Methods and Compositions for the Treatment of Non-Small Cell Lung Cancer
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Table 2. Significant
Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S. -
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. -
Steroid-Dependent Regulation of the Oviduct: a Cross-Species Transcriptomal Analysis
University of Kentucky UKnowledge Theses and Dissertations--Animal and Food Sciences Animal and Food Sciences 2015 Steroid-dependent regulation of the oviduct: A cross-species transcriptomal analysis Katheryn L. Cerny University of Kentucky, [email protected] Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Cerny, Katheryn L., "Steroid-dependent regulation of the oviduct: A cross-species transcriptomal analysis" (2015). Theses and Dissertations--Animal and Food Sciences. 49. https://uknowledge.uky.edu/animalsci_etds/49 This Doctoral Dissertation is brought to you for free and open access by the Animal and Food Sciences at UKnowledge. It has been accepted for inclusion in Theses and Dissertations--Animal and Food Sciences by an authorized administrator of UKnowledge. For more information, please contact [email protected]. STUDENT AGREEMENT: I represent that my thesis or dissertation and abstract are my original work. Proper attribution has been given to all outside sources. I understand that I am solely responsible for obtaining any needed copyright permissions. I have obtained needed written permission statement(s) from the owner(s) of each third-party copyrighted matter to be included in my work, allowing electronic distribution (if such use is not permitted by the fair use doctrine) which will be submitted to UKnowledge as Additional File. I hereby grant to The University of Kentucky and its agents the irrevocable, non-exclusive, and royalty-free license to archive and make accessible my work in whole or in part in all forms of media, now or hereafter known. -
Regulation of Xenobiotic and Bile Acid Metabolism by the Anti-Aging Intervention Calorie Restriction in Mice
REGULATION OF XENOBIOTIC AND BILE ACID METABOLISM BY THE ANTI-AGING INTERVENTION CALORIE RESTRICTION IN MICE By Zidong Fu Submitted to the Graduate Degree Program in Pharmacology, Toxicology, and Therapeutics and the Graduate Faculty of the University of Kansas in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Dissertation Committee ________________________________ Chairperson: Curtis Klaassen, Ph.D. ________________________________ Udayan Apte, Ph.D. ________________________________ Wen-Xing Ding, Ph.D. ________________________________ Thomas Pazdernik, Ph.D. ________________________________ Hao Zhu, Ph.D. Date Defended: 04-11-2013 The Dissertation Committee for Zidong Fu certifies that this is the approved version of the following dissertation: REGULATION OF XENOBIOTIC AND BILE ACID METABOLISM BY THE ANTI-AGING INTERVENTION CALORIE RESTRICTION IN MICE ________________________________ Chairperson: Curtis Klaassen, Ph.D. Date approved: 04-11-2013 ii ABSTRACT Calorie restriction (CR), defined as reduced calorie intake without causing malnutrition, is the best-known intervention to increase life span and slow aging-related diseases in various species. However, current knowledge on the exact mechanisms of aging and how CR exerts its anti-aging effects is still inadequate. The detoxification theory of aging proposes that the up-regulation of xenobiotic processing genes (XPGs) involved in phase-I and phase-II xenobiotic metabolism as well as transport, which renders a wide spectrum of detoxification, is a longevity mechanism. Interestingly, bile acids (BAs), the metabolites of cholesterol, have recently been connected with longevity. Thus, this dissertation aimed to determine the regulation of xenobiotic and BA metabolism by the well-known anti-aging intervention CR. First, the mRNA expression of XPGs in liver during aging was investigated. -
Flavin-Containing Monooxygenases: Mutations, Disease and Drug Response Phillips, IR; Shephard, EA
Flavin-containing monooxygenases: mutations, disease and drug response Phillips, IR; Shephard, EA For additional information about this publication click this link. http://qmro.qmul.ac.uk/jspui/handle/123456789/1015 Information about this research object was correct at the time of download; we occasionally make corrections to records, please therefore check the published record when citing. For more information contact [email protected] Flavin-containing monooxygenases: mutations, disease and drug response Ian R. Phillips1 and Elizabeth A. Shephard2 1School of Biological and Chemical Sciences, Queen Mary, University of London, Mile End Road, London E1 4NS, UK 2Department of Biochemistry and Molecular Biology, University College London, Gower Street, London WC1E 6BT, UK Corresponding author: Shephard, E.A. ([email protected]). and, thus, contribute to drug development. This review Flavin-containing monooxygenases (FMOs) metabolize considers the role of FMOs and their genetic variants in numerous foreign chemicals, including drugs, pesticides disease and drug response. and dietary components and, thus, mediate interactions between humans and their chemical environment. We Mechanism and structure describe the mechanism of action of FMOs and insights For catalysis FMOs require flavin adenine dinucleotide gained from the structure of yeast FMO. We then (FAD) as a prosthetic group, NADPH as a cofactor and concentrate on the three FMOs (FMOs 1, 2 and 3) that are molecular oxygen as a cosubstrate [5,6]. In contrast to most important for metabolism of foreign chemicals in CYPs FMOs accept reducing equivalents directly from humans, focusing on the role of the FMOs and their genetic NADPH and, thus, do not require accessory proteins. -
Type of the Paper (Article
Supplementary Material A Proteomics Study on the Mechanism of Nutmeg-induced Hepatotoxicity Wei Xia 1, †, Zhipeng Cao 1, †, Xiaoyu Zhang 1 and Lina Gao 1,* 1 School of Forensic Medicine, China Medical University, Shenyang 110122, P. R. China; lessen- [email protected] (W.X.); [email protected] (Z.C.); [email protected] (X.Z.) † The authors contributed equally to this work. * Correspondence: [email protected] Figure S1. Table S1. Peptide fraction separation liquid chromatography elution gradient table. Time (min) Flow rate (mL/min) Mobile phase A (%) Mobile phase B (%) 0 1 97 3 10 1 95 5 30 1 80 20 48 1 60 40 50 1 50 50 53 1 30 70 54 1 0 100 1 Table 2. Liquid chromatography elution gradient table. Time (min) Flow rate (nL/min) Mobile phase A (%) Mobile phase B (%) 0 600 94 6 2 600 83 17 82 600 60 40 84 600 50 50 85 600 45 55 90 600 0 100 Table S3. The analysis parameter of Proteome Discoverer 2.2. Item Value Type of Quantification Reporter Quantification (TMT) Enzyme Trypsin Max.Missed Cleavage Sites 2 Precursor Mass Tolerance 10 ppm Fragment Mass Tolerance 0.02 Da Dynamic Modification Oxidation/+15.995 Da (M) and TMT /+229.163 Da (K,Y) N-Terminal Modification Acetyl/+42.011 Da (N-Terminal) and TMT /+229.163 Da (N-Terminal) Static Modification Carbamidomethyl/+57.021 Da (C) 2 Table S4. The DEPs between the low-dose group and the control group. Protein Gene Fold Change P value Trend mRNA H2-K1 0.380 0.010 down Glutamine synthetase 0.426 0.022 down Annexin Anxa6 0.447 0.032 down mRNA H2-D1 0.467 0.002 down Ribokinase Rbks 0.487 0.000 -
Association of Gene Ontology Categories with Decay Rate for Hepg2 Experiments These Tables Show Details for All Gene Ontology Categories
Supplementary Table 1: Association of Gene Ontology Categories with Decay Rate for HepG2 Experiments These tables show details for all Gene Ontology categories. Inferences for manual classification scheme shown at the bottom. Those categories used in Figure 1A are highlighted in bold. Standard Deviations are shown in parentheses. P-values less than 1E-20 are indicated with a "0". Rate r (hour^-1) Half-life < 2hr. Decay % GO Number Category Name Probe Sets Group Non-Group Distribution p-value In-Group Non-Group Representation p-value GO:0006350 transcription 1523 0.221 (0.009) 0.127 (0.002) FASTER 0 13.1 (0.4) 4.5 (0.1) OVER 0 GO:0006351 transcription, DNA-dependent 1498 0.220 (0.009) 0.127 (0.002) FASTER 0 13.0 (0.4) 4.5 (0.1) OVER 0 GO:0006355 regulation of transcription, DNA-dependent 1163 0.230 (0.011) 0.128 (0.002) FASTER 5.00E-21 14.2 (0.5) 4.6 (0.1) OVER 0 GO:0006366 transcription from Pol II promoter 845 0.225 (0.012) 0.130 (0.002) FASTER 1.88E-14 13.0 (0.5) 4.8 (0.1) OVER 0 GO:0006139 nucleobase, nucleoside, nucleotide and nucleic acid metabolism3004 0.173 (0.006) 0.127 (0.002) FASTER 1.28E-12 8.4 (0.2) 4.5 (0.1) OVER 0 GO:0006357 regulation of transcription from Pol II promoter 487 0.231 (0.016) 0.132 (0.002) FASTER 6.05E-10 13.5 (0.6) 4.9 (0.1) OVER 0 GO:0008283 cell proliferation 625 0.189 (0.014) 0.132 (0.002) FASTER 1.95E-05 10.1 (0.6) 5.0 (0.1) OVER 1.50E-20 GO:0006513 monoubiquitination 36 0.305 (0.049) 0.134 (0.002) FASTER 2.69E-04 25.4 (4.4) 5.1 (0.1) OVER 2.04E-06 GO:0007050 cell cycle arrest 57 0.311 (0.054) 0.133 (0.002) -
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. -
Appendix Table A.2.3.1 Full Table of All Chicken Proteins and Human Orthologs Pool Accession Human Human Protein Human Product Cell Angios Log2( Endo Gene Comp
Appendix table A.2.3.1 Full table of all chicken proteins and human orthologs Pool Accession Human Human Protein Human Product Cell AngioS log2( Endo Gene comp. core FC) Specific CIKL F1NWM6 KDR NP_002244 kinase insert domain receptor (a type III receptor tyrosine M 94 4 kinase) CWT Q8AYD0 CDH5 NP_001786 cadherin 5, type 2 (vascular endothelium) M 90 8.45 specific CWT Q8AYD0 CDH5 NP_001786 cadherin 5, type 2 (vascular endothelium) M 90 8.45 specific CIKL F1P1Y9 CDH5 NP_001786 cadherin 5, type 2 (vascular endothelium) M 90 8.45 specific CIKL F1P1Y9 CDH5 NP_001786 cadherin 5, type 2 (vascular endothelium) M 90 8.45 specific CIKL F1N871 FLT4 NP_891555 fms-related tyrosine kinase 4 M 86 -1.71 CWT O73739 EDNRA NP_001948 endothelin receptor type A M 81 -8 CIKL O73739 EDNRA NP_001948 endothelin receptor type A M 81 -8 CWT Q4ADW2 PROCR NP_006395 protein C receptor, endothelial M 80 -0.36 CIKL Q4ADW2 PROCR NP_006395 protein C receptor, endothelial M 80 -0.36 CIKL F1NFQ9 TEK NP_000450 TEK tyrosine kinase, endothelial M 77 7.3 specific CWT Q9DGN6 ECE1 NP_001106819 endothelin converting enzyme 1 M 74 -0.31 CIKL Q9DGN6 ECE1 NP_001106819 endothelin converting enzyme 1 M 74 -0.31 CWT F1NIF0 CA9 NP_001207 carbonic anhydrase IX I 74 CIKL F1NIF0 CA9 NP_001207 carbonic anhydrase IX I 74 CWT E1BZU7 AOC3 NP_003725 amine oxidase, copper containing 3 (vascular adhesion protein M 70 1) CIKL E1BZU7 AOC3 NP_003725 amine oxidase, copper containing 3 (vascular adhesion protein M 70 1) CWT O93419 COL18A1 NP_569712 collagen, type XVIII, alpha 1 E 70 -2.13 CIKL O93419 -
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 -
Hanna Joleen 2018 Thesis.Pdf
Validation of an In Vitro Mutagenicity Assay Based on Pulmonary Epithelial Cells from the Transgenic MutaMouse: Intra-Laboratory Variability and Metabolic Competence By: Joleen Hanna, B.Sc. A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science In Biology Specializing in Chemical and Environmental Toxicology Supervisor: Dr. Paul White (University of Ottawa) Thesis Advisory Committee: Dr. Frances Pick (University of Ottawa) Dr. Iain Lambert (Carleton University) University of Ottawa Ontario, Canada March 2018 © Joleen Hanna, Ottawa, Canada, 2018 Abstract: Genetic toxicity tests used for regulatory screening must be rigorously validated to ensure accuracy, reliability and relevance. Hence, prior to establishment of an internationally- accepted test guideline, a new assay must undergo multi-stage validation. An in vitro transgene mutagenicity assay based on an immortalized cell line derived from MutaMouse lung (i.e., FE1 cells) is currently undergoing formal validation. FE1 cells retain a lacZ transgene in a λgt10 shuttle vector that can be retrieved for scoring of chemically-induced mutations. This work contributes to validation of the in vitro transgene (lacZ) mutagenicity assay in MutaMouse FE1 cells. More specifically, the work includes an intra-laboratory variability study, and a follow-up study to assess the endogenous metabolic capacity of FE1 cells. The former is essential to determine assay reliability, the latter to define the range of chemicals that can be reliably screened without an exogenous metabolic activation mixture (i.e., rat liver S9). The intra- laboratory variability assessment revealed minimal variability; thus, assay reproducibility can be deemed acceptable. Assessment of metabolic capacity involved exposure of FE1 cells to 5 known mutagens, and subsequent assessment of changes in the expression of genes involved in xenobiotic metabolism; induced transgene mutant frequency (±S9) was assessed in parallel. -
Bioinformatics Analysis of Potential Key Genes and Mechanisms in Type 2 Diabetes Mellitus Basavaraj Vastrad1, Chanabasayya Vastrad*2
bioRxiv preprint doi: https://doi.org/10.1101/2021.03.28.437386; this version posted March 29, 2021. 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. Bioinformatics analysis of potential key genes and mechanisms in type 2 diabetes mellitus 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, Karnataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2021.03.28.437386; this version posted March 29, 2021. 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 Type 2 diabetes mellitus (T2DM) is etiologically related to metabolic disorder. The aim of our study was to screen out candidate genes of T2DM and to elucidate the underlying molecular mechanisms by bioinformatics methods. Expression profiling by high throughput sequencing data of GSE154126 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between T2DM and normal control were identified. And then, functional enrichment analyses of gene ontology (GO) and REACTOME pathway analysis was performed. Protein–protein interaction (PPI) network and module analyses were performed based on the DEGs. Additionally, potential miRNAs of hub genes were predicted by miRNet database . Transcription factors (TFs) of hub genes were detected by NetworkAnalyst database. Further, validations were performed by receiver operating characteristic curve (ROC) analysis and real-time polymerase chain reaction (RT-PCR).