Extended Lifespan of Bronchial Epithelial Cells Maintains Normal Cellular Phenotype and Transcriptome Integrity
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PSPC1 Potentiates IGF1R Expression to Augment Cell Adhesion and Motility
1 Supplementary information 2 PSPC1 potentiates IGF1R expression to augment cell 3 adhesion and motility 4 Hsin-Wei Jen1,2 , De-Leung Gu 2, Yaw-Dong Lang 2 and Yuh-Shan Jou 1,2,* 5 1 Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan 6 2 Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan 7 * Author to whom correspondence should be addressed 8 Cells 2020, 9, x; doi: FOR PEER REVIEW www.mdpi.com/journal/cells Cells 2020, 9, x FOR PEER REVIEW 2 of 10 9 10 11 Supplementary Figure S1: Expression of IGF1R and integrin in PSPC1-expressing or PSPC1-depleted 12 HCC cells by Western blotting analysis 13 (A) Detection of IGF1R protein levels in three PSPC1-knockdown cells Huh7, HepG2 and Mahlavu. (B) 14 Detection of selected integrin expression in PSPC1-overexpressing or PSPC1-depleted HCC cells by using 15 their total cell lysates immunoblotted with specific integrin antibodies as shown. 16 17 18 Supplementary Figure S2: PSPC1-modulated IGF1R downstream signaling in HCC cells. Cells 2020, 9, x FOR PEER REVIEW 3 of 10 19 (A, B) Immunoblotting of IGF1R expression in PSPC1-overexpressing SK-Hep1 and PLC5 cells 20 treated with IGF1R shRNAs. (C, D) Cell migration and adhesion were measured in PSPC1- 21 knockdown Hep3B cells rescued with exogenous expression of IGF1R. Exogenous expression of 22 IGF1R in PSPC1-knockdown Hep3B cells were then applied for detection of altered AKT/ERK 23 signaling including (E) total PSPC1, IGF1R, AKT, ERK, p-IGF1R, p-AKT(S473), and 24 p-ERK(T202/Y204) as well as altered FAK/Src signaling including (F) total FAK, Src, p-FAK(Y397) 25 and p-Src(Y416) by immunoblotting assay. -
ONCOGENOMICS Through the Use of Chemotherapeutic Agents
Oncogene (2002) 21, 7749 – 7763 ª 2002 Nature Publishing Group All rights reserved 0950 – 9232/02 $25.00 www.nature.com/onc Expression profiling of primary non-small cell lung cancer for target identification Jim Heighway*,1, Teresa Knapp1, Lenetta Boyce1, Shelley Brennand1, John K Field1, Daniel C Betticher2, Daniel Ratschiller2, Mathias Gugger2, Michael Donovan3, Amy Lasek3 and Paula Rickert*,3 1Target Identification Group, Roy Castle International Centre for Lung Cancer Research, University of Liverpool, 200 London Road, Liverpool L3 9TA, UK; 2Institute of Medical Oncology, University of Bern, 3010 Bern, Switzerland; 3Incyte Genomics Inc., 3160 Porter Drive, Palo Alto, California, CA 94304, USA Using a panel of cDNA microarrays comprising 47 650 Introduction transcript elements, we have carried out a dual-channel analysis of gene expression in 39 resected primary The lack of generally effective therapies for lung cancer human non-small cell lung tumours versus normal lung leads to the high mortality rate associated with this tissue. Whilst *11 000 elements were scored as common disease. Recorded 5-year survival figures vary, differentially expressed at least twofold in at least one to some extent perhaps reflecting differences in sample, 96 transcripts were scored as over-represented ascertainment methods, but across Europe are approxi- fourfold or more in at least seven out of 39 tumours mately 10% (Bray et al., 2002). Whilst surgery is an and 30 sequences 16-fold in at least two out of 39 effective strategy for the treatment of localized lesions, tumours, including 24 transcripts in common. Tran- most patients present with overtly or covertly dissemi- scripts (178) were found under-represented fourfold in nated disease. -
Β-Catenin Knockdown Affects Mitochondrial Biogenesis and Lipid Metabolism in Breast Cancer Cells
ORIGINAL RESEARCH published: 27 July 2017 doi: 10.3389/fphys.2017.00544 β-Catenin Knockdown Affects Mitochondrial Biogenesis and Lipid Metabolism in Breast Cancer Cells Daniele Vergara 1, 2 †, Eleonora Stanca 1, 2 †, Flora Guerra 1, Paola Priore 3, Antonio Gaballo 3, Julien Franck 4, Pasquale Simeone 5, Marco Trerotola 6, Stefania De Domenico 7, Isabelle Fournier 4, Cecilia Bucci 1, Michel Salzet 4, Anna M. Giudetti 1* and Michele Maffia 1, 2* 1 Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy, 2 Laboratory of Clinical Proteomic, “Giovanni Paolo II” Hospital, Lecce, Italy, 3 CNR NANOTEC - Institute of Nanotechnology, Lecce, Italy, 4 University of Lille, Institut national de la santé et de la recherche médicale, U-1192 - Laboratoire Protéomique, Réponse Edited by: Inflammatoire et Spectrométrie de Masse-PRISM, Lille, France, 5 Unit of Cytomorphology, CeSI-MeT and Department of Andrei Surguchov, Medicine and Aging Sciences, School of Medicine and Health Sciences, University “G. d’Annunzio,” Chieti, Italy, 6 Unit of Kansas University of Medical Center Cancer Pathology, CeSI-MeT and Department of Medical, Oral and Biotechnological Sciences, University “G. d’Annunzio,” Research Institute, United States Chieti, Italy, 7 C.N.R. Unit of Lecce, Institute of Food Production Sciences, Lecce, Italy Reviewed by: Kamal Datta, β-catenin plays an important role as regulatory hub in several cellular processes including Georgetown University, United States Silvana Gaetani, cell adhesion, metabolism, and epithelial mesenchymal transition. This is mainly achieved Sapienza Università di Roma, Italy by its dual role as structural component of cadherin-based adherens junctions, and as Clizia Chinello, University of Milano-Bicocca, Italy a key nuclear effector of the Wnt pathway. -
Salivary Exrna Biomarkers to Detect Gingivitis and Monitor Disease Regression
Received: 3 December 2017 | Revised: 14 April 2018 | Accepted: 13 May 2018 DOI: 10.1111/jcpe.12930 OTHER Salivary exRNA biomarkers to detect gingivitis and monitor disease regression Karolina E. Kaczor-Urbanowicz1,2* | Harsh M. Trivedi3* | Patricia O. Lima1,4 | Paulo M. Camargo5 | William V. Giannobile6 | Tristan R. Grogan7 | Frederico O. Gleber-Netto8 | Yair Whiteman9 | Feng Li1 | Hyo Jung Lee10 | Karan Dharia11 | Katri Aro1 | Carmen Martin Carerras-Presas12 | Saarah Amuthan11 | Manjiri Vartak11 | David Akin1 | Hiba Al-adbullah11 | Kanika Bembey11 | Perry R. Klokkevold5 | David Elashoff7 | Virginia M. Barnes13 | Rose Richter13 | William DeVizio13 | James G. Masters3 | David T. W. Wong1 1UCLA School of Dentistry, Center for Oral/Head & Neck Oncology Research, University of California at Los Angeles, Los Angeles, California 2Section of Orthodontics, UCLA School of Dentistry, University of California at Los Angeles, Los Angeles, California 3Early Research Oral Care, Colgate Palmolive Co., Piscataway, New Jersey 4Department of Physiological Sciences, Piracicaba Dental School, University of Campinas, Piracicaba, São Paulo, Brazil 5Section of Periodontics, UCLA School of Dentistry, University of California at Los Angeles, Los Angeles, California 6Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, Michigan 7Department of Biostatistics, University of California at Los Angeles, Los Angeles, California 8Medical Genomics Laboratory, Centro Internacional de Pesquisa e Ensino (CIPE), AC Camargo Cancer -
Communication Pathways in Human Nonmuscle Myosin-2C 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Authors: 25 Krishna Chinthalapudia,B,C,1, Sarah M
1 Mechanistic Insights into the Active Site and Allosteric 2 Communication Pathways in Human Nonmuscle Myosin-2C 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Authors: 25 Krishna Chinthalapudia,b,c,1, Sarah M. Heisslera,d,1, Matthias Prellera,e, James R. Sellersd,2, and 26 Dietmar J. Mansteina,b,2 27 28 Author Affiliations 29 aInstitute for Biophysical Chemistry, OE4350 Hannover Medical School, 30625 Hannover, 30 Germany. 31 bDivision for Structural Biochemistry, OE8830, Hannover Medical School, 30625 Hannover, 32 Germany. 33 cCell Adhesion Laboratory, Department of Integrative Structural and Computational Biology, The 34 Scripps Research Institute, Jupiter, Florida 33458, USA. 35 dLaboratory of Molecular Physiology, NHLBI, National Institutes of Health, Bethesda, Maryland 36 20892, USA. 37 eCentre for Structural Systems Biology (CSSB), German Electron Synchrotron (DESY), 22607 38 Hamburg, Germany. 39 1K.C. and S.M.H. contributed equally to this work 40 2To whom correspondence may be addressed: E-mail: [email protected] or 41 [email protected] 42 43 1 44 Abstract 45 Despite a generic, highly conserved motor domain, ATP turnover kinetics and their activation by 46 F-actin vary greatly between myosin-2 isoforms. Here, we present a 2.25 Å crystal pre- 47 powerstroke state (ADPVO4) structure of the human nonmuscle myosin-2C motor domain, one 48 of the slowest myosins characterized. In combination with integrated mutagenesis, ensemble- 49 solution kinetics, and molecular dynamics simulation approaches, the structure reveals an 50 allosteric communication pathway that connects the distal end of the motor domain with the 51 active site. -
Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database. -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
Serum Albumin OS=Homo Sapiens
Protein Name Cluster of Glial fibrillary acidic protein OS=Homo sapiens GN=GFAP PE=1 SV=1 (P14136) Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 Cluster of Isoform 3 of Plectin OS=Homo sapiens GN=PLEC (Q15149-3) Cluster of Hemoglobin subunit beta OS=Homo sapiens GN=HBB PE=1 SV=2 (P68871) Vimentin OS=Homo sapiens GN=VIM PE=1 SV=4 Cluster of Tubulin beta-3 chain OS=Homo sapiens GN=TUBB3 PE=1 SV=2 (Q13509) Cluster of Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 (P60709) Cluster of Tubulin alpha-1B chain OS=Homo sapiens GN=TUBA1B PE=1 SV=1 (P68363) Cluster of Isoform 2 of Spectrin alpha chain, non-erythrocytic 1 OS=Homo sapiens GN=SPTAN1 (Q13813-2) Hemoglobin subunit alpha OS=Homo sapiens GN=HBA1 PE=1 SV=2 Cluster of Spectrin beta chain, non-erythrocytic 1 OS=Homo sapiens GN=SPTBN1 PE=1 SV=2 (Q01082) Cluster of Pyruvate kinase isozymes M1/M2 OS=Homo sapiens GN=PKM PE=1 SV=4 (P14618) Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 Clathrin heavy chain 1 OS=Homo sapiens GN=CLTC PE=1 SV=5 Filamin-A OS=Homo sapiens GN=FLNA PE=1 SV=4 Cytoplasmic dynein 1 heavy chain 1 OS=Homo sapiens GN=DYNC1H1 PE=1 SV=5 Cluster of ATPase, Na+/K+ transporting, alpha 2 (+) polypeptide OS=Homo sapiens GN=ATP1A2 PE=3 SV=1 (B1AKY9) Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 Dihydropyrimidinase-related protein 2 OS=Homo sapiens GN=DPYSL2 PE=1 SV=1 Cluster of Alpha-actinin-1 OS=Homo sapiens GN=ACTN1 PE=1 SV=2 (P12814) 60 kDa heat shock protein, mitochondrial OS=Homo -
A Database of Gene Regulatory Network Models Across Human Conditions
bioRxiv preprint doi: https://doi.org/10.1101/2021.06.18.448997; this version posted June 18, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. GRAND: A database of gene regulatory network models across human conditions Marouen Ben Guebila1†, Camila M Lopes-Ramos1†, Deborah Weighill1,7, Abhijeet Rajendra Sonawane2, Rebekka Burkholz1, Behrouz Shamsaei3, John Platig4, Kimberly Glass1,4, Marieke L Kuijjer5,6, John Quackenbush1,4,*. 1Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA. 2Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. 3Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. 4Channing Division of Network Medicine, Department of Medicine, Harvard Medical School and Brigham and Women’s Hospital 5Center for Molecular Medicine Norway, Faculty of Medicine, University of Oslo, Norway 6Leiden University Medical Center, Leiden, the Netherlands 7Current address: UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA † Joint Authors. * To whom correspondence should be addressed. Email: [email protected]. 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.18.448997; this version posted June 18, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. -
Myosin Motors: Novel Regulators and Therapeutic Targets in Colorectal Cancer
cancers Review Myosin Motors: Novel Regulators and Therapeutic Targets in Colorectal Cancer Nayden G. Naydenov 1, Susana Lechuga 1, Emina H. Huang 2 and Andrei I. Ivanov 1,* 1 Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44195, USA; [email protected] (N.G.N.); [email protected] (S.L.) 2 Departments of Cancer Biology and Colorectal Surgery, Cleveland Clinic Foundation, Cleveland, OH 44195, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-216-445-5620 Simple Summary: Colorectal cancer (CRC) is a deadly disease that may go undiagnosed until it presents at an advanced metastatic stage for which few interventions are available. The develop- ment and metastatic spread of CRC is driven by remodeling of the actin cytoskeleton in cancer cells. Myosins represent a large family of actin motor proteins that play key roles in regulating actin cytoskeleton architecture and dynamics. Different myosins can move and cross-link actin filaments, attach them to the membrane organelles and translocate vesicles along the actin filaments. These diverse activities determine the key roles of myosins in regulating cell proliferation, differ- entiation and motility. Either mutations or the altered expression of different myosins have been well-documented in CRC; however, the roles of these actin motors in colon cancer development remain poorly understood. The present review aims at summarizing the evidence that implicate myosin motors in regulating CRC growth and metastasis and discusses the mechanisms underlying the oncogenic and tumor-suppressing activities of myosins. Abstract: Colorectal cancer (CRC) remains the third most common cause of cancer and the second most common cause of cancer deaths worldwide. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Microrna Regulatory Pathways in the Control of the Actin–Myosin Cytoskeleton
cells Review MicroRNA Regulatory Pathways in the Control of the Actin–Myosin Cytoskeleton , , Karen Uray * y , Evelin Major and Beata Lontay * y Department of Medical Chemistry, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary; [email protected] * Correspondence: [email protected] (K.U.); [email protected] (B.L.); Tel.: +36-52-412345 (K.U. & B.L.) The authors contributed equally to the manuscript. y Received: 11 June 2020; Accepted: 7 July 2020; Published: 9 July 2020 Abstract: MicroRNAs (miRNAs) are key modulators of post-transcriptional gene regulation in a plethora of processes, including actin–myosin cytoskeleton dynamics. Recent evidence points to the widespread effects of miRNAs on actin–myosin cytoskeleton dynamics, either directly on the expression of actin and myosin genes or indirectly on the diverse signaling cascades modulating cytoskeletal arrangement. Furthermore, studies from various human models indicate that miRNAs contribute to the development of various human disorders. The potentially huge impact of miRNA-based mechanisms on cytoskeletal elements is just starting to be recognized. In this review, we summarize recent knowledge about the importance of microRNA modulation of the actin–myosin cytoskeleton affecting physiological processes, including cardiovascular function, hematopoiesis, podocyte physiology, and osteogenesis. Keywords: miRNA; actin; myosin; actin–myosin complex; Rho kinase; cancer; smooth muscle; hematopoiesis; stress fiber; gene expression; cardiovascular system; striated muscle; muscle cell differentiation; therapy 1. Introduction Actin–myosin interactions are the primary source of force generation in mammalian cells. Actin forms a cytoskeletal network and the myosin motor proteins pull actin filaments to produce contractile force. All eukaryotic cells contain an actin–myosin network inferring contractile properties to these cells.