How to Identify Physiologically Relevant Protein Interactions Using Covalent-Capture Halotag® Technology Rob Chumanov, Phd, MBA
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Supplementary Materials
DEPs in osteosarcoma cells comparing to osteoblastic cells Biological Process Protein Percentage of Hits metabolic process (GO:0008152) 29.3 29.3% cellular process (GO:0009987) 20.2 20.2% localization (GO:0051179) 9.4 9.4% biological regulation (GO:0065007) 8 8.0% developmental process (GO:0032502) 7.8 7.8% response to stimulus (GO:0050896) 5.6 5.6% cellular component organization (GO:0071840) 5.6 5.6% multicellular organismal process (GO:0032501) 4.4 4.4% immune system process (GO:0002376) 4.2 4.2% biological adhesion (GO:0022610) 2.7 2.7% apoptotic process (GO:0006915) 1.6 1.6% reproduction (GO:0000003) 0.8 0.8% locomotion (GO:0040011) 0.4 0.4% cell killing (GO:0001906) 0.1 0.1% 100.1% Genes 2179Hits 3870 biological adhesion apoptotic process … reproduction (GO:0000003) , 0.8% (GO:0022610) , 2.7% locomotion (GO:0040011) ,… immune system process cell killing (GO:0001906) , 0.1% (GO:0002376) , 4.2% multicellular organismal process (GO:0032501) , metabolic process 4.4% (GO:0008152) , 29.3% cellular component organization (GO:0071840) , 5.6% response to stimulus (GO:0050896), 5.6% developmental process (GO:0032502) , 7.8% biological regulation (GO:0065007) , 8.0% cellular process (GO:0009987) , 20.2% localization (GO:0051179) , 9. -
A Network Propagation Approach to Prioritize Long Tail Genes in Cancer
bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429983; this version posted February 8, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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-NC-ND 4.0 International license. A Network Propagation Approach to Prioritize Long Tail Genes in Cancer Hussein Mohsen1,*, Vignesh Gunasekharan2, Tao Qing2, Sahand Negahban3, Zoltan Szallasi4, Lajos Pusztai2,*, Mark B. Gerstein1,5,6,3,* 1 Computational Biology & Bioinformatics Program, Yale University, New Haven, CT 06511, USA 2 Breast Medical Oncology, Yale School of Medicine, New Haven, CT 06511, USA 3 Department of Statistics & Data Science, Yale University, New Haven, CT 06511, USA 4 Children’s Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA 5 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA 6 Department of Computer Science, Yale University, New Haven, CT 06511, USA * Corresponding author Abstract Introduction. The diversity of genomic alterations in cancer pose challenges to fully understanding the etiologies of the disease. Recent interest in infrequent mutations, in genes that reside in the “long tail” of the mutational distribution, uncovered new genes with significant implication in cancer development. The study of these genes often requires integrative approaches with multiple types of biological data. Network propagation methods have demonstrated high efficacy in uncovering genomic patterns underlying cancer using biological interaction networks. Yet, the majority of these analyses have focused their assessment on detecting known cancer genes or identifying altered subnetworks. -
Protein Interaction Networks and Their Applications to Protein Characterization and Cancer Genes Prediction
Ramón Aragüés Peleato Protein interaction networks and their applications to protein characterization and cancer genes prediction PhD Thesis Barcelona, May 2007 1 The image of the cover shows the happiness protein interaction network (i.e. the protein interaction network for proteins involved in the serotonin pathway) ii Protein Interaction Networks and their Applications to Protein Characterization and Cancer Genes Prediction Ramón Aragüés Peleato Memòria presentada per optar al grau de Doctor en Biologia per la Universitat Pompeu Fabra. Aquesta Tesi Doctoral ha estat realitzada sota la direcció del Dr. Baldo Oliva al Departament de Ciències Experimentals i de la Salut de la Universitat Pompeu Fabra Baldo Oliva Miguel Ramón Aragüés Peleato Barcelona, Maig 2007 The research in this thesis has been carried out at the Structural Bioinformatics Lab (SBI) within the Grup de Recerca en Informàtica Biomèdica at the Parc de Recerca Biomèdica de Barcelona (PRBB). The research carried out in this thesis has been supported by a “Formación de Personal Investigador (FPI)” grant from the Ministerio de Educación y Ciencia awarded to Dr. Baldo Oliva. A mis padres, que me hicieron querer conseguirlo A Natalia, que me ayudó a conseguir hacerlo ii TABLE OF CONTENTS TABLE OF CONTENTS................................................................................................................................... I ACKNOWLEDGEMENTS........................................................................................................................... -
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Supplementary Figure S1. Results of flow cytometry analysis, performed to estimate CD34 positivity, after immunomagnetic separation in two different experiments. As monoclonal antibody for labeling the sample, the fluorescein isothiocyanate (FITC)- conjugated mouse anti-human CD34 MoAb (Mylteni) was used. Briefly, cell samples were incubated in the presence of the indicated MoAbs, at the proper dilution, in PBS containing 5% FCS and 1% Fc receptor (FcR) blocking reagent (Miltenyi) for 30 min at 4 C. Cells were then washed twice, resuspended with PBS and analyzed by a Coulter Epics XL (Coulter Electronics Inc., Hialeah, FL, USA) flow cytometer. only use Non-commercial 1 Supplementary Table S1. Complete list of the datasets used in this study and their sources. GEO Total samples Geo selected GEO accession of used Platform Reference series in series samples samples GSM142565 GSM142566 GSM142567 GSM142568 GSE6146 HG-U133A 14 8 - GSM142569 GSM142571 GSM142572 GSM142574 GSM51391 GSM51392 GSE2666 HG-U133A 36 4 1 GSM51393 GSM51394 only GSM321583 GSE12803 HG-U133A 20 3 GSM321584 2 GSM321585 use Promyelocytes_1 Promyelocytes_2 Promyelocytes_3 Promyelocytes_4 HG-U133A 8 8 3 GSE64282 Promyelocytes_5 Promyelocytes_6 Promyelocytes_7 Promyelocytes_8 Non-commercial 2 Supplementary Table S2. Chromosomal regions up-regulated in CD34+ samples as identified by the LAP procedure with the two-class statistics coded in the PREDA R package and an FDR threshold of 0.5. Functional enrichment analysis has been performed using DAVID (http://david.abcc.ncifcrf.gov/) -
WO 2015/065964 Al 7 May 2015 (07.05.2015) W P O P C T
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2015/065964 Al 7 May 2015 (07.05.2015) W P O P C T (51) International Patent Classification: (74) Agents: KOWALSKI, Thomas, J. et al; Vedder Price C12N 15/90 (2006.01) C12N 15/113 (2010.01) P.C., 1633 Broadway, New York, NY 1001 9 (US). C12N 15/10 (2006.01) C12N 15/63 (2006.01) (81) Designated States (unless otherwise indicated, for every (21) International Application Number: kind of national protection available): AE, AG, AL, AM, PCT/US2014/062558 AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, (22) International Filing Date: DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, 28 October 2014 (28.10.2014) HN, HR, HU, ID, IL, IN, IR, IS, JP, KE, KG, KN, KP, KR, (25) Filing Language: English KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, (26) Publication Language: English PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, (30) Priority Data: SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 61/96 1,980 28 October 20 13 (28. 10.20 13) US TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. 61/963,643 9 December 2013 (09. -
The Middle Temporal Gyrus Is Transcriptionally Altered in Patients with Alzheimer’S Disease
1 The middle temporal gyrus is transcriptionally altered in patients with Alzheimer’s Disease. 2 1 3 Shahan Mamoor 1Thomas Jefferson School of Law 4 East Islip, NY 11730 [email protected] 5 6 We sought to understand, at the systems level and in an unbiased fashion, how gene 7 expression was most different in the brains of patients with Alzheimer’s Disease (AD) by mining published microarray datasets (1, 2). Comparing global gene expression profiles between 8 patient and control revealed that a set of 84 genes were expressed at significantly different levels in the middle temporal gyrus (MTG) of patients with Alzheimer’s Disease (1, 2). We used 9 computational analyses to classify these genes into known pathways and existing gene sets, 10 and to describe the major differences in the epigenetic marks at the genomic loci of these genes. While a portion of these genes is computationally cognizable as part of a set of genes 11 up-regulated in the brains of patients with AD (3), many other genes in the gene set identified here have not previously been studied in association with AD. Transcriptional repression, both 12 pre- and post-transcription appears to be affected; nearly 40% of these genes are transcriptional 13 targets of MicroRNA-19A/B (miR-19A/B), the zinc finger protein 10 (ZNF10), or of the AP-1 repressor jun dimerization protein 2 (JDP2). 14 15 16 17 18 19 20 21 22 23 24 25 26 Keywords: Alzheimer’s Disease, systems biology of Alzheimer’s Disease, differential gene 27 expression, middle temporal gyrus. -
SLFN11 Promotes CDT1 Degradation by CUL4 in Response to Replicative DNA Damage, While Its Absence Leads to Synthetic Lethality with ATR/CHK1 Inhibitors
SLFN11 promotes CDT1 degradation by CUL4 in response to replicative DNA damage, while its absence leads to synthetic lethality with ATR/CHK1 inhibitors Ukhyun Joa,1, Yasuhisa Muraia, Sirisha Chakkab, Lu Chenb, Ken Chengb, Junko Muraic, Liton Kumar Sahaa, Lisa M. Miller Jenkinsd, and Yves Pommiera,1 aDevelopmental Therapeutics Branch, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20814; bNational Center for Advancing Translational Sciences, Functional Genomics Laboratory, NIH, Rockville, MD 20850; cInstitute for Advanced Biosciences, Keio University, 997-0052 Yamagata, Japan; and dLaboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892 Edited by Richard D. Kolodner, Ludwig Institute for Cancer Research, La Jolla, CA, and approved December 8, 2020 (received for review July 29, 2020) Schlafen-11 (SLFN11) inactivation in ∼50% of cancer cells confers condensation related to deposition of H3K27me3 in the gene broad chemoresistance. To identify therapeutic targets and under- body of SLFN11 by EZH2, a histone methyltransferase (11). lying molecular mechanisms for overcoming chemoresistance, we Targeting epigenetic regulators is therefore an attractive com- performed an unbiased genome-wide RNAi screen in SLFN11-WT bination strategy to overcome chemoresistance of SLFN11- and -knockout (KO) cells. We found that inactivation of Ataxia deficient cancers (10, 25, 26). An alternative approach is to at- Telangiectasia- and Rad3-related (ATR), CHK1, BRCA2, and RPA1 tack SLFN11-negative cancer cells by targeting the essential SLFN11 overcome chemoresistance to camptothecin (CPT) in -KO pathways that cells use to overcome replicative damage and cells. Accordingly, we validate that clinical inhibitors of ATR replication stress. -
Identification of the Key Genes and Pathways in Prostate Cancer
ONCOLOGY LETTERS 16: 6663-6669, 2018 Identification of the key genes and pathways in prostate cancer SHUTONG FAN1*, ZUMU LIANG1*, ZHIQIN GAO1, ZHIWEI PAN2, SHAOJIE HAN3, XIAOYING LIU1, CHUNLING ZHAO1, WEIWEI YANG1, ZHIFANG PAN1 and WEIGUO FENG1 1College of Bioscience and Technology, Weifang Medical University, Weifang, Shandong 261053; 2Department of Internal Medicine, Laizhou Development Zone Hospital, Yantai, Shandong 261400; 3Animal Epidemic Prevention and Epidemic Control Center, Changle County Bureau of Animal Health and Production, Weifang, Shandong 262400, P.R. China Received March 5, 2018; Accepted September 17, 2018 DOI: 10.3892/ol.2018.9491 Abstract. Prostate cancer (PCa) is one of the most common Introduction malignancies in men globally. The aim of the present study was to identify the key genes and pathways involved in the Prostate cancer (PCa) is one of the most common malignancies occurrence of PCa. Gene expression profile (GSE55945) in men globally and the second leading cause of cancer was downloaded from Gene Expression Omnibus, and associated mortality in developed countries (1,2). Like other the differentially expressed genes (DEGs) were identified. cancers, PCa is considered to be a disease which caused by Subsequently, Gene ontology analysis, KEGG pathway age, diet and gene aberrations (3). Accumulating evidences analysis and protein-protein interaction (PPI) analysis of have demonstrated that a series of genes and pathways involved DEGs were performed. Finally, the identified key genes were in the occurrence, progression and metastasis of PCa (4). At confirmed by immunohistochemistry. The GO analysis results present, the underlying mechanism of PCa occurrence is still showed that the DEGs were mainly participated in cell cycle, unclear, which limits the diagnosis and therapy. -
Protein Purification Protein Localization in Vivo Fluorescent Imaging Protein Arrays Real Time Imaging Protein Interactions Protein Trafficking Protein Turnover
Overcoming Challenges of Protein Analysis in Mammalian Systems Danette L. Daniels, Ph.D. Current Technologies for Protein Analysis Biochemical/ In Vivo Proteomic Cell Based Animal Analysis Analysis Models Fluorescent proteins Affinity tags Antibodies How about a system applicable to the all approaches that also addresses limitations of current methods? • Minimal interference with protein of interest • Efficient capture/isolation • Detection/real-time imaging • Differential labeling • High Signal/background HaloTag Platform Biochemical/ In Vivo Proteomic Cell Based Animal Analysis Analysis Models Protein purification Protein localization In vivo fluorescent imaging Protein arrays Real time imaging Protein interactions Protein trafficking Protein turnover HaloTag® HaloCHIP™ HaloLink™ HaloTag® Fluorescent Purification Protein:DNA Protein Arrays Pull-Down Ligands HaloTag is a Genetically Engineered Protein Fusion Tag O Functional Protein of Cl O Interest HT + group Protein of Functional HT O O Interest group . A monomeric , 34 kDa, modified bacterial dehalogenase genetically engineered to covalently bind specific, synthetic HaloTag® ligands . Irreversible, covalent attachment of chemical functionalities . Suitable as either N- or C- terminal fusion Mutagenized HaloTag® Protein Enables Covalent HaloTag®-Ligand Complex Hydrolase (DhaA) HaloTag® Catalytic process Facilitated bond formation T r p 1 0 7 T r p 1 0 7 HaloTag®: • 34kDa protein • Monomeric N N H N 4 1 H N A s n - H H • Single change: C l 4 1 A s n C l 2 1 His272Phe for covalent O R O O - C C bond. 3 R O 1 0 6 A s p 1 0 6 A s p O H O H Covalent bond: H H O O • Stable after N C G l u 1 3 0 C G l u 1 3 0 N - O denaturation. -
Supplementary Materials
Supplementary Materials COMPARATIVE ANALYSIS OF THE TRANSCRIPTOME, PROTEOME AND miRNA PROFILE OF KUPFFER CELLS AND MONOCYTES Andrey Elchaninov1,3*, Anastasiya Lokhonina1,3, Maria Nikitina2, Polina Vishnyakova1,3, Andrey Makarov1, Irina Arutyunyan1, Anastasiya Poltavets1, Evgeniya Kananykhina2, Sergey Kovalchuk4, Evgeny Karpulevich5,6, Galina Bolshakova2, Gennady Sukhikh1, Timur Fatkhudinov2,3 1 Laboratory of Regenerative Medicine, National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov of Ministry of Healthcare of Russian Federation, Moscow, Russia 2 Laboratory of Growth and Development, Scientific Research Institute of Human Morphology, Moscow, Russia 3 Histology Department, Medical Institute, Peoples' Friendship University of Russia, Moscow, Russia 4 Laboratory of Bioinformatic methods for Combinatorial Chemistry and Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia 5 Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow, Russia 6 Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia Figure S1. Flow cytometry analysis of unsorted blood sample. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S2. Flow cytometry analysis of unsorted liver stromal cells. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S3. MiRNAs expression analysis in monocytes and Kupffer cells. Full-length of heatmaps are presented. -
Novel Driver Strength Index Highlights Important Cancer Genes in TCGA Pancanatlas Patients
medRxiv preprint doi: https://doi.org/10.1101/2021.08.01.21261447; this version posted August 5, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients Aleksey V. Belikov*, Danila V. Otnyukov, Alexey D. Vyatkin and Sergey V. Leonov Laboratory of Innovative Medicine, School of Biological and Medical Physics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Moscow Region, Russia *Corresponding author: [email protected] NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. 1 medRxiv preprint doi: https://doi.org/10.1101/2021.08.01.21261447; this version posted August 5, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Abstract Elucidating crucial driver genes is paramount for understanding the cancer origins and mechanisms of progression, as well as selecting targets for molecular therapy. Cancer genes are usually ranked by the frequency of mutation, which, however, does not necessarily reflect their driver strength. Here we hypothesize that driver strength is higher for genes that are preferentially mutated in patients with few driver mutations overall, because these few mutations should be strong enough to initiate cancer. -
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Produktinformation Diagnostik & molekulare Diagnostik Laborgeräte & Service Zellkultur & Verbrauchsmaterial Forschungsprodukte & Biochemikalien Weitere Information auf den folgenden Seiten! See the following pages for more information! Lieferung & Zahlungsart Lieferung: frei Haus Bestellung auf Rechnung SZABO-SCANDIC Lieferung: € 10,- HandelsgmbH & Co KG Erstbestellung Vorauskassa Quellenstraße 110, A-1100 Wien T. +43(0)1 489 3961-0 Zuschläge F. +43(0)1 489 3961-7 [email protected] • Mindermengenzuschlag www.szabo-scandic.com • Trockeneiszuschlag • Gefahrgutzuschlag linkedin.com/company/szaboscandic • Expressversand facebook.com/szaboscandic SANTA CRUZ BIOTECHNOLOGY, INC. POLR2G CRISPR/Cas9 KO Plasmid (h): sc-406059 BACKGROUND APPLICATIONS The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and POLR2G CRISPR/Cas9 KO Plasmid (h) is recommended for the disruption of CRISPR-associated protein (Cas9) system is an adaptive immune response gene expression in human cells. defense mechanism used by archea and bacteria for the degradation of foreign genetic material (4,6). This mechanism can be repurposed for other 20 nt non-coding RNA sequence: guides Cas9 functions, including genomic engineering for mammalian systems, such as to a specific target location in the genomic DNA gene knockout (KO) (1,2,3,5). CRISPR/Cas9 KO Plasmid products enable the U6 promoter: drives gRNA scaffold: helps Cas9 identification and cleavage of specific genes by utilizing guide RNA (gRNA) expression of gRNA bind to target DNA sequences derived from the Genome-scale CRISPR Knock-Out (GeCKO) v2 library developed in the Zhang Laboratory at the Broad Institute (3,5). Termination signal Green Fluorescent Protein: to visually REFERENCES verify transfection CRISPR/Cas9 Knockout Plasmid CBh (chicken β-Actin 1. Cong, L., et al.