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Markovits E, Harush O, Baruch EN, Shulman ED, Debby A, Itzhaki O, Anafi L, Danilevsky A, Shomron N, Ben-Betzalel G, Asher N, Shapira-Frommer R, Schachter J, Barshack I, Geiger T, Elkon R, Besser MJ, Markel G. MYC Induces Immunotherapy and IFNγ Resistance Through Downregulation of JAK2. Cancer Immunol Res 2023; 11:909-924. [PMID: 37074069 DOI: 10.1158/2326-6066.cir-22-0184] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 11/27/2022] [Accepted: 04/17/2023] [Indexed: 04/20/2023]
Abstract
Immunotherapy has revolutionized the treatment of advanced melanoma. Because the pathways mediating resistance to immunotherapy are largely unknown, we conducted transcriptome profiling of preimmunotherapy tumor biopsies from patients with melanoma that received PD-1 blockade or adoptive cell therapy with tumor-infiltrating lymphocytes. We identified two melanoma-intrinsic, mutually exclusive gene programs, which were controlled by IFNγ and MYC, and the association with immunotherapy outcome. MYC-overexpressing melanoma cells exhibited lower IFNγ responsiveness, which was linked with JAK2 downregulation. Luciferase activity assays, under the control of JAK2 promoter, demonstrated reduced activity in MYC-overexpressing cells, which was partly reversible upon mutagenesis of a MYC E-box binding site in the JAK2 promoter. Moreover, silencing of MYC or its cofactor MAX with siRNA increased JAK2 expression and IFNγ responsiveness of melanomas, while concomitantly enhancing the effector functions of T cells coincubated with MYC-overexpressing cells. Thus, we propose that MYC plays a pivotal role in immunotherapy resistance through downregulation of JAK2.
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Affiliation(s)
- Ettai Markovits
- Ella Lemelbaum Institute for Immuno-oncology, Sheba Medical Center, Tel Hashomer, Israel
- Department of Clinical Microbiology and Immunology, The Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Ortal Harush
- Ella Lemelbaum Institute for Immuno-oncology, Sheba Medical Center, Tel Hashomer, Israel
- Department of Clinical Microbiology and Immunology, The Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Erez N Baruch
- Ella Lemelbaum Institute for Immuno-oncology, Sheba Medical Center, Tel Hashomer, Israel
- Department of Clinical Microbiology and Immunology, The Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Eldad D Shulman
- Department of Human Molecular Genetics and Biochemistry, The Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Assaf Debby
- Institute of Pathology, Sheba Medical Center, Tel Hashomer, Israel
- Department of Dermatology, Sheba Medical Center, Tel Hashomer, Israel
| | - Orit Itzhaki
- Ella Lemelbaum Institute for Immuno-oncology, Sheba Medical Center, Tel Hashomer, Israel
| | - Liat Anafi
- Institute of Pathology, Sheba Medical Center, Tel Hashomer, Israel
| | - Artem Danilevsky
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Noam Shomron
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Guy Ben-Betzalel
- Ella Lemelbaum Institute for Immuno-oncology, Sheba Medical Center, Tel Hashomer, Israel
| | - Nethanel Asher
- Ella Lemelbaum Institute for Immuno-oncology, Sheba Medical Center, Tel Hashomer, Israel
| | - Ronnie Shapira-Frommer
- Ella Lemelbaum Institute for Immuno-oncology, Sheba Medical Center, Tel Hashomer, Israel
| | - Jacob Schachter
- Ella Lemelbaum Institute for Immuno-oncology, Sheba Medical Center, Tel Hashomer, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Iris Barshack
- Institute of Pathology, Sheba Medical Center, Tel Hashomer, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Tamar Geiger
- Department of Human Molecular Genetics and Biochemistry, The Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Ran Elkon
- Department of Human Molecular Genetics and Biochemistry, The Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Michal J Besser
- Ella Lemelbaum Institute for Immuno-oncology, Sheba Medical Center, Tel Hashomer, Israel
- Department of Clinical Microbiology and Immunology, The Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
- Davidoff Cancer Center, Rabin Medical Center-Beilinson Hospital, Petah Tikva, Israel
- Felsenstein Medical Research Center, Rabin Medical Center-Beilinson Hospital, Petah Tikva, Israel
| | - Gal Markel
- Department of Clinical Microbiology and Immunology, The Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
- Davidoff Cancer Center, Rabin Medical Center-Beilinson Hospital, Petah Tikva, Israel
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2
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Ofek P, Yeini E, Arad G, Danilevsky A, Pozzi S, Luna CB, Dangoor SI, Grossman R, Ram Z, Shomron N, Brem H, Hyde TM, Geiger T, Satchi-Fainaro R. Deoxyhypusine hydroxylase: A novel therapeutic target differentially expressed in short-term vs long-term survivors of glioblastoma. Int J Cancer 2023. [PMID: 37141410 DOI: 10.1002/ijc.34545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 02/13/2023] [Accepted: 03/10/2023] [Indexed: 05/06/2023]
Abstract
Glioblastoma (GB) is the most aggressive neoplasm of the brain. Poor prognosis is mainly attributed to tumor heterogeneity, invasiveness and drug resistance. Only a small fraction of GB patients survives longer than 24 months from the time of diagnosis (ie, long-term survivors [LTS]). In our study, we aimed to identify molecular markers associated with favorable GB prognosis as a basis to develop therapeutic applications to improve patients' outcome. We have recently assembled a proteogenomic dataset of 87 GB clinical samples of varying survival rates. Following RNA-seq and mass spectrometry (MS)-based proteomics analysis, we identified several differentially expressed genes and proteins, including some known cancer-related pathways and some less established that showed higher expression in short-term (<6 months) survivors (STS) compared to LTS. One such target found was deoxyhypusine hydroxylase (DOHH), which is known to be involved in the biosynthesis of hypusine, an unusual amino acid essential for the function of the eukaryotic translation initiation factor 5A (eIF5A), which promotes tumor growth. We consequently validated DOHH overexpression in STS samples by quantitative polymerase chain reaction (qPCR) and immunohistochemistry. We further showed robust inhibition of proliferation, migration and invasion of GB cells following silencing of DOHH with short hairpin RNA (shRNA) or inhibition of its activity with small molecules, ciclopirox and deferiprone. Moreover, DOHH silencing led to significant inhibition of tumor progression and prolonged survival in GB mouse models. Searching for a potential mechanism by which DOHH promotes tumor aggressiveness, we found that it supports the transition of GB cells to a more invasive phenotype via epithelial-mesenchymal transition (EMT)-related pathways.
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Affiliation(s)
- Paula Ofek
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eilam Yeini
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Gali Arad
- Department of Molecular Genetics, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Artem Danilevsky
- Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
| | - Sabina Pozzi
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Christian Burgos Luna
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Sahar Israeli Dangoor
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Rachel Grossman
- Department of Neurosurgery, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Zvi Ram
- Department of Neurosurgery, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Noam Shomron
- Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neurosciences, Tel Aviv University, Tel Aviv, Israel
| | - Henry Brem
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA
- Department of Psychiatry & Behavioral Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Tamar Geiger
- Department of Molecular Genetics, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ronit Satchi-Fainaro
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neurosciences, Tel Aviv University, Tel Aviv, Israel
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3
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Danilevsky A, Polsky AL, Shomron N. Adaptive sequencing using nanopores and deep learning of mitochondrial DNA. Brief Bioinform 2022; 23:6634223. [PMID: 35804265 DOI: 10.1093/bib/bbac251] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 05/13/2022] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Nanopore sequencing is an emerging technology that reads DNA by utilizing a unique method of detecting nucleic acid sequences and identifies the various chemical modifications they carry. Deep learning has increased in popularity as a useful technique to solve many complex computational tasks. 'Adaptive sequencing' is an implementation of selective sequencing, intended for use on the nanopore sequencing platform. In this study, we demonstrated an alternative method of software-based selective sequencing that is performed in real time by combining nanopore sequencing and deep learning. Our results showed the feasibility of using deep learning for classifying signals from only the first 200 nucleotides in a raw nanopore sequencing signal format. This was further demonstrated by comparing the accuracy of our deep learning classification model across data from several human cell lines and other eukaryotic organisms. We used custom deep learning models and a script that utilizes a 'Read Until' framework to target mitochondrial molecules in real time from a human cell line sample. This achieved a significant separation and enrichment ability of 2.3-fold. In a series of very short sequencing experiments (10, 30 and 120 min), we identified genomic and mitochondrial reads with accuracy above 90%, although mitochondrial DNA comprised only 0.1% of the total input material. The uniqueness of our method is the ability to distinguish two groups of DNA even without a labeled reference. This contrasts with studies that required a well-defined reference, whether of a DNA sequence or of another type of representation. Additionally, our method showed higher correlation to the theoretically possible enrichment factor, compared with other published methods. We believe that our results will lay the foundation for rapid and selective sequencing using nanopore technology and will pave the approach for clinical applications that use nanopore sequencing data.
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Affiliation(s)
- Artem Danilevsky
- Faculty of Medicine and Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel
| | - Avital Luba Polsky
- Faculty of Medicine and Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel
| | - Noam Shomron
- Faculty of Medicine and Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel
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Shmerling M, Chalik M, Smorodinsky NI, Meeker A, Roy S, Sagi-Assif O, Meshel T, Danilevsky A, Shomron N, Levinger S, Nishry B, Baruchi D, Shargorodsky A, Ziv R, Sarusi-Portuguez A, Lahav M, Ehrlich M, Braschi B, Bruford E, Witz IP, Wreschner DH. LY6S, a New IFN-Inducible Human Member of the Ly6a Subfamily Expressed by Spleen Cells and Associated with Inflammation and Viral Resistance. Immunohorizons 2022; 6:253-272. [PMID: 35440514 PMCID: PMC9574348 DOI: 10.4049/immunohorizons.2200018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 03/10/2022] [Indexed: 11/19/2022] Open
Abstract
Syntenic genomic loci on human chromosome 8 and mouse chromosome 15 (mChr15) code for LY6/Ly6 (lymphocyte Ag 6) family proteins. The 23 murine Ly6 family genes include eight genes that are flanked by the murine Ly6e and Ly6l genes and form an Ly6 subgroup referred to in this article as the Ly6a subfamily gene cluster. Ly6a, also known as Stem Cell Ag-1 and T cell–activating protein, is a member of the Ly6a subfamily gene cluster. No LY6 genes have been annotated within the syntenic LY6E to LY6L human locus. We report in this article on LY6S, a solitary human LY6 gene that is syntenic with the murine Ly6a subfamily gene cluster, and with which it shares a common ancestry. LY6S codes for the IFN-inducible GPI-linked LY6S-iso1 protein that contains only 9 of the 10 consensus LY6 cysteine residues and is most highly expressed in a nonclassical spleen cell population. Its expression leads to distinct shifts in patterns of gene expression, particularly of genes coding for inflammatory and immune response proteins, and LY6S-iso1–expressing cells show increased resistance to viral infection. Our findings reveal the presence of a previously unannotated human IFN-stimulated gene, LY6S, which has a 1:8 ortholog relationship with the genes of the Ly6a subfamily gene cluster, is most highly expressed in spleen cells of a nonclassical cell lineage, and whose expression induces viral resistance and is associated with an inflammatory phenotype and with the activation of genes that regulate immune responses.
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Affiliation(s)
- Moriya Shmerling
- Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Ramat Aviv, Israel
| | - Michael Chalik
- Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Ramat Aviv, Israel
| | - Nechama I Smorodinsky
- Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Ramat Aviv, Israel
| | - Alan Meeker
- The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Sujayita Roy
- The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Orit Sagi-Assif
- Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Ramat Aviv, Israel
| | - Tsipi Meshel
- Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Ramat Aviv, Israel
| | - Artem Danilevsky
- Faculty of Medicine and Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Ramat Aviv, Israel
| | - Noam Shomron
- Faculty of Medicine and Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Ramat Aviv, Israel
| | - Shmuel Levinger
- Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Ramat Aviv, Israel
| | - Bar Nishry
- Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Ramat Aviv, Israel
| | - David Baruchi
- Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Ramat Aviv, Israel
| | - Avital Shargorodsky
- Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Ramat Aviv, Israel
| | - Ravit Ziv
- Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Ramat Aviv, Israel
| | - Avital Sarusi-Portuguez
- Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel
| | - Maoz Lahav
- Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Ramat Aviv, Israel
| | - Marcelo Ehrlich
- Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Ramat Aviv, Israel
| | - Bryony Braschi
- HUGO Gene Nomenclature Committee, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom; and
| | - Elspeth Bruford
- HUGO Gene Nomenclature Committee, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom; and.,Department of Haematology, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Isaac P Witz
- Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Ramat Aviv, Israel;
| | - Daniel H Wreschner
- Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Ramat Aviv, Israel;
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Abstract
Since its inception, deep learning has revolutionized the field of machine learning and data-driven science. One such data-driven science to be transformed by deep learning is genomics. In the past decade, numerous genomics studies have adopted deep learning and its applications range from predicting regulatory elements to cancer classification. Despite its dominating efficacy in these applications, deep learning is not without drawbacks. A prominent shortcoming of deep learning is the lack of interpretability. Hence, the main objective of this study is to address this obstacle in the deep learning cancer classification. Here we adopt a feature importance scoring methodology (Gradient-based class activation mapping or Grad-CAM) on a quasi-recurrent neural network model that classify cancer based on FASTA sequencing data. In this study, we managed to formulate a nucleotide-to-genomic-region Grad-CAM scoring methodology, as well as, validate the use this methodology for the chosen model. Consequently, this allows for the utilization of the Grad-CAM scoring methodology for feature importance in deep learning cancer classification. The results from our study identify potential novel candidate genes, genomic elements, and mechanisms for future cancer research.
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Affiliation(s)
| | | | - Noam Shomron
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
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Rabinowitz T, Polsky A, Golan D, Danilevsky A, Shapira G, Raff C, Basel-Salmon L, Matar RT, Shomron N. Bayesian-based noninvasive prenatal diagnosis of single-gene disorders. Genome Res 2019; 29:428-438. [PMID: 30787035 PMCID: PMC6396420 DOI: 10.1101/gr.235796.118] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Accepted: 01/23/2019] [Indexed: 12/04/2022]
Abstract
In the last decade, noninvasive prenatal diagnosis (NIPD) has emerged as an effective procedure for early detection of inherited diseases during pregnancy. This technique is based on using cell-free DNA (cfDNA) and fetal cfDNA (cffDNA) in maternal blood, and hence, has minimal risk for the mother and fetus compared with invasive techniques. NIPD is currently used for identifying chromosomal abnormalities (in some instances) and for single-gene disorders (SGDs) of paternal origin. However, for SGDs of maternal origin, sensitivity poses a challenge that limits the testing to one genetic disorder at a time. Here, we present a Bayesian method for the NIPD of monogenic diseases that is independent of the mode of inheritance and parental origin. Furthermore, we show that accounting for differences in the length distribution of fetal- and maternal-derived cfDNA fragments results in increased accuracy. Our model is the first to predict inherited insertions–deletions (indels). The method described can serve as a general framework for the NIPD of SGDs; this will facilitate easy integration of further improvements. One such improvement that is presented in the current study is a machine learning model that corrects errors based on patterns found in previously processed data. Overall, we show that next-generation sequencing (NGS) can be used for the NIPD of a wide range of monogenic diseases, simultaneously. We believe that our study will lead to the achievement of a comprehensive NIPD for monogenic diseases.
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Affiliation(s)
- Tom Rabinowitz
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Avital Polsky
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - David Golan
- Faculty of Industrial Engineering and Management, Technion, Haifa, 3200003, Israel
| | - Artem Danilevsky
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Guy Shapira
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Chen Raff
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Lina Basel-Salmon
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel.,Raphael Recanati Genetic Institute, Rabin Medical Center, Beilinson Hospital, Petah Tikva, 4941494, Israel
| | - Reut Tomashov Matar
- Raphael Recanati Genetic Institute, Rabin Medical Center, Beilinson Hospital, Petah Tikva, 4941494, Israel
| | - Noam Shomron
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
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7
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Schipiloff C, Danilevsky A. Ueber die Natur der anisotropen Substanzen des quergestreiften Muskels und ihre räumliche Vertheilung im Muskelbündel. Biol Chem 2009. [DOI: 10.1515/bchm1.1881.5.5.349] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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