images. In a biomedical context promising results have been achieved in image-based diagnostics ranging from ophthalmology 4 to diagnostic pathology 5 . Within digital pathology, quantification and classification of digitized tissue samples by supervised deep learning has shown good results even for tasks previously considered too challenging to be accomplished with conventional image analysis methods 6-8 . Often, the purpose of many tasks in digital pathology, such as counting mitoses 9-11 , quantifying tumour infiltrating immune cells 12,13 , assessing the grade of tumour differentiation 14 or characterization of specific tissue entities 15-17 aim to ultimately predict patient outcome [18] [19] [20] [21] [22] . Therefore, an interesting question is whether these intermediate proxies for outcome could be bypassed and the novel machine learning techniques could be used to directly learn the prognostically relevant features in microscopy images of the tumour, without prior identification of the known tissue entities, e.g. mitoses, nuclear pleomorphism, infiltrating immune cells, tumour budding. Our hypothesis is that training a machine learning classifier by using patient outcome as the endpoint could reveal known prognostic morphologies, but also has the potential to identify previously unknown prognostic features. Tissue images typically comprise a combination of a complex set of patterns and conventional design of an automated tissue classifier requires substantial domain expertise to plan which particular features to extract and feed into a classification algorithm. This task, known as feature engineering, is often laborious and time-consuming. Deep learning eliminates feature engineering and can learn representative features
doi:10.1038/s41598-018-21758-3 pmid:29467373 pmcid:PMC5821847 fatcat:a46t3x5egza5fnqja2ho4prvqm

Serum Biomarkers of Allergic Contact Dermatitis: A Pilot Study

Auksė Zinkevičienė, Denis Kainov, Eglė Lastauskienė, Violeta Kvedarienė, Dmitrii Bychkov, Myles Byrne, Irutė Girkontaitė
2015 International Archives of Allergy and Immunology  
Allergic contact dermatitis (ACD) is an inflammatory skin disease caused by repeated skin exposure to contact allergens. The goal of this pilot study was to identify inflammatory proteins which can serve as biomarkers for ACD. Methods: We measured levels of 102 cytokines, chemokines, and growth factors in the sera of 16 ACD patients during acute and remission phases, and 16 healthy volunteers. Results: Serum levels of adiponectin, chemokine (C-C motif) ligand 5 (CCL5), C-reactive protein (CRP),
more » ... chitinase 3-like 1 (CHI3L1), complement factor D (CFD), endoglin, lipocalin-2, osteopontin, retinol-binding protein 4 (RBP4), and platelet factor 4 (PF4) were significantly higher, whereas levels of trefoil factor 3 (TFF3) were significantly lower, in ACD patients than in healthy controls. In ACD patients, serum levels of CCL5 were elevated, whereas levels of TFF3, soluble intercellular adhesion molecule-1 (sICAM-1), and plateletderived growth factor (PDGF)-AB/BB were found to be lower during the remission phase of the disease. Conclusions: Serum levels of adiponectin, CCL5, CRP, CHI3L1, CFD, endoglin, lipocalin-2, osteopontin, RBP4, PF4, and TFF3 might be ex-
doi:10.1159/000442749 pmid:26790150 fatcat:rgeqgibibvg7hf4f6lcbfiy65a

Systems pathology by multiplexed immunohistochemistry and whole-slide digital image analysis

Sami Blom, Lassi Paavolainen, Dmitrii Bychkov, Riku Turkki, Petra Mäki-Teeri, Annabrita Hemmes, Katja Välimäki, Johan Lundin, Olli Kallioniemi, Teijo Pellinen
2017 Scientific Reports  
The paradigm of molecular histopathology is shifting from a single-marker immunohistochemistry towards multiplexed detection of markers to better understand the complex pathological processes. However, there are no systems allowing multiplexed IHC (mIHC) with high-resolution whole-slide tissue imaging and analysis, yet providing feasible throughput for routine use. We present an mIHC platform combining fluorescent and chromogenic staining with automated whole-slide imaging and integrated
more » ... lide image analysis, enabling simultaneous detection of six protein markers and nuclei, and automatic quantification and classification of hundreds of thousands of cells in situ in formalin-fixed paraffin-embedded tissues. In the first proof-of-concept, we detected immune cells at cell-level resolution (n = 128,894 cells) in human prostate cancer, and analysed T cell subpopulations in different tumour compartments (epithelium vs. stroma). In the second proof-of-concept, we demonstrated an automatic classification of epithelial cell populations (n = 83,558) and glands (benign vs. cancer) in prostate cancer with simultaneous analysis of androgen receptor (AR) and alpha-methylacyl-CoA (AMACR) expression at cell-level resolution. We conclude that the open-source combination of 8-plex mIHC detection, whole-slide image acquisition and analysis provides a robust tool allowing quantitative, spatially resolved whole-slide tissue cytometry directly in formalin-fixed human tumour tissues for improved characterization of histology and the tumour microenvironment. Published: xx xx xxxx OPEN www.nature.com/scientificreports/ 2 SCIenTIFIC REPORTS | 7: 15580 | up to 61 markers per section 17 . However, major drawbacks of dye cycling are the laborious staining/imaging cycles 5,17 , the primary antibody labelling for direct fluorescence detection 5,17 , and potential changes of the tissue morphology and antigenicity due to the repetitive exposure of the tissue to the dye bleaching and/or antibody stripping conditions 5 . In contrast to fluorescence, mass spectrometry based methods provide highly multiplexed mIHC assays 10-12,17 omitting most of the pitfalls of fluorescence imaging. Mass spectrometry holds a great potential for the future, but the instrumentation is still expensive, not easily accessible, and the spectrometry "image" acquisition is extremely slow, even when compared to multispectral fluorescence acquisition, being impractical for routine whole-slide analytics at cell-level resolution. Despite of the issue in terms of scalability and throughput, multiplexed IHC (mIHC) methods allow simultaneous detection and co-localization analysis of multiple markers in situ in the intact spatial context of tissues [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [17] [18] [19] . Moreover, multiplexing allows for a simple and easily automated, marker-guided tissue segmentation (e.g. epithelium vs. stroma), and provides more information from each tissue section, which may be critically important for small samples, such as needle biopsies of tissues or small metastatic tumour samples. However, as tumours often exhibit significant cellular and spatial heterogeneity, it would be important to be able to perform high-resolution, multiplexed analysis across whole-sections of tumours 20 . Hence, there is a growing need for an integrated "workhorse" mIHC system enabling not only moderate degree of multiplexing but also imaging and image analysis for high-resolution whole-slide analytics. Here, we describe a whole-slide 8-plex mIHC platform combining fluorescence (five-channel) and chromogenic (three-channel) mIHC allowing for a quantitative whole-slide analysis of six markers and cell nuclei at cell-level resolution. Our mIHC method is based on a fixed set of secondary reagents instead of labelled primary antibodies, thus enabling rapid implementation and virtually unlimited design of custom antibody panels for different targets of interest. We also provide protocols and methods for implementing new antibodies and targets for mIHC. As a proof-of-concept, we demonstrate automated classification of epithelial and immune cells in human prostate cancer and a simultaneous marker analysis at single cell level. To our knowledge, this is the first open-source 8-plex mIHC assay design that enables whole-slide imaging with true quantitative whole-slide analysis in FFPE samples at high resolution across the whole tissue.
doi:10.1038/s41598-017-15798-4 pmid:29138507 pmcid:PMC5686230 fatcat:smkuvtfz35g5xbp2xhbnr7dpje

German-Russian Astroparticle Data Life Cycle Initiative [article]

Andreas Haungs, Igor Bychkov, Julia Dubenskaya, Oleg Fedorov, Andreas Heiss, Donghwa Kang, Yulia Kazarina, Elena Korosteleva, Dmitriy Kostunin, Alexander Kryukov, Andrey Mikhailov, Minh-Duc Nguyen, Frank Polgart, Stanislav Polyakov (+8 others)
2019 arXiv   pre-print
A data life cycle (DLC) is a high-level data processing pipeline that involves data acquisition, event reconstruction, data analysis, publication, archiving, and sharing. For astroparticle physics a DLC is particularly important due to the geographical and content diversity of the research field. A dedicated and experiment spanning analysis and data centre would ensure that multi-messenger analyses can be carried out using state-of-the-art methods. The German-Russian Astroparticle Data Life
more » ... e Initiative (GRADLCI) is a joint project of the KASCADE-Grande and TAIGA collaborations, aimed at developing a concept and creating a DLC prototype that takes into account the data processing features specific for the research field. An open science system based on the KASCADE Cosmic Ray Data Centre (KCDC), which is a web-based platform to provide the astroparticle physics data for the general public, must also include effective methods for distributed data storage algorithms and techniques to allow the community to perform simulations and analyses with sophisticated machine learning methods. The aim is to achieve more efficient analyses of the data collected in different, globally dispersed observatories, as well as a modern education to Big Data Scientist in the synergy between basic research and the information society. The contribution covers the status and future plans of the initiative.
arXiv:1907.13303v1 fatcat:lkq3ykhjdjaz3atxzdehlibh24

Antibody Supervised Training of a Deep Learning Based Algorithm for Leukocyte Segmentation in Papillary Thyroid Carcinoma

Sebastian Emil Stenman, Dmitrii Bychkov, Hakan Kucukel, Nina Linder, Caj Haglund, Johanna Arola, Lundin Johan
2020 IEEE journal of biomedical and health informatics  
The quantity of leukocytes in papillary thyroid carcinoma (PTC) potentially have prognostic and treatment predictive value. Here, we propose a novel method for training a convolutional neural network (CNN) algorithm for segmenting leukocytes in PTCs. Tissue samples from two retrospective PTC cohort were obtained and representative tissue slides from twelve patients were stained with hematoxylin and eosin (HE) and digitized. Then, the HE slides were destained and restained immunohistochemically
more » ... IHC) with antibodies to the pan-leukocyte anti CD45 antigen and scanned again. The two stain-pairs of all representative tissue slides were registered, and image tiles of regions of interests were exported. The image tiles were processed and the 3,3'-diaminobenzidine (DAB) stained areas representing anti CD45 expression were turned into binary masks. These binary masks were applied as annotations on the HE image tiles and used in the training of a CNN algorithm. Ten whole slide images (WSIs) were used for training using a five-fold cross-validation and the remaining two slides were used as an independent test set for the trained model. For visual evaluation, the algorithm was run on all twelve WSIs, and in total 238,144 tiles sized 500x500 pixels were analyzed. The trained CNN algorithm had an intersection over union of 0.82 for detection of leukocytes in the HE image tiles when comparing the prediction masks to the ground truth anti CD45 mask. We conclude that this method for generating antibody supervised annotations using the destain-restain IHC guided annotations resulted in high accuracy segmentations of leukocytes in HE tissue images.
doi:10.1109/jbhi.2020.2994970 pmid:32750899 fatcat:eqibat4q6ffb3pb5c64wefqgom

Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose–response data

John-Patrick Mpindi, Potdar Swapnil, Bychkov Dmitrii, Saarela Jani, Khalid Saeed, Krister Wennerberg, Tero Aittokallio, Päivi Östling, Olli Kallioniemi
2015 Bioinformatics  
Motivation: Most data analysis tools for high-throughput screening (HTS) seek to uncover interesting hits for further analysis. They typically assume a low hit rate per plate. Hit rates can be dramatically higher in secondary screening, RNAi screening and in drug sensitivity testing using biologically active drugs. In particular, drug sensitivity testing on primary cells is often based on dose-response experiments, which pose a more stringent requirement for data quality and for intra-and
more » ... plate variation. Here, we compared common plate normalization and noise-reduction methods, including the B-score and the Loess a local polynomial fit method under high hit-rate scenarios of drug sensitivity testing. We generated simulated 384-well plate HTS datasets, each with 71 plates having a range of 20 (5%) to 160 (42%) hits per plate, with controls placed either at the edge of the plates or in a scattered configuration. Results: We identified 20% (77/384) as the critical hit-rate after which the normalizations started to perform poorly. Results from real drug testing experiments supported this estimation. In particular, the B-score resulted in incorrect normalization of high hit-rate plates, leading to poor data quality, which could be attributed to its dependency on the median polish algorithm. We conclude that a combination of a scattered layout of controls per plate and normalization using a polynomial least squares fit method, such as Loess helps to reduce column, row and edge effects in HTS experiments with high hit-rates and is optimal for generating accurate dose-response curves.
doi:10.1093/bioinformatics/btv455 pmid:26254433 pmcid:PMC4653387 fatcat:5kp6vw4jsfan7bxz6ijuehardm

Proliferative activity of salivary tumor cells
Пролиферативная активность клеток опухолей слюнных желез

Немчинова, Svetlana Nemchinova, Раевская, Lidiya Raevskaya, Казимирский, Viktor Kazimirskiy, Кислицина, Lada Kislitsina, Алёшкин, Igor Aleshkin, Батороев, Yuriy Batoroev (+4 others)
2016 Бюллетень Восточно-Сибирского научного центра Сибирского отделения Российской академии медицинских наук  
Salivary carcinomas comprise 2–3 % of malignant tumors of the head and neck. The basic method of morphological study in early diagnostics is aspiration puncture with a fine needle (FNAB). Complex histologic structure and great diversity of morphological items complicate cytologic diagnostics of salivary tumors considerably. Immunocytochemical examination must be used to reduce the errors, it enables to reveal neoplastic cells at early stages of malignization. High proliferative activity of
more » ... cells is one of their biological peculiarities. Accuracy in differential diagnostics between benign and malignant tumors is improved through determination of mitotic index combined with a fine needle aspiration puncture.
doi:10.12737/23393 fatcat:75xvv5ekxbgt5msqwypqvbvbqq

Akt Inhibitor MK2206 Prevents Influenza pH1N1 Virus InfectionIn Vitro

Oxana V. Denisova, Sandra Söderholm, Salla Virtanen, Carina Von Schantz, Dmitrii Bychkov, Elena Vashchinkina, Jens Desloovere, Janne Tynell, Niina Ikonen, Linda L. Theisen, Tuula A. Nyman, Sampsa Matikainen (+6 others)
2014 Antimicrobial Agents and Chemotherapy  
ABSTRACTThe influenza pH1N1 virus caused a global flu pandemic in 2009 and continues manifestation as a seasonal virus. Better understanding of the virus-host cell interaction could result in development of better prevention and treatment options. Here we show that the Akt inhibitor MK2206 blocks influenza pH1N1 virus infectionin vitro. In particular, at noncytotoxic concentrations, MK2206 alters Akt signaling and inhibits endocytic uptake of the virus. Interestingly, MK2206 is unable to
more » ... H3N2, H7N9, and H5N1 viruses, indicating that pH1N1 evolved specific requirements for efficient infection. Thus, Akt signaling could be exploited further for development of better therapeutics against pH1N1 virus.
doi:10.1128/aac.02798-13 pmid:24752266 pmcid:PMC4068572 fatcat:e4j2xs7r3zazlkhjqew46wr5ue

Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy

Dmitrii Bychkov, Nina Linder, Aleksei Tiulpin, Hakan Kücükel, Mikael Lundin, Stig Nordling, Harri Sihto, Jorma Isola, Tiina Lehtimäki, Pirkko-Liisa Kellokumpu-Lehtinen, Karl von Smitten, Heikki Joensuu (+1 others)
2021 Scientific Reports  
The treatment of patients with ERBB2 (HER2)-positive breast cancer with anti-ERBB2 therapy is based on the detection of ERBB2 gene amplification or protein overexpression. Machine learning (ML) algorithms can predict the amplification of ERBB2 based on tumor morphological features, but it is not known whether ML-derived features can predict survival and efficacy of anti-ERBB2 treatment. In this study, we trained a deep learning model with digital images of hematoxylin-eosin (H&E)-stained
more » ... n-fixed primary breast tumor tissue sections, weakly supervised by ERBB2 gene amplification status. The gene amplification was determined by chromogenic in situ hybridization (CISH). The training data comprised digitized tissue microarray (TMA) samples from 1,047 patients. The correlation between the deep learning-predicted ERBB2 status, which we call H&E-ERBB2 score, and distant disease-free survival (DDFS) was investigated on a fully independent test set, which included whole-slide tumor images from 712 patients with trastuzumab treatment status available. The area under the receiver operating characteristic curve (AUC) in predicting gene amplification in the test sets was 0.70 (95% CI, 0.63-0.77) on 354 TMA samples and 0.67 (95% CI, 0.62-0.71) on 712 whole-slide images. Among patients with ERBB2-positive cancer treated with trastuzumab, those with a higher than the median morphology-based H&E-ERBB2 score derived from machine learning had more favorable DDFS than those with a lower score (hazard ratio [HR] 0.37; 95% CI, 0.15-0.93; P = 0.034). A high H&E-ERBB2 score was associated with unfavorable survival in patients with ERBB2-negative cancer as determined by CISH. ERBB2-associated morphology correlated with the efficacy of adjuvant anti-ERBB2 treatment and can contribute to treatment-predictive information in breast cancer.
doi:10.1038/s41598-021-83102-6 pmid:33597560 pmcid:PMC7890057 fatcat:uiisfdrc2ncs7eqzjfomlvplgu

Russian–German Astroparticle Data Life Cycle Initiative

Igor Bychkov, Andrey Demichev, Julia Dubenskaya, Oleg Fedorov, Andreas Haungs, Andreas Heiss, Donghwa Kang, Yulia Kazarina, Elena Korosteleva, Dmitriy Kostunin, Alexander Kryukov, Andrey Mikhailov (+10 others)
2018 Data  
(Dmitriy Kostunin), and A.K.; Validation, O.F., E.K., A.M., S.P., E.P., D.S., and D.Z.; Writing-original draft, A.H. (Andreas Haungs), Y.K., D.K. (Dmitriy Kostunin), A.K., E.P., and A.S.  ...  (Dmitriy Kostunin), Y.K., A.M., M.-D.N., S.P., A.S. (Alexey Shigarov), and D.S.; Software, A.D., J.D., A.H. (Andreas Heiss), D.K. (Dmitriy Kostunin), A.K., A.M., M.-D.N., S.P., D.S., A.S.  ... 
doi:10.3390/data3040056 fatcat:qnapj52ptvftvfzfjq4yvcnxoe

Genome-Wide Analysis of Evolutionary Markers of Human Influenza A(H1N1)pdm09 and A(H3N2) Viruses May Guide Selection of Vaccine Strain Candidates

Sergei S. Belanov, Dmitrii Bychkov, Christian Benner, Samuli Ripatti, Teija Ojala, Matti Kankainen, Hong Kai Lee, Julian Wei-Tze Tang, Denis E. Kainov
2015 Genome Biology and Evolution  
Here we analyzed whole-genome sequences of 3,969 influenza A(H1N1)pdm09 and 4,774 A(H3N2) strains that circulated during 2009-2015 in the world. The analysis revealed changes at 481 and 533 amino acid sites in proteins of influenza A(H1N1)pdm09 and A(H3N2) strains, respectively. Many of these changes were introduced as a result of random drift. However, there were 61 and 68 changes that were present in relatively large number of A(H1N1)pdm09 and A(H3N2) strains, respectively, that circulated
more » ... ing relatively long time. We named these amino acid substitutions evolutionary markers, as they seemed to contain valuable information regarding the viral evolution. Interestingly, influenza A(H1N1)pdm09 and A(H3N2) viruses acquired non-overlapping sets of evolutionary markers. We next analyzed these characteristic markers in vaccine strains recommended by the World Health Organization for the past five years. Our analysis revealed that vaccine strains carried only few evolutionary markers at antigenic sites of viral hemagglutinin (HA) and neuraminidase (NA). The absence of these markers at antigenic sites could affect the recognition of HA and NA by human antibodies generated in response to vaccinations. This could, in part, explain moderate efficacy of influenza vaccines during 2009-2014. Finally, we identified influenza A(H1N1)pdm09 and A(H3N2) strains, which contain all the evolutionary markers of influenza A strains circulated in 2015, and which could be used as vaccine candidates for the 2015/2016 season. Thus, genome-wide analysis of evolutionary markers of influenza A(H1N1)pdm09 and A(H3N2) viruses may guide selection of vaccine strain candidates.
doi:10.1093/gbe/evv240 pmid:26615216 pmcid:PMC4700966 fatcat:vbcw77hpjzb2fjz557k5vwqlta

Human Three-Finger Protein Lypd6 Is a Negative Modulator of the Cholinergic System in the Brain

Dmitrii Kulbatskii, Zakhar Shenkarev, Maxim Bychkov, Eugene Loktyushov, Mikhail Shulepko, Sergey Koshelev, Igor Povarov, Alexander Popov, Steve Peigneur, Anton Chugunov, Sergey Kozlov, Irina Sharonova (+5 others)
2021 Frontiers in Cell and Developmental Biology  
The connection of the nAChRs modulation in the neurons with the expression of nAChRs and other Ly-6/uPAR proteins was recently described (Bychkov et al., 2018) .  ...  Activation of nAChRs by nicotine results in the stimulation of LTP in the hippocampus (Welsby et al., 2009) , while α-Bgtx and β-amyloid peptide (1-42) decrease LTP (Kapay et al., 2013; Bychkov et al  ...  Copyright © 2021 Kulbatskii, Shenkarev, Bychkov, Loktyushov, Shulepko, Koshelev, Povarov, Popov, Peigneur, Chugunov, Kozlov, Sharonova, Efremov, Skrebitsky, Tytgat, Kirpichnikov and Lyukmanova.  ... 
doi:10.3389/fcell.2021.662227 pmid:34631692 pmcid:PMC8494132 fatcat:7pggdofo3vfd3hqs4xq2dmsny4

Human Secreted Ly-6/uPAR Related Protein-1 (SLURP-1) Is a Selective Allosteric Antagonist of α7 Nicotinic Acetylcholine Receptor

Ekaterina N. Lyukmanova, Mikhail A. Shulepko, Denis Kudryavtsev, Maxim L. Bychkov, Dmitrii S. Kulbatskii, Igor E. Kasheverov, Maria V. Astapova, Alexey V. Feofanov, Morten S. Thomsen, Jens D. Mikkelsen, Zakhar O. Shenkarev, Victor I. Tsetlin (+3 others)
2016 PLoS ONE  
SLURP-1 is a secreted toxin-like Ly-6/uPAR protein found in epithelium, sensory neurons and immune cells. Point mutations in the slurp-1 gene cause the autosomal inflammation skin disease Mal de Meleda. SLURP-1 is considered an autocrine/paracrine hormone that regulates growth and differentiation of keratinocytes and controls inflammation and malignant cell transformation. The majority of previous studies of SLURP-1 have been made using fusion constructs containing, in addition to the native
more » ... tein, extra polypeptide sequences. Here we describe the activity and pharmacological profile of a recombinant analogue of human SLURP-1 (rSLURP-1) differing from the native protein only by one additional N-terminal Met residue. rSLURP-1 significantly inhibited proliferation (up to~40%, EC 50~4 nM) of human oral keratinocytes (Het-1A cells). Application of mecamylamine and atropine,-non-selective inhibitors of nicotinic acetylcholine receptors (nAChRs) and muscarinic acetylcholine receptors, respectively, and anti-α7-nAChRs antibodies revealed α7 type nAChRs as an rSLURP-1 target in keratinocytes. Using affinity purification from human cortical extracts, we confirmed that rSLURP-1 binds selectively to the α7-nAChRs. Exposure of Xenopus oocytes expressing α7-nAChRs to rSLURP-1 caused a significant noncompetitive inhibition of the response to acetylcholine (up to~70%, IC 50~1 μM). It was shown that rSLURP-1 binds to α7-nAChRs overexpressed in GH 4 C l cells, but does not compete with 125 I-α-bungarotoxin for binding to the receptor. These findings imply an allosteric antagonist-like mode of SLURP-1 interaction with α7-nAChRs outside the classical ligand-binding site. Contrary to rSLURP-1, other inhibitors of α7-nAChRs (mecamylamine, α-bungarotoxin and Lynx1) did not suppress the proliferation of keratinocytes. Moreover, PLOS ONE | issued to ENL and MAS). The work on affinity purification from cortical extracts and test of antiproliferative activity of rSLURP-1 was done with the support from the the co-application of α-bungarotoxin with rSLURP-1 did not influence antiproliferative activity of the latter. This supports the hypothesis that the antiproliferative activity of SLURP-1 is related to 'metabotropic' signaling pathway through α7-nAChR, that activates intracellular signaling cascades without opening the receptor channel. Human SLURP-1 Is a Selective Allosteric Antagonist of α7 nAChRs PLOS ONE | Abbreviations: ACh, acetylcholine; α-Bgtx, αbungarotoxin; Ls-AChBP, Lymnaea stagnalis acetylcholine binding protein; mAChR, muscarinic acetylcholine receptor; Mec, mecamylamine; nAChR, nicotinic acetylcholine receptor; rSLURP-1, recombinant analogue of human SLURP-1; ws-Lynx1, water-soluble domain of human Lynx1.
doi:10.1371/journal.pone.0149733 pmid:26905431 pmcid:PMC4764493 fatcat:zjpbey6r3rczjbtum5esdlzcd4
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