Podocin Inactivation in Mature Kidneys Causes Focal Segmental Glomerulosclerosis and Nephrotic Syndrome
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Title a New Centrosomal Protein Regulates Neurogenesis By
Title A new centrosomal protein regulates neurogenesis by microtubule organization Authors: Germán Camargo Ortega1-3†, Sven Falk1,2†, Pia A. Johansson1,2†, Elise Peyre4, Sanjeeb Kumar Sahu5, Loïc Broic4, Camino De Juan Romero6, Kalina Draganova1,2, Stanislav Vinopal7, Kaviya Chinnappa1‡, Anna Gavranovic1, Tugay Karakaya1, Juliane Merl-Pham8, Arie Geerlof9, Regina Feederle10,11, Wei Shao12,13, Song-Hai Shi12,13, Stefanie M. Hauck8, Frank Bradke7, Victor Borrell6, Vijay K. Tiwari§, Wieland B. Huttner14, Michaela Wilsch- Bräuninger14, Laurent Nguyen4 and Magdalena Götz1,2,11* Affiliations: 1. Institute of Stem Cell Research, Helmholtz Center Munich, German Research Center for Environmental Health, Munich, Germany. 2. Physiological Genomics, Biomedical Center, Ludwig-Maximilian University Munich, Germany. 3. Graduate School of Systemic Neurosciences, Biocenter, Ludwig-Maximilian University Munich, Germany. 4. GIGA-Neurosciences, Molecular regulation of neurogenesis, University of Liège, Belgium 5. Institute of Molecular Biology (IMB), Mainz, Germany. 6. Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas and Universidad Miguel Hernández, Sant Joan d’Alacant, Spain. 7. Laboratory for Axon Growth and Regeneration, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany. 8. Research Unit Protein Science, Helmholtz Centre Munich, German Research Center for Environmental Health, Munich, Germany. 9. Protein Expression and Purification Facility, Institute of Structural Biology, Helmholtz Center Munich, German Research Center for Environmental Health, Munich, Germany. 10. Institute for Diabetes and Obesity, Monoclonal Antibody Core Facility, Helmholtz Center Munich, German Research Center for Environmental Health, Munich, Germany. 11. SYNERGY, Excellence Cluster of Systems Neurology, Biomedical Center, Ludwig- Maximilian University Munich, Germany. 12. Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, USA 13. -
Bioinformatics Identi Cation of Prognostic Factors Associated With
Bioinformatics Identication of Prognostic Factors Associated with Breast Cancer Ying Wei Sichuan University https://orcid.org/0000-0001-8178-4705 Shipeng Zhang College of Pharmacy, North Sichuan Medical College Li Xiao West China School of Basic Medical Sciences and Forensic Medicine Jing Zou West China School of Basic Medical Sciences and Forensic Medicine Yingqing Fu West China School of Basic Medical Sciences and Forensic Medicine Yi Ye West China School of Basic Medical Sciences and Forensic Medicine Linchuan Liao ( [email protected] ) West China School of Basic Medical Sciences and Forensic Medicine https://orcid.org/0000-0003-3700-8471 Research Keywords: Breast cancer, Differentially expressed genes, miRNAs, Transcription factors, Bioinformatic analysis Posted Date: December 2nd, 2020 DOI: https://doi.org/10.21203/rs.3.rs-117477/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/23 Abstract Background: Breast cancer (BRCA) remains one of the most common forms of cancer and is the most prominent driver of cancer-related death among women. The mechanistic basis for BRCA, however, remains incompletely understood. In particular, the relationships between driver mutations and signaling pathways in BRCA are poorly characterized, making it dicult to identify reliable clinical biomarkers that can be employed in diagnostic, therapeutic, or prognostic contexts. Methods: First, we downloaded publically available BRCA datasets (GSE45827, GSE42568, and GSE61304) from the Gene Expression Omnibus (GEO) database. We then compared gene expression proles between tumor and control tissues in these datasets using Venn diagrams and the GEO2R analytical tool. We further explore the functional relevance of BRCA-associated differentially expressed genes (DEGs) via functional and pathway enrichment analyses using the DAVID tool, and we then constructed a protein-protein interaction network incorporating DEGs of interest using the Search Tool for the Retrieval of Interacting Genes (STRING) database. -
Oocyte Aneuploidy—More Tools to Tackle an Old Problem
COMMENTARY Oocyte aneuploidy—more tools to tackle an old problem COMMENTARY Chris Lodgea and Mary Herberta,1 Meiosis generates a single-copy genome during two centromeric cohesin enables sister centromeres to successive rounds of cell division after a single round biorient on the meiosis II spindle (2, 5). Upon cleavage of DNA replication. Failure to transmit exactly one of centromeric cohesin, dyads are converted to single copy of each chromosome during fertilization gives chromatids, which segregate equationally to opposite rise to aneuploid embryos resulting in infertility and poles of the meiosis II spindle. Protection of a centro- congenital abnormalities such as Down’s syndrome. meric cohesin by Shugoshin proteins (SGOL2 in mam- Aneuploidy attributable to meiotic errors is over- mals) until the onset of anaphase II is essential for whelming due to chromosome segregation errors dur- orderly segregation of chromatids (7, 8). In oocytes, ing female meiosis, and the incidence of these both meiotic divisions are highly asymmetrical, giving increases dramatically as women get older (1, 2). Be- rise to two small nonviable polar bodies (Fig. 1). cause the oocyte is the only viable product of female Consistent with previous studies (9), Tyc et al. (3) meiosis, our understanding of predisposing events in found that only a tiny fraction (<1%) of meiotic errors human oocytes relies largely on inferences from ge- are of paternal origin. Compared with males, the es- netic studies in cases of trisomy and on analysis of tablishment and maintenance of bivalent chromo- oocytes obtained from in vitro fertilization clinics. somes are compromised in female meiosis. In females, Progress toward understanding the underlying mech- there is a greater risk of homologs failing to form cross- anisms has been hampered by a paucity of informa- overs, or of forming them in precarious positions that are tion on the outcome of both meiotic divisions. -
Mutational Inactivation of STAG2 Causes Aneuploidy in Human Cancer
REPORTS mean difference for all rubric score elements was ing becomes a more commonly supported facet 18. C. L. Townsend, E. Heit, Mem. Cognit. 39, 204 (2011). rejected. Univariate statistical tests of the observed of STEM graduate education then students’ in- 19. D. F. Feldon, M. Maher, B. Timmerman, Science 329, 282 (2010). mean differences between the teaching-and- structional training and experiences would alle- 20. B. Timmerman et al., Assess. Eval. High. Educ. 36,509 research and research-only conditions indicated viate persistent concerns that current programs (2011). significant results for the rubric score elements underprepare future STEM faculty to perform 21. No outcome differences were detected as a function of “testability of hypotheses” [mean difference = their teaching responsibilities (28, 29). the type of teaching experience (TA or GK-12) within the P sample population participating in both research and 0.272, = 0.006; CI = (.106, 0.526)] with the null teaching. hypothesis rejected in 99.3% of generated data References and Notes 22. Materials and methods are available as supporting samples (Fig. 1) and “research/experimental de- 1. W. A. Anderson et al., Science 331, 152 (2011). material on Science Online. ” P 2. J. A. Bianchini, D. J. Whitney, T. D. Breton, B. A. Hilton-Brown, 23. R. L. Johnson, J. Penny, B. Gordon, Appl. Meas. Educ. 13, sign [mean difference = 0.317, = 0.002; CI = Sci. Educ. 86, 42 (2001). (.106, 0.522)] with the null hypothesis rejected in 121 (2000). 3. C. E. Brawner, R. M. Felder, R. Allen, R. Brent, 24. R. J. A. Little, J. -
Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. -
A Cytoskeleton Regulator AVIL Drives Tumorigenesis in Glioblastoma
ARTICLE https://doi.org/10.1038/s41467-020-17279-1 OPEN A cytoskeleton regulator AVIL drives tumorigenesis in glioblastoma Zhongqiu Xie 1, Pawel Ł. Janczyk2,8, Ying Zhang3,8, Aiqun Liu4, Xinrui Shi1, Sandeep Singh 1, Loryn Facemire1, ✉ Kristopher Kubow5,ZiLi6, Yuemeng Jia1, Dorothy Schafer7, James W. Mandell1, Roger Abounader3 & Hui Li1,2 Glioblastoma is a deadly cancer, with no effective therapies. Better understanding and identification of selective targets are urgently needed. We found that advillin (AVIL) is 1234567890():,; overexpressed in all the glioblastomas we tested including glioblastoma stem/initiating cells, but hardly detectable in non-neoplastic astrocytes, neural stem cells or normal brain. Glioma patients with increased AVIL expression have a worse prognosis. Silencing AVIL nearly eradicated glioblastoma cells in culture, and dramatically inhibited in vivo xenografts in mice, but had no effect on normal control cells. Conversely, overexpressing AVIL promoted cell proliferation and migration, enabled fibroblasts to escape contact inhibition, and transformed immortalized astrocytes, supporting AVIL being a bona fide oncogene. We provide evidence that the tumorigenic effect of AVIL is partly mediated by FOXM1, which regulates LIN28B, whose expression also correlates with clinical prognosis. AVIL regulates the cytoskeleton through modulating F-actin, while mutants disrupting F-actin binding are defective in its tumorigenic capabilities. 1 Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA. 2 Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA. 3 Department of Microbiology, Immunology, and Cancer Biology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA. 4 Tumor Hospital, Guangxi Medical University, Nanning 530021, China. -
And MMP-Mediated Cell–Matrix Interactions in the Tumor Microenvironment
International Journal of Molecular Sciences Review Hold on or Cut? Integrin- and MMP-Mediated Cell–Matrix Interactions in the Tumor Microenvironment Stephan Niland and Johannes A. Eble * Institute of Physiological Chemistry and Pathobiochemistry, University of Münster, 48149 Münster, Germany; [email protected] * Correspondence: [email protected] Abstract: The tumor microenvironment (TME) has become the focus of interest in cancer research and treatment. It includes the extracellular matrix (ECM) and ECM-modifying enzymes that are secreted by cancer and neighboring cells. The ECM serves both to anchor the tumor cells embedded in it and as a means of communication between the various cellular and non-cellular components of the TME. The cells of the TME modify their surrounding cancer-characteristic ECM. This in turn provides feedback to them via cellular receptors, thereby regulating, together with cytokines and exosomes, differentiation processes as well as tumor progression and spread. Matrix remodeling is accomplished by altering the repertoire of ECM components and by biophysical changes in stiffness and tension caused by ECM-crosslinking and ECM-degrading enzymes, in particular matrix metalloproteinases (MMPs). These can degrade ECM barriers or, by partial proteolysis, release soluble ECM fragments called matrikines, which influence cells inside and outside the TME. This review examines the changes in the ECM of the TME and the interaction between cells and the ECM, with a particular focus on MMPs. Keywords: tumor microenvironment; extracellular matrix; integrins; matrix metalloproteinases; matrikines Citation: Niland, S.; Eble, J.A. Hold on or Cut? Integrin- and MMP-Mediated Cell–Matrix 1. Introduction Interactions in the Tumor Microenvironment. -
Human ADAM12 Quantikine ELISA
Quantikine® ELISA Human ADAM12 Immunoassay Catalog Number DAD120 For the quantitative determination of A Disintegrin And Metalloproteinase domain- containing protein 12 (ADAM12) concentrations in cell culture supernates, serum, plasma, and urine. This package insert must be read in its entirety before using this product. For research use only. Not for use in diagnostic procedures. TABLE OF CONTENTS SECTION PAGE INTRODUCTION .....................................................................................................................................................................1 PRINCIPLE OF THE ASSAY ...................................................................................................................................................2 LIMITATIONS OF THE PROCEDURE .................................................................................................................................2 TECHNICAL HINTS .................................................................................................................................................................2 MATERIALS PROVIDED & STORAGE CONDITIONS ...................................................................................................3 OTHER SUPPLIES REQUIRED .............................................................................................................................................3 PRECAUTIONS .........................................................................................................................................................................4 -
Quantikine® ELISA
Quantikine® ELISA Human ADAMTS13 Immunoassay Catalog Number DADT130 For the quantitative determination of human A Disintegrin And Metalloproteinase with Thombospondin type 1 motif, 13 (ADAMTS13) concentrations in cell culture supernates, serum, and plasma. This package insert must be read in its entirety before using this product. For research use only. Not for use in diagnostic procedures. TABLE OF CONTENTS SECTION PAGE INTRODUCTION .....................................................................................................................................................................1 PRINCIPLE OF THE ASSAY ...................................................................................................................................................2 LIMITATIONS OF THE PROCEDURE .................................................................................................................................2 TECHNICAL HINTS .................................................................................................................................................................2 MATERIALS PROVIDED & STORAGE CONDITIONS ...................................................................................................3 OTHER SUPPLIES REQUIRED .............................................................................................................................................4 PRECAUTIONS .........................................................................................................................................................................4 -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
A Gene Expression Resource Generated by Genome-Wide Lacz
© 2015. Published by The Company of Biologists Ltd | Disease Models & Mechanisms (2015) 8, 1467-1478 doi:10.1242/dmm.021238 RESOURCE ARTICLE A gene expression resource generated by genome-wide lacZ profiling in the mouse Elizabeth Tuck1,**, Jeanne Estabel1,*,**, Anika Oellrich1, Anna Karin Maguire1, Hibret A. Adissu2, Luke Souter1, Emma Siragher1, Charlotte Lillistone1, Angela L. Green1, Hannah Wardle-Jones1, Damian M. Carragher1,‡, Natasha A. Karp1, Damian Smedley1, Niels C. Adams1,§, Sanger Institute Mouse Genetics Project1,‡‡, James N. Bussell1, David J. Adams1, Ramiro Ramırez-Soliś 1, Karen P. Steel1,¶, Antonella Galli1 and Jacqueline K. White1,§§ ABSTRACT composite of RNA-based expression data sets. Strong agreement was observed, indicating a high degree of specificity in our data. Knowledge of the expression profile of a gene is a critical piece of Furthermore, there were 1207 observations of expression of a information required to build an understanding of the normal and particular gene in an anatomical structure where Bgee had no essential functions of that gene and any role it may play in the data, indicating a large amount of novelty in our data set. development or progression of disease. High-throughput, large- Examples of expression data corroborating and extending scale efforts are on-going internationally to characterise reporter- genotype-phenotype associations and supporting disease gene tagged knockout mouse lines. As part of that effort, we report an candidacy are presented to demonstrate the potential of this open access adult mouse expression resource, in which the powerful resource. expression profile of 424 genes has been assessed in up to 47 different organs, tissues and sub-structures using a lacZ reporter KEY WORDS: Gene expression, lacZ reporter, Mouse, Resource gene. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4).