Function Study of Arabidopsis Thaliana Core Alpha1,3-Fucosyltransferase (Fucta) Peter Both
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Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer Kumardeep Chaudhary1, Olivier B
Published OnlineFirst October 5, 2017; DOI: 10.1158/1078-0432.CCR-17-0853 Statistics in CCR Clinical Cancer Research Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer Kumardeep Chaudhary1, Olivier B. Poirion1, Liangqun Lu1,2, and Lana X. Garmire1,2 Abstract Identifying robust survival subgroups of hepatocellular car- index (C-index) ¼ 0.68]. More aggressive subtype is associated cinoma (HCC) will significantly improve patient care. Current- with frequent TP53 inactivation mutations, higher expression ly, endeavor of integrating multi-omicsdatatoexplicitlypredict of stemness markers (KRT19 and EPCAM)andtumormarker HCC survival from multiple patient cohorts is lacking. To fill BIRC5, and activated Wnt and Akt signaling pathways. We this gap, we present a deep learning (DL)–based model on HCC validated this multi-omics model on five external datasets of that robustly differentiates survival subpopulations of patients various omics types: LIRI-JP cohort (n ¼ 230, C-index ¼ 0.75), in six cohorts. We built the DL-based, survival-sensitive model NCI cohort (n ¼ 221, C-index ¼ 0.67), Chinese cohort (n ¼ on 360 HCC patients' data using RNA sequencing (RNA-Seq), 166, C-index ¼ 0.69), E-TABM-36 cohort (n ¼ 40, C-index ¼ miRNA sequencing (miRNA-Seq), and methylation data from 0.77), and Hawaiian cohort (n ¼ 27, C-index ¼ 0.82). This TheCancerGenomeAtlas(TCGA),whichpredictsprognosis is the first study to employ DL to identify multi-omics features as good as an alternative model where genomics and clinical linked to the differential survival of patients with HCC. Given data are both considered. This DL-based model provides two its robustness over multiple cohorts, we expect this workflow to optimal subgroups of patients with significant survival differ- be useful at predicting HCC prognosis prediction. -
Molecular Mechanisms Involved Involved in the Interaction Effects of HCV and Ethanol on Liver Cirrhosis
Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2010 Molecular Mechanisms Involved Involved in the Interaction Effects of HCV and Ethanol on Liver Cirrhosis Ryan Fassnacht Virginia Commonwealth University Follow this and additional works at: https://scholarscompass.vcu.edu/etd Part of the Physiology Commons © The Author Downloaded from https://scholarscompass.vcu.edu/etd/2246 This Thesis is brought to you for free and open access by the Graduate School at VCU Scholars Compass. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of VCU Scholars Compass. For more information, please contact [email protected]. Ryan C. Fassnacht 2010 All Rights Reserved Molecular Mechanisms Involved in the Interaction Effects of HCV and Ethanol on Liver Cirrhosis A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University. by Ryan Christopher Fassnacht, B.S. Hampden Sydney University, 2005 M.S. Virginia Commonwealth University, 2010 Director: Valeria Mas, Ph.D., Associate Professor of Surgery and Pathology Division of Transplant Department of Surgery Virginia Commonwealth University Richmond, Virginia July 9, 2010 Acknowledgement The Author wishes to thank his family and close friends for their support. He would also like to thank the members of the molecular transplant team for their help and advice. This project would not have been possible with out the help of Dr. Valeria Mas and her endearing -
Primary Antibodies Flyer
Primary Antibodies Your choice of size and format Format Concentration Size CF® dye conjugates (13 colors) 0.1 mg/mL 100 or 500 uL Biotin, HRP or AP conjugates 0.1 mg/mL 100 or 500 uL R-PE, APC, or Per-CP conjugates 0.1 mg/mL 250 uL Purified, with BSA 0.1 mg/mL 100 or 500 uL Purified, BSA-free (Mix-n-Stain™ Ready) 1 mg/mL 50 uL Advantages Figure 1. IHC staining of human prostate Figure 2. Flow cytometry analysis of U937 • More than 1000 monoclonal antibodies carcinoma with anti-ODC1 clone cells with anti-CD31/PECAM clone C31.7, • Growing selection of monoclonal rabbit ODC1/485. CF647 conjugate (blue) or isotype control (orange). antibodies • Validated in IHC and other applications Your choice of 13 bright and photostable CF® dyes • Choose from 13 bright and stable CF® dyes CF® dye Ex/Em (nm) Features • Also available with R-PE, APC, PerCP, HRP, AP, CF®405S 404/431 • Better fit for the 450/50 flow cytometer channel than Alexa Fluor® 405 or biotin CF®405M 408/452 • More photostable than Pacific Blue®, with less green spill-over • Purified antibodies available BSA-free, 1 mg/mL, • Compatible with super-resolution imaging by SIM ready to use for Mix-n-Stain™ labeling or other CF®488A 490/515 • Less non-specific binding and spill-over than Alexa Fluor® 488 conjugation • Very photostable and pH-insensitive • Compatible with super-resolution imaging by TIRF • Offered in affordable 100 uL size CF®543 541/560 • Brighter than Alexa Fluor® 546 CF®555 555/565 • Brighter than Cy®3 • Validated in multicolor super-resolution imaging by STORM CF®568 -
A Systematic Review on the Implications of O-Linked Glycan Branching and Truncating Enzymes on Cancer Progression and Metastasis
cells Review A Systematic Review on the Implications of O-linked Glycan Branching and Truncating Enzymes on Cancer Progression and Metastasis 1, 1, 1 1,2,3, Rohitesh Gupta y, Frank Leon y, Sanchita Rauth , Surinder K. Batra * and Moorthy P. Ponnusamy 1,2,* 1 Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68105, USA; [email protected] (R.G.); [email protected] (F.L.); [email protected] (S.R.) 2 Fred and Pamela Buffett Cancer Center, Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE 681980-5900, USA 3 Department of Pathology and Microbiology, UNMC, Omaha, NE 68198-5900, USA * Correspondence: [email protected] (S.K.B.); [email protected] (M.P.P.); Tel.: +402-559-5455 (S.K.B.); +402-559-1170 (M.P.P.); Fax: +402-559-6650 (S.K.B. & M.P.P.) Equal contribution. y Received: 21 January 2020; Accepted: 12 February 2020; Published: 14 February 2020 Abstract: Glycosylation is the most commonly occurring post-translational modifications, and is believed to modify over 50% of all proteins. The process of glycan modification is directed by different glycosyltransferases, depending on the cell in which it is expressed. These small carbohydrate molecules consist of multiple glycan families that facilitate cell–cell interactions, protein interactions, and downstream signaling. An alteration of several types of O-glycan core structures have been implicated in multiple cancers, largely due to differential glycosyltransferase expression or activity. Consequently, aberrant O-linked glycosylation has been extensively demonstrated to affect biological function and protein integrity that directly result in cancer growth and progression of several diseases. -
Selectin Ligand Sialyl-Lewis X Antigen Drives Metastasis of Hormone-Dependent Breast Cancers
Published OnlineFirst October 24, 2011; DOI: 10.1158/0008-5472.CAN-11-1139 Cancer Tumor and Stem Cell Biology Research Selectin Ligand Sialyl-Lewis x Antigen Drives Metastasis of Hormone-Dependent Breast Cancers Sylvain Julien1, Aleksandar Ivetic2, Anita Grigoriadis3, Ding QiZe1, Brian Burford1, Daisy Sproviero1, Gianfranco Picco1, Cheryl Gillett4, Suzanne L. Papp5, Lana Schaffer5, Andrew Tutt3, Joyce Taylor-Papadimitriou1, Sarah E. Pinder4, and Joy M. Burchell1 Abstract The glycome acts as an essential interface between cells and the surrounding microenvironment. However, changes in glycosylation occur in nearly all breast cancers, which can alter this interaction. Here, we report that profiles of glycosylation vary between ER-positive and ER-negative breast cancers. We found that genes involved in the synthesis of sialyl-Lewis x (sLex; FUT3, FUT4, and ST3GAL6) are significantly increased in estrogen receptor alpha-negative (ER-negative) tumors compared with ER-positive ones. SLex expression had no influence on the survival of patients whether they had ER-negative or ER-positive tumors. However, high expression of sLex in ER- positive tumors was correlated with metastasis to the bone where sLex receptor E-selectin is constitutively expressed. The ER-positive ZR-75-1 and the ER-negative BT20 cell lines both express sLex but only ZR-75-1 cells could adhere to activated endothelial cells under dynamic flow conditions in a sLex and E-selectin–dependent manner. Moreover, L/P-selectins bound strongly to ER-negative MDA-MB-231 and BT-20 cell lines in a heparan sulfate (HS)–dependent manner that was independent of sLex expression. Expression of glycosylation genes involved in heparan biosynthesis (EXT1 and HS3ST1) was increased in ER-negative tumors. -
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. -
Protein Identities in Evs Isolated from U87-MG GBM Cells As Determined by NG LC-MS/MS
Protein identities in EVs isolated from U87-MG GBM cells as determined by NG LC-MS/MS. No. Accession Description Σ Coverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs # AAs MW [kDa] calc. pI 1 A8MS94 Putative golgin subfamily A member 2-like protein 5 OS=Homo sapiens PE=5 SV=2 - [GG2L5_HUMAN] 100 1 1 7 88 110 12,03704523 5,681152344 2 P60660 Myosin light polypeptide 6 OS=Homo sapiens GN=MYL6 PE=1 SV=2 - [MYL6_HUMAN] 100 3 5 17 173 151 16,91913397 4,652832031 3 Q6ZYL4 General transcription factor IIH subunit 5 OS=Homo sapiens GN=GTF2H5 PE=1 SV=1 - [TF2H5_HUMAN] 98,59 1 1 4 13 71 8,048185945 4,652832031 4 P60709 Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 - [ACTB_HUMAN] 97,6 5 5 35 917 375 41,70973209 5,478027344 5 P13489 Ribonuclease inhibitor OS=Homo sapiens GN=RNH1 PE=1 SV=2 - [RINI_HUMAN] 96,75 1 12 37 173 461 49,94108966 4,817871094 6 P09382 Galectin-1 OS=Homo sapiens GN=LGALS1 PE=1 SV=2 - [LEG1_HUMAN] 96,3 1 7 14 283 135 14,70620005 5,503417969 7 P60174 Triosephosphate isomerase OS=Homo sapiens GN=TPI1 PE=1 SV=3 - [TPIS_HUMAN] 95,1 3 16 25 375 286 30,77169764 5,922363281 8 P04406 Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 - [G3P_HUMAN] 94,63 2 13 31 509 335 36,03039959 8,455566406 9 Q15185 Prostaglandin E synthase 3 OS=Homo sapiens GN=PTGES3 PE=1 SV=1 - [TEBP_HUMAN] 93,13 1 5 12 74 160 18,68541938 4,538574219 10 P09417 Dihydropteridine reductase OS=Homo sapiens GN=QDPR PE=1 SV=2 - [DHPR_HUMAN] 93,03 1 1 17 69 244 25,77302971 7,371582031 11 P01911 HLA class II histocompatibility antigen, -
Integrated Metabolomics and Proteomics Highlight Altered
Carcinogenesis, 2017, Vol. 38, No. 3, 271–280 doi:10.1093/carcin/bgw205 Advance Access publication January 3, 2017 Original Manuscript original manuscript Integrated metabolomics and proteomics highlight altered nicotinamide and polyamine pathways in lung adenocarcinoma Johannes F.Fahrmann1,†, Dmitry Grapov2,†, Kwanjeera Wanichthanarak1, Brian C.DeFelice1, Michelle R.Salemi3, William N.Rom4, David R.Gandara5, Brett S.Phinney3, Oliver Fiehn1,6, Harvey Pass7 and Suzanne Miyamoto5,* 1University of California, Davis, West Coast Metabolomics Center, Davis, CA, USA, 2CDS Creative Data Solutions, Ballwin, MO, USA, 3Genome Center Proteomics Core Facility, UC Davis, Davis CA, USA 4Division of Pulmonary, Critical Care, and Sleep, NYU School of Medicine, New York, NY, USA, 5Division of Hematology and Oncology, Department of Internal Medicine, School of Medicine, University of California, Davis Medical Center, Sacramento, CA, USA, 6Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi-Arabia, 7Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Langone Medical Center, New York University, New York, NY, USA *To whom correspondence should be addressed. Tel: 916-734-3769; Email: [email protected] †-These authors contributed equally to this work. Abstract Lung cancer is the leading cause of cancer mortality in the United States with non-small cell lung cancer adenocarcinoma being the most common histological type. Early perturbations in cellular metabolism are a hallmark of cancer, but the extent of these changes in early stage lung adenocarcinoma remains largely unknown. In the current study, an integrated metabolomics and proteomics approach was utilized to characterize the biochemical and molecular alterations between malignant and matched control tissue from 27 subjects diagnosed with early stage lung adenocarcinoma. -
Promiscuity and Specificity of Eukaryotic Glycosyltransferases
Biochemical Society Transactions (2020) 48 891–900 https://doi.org/10.1042/BST20190651 Review Article Promiscuity and specificity of eukaryotic glycosyltransferases Ansuman Biswas and Mukund Thattai Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, TIFR, Bangalore, India Correspondence: Mukund Thattai ([email protected]) Glycosyltransferases are a large family of enzymes responsible for covalently linking sugar monosaccharides to a variety of organic substrates. These enzymes drive the synthesis of complex oligosaccharides known as glycans, which play key roles in inter-cellular interac- tions across all the kingdoms of life; they also catalyze sugar attachment during the syn- thesis of small-molecule metabolites such as plant flavonoids. A given glycosyltransferase enzyme is typically responsible for attaching a specific donor monosaccharide, via a spe- cific glycosidic linkage, to a specific moiety on the acceptor substrate. However these enzymes are often promiscuous, able catalyze linkages between a variety of donors and acceptors. In this review we discuss distinct classes of glycosyltransferase promiscuity, each illustrated by enzymatic examples from small-molecule or glycan synthesis. We high- light the physical causes of promiscuity, and its biochemical consequences. Structural studies of glycosyltransferases involved in glycan synthesis show that they make specific contacts with ‘recognition motifs’ that are much smaller than the full oligosaccharide sub- strate. There is a wide range in the sizes of glycosyltransferase recognition motifs: highly promiscuous enzymes recognize monosaccharide or disaccharide motifs across multiple oligosaccharides, while highly specific enzymes recognize large, complex motifs found on few oligosaccharides. In eukaryotes, the localization of glycosyltransferases within compartments of the Golgi apparatus may play a role in mitigating the glycan variability caused by enzyme promiscuity. -
ST8SIA2) Expression in Schizophrenia Superior Temporal Gyrus
bioRxiv preprint doi: https://doi.org/10.1101/377770; this version posted July 26, 2018. 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. Increased α-2,8-sialyltransferase 8B (ST8SIA2) expression in schizophrenia superior temporal gyrus Toni M. Mueller*, Stefani D. Yates, and James H. Meador-Woodruff Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA *Corresponding author Reduced polysialylation of neural cell adhesion molecule (NCAM) in schizophrenia has been suggested to contribute to abnormal neuroplasticity and neurodevelopmental features of this illness. The posttranslational addition of sialic acid is mediated by sialyltransferases, and polysialylation (the addition of ≥ 8 α-2,8-linked sialic acid residues) is catalyzed by three enzymes: ST8SIA2 (also called STX), ST8SIA4 (also called PST), and/or ST8SIA3. ST8SIA2 and ST8SIA4 are the primary mediators of NCAM polysialylation. The gene encoding ST8SIA2 maps to schizophrenia risk locus 15q26, and single nucleotide polymorphisms (SNPs) and SNP haplotypes of the ST8SIA2 gene have been associated with schizophrenia in multiple populations. The current study in elderly schizophrenia (N = 16) and comparison (N = 14) subjects measured the protein expression of NCAM, polysialylated-NCAM (PSA- NCAM), and three poly-α-2,8-sialyltransferases (ST8SIA2, ST8SIA3, and ST8SIA4) in postmortem superior temporal gyrus. Although expression of NCAM, PSA-NCAM, ST8SIA3, and ST8SIA4 were not different in schizophrenia, increased protein levels of ST8SIA2 were identified. -
Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract The high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a proteinprotein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes. -
Fucosyltransferase Genes on Porcine Chromosome 6Q11 Are Closely Linked to the Blood Group Inhibitor (S) and Escherichia Coli F18 Receptor (ECF18R) Loci
Mammalian Genome 8, 736–741 (1997). © Springer-Verlag New York Inc. 1997 Two a(1,2) fucosyltransferase genes on porcine Chromosome 6q11 are closely linked to the blood group inhibitor (S) and Escherichia coli F18 receptor (ECF18R) loci E. Meijerink,1 R. Fries,1,*P.Vo¨geli,1 J. Masabanda,1 G. Wigger,1 C. Stricker,1 S. Neuenschwander,1 H.U. Bertschinger,2 G. Stranzinger1 1Institute of Animal Science, Swiss Federal Institute of Technology, ETH-Zentrum, CH-8092 Zurich, Switzerland 2Institute of Veterinary Bacteriology, University of Zurich, CH 8057 Zurich, Switzerland Received: 17 February 1997 / Accepted: 30 May 1997 Abstract. The Escherichia coli F18 receptor locus (ECF18R) has fimbriae F107, has been shown to be genetically controlled by the been genetically mapped to the halothane linkage group on porcine host and is inherited as a dominant trait (Bertschinger et al. 1993) Chromosome (Chr) 6. In an attempt to obtain candidate genes for with B being the susceptibility allele and b the resistance allele. this locus, we isolated 5 cosmids containing the a(1,2)fucosyl- The genetic locus for this E. coli F18 receptor (ECF18R) has been transferase genes FUT1, FUT2, and the pseudogene FUT2P from mapped to porcine Chr 6 (SSC6), based on its close linkage to the a porcine genomic library. Mapping by fluorescence in situ hy- S locus and other loci of the halothane (HAL) linkage group (Vo¨- bridization placed all these clones in band q11 of porcine Chr 6 geli et al. 1996). The epistatic S locus suppresses the phenotypic (SSC6q11). Sequence analysis of the cosmids resulted in the char- expression of the A-0 blood group system when being SsSs (Vo¨geli acterization of an open reading frame (ORF), 1098 bp in length, et al.