TY - THES AU - Balassa, Tamás TI - Cell identification and phenotyping using classical machine learning and deep learning PB - Eötvös Loránd Tudományegyetem (ELTE) PY - 2022 SP - 100 DO - 10.15476/ELTE.2021.207 UR - https://m2.mtmt.hu/api/publication/33862507 ID - 33862507 LA - English DB - MTMT ER - TY - JOUR AU - Szkalisity, Ábel AU - Piccinini, Filippo AU - Beleon, Attila AU - Balassa, Tamás AU - Varga, Gergely István AU - Migh, Ede AU - Molnár, Csaba AU - Paavolainen, Lassi AU - Timonen, Sanna AU - Banerjee, Indranil AU - Ikonen, Elina AU - Yamauchi, Yohei AU - Andó, István AU - Peltonen, Jaakko AU - Pietiäinen, Vilja AU - Honti, Viktor AU - Horváth, Péter TI - Regression plane concept for analysing continuous cellular processes with machine learning JF - NATURE COMMUNICATIONS J2 - NAT COMMUN VL - 12 PY - 2021 IS - 1 PG - 9 SN - 2041-1723 DO - 10.1038/s41467-021-22866-x UR - https://m2.mtmt.hu/api/publication/32023128 ID - 32023128 N1 - Cited By :2 Export Date: 14 June 2022 LA - English DB - MTMT ER - TY - JOUR AU - Dukay, Brigitta AU - Walter, Fruzsina AU - Vigh, Judit Piroska AU - Barabási, Beáta AU - Hajdu, Petra AU - Balassa, Tamás AU - Migh, Ede AU - Kincses, András AU - Hoyk, Zsófia AU - Szögi, Titanilla AU - Borbély, Emőke AU - Csoboz, Bálint AU - Horváth, Péter AU - Fülöp, Lívia AU - Penke, Botond AU - Vigh, László AU - Deli, Mária Anna AU - Sántha, Miklós AU - Tóth, Erzsébet Melinda TI - Neuroinflammatory processes are augmented in mice overexpressing human heat-shock protein B1 following ethanol-induced brain injury JF - JOURNAL OF NEUROINFLAMMATION J2 - J NEUROINFLAMM VL - 18 PY - 2021 IS - 1 PG - 24 SN - 1742-2094 DO - 10.1186/s12974-020-02070-2 UR - https://m2.mtmt.hu/api/publication/31807147 ID - 31807147 N1 - Miklós Sántha and Melinda E. Tóth are shared last authors. LA - English DB - MTMT ER - TY - JOUR AU - Koós, Krisztián AU - Oláh, Gáspár AU - Balassa, Tamás AU - Mihut, Norbert AU - Rózsa, Márton AU - Ozsvár, Attila AU - Tasnádi, Ervin Áron AU - Barzó, Pál AU - Faragó, Nóra AU - Puskás, László AU - Molnár, Gábor AU - Molnár, József AU - Tamás, Gábor AU - Horváth, Péter TI - Automatic deep learning-driven label-free image-guided patch clamp system JF - NATURE COMMUNICATIONS J2 - NAT COMMUN VL - 12 PY - 2021 IS - 1 PG - 11 SN - 2041-1723 DO - 10.1038/s41467-021-21291-4 UR - https://m2.mtmt.hu/api/publication/31598376 ID - 31598376 N1 - Cited By :6 Export Date: 17 June 2022 LA - English DB - MTMT ER - TY - JOUR AU - Piccinini, Filippo AU - Balassa, Tamás AU - Carbonaro, Antonella AU - Diósdi, Ákos AU - Tóth, Tímea AU - Moshkov, Nikita AU - Tasnádi, Ervin Áron AU - Horváth, Péter TI - Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates JF - COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL J2 - CSBJ VL - 18 PY - 2020 SP - 1287 EP - 1300 PG - 14 SN - 2001-0370 DO - 10.1016/j.csbj.2020.05.022 UR - https://m2.mtmt.hu/api/publication/31360966 ID - 31360966 N1 - Journal Article; Review Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Cancer Research Hospital, Meldola, FC, Italy Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary Department of Computer Science and Engineering, University of Bologna, Italy Doctoral School of Biology, University of Szeged, Hungary Doctoral School of Interdisciplinary Medicine, University of Szeged, Hungary National Research University Higher School of Economics, Moscow, Russian Federation Doctoral School of Computer Science, University of Szeged, Hungary Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland Single-Cell Technologies Ltd., Szeged, Hungary Export Date: 16 September 2020 Correspondence Address: Horvath, P.; Synthetic and Systems Biology Unit, Biological Research Centre (BRC)Hungary; email: horvath.peter@brc.hu Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Cancer Research Hospital, Meldola, FC, Italy Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary Department of Computer Science and Engineering, University of Bologna, Italy Doctoral School of Biology, University of Szeged, Hungary Doctoral School of Interdisciplinary Medicine, University of Szeged, Hungary National Research University Higher School of Economics, Moscow, Russian Federation Doctoral School of Computer Science, University of Szeged, Hungary Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland Single-Cell Technologies Ltd., Szeged, Hungary Cited By :1 Export Date: 22 April 2021 Correspondence Address: Horvath, P.; Synthetic and Systems Biology Unit, Hungary; email: horvath.peter@brc.hu LA - English DB - MTMT ER - TY - JOUR AU - Hollandi, Réka AU - Szkalisity, Ábel AU - Tóth, Tímea AU - Tasnádi, Ervin Áron AU - Molnár, Csaba AU - Mathe, Botond AU - Grexa, István AU - Molnár, József AU - Bálind, Árpád AU - Gorbe, Mate AU - Kovács, Mária AU - Migh, Ede AU - Goodman, Allen AU - Balassa, Tamás AU - Koós, Krisztián AU - Wang, Wenyu AU - Caicedo, Juan Carlos AU - Bara, Norbert AU - Kovács, Ferenc AU - Paavolainen, Lassi AU - Danka, Tivadar AU - Kriston, András AU - Carpenter, Anne Elizabeth AU - Smith, Kevin AU - Horváth, Péter TI - nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer JF - CELL SYSTEMS J2 - CELL SYST VL - 10 PY - 2020 IS - 5 SP - 453 EP - 458 PG - 6 SN - 2405-4712 DO - 10.1016/j.cels.2020.04.003 UR - https://m2.mtmt.hu/api/publication/31334623 ID - 31334623 AB - Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is that during training nucleAIzer automatically adapts its nucleus-style model to unseen and unlabeled data using image style transfer to automatically generate augmented training samples. This allows the model to recognize nuclei in new and different experiments efficiently without requiring expert annotations, making deep learning for nucleus segmentation fairly simple and labor free for most biological light microscopy experiments. It can also be used online, integrated into CellProfiler and freely downloaded at www.nucleaizer.org. A record of this paper's transparent peer review process is included in the Supplemental Information. LA - English DB - MTMT ER - TY - JOUR AU - Smith, Kevin AU - Piccinini, Filippo AU - Balassa, Tamás AU - Koós, Krisztián AU - Danka, Tivadar AU - Azizpour, Hossein AU - Horváth, Péter TI - Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays JF - CELL SYSTEMS J2 - CELL SYST VL - 6 PY - 2018 IS - 6 SP - 636 EP - 653 PG - 18 SN - 2405-4712 DO - 10.1016/j.cels.2018.06.001 UR - https://m2.mtmt.hu/api/publication/27598077 ID - 27598077 N1 - KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, Lindstedtsvägen 3, Stockholm, 10044, Sweden Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, Meldola, FC 47014, Italy Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári krt. 62, Szeged, 6726, Hungary Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, Helsinki, 00014, Finland Cited By :6 Export Date: 26 August 2019 Correspondence Address: Horvath, P.; Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, Finland; email: horvath.peter@brc.mta.hu Funding details: MSD K.K. Funding details: GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-15-2016-00026 Funding details: Tekes, 40294/13 Funding details: European Association for Cancer Research Funding text 1: The authors wish to thank Benjamin Misselwitz for providing source code of Enhanced CellClassifier. P.H. acknowledges support from the Finnish TEKES FiDiPro Fellow Grant 40294/13 . F.P. acknowledges support from the European Association for Cancer Research (EACR) for a granted travel fellowship (ref. 573) and from NEUBIAS COST Action (European Cooperation in Science and Technology) for a granted short-term scientific mission (ref. CA15124). B.T., K.K., T.D., and P.H. acknowledge support from the European Regional Development Funds ( GINOP-2.3.2-15-2016-00006 and GINOP-2.3.2-15-2016-00026 ). KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, Lindstedtsvägen 3, Stockholm, 10044, Sweden Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, Meldola, FC 47014, Italy Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári krt. 62, Szeged, 6726, Hungary Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, Helsinki, 00014, Finland Cited By :13 Export Date: 28 January 2020 Correspondence Address: Horvath, P.; Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, Finland; email: horvath.peter@brc.mta.hu Funding details: MSD K.K. Funding details: GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-15-2016-00026 Funding details: Tekes, 40294/13 Funding details: European Association for Cancer Research Funding text 1: The authors wish to thank Benjamin Misselwitz for providing source code of Enhanced CellClassifier. P.H. acknowledges support from the Finnish TEKES FiDiPro Fellow Grant 40294/13 . F.P. acknowledges support from the European Association for Cancer Research (EACR) for a granted travel fellowship (ref. 573) and from NEUBIAS COST Action (European Cooperation in Science and Technology) for a granted short-term scientific mission (ref. CA15124). B.T., K.K., T.D., and P.H. acknowledge support from the European Regional Development Funds ( GINOP-2.3.2-15-2016-00006 and GINOP-2.3.2-15-2016-00026 ). KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, Lindstedtsvägen 3, Stockholm, 10044, Sweden Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, Meldola, FC 47014, Italy Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári krt. 62, Szeged, 6726, Hungary Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, Helsinki, 00014, Finland Cited By :14 Export Date: 17 February 2020 Correspondence Address: Horvath, P.; Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, Finland; email: horvath.peter@brc.mta.hu Funding Agency and Grant Number: Finnish TEKES FiDiPro Fellow Grant [40294/13]; European Association for Cancer Research (EACR) [573]; NEUBIAS COST Action (European Cooperation in Science and Technology) [CA15124]; European Regional Development FundsEuropean Union (EU) [GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-152016-00026] Funding text: The authors wish to thank Benjamin Misselwitz for providing source code of Enhanced CellClassifier. P.H. acknowledges support from the Finnish TEKES FiDiPro Fellow Grant 40294/13. F.P. acknowledges support from the European Association for Cancer Research (EACR) for a granted travel fellowship (ref. 573) and from NEUBIAS COST Action (European Cooperation in Science and Technology) for a granted short-term scientific mission (ref. CA15124). B.T., K.K., T.D., and P.H. acknowledge support from the European Regional Development Funds (GINOP-2.3.2-15-2016-00006 and GINOP-2.3.2-152016-00026). KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, Lindstedtsvägen 3, Stockholm, 10044, Sweden Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, Meldola, FC 47014, Italy Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári krt. 62, Szeged, 6726, Hungary Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, Helsinki, 00014, Finland Cited By :19 Export Date: 28 August 2020 Correspondence Address: Horvath, P.; Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, Finland; email: horvath.peter@brc.mta.hu Funding details: GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-15-2016-00026 Funding details: European Association for Cancer Research, EACR Funding details: 40294/13 Funding text 1: The authors wish to thank Benjamin Misselwitz for providing source code of Enhanced CellClassifier. P.H. acknowledges support from the Finnish TEKES FiDiPro Fellow Grant 40294/13 . F.P. acknowledges support from the European Association for Cancer Research (EACR) for a granted travel fellowship (ref. 573) and from NEUBIAS COST Action (European Cooperation in Science and Technology) for a granted short-term scientific mission (ref. CA15124). B.T., K.K., T.D., and P.H. acknowledge support from the European Regional Development Funds ( GINOP-2.3.2-15-2016-00006 and GINOP-2.3.2-15-2016-00026 ). LA - English DB - MTMT ER - TY - JOUR AU - Suleymanova, I AU - Balassa, Tamás AU - Tripathi, S AU - Molnár, Csaba AU - Saarma, M AU - Sidorova, Y AU - Horváth, Péter TI - A deep convolutional neural network approach for astrocyte detection JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 8 PY - 2018 PG - 7 SN - 2045-2322 DO - 10.1038/s41598-018-31284-x UR - https://m2.mtmt.hu/api/publication/3414411 ID - 3414411 N1 - OA gold AB - Astrocytes are involved in various brain pathologies including trauma, stroke, neurodegenerative disorders such as Alzheimer's and Parkinson's diseases, or chronic pain. Determining cell density in a complex tissue environment in microscopy images and elucidating the temporal characteristics of morphological and biochemical changes is essential to understand the role of astrocytes in physiological and pathological conditions. Nowadays, manual stereological cell counting or semi-automatic segmentation techniques are widely used for the quantitative analysis of microscopy images. Detecting astrocytes automatically is a highly challenging computational task, for which we currently lack efficient image analysis tools. We have developed a fast and fully automated software that assesses the number of astrocytes using Deep Convolutional Neural Networks (DCNN). The method highly outperforms state-of-the-art image analysis and machine learning methods and provides precision comparable to those of human experts. Additionally, the runtime of cell detection is significantly less than that of other three computational methods analysed, and it is faster than human observers by orders of magnitude. We applied our DCNN-based method to examine the number of astrocytes in different brain regions of rats with opioid-induced hyperalgesia/tolerance (OIH/OIT), as morphine tolerance is believed to activate glia. We have demonstrated a strong positive correlation between manual and DCNN-based quantification of astrocytes in rat brain. LA - English DB - MTMT ER - TY - JOUR AU - Tóth, Tímea AU - Balassa, Tamás AU - Bara, Norbert AU - Kovács, Ferenc AU - Kriston, András AU - Molnár, Csaba AU - Haracska, Lajos AU - Sükösd, Farkas AU - Horváth, Péter TI - Environmental properties of cells improve machine learning-based phenotype recognition accuracy. JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 8 PY - 2018 IS - 1 PG - 9 SN - 2045-2322 DO - 10.1038/s41598-018-28482-y UR - https://m2.mtmt.hu/api/publication/3396221 ID - 3396221 N1 - Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary Single-Cell Technologies Ltd, Szeged, Hungary University of Szeged, Department of Pathology, Szeged, Hungary Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland Export Date: 26 August 2019 Correspondence Address: Horvath, P.; Biological Research Centre of the Hungarian Academy of SciencesHungary; email: horvath.peter@brc.mta.hu Funding details: 2018-342 Funding details: GINOP-2.3.2-15-2016-00037, GINOP-2.3.2-15-2016-00001 Funding details: Tekes, 40294/13 Funding text 1: P.H. acknowledges support from the Finnish TEKES FiDiPro Fellow Grant 40294/13. T.T., T.B., C.M., L.H. and P.H. acknowledge support from the LENDULET-BIOMAG Grant (2018-342) and support from the European Regional Development Funds (GINOP-2.3.2-15-2016-00001, GINOP-2.3.2-15-2016-00037). The authors thank Gabriella Tick and Dora Bokor PharmD for proofreading the manuscript. Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary Single-Cell Technologies Ltd, Szeged, Hungary University of Szeged, Department of Pathology, Szeged, Hungary Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland Cited By :3 Export Date: 28 January 2020 Correspondence Address: Horvath, P.; Biological Research Centre of the Hungarian Academy of SciencesHungary; email: horvath.peter@brc.mta.hu Funding details: 2018-342 Funding details: GINOP-2.3.2-15-2016-00037, GINOP-2.3.2-15-2016-00001 Funding details: Tekes, 40294/13 Funding text 1: P.H. acknowledges support from the Finnish TEKES FiDiPro Fellow Grant 40294/13. T.T., T.B., C.M., L.H. and P.H. acknowledge support from the LENDULET-BIOMAG Grant (2018-342) and support from the European Regional Development Funds (GINOP-2.3.2-15-2016-00001, GINOP-2.3.2-15-2016-00037). The authors thank Gabriella Tick and Dora Bokor PharmD for proofreading the manuscript. Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary Single-Cell Technologies Ltd, Szeged, Hungary University of Szeged, Department of Pathology, Szeged, Hungary Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland Cited By :3 Export Date: 17 February 2020 Correspondence Address: Horvath, P.; Biological Research Centre of the Hungarian Academy of SciencesHungary; email: horvath.peter@brc.mta.hu Funding Agency and Grant Number: Finnish TEKES FiDiPro Fellow Grant [40294/13]; LENDULET-BIOMAG Grant [2018-342]; European Regional Development FundsEuropean Union (EU) [GINOP-2.3.2-15-2016-00001, GINOP-2.3.2-15-2016-00037] Funding text: P.H. acknowledges support from the Finnish TEKES FiDiPro Fellow Grant 40294/13. T.T., T.B., C.M., L.H. and P.H. acknowledge support from the LENDULET-BIOMAG Grant (2018-342) and support from the European Regional Development Funds (GINOP-2.3.2-15-2016-00001, GINOP-2.3.2-15-2016-00037). The authors thank Gabriella Tick and Dora Bokor PharmD for proofreading the manuscript. Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary Single-Cell Technologies Ltd, Szeged, Hungary University of Szeged, Department of Pathology, Szeged, Hungary Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland Cited By :4 Export Date: 28 August 2020 Correspondence Address: Horvath, P.; Biological Research Centre of the Hungarian Academy of SciencesHungary; email: horvath.peter@brc.mta.hu Funding details: 2018-342 Funding details: GINOP-2.3.2-15-2016-00037, GINOP-2.3.2-15-2016-00001 Funding details: 40294/13 Funding text 1: P.H. acknowledges support from the Finnish TEKES FiDiPro Fellow Grant 40294/13. T.T., T.B., C.M., L.H. and P.H. acknowledge support from the LENDULET-BIOMAG Grant (2018-342) and support from the European Regional Development Funds (GINOP-2.3.2-15-2016-00001, GINOP-2.3.2-15-2016-00037). The authors thank Gabriella Tick and Dora Bokor PharmD for proofreading the manuscript. Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary Single-Cell Technologies Ltd, Szeged, Hungary University of Szeged, Department of Pathology, Szeged, Hungary Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland Cited By :4 Export Date: 16 September 2020 Correspondence Address: Horvath, P.; Biological Research Centre of the Hungarian Academy of SciencesHungary; email: horvath.peter@brc.mta.hu Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary Single-Cell Technologies Ltd, Szeged, Hungary University of Szeged, Department of Pathology, Szeged, Hungary Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland Cited By :4 Export Date: 21 April 2021 Correspondence Address: Horvath, P.; Biological Research Centre of the Hungarian Academy of SciencesHungary; email: horvath.peter@brc.mta.hu AB - To answer major questions of cell biology, it is often essential to understand the complex phenotypic composition of cellular systems precisely. Modern automated microscopes produce vast amounts of images routinely, making manual analysis nearly impossible. Due to their efficiency, machine learning-based analysis software have become essential tools to perform single-cell-level phenotypic analysis of large imaging datasets. However, an important limitation of such methods is that they do not use the information gained from the cellular micro- and macroenvironment: the algorithmic decision is based solely on the local properties of the cell of interest. Here, we present how various features from the surrounding environment contribute to identifying a cell and how such additional information can improve single-cell-level phenotypic image analysis. The proposed methodology was tested for different sizes of Euclidean and nearest neighbour-based cellular environments both on tissue sections and cell cultures. Our experimental data verify that the surrounding area of a cell largely determines its entity. This effect was found to be especially strong for established tissues, while it was somewhat weaker in the case of cell cultures. Our analysis shows that combining local cellular features with the properties of the cell's neighbourhood significantly improves the accuracy of machine learning-based phenotyping. LA - English DB - MTMT ER - TY - JOUR AU - Farkas, Zoltán AU - Kalapis, Dorottya AU - Bódi, Zoltán AU - Szamecz, Béla AU - Daraba, Andreea AU - Almási, Karola AU - Kovács, Károly AU - Boross, Gábor AU - Pál, Ferenc AU - Horváth, Péter AU - Balassa, Tamás AU - Molnár, Csaba AU - Pettkó-Szandtner, Aladár AU - Klement, Éva AU - Rutkai, E AU - Szvetnik, Attila AU - Papp, Balázs AU - Pál, Csaba TI - Hsp70-associated chaperones have a critical role in buffering protein production costs. JF - ELIFE J2 - ELIFE VL - 7 PY - 2018 PG - 23 SN - 2050-084X DO - 10.7554/eLife.29845 UR - https://m2.mtmt.hu/api/publication/3325350 ID - 3325350 N1 - Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary Institute of Plant Biology, Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary Laboratory of Proteomic Research, Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary Division for Biotechnology, Budapest, Hungary Cited By :11 Export Date: 9 April 2021 Correspondence Address: Farkas, Z.; Synthetic and Systems Biology Unit, Hungary; email: farkas.zoltan@brc.mta.hu AB - Proteins are necessary for cellular growth. Concurrently, however, protein production has high energetic demands associated with transcription and translation. Here, we propose that activity of molecular chaperones shape protein burden, that is the fitness costs associated with expression of unneeded proteins. To test this hypothesis, we performed a genome-wide genetic interaction screen in baker's yeast. Impairment of transcription, translation, and protein folding rendered cells hypersensitive to protein burden. Specifically, deletion of specific regulators of the Hsp70-associated chaperone network increased protein burden. In agreement with expectation, temperature stress, increased mistranslation and a chemical misfolding agent all substantially enhanced protein burden. Finally, unneeded protein perturbed interactions between key components of the Hsp70-Hsp90 network involved in folding of native proteins. We conclude that specific chaperones contribute to protein burden. Our work indicates that by minimizing the damaging impact of gratuitous protein overproduction, chaperones enable tolerance to massive changes in genomic expression. LA - English DB - MTMT ER -