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Meta-Analysis and Inding from Seoul Breast Cancer Study (SEBCS)
The Pharmacogenomics Journal https://doi.org/10.1038/s41397-018-0016-6 ARTICLE Associations between genetic polymorphisms of membrane transporter genes and prognosis after chemotherapy: meta-analysis and finding from Seoul Breast Cancer Study (SEBCS) 1 1 1 1 2 3 4 Ji-Eun Kim ● Jaesung Choi ● JooYong Park ● Chulbum Park ● Se Mi Lee ● Seong Eun Park ● Nan Song ● 5 6 4,7 8 1,4,9 10 Seokang Chung ● Hyuna Sung ● Wonshik Han ● Jong Won Lee ● Sue K. Park ● Mi Kyung Kim ● 4,7 9,11 1,4,9,12 1,4,9 Dong-Young Noh ● Keun-Young Yoo ● Daehee Kang ● Ji-Yeob Choi Received: 7 June 2017 / Revised: 13 October 2017 / Accepted: 4 December 2017 © Macmillan Publishers Limited, part of Springer Nature 2018 Abstract Membrane transporters can be major determinants of the pharmacokinetic profiles of anticancer drugs. The associations between genetic variations of ATP-binding cassette (ABC) and solute carrier (SLC) genes and cancer survival were investigated through a meta-analysis and an association study in the Seoul Breast Cancer Study (SEBCS). Including the SEBCS, the meta-analysis was conducted among 38 studies of genetic variations of transporters on various cancer survivors. 1234567890();,: The population of SEBCS consisted of 1 338 breast cancer patients who had been treated with adjuvant chemotherapy. A total of 7 750 SNPs were selected from 453 ABC and/or SLC genes typed by an Affymetrix 6.0 chip. ABCB1 rs1045642 was associated with poor progression-free survival in a meta-analysis (HR = 1.33, 95% CI: 1.07–1.64). ABCB1, SLC8A1, and SLC12A8 were associated with breast cancer survival in SEBCS (Pgene < 0.05). -
Transcriptome Response of High- and Low-Light-Adapted Prochlorococcus Strains to Changing Iron Availability
Transcriptome response of high- and low-light-adapted Prochlorococcus strains to changing iron availability The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Thompson, Anne W et al. “Transcriptome Response of High- and Low-light-adapted Prochlorococcus Strains to Changing Iron Availability.” ISME Journal (2011), 1-15. As Published http://dx.doi.org/10.1038/ismej.2011.49 Publisher Nature Publishing Group Version Author's final manuscript Citable link http://hdl.handle.net/1721.1/64705 Terms of Use Creative Commons Attribution-Noncommercial-Share Alike 3.0 Detailed Terms http://creativecommons.org/licenses/by-nc-sa/3.0/ Title: Transcriptome response of high- and low-light adapted Prochlorococcus strains to changing iron availability Running title: Prochlorococcus response to iron stress 5 Contributors: Anne W. Thompson1, Katherine Huang1, Mak A. Saito* 2, Sallie W. Chisholm* 1, 3 10 1 MIT Department of Civil and Environmental Engineering 2 Woods Hole Oceanographic Institution – Department of Marine Chemistry and Geochemistry 3 MIT Department of Biology 15 * To whom correspondence should be addressed: E-mail: [email protected] and [email protected] Subject Category: Microbial population and community ecology 20 Abstract Prochlorococcus contributes significantly to ocean primary productivity. The link between primary productivity and iron in specific ocean regions is well established and iron-limitation of Prochlorococcus cell division rates in these regions has been 25 demonstrated. However, the extent of ecotypic variation in iron metabolism among Prochlorococcus and the molecular basis for differences is not understood. Here, we examine the growth and transcriptional response of Prochlorococcus strains, MED4 and MIT9313, to changing iron concentrations. -
Cyclin D1/Cyclin-Dependent Kinase 4 Interacts with Filamin a and Affects the Migration and Invasion Potential of Breast Cancer Cells
Published OnlineFirst February 28, 2010; DOI: 10.1158/0008-5472.CAN-08-1108 Tumor and Stem Cell Biology Cancer Research Cyclin D1/Cyclin-Dependent Kinase 4 Interacts with Filamin A and Affects the Migration and Invasion Potential of Breast Cancer Cells Zhijiu Zhong, Wen-Shuz Yeow, Chunhua Zou, Richard Wassell, Chenguang Wang, Richard G. Pestell, Judy N. Quong, and Andrew A. Quong Abstract Cyclin D1 belongs to a family of proteins that regulate progression through the G1-S phase of the cell cycle by binding to cyclin-dependent kinase (cdk)-4 to phosphorylate the retinoblastoma protein and release E2F transcription factors for progression through cell cycle. Several cancers, including breast, colon, and prostate, overexpress the cyclin D1 gene. However, the correlation of cyclin D1 overexpression with E2F target gene regulation or of cdk-dependent cyclin D1 activity with tumor development has not been identified. This suggests that the role of cyclin D1 in oncogenesis may be independent of its function as a cell cycle regulator. One such function is the role of cyclin D1 in cell adhesion and motility. Filamin A (FLNa), a member of the actin-binding filamin protein family, regulates signaling events involved in cell motility and invasion. FLNa has also been associated with a variety of cancers including lung cancer, prostate cancer, melanoma, human bladder cancer, and neuroblastoma. We hypothesized that elevated cyclin D1 facilitates motility in the invasive MDA-MB-231 breast cancer cell line. We show that MDA-MB-231 motility is affected by disturbing cyclin D1 levels or cyclin D1-cdk4/6 kinase activity. -
New Products JULY 2013
R&D Systems Tools for Cell Biology Research™ New Products JULY 2013 GMP-grade Recombinant Proteins Contents R&D Systems now offers GMP-grade cytokines and growth factors for research and further manu- facturing applications where current Good Manufacturing Practices (GMP) are required. GMP-grade Recombinant Proteins 2 proteins are manufactured in our ISO-certified facility in compliance with relevant guidelines1 and are produced with extensive documentation at every stage of development from cell culture to final fill and Quantikine® ELISA Kits 3 formulation. GMP-grade Recombinant Human IL-6 and Recombinant Human TNF-a have recently been added to our line of GMP-grade proteins. Additional GMP-grade proteins will be available in the next several months. For an up-to-date product listing or additional information, please visit our website at Luminex® Screening Assays 4 www.RnDSystems.com/GMP. Luminex® Performance Assays 4-5 Features New GMP Proteins ✓ Extensive documentation at every stage of development ProtEin SOURCE Catalog # SIZE Polyclonal Antibodies 6-7 ✓ Documentation of lot-to-lot consistency and traceability Human IL-6 E. coli 206-GMP-010 10 µg Monoclonal Antibodies 7-8 of materials used 206-GMP-050 50 µg ✓ Rigorous quality control using stringent analytical 206-GMP-01M 1 mg processes Biotinylated Antibodies 8 Human TNF-a/ E. coli 210-GMP-010 10 µg ✓ Proven formulations to ensure consistent reconstitution TNFSF1A and results 210-GMP-050 50 µg Antibody Controls 8 210-GMP-01M 1 mg ELISpot Kits & Development Modules 8 30000 60 Fluorokine® Flow Cytometry Kits 8 25000 17348 ) 2 50 20000 Fluorochrome-labeled Antibodies 9 40 8673 DuoSet® ELISA and DuoSet IC ELISA 15000 Development Systems 10 30 Peak Intensity Peak 10000 20 Cell-Based ELISA Assay Kits 10 5000 17565 (Mean RFU x10 Viability Cell 8780 10 Parameter Assay Kits 10 0 0 6000 9000 12000 15000 18000 21000 24000 -3 -2 -1 0 1 10 10 10 10 10 Apoptosis Detection 10 Mass Charge Ratio Recombinant Human TNF-α GMP (ng/mL) MALDI-TOF Analysis of GMP-grade Recombinant Human TNF-a. -
In Vivo Studies Using the Classical Mouse Diversity Panel
The Mouse Diversity Panel Predicts Clinical Drug Toxicity Risk Where Classical Models Fail Alison Harrill, Ph.D The Hamner-UNC Institute for Drug Safety Sciences 0 The Importance of Predicting Clinical Adverse Drug Reactions (ADR) Figure: Cath O’Driscoll Nature Publishing 2004 Risk ID PGx Testing 1 People Respond Differently to Drugs Pharmacogenetic Markers Identified by Genome-Wide Association Drug Adverse Drug Risk Allele Reaction (ADR) Abacavir Hypersensitivity HLA-B*5701 Flucloxacillin Hepatotoxicity Allopurinol Cutaneous ADR HLA-B*5801 Carbamazepine Stevens-Johnson HLA-B*1502 Syndrome Augmentin Hepatotoxicity DRB1*1501 Ximelagatran Hepatotoxicity DRB1*0701 Ticlopidine Hepatotoxicity HLA-A*3303 Average preclinical populations and human hepatocytes lack the diversity to detect incidence of adverse events that occur only in 1/10,000 people. Current Rodent Models of Risk Assessment The Challenge “At a time of extraordinary scientific progress, methods have hardly changed in several decades ([FDA] 2004)… Toxicologists face a major challenge in the twenty-first century. They need to embrace the new “omics” techniques and ensure that they are using the most appropriate animals if their discipline is to become a more effective tool in drug development.” -Dr. Michael Festing Quantitative geneticist Toxicol Pathol. 2010;38(5):681-90 Rodent Models as a Strategy for Hazard Characterization and Pharmacogenetics Genetically defined rodent models may provide ability to: 1. Improve preclinical prediction of drugs that carry a human safety risk 2. -
An Interactive Web Application to Explore Regeneration-Associated Gene Expression and Chromatin Accessibility
Supplementary Materials Regeneration Rosetta: An interactive web application to explore regeneration-associated gene expression and chromatin accessibility Andrea Rau, Sumona P. Dhara, Ava J. Udvadia, Paul L. Auer 1. Table S1. List of cholesterol metabolic genes from MGI database 2. Table S2. List of differentially expressed transcripts during optic nerve regeneration in zebrafish using the MGI cholesterol metabolic gene queries in the Regeneration Rosetta app 3. Table S3. List of transcription factor encoding genes from brain cell bodies following spinal cord injury in lamprey over a course of 12 weeKs 4. Table S4. List of transcription factor encoding genes from spinal cell bodies following spinal cord injury in lamprey over a course of 12 weeks Ensembl ID MGI Gene ID Symbol Name ENSMUSG00000015243 MGI:99607 Abca1 ATP-binding cassette, sub-family A (ABC1), member 1 ENSMUSG00000026944 MGI:99606 Abca2 ATP-binding cassette, sub-family A (ABC1), member 2 ENSMUSG00000024030 MGI:107704 Abcg1 ATP binding cassette subfamily G member 1 ENSMUSG00000026003 MGI:87866 Acadl acyl-Coenzyme A dehydrogenase, long-chain ENSMUSG00000018574 MGI:895149 Acadvl acyl-Coenzyme A dehydrogenase, very long chain ENSMUSG00000038641 MGI:2384785 Akr1d1 aldo-keto reductase family 1, member D1 ENSMUSG00000028553 MGI:1353627 Angptl3 angiopoietin-like 3 ENSMUSG00000031996 MGI:88047 Aplp2 amyloid beta (A4) precursor-like protein 2 ENSMUSG00000032083 MGI:88049 Apoa1 apolipoprotein A-I ENSMUSG00000005681 MGI:88050 Apoa2 apolipoprotein A-II ENSMUSG00000032080 MGI:88051 Apoa4 -
Edinburgh Research Explorer
Edinburgh Research Explorer International Union of Basic and Clinical Pharmacology. LXXXVIII. G protein-coupled receptor list Citation for published version: Davenport, AP, Alexander, SPH, Sharman, JL, Pawson, AJ, Benson, HE, Monaghan, AE, Liew, WC, Mpamhanga, CP, Bonner, TI, Neubig, RR, Pin, JP, Spedding, M & Harmar, AJ 2013, 'International Union of Basic and Clinical Pharmacology. LXXXVIII. G protein-coupled receptor list: recommendations for new pairings with cognate ligands', Pharmacological reviews, vol. 65, no. 3, pp. 967-86. https://doi.org/10.1124/pr.112.007179 Digital Object Identifier (DOI): 10.1124/pr.112.007179 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: Pharmacological reviews Publisher Rights Statement: U.S. Government work not protected by U.S. copyright General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 02. Oct. 2021 1521-0081/65/3/967–986$25.00 http://dx.doi.org/10.1124/pr.112.007179 PHARMACOLOGICAL REVIEWS Pharmacol Rev 65:967–986, July 2013 U.S. -
Modes of Interaction of KMT2 Histone H3 Lysine 4 Methyltransferase/COMPASS Complexes with Chromatin
cells Review Modes of Interaction of KMT2 Histone H3 Lysine 4 Methyltransferase/COMPASS Complexes with Chromatin Agnieszka Bochy ´nska,Juliane Lüscher-Firzlaff and Bernhard Lüscher * ID Institute of Biochemistry and Molecular Biology, Medical School, RWTH Aachen University, Pauwelsstrasse 30, 52057 Aachen, Germany; [email protected] (A.B.); jluescher-fi[email protected] (J.L.-F.) * Correspondence: [email protected]; Tel.: +49-241-8088850; Fax: +49-241-8082427 Received: 18 January 2018; Accepted: 27 February 2018; Published: 2 March 2018 Abstract: Regulation of gene expression is achieved by sequence-specific transcriptional regulators, which convey the information that is contained in the sequence of DNA into RNA polymerase activity. This is achieved by the recruitment of transcriptional co-factors. One of the consequences of co-factor recruitment is the control of specific properties of nucleosomes, the basic units of chromatin, and their protein components, the core histones. The main principles are to regulate the position and the characteristics of nucleosomes. The latter includes modulating the composition of core histones and their variants that are integrated into nucleosomes, and the post-translational modification of these histones referred to as histone marks. One of these marks is the methylation of lysine 4 of the core histone H3 (H3K4). While mono-methylation of H3K4 (H3K4me1) is located preferentially at active enhancers, tri-methylation (H3K4me3) is a mark found at open and potentially active promoters. Thus, H3K4 methylation is typically associated with gene transcription. The class 2 lysine methyltransferases (KMTs) are the main enzymes that methylate H3K4. KMT2 enzymes function in complexes that contain a necessary core complex composed of WDR5, RBBP5, ASH2L, and DPY30, the so-called WRAD complex. -
A Minimum-Labeling Approach for Reconstructing Protein Networks Across Multiple Conditions
A Minimum-Labeling Approach for Reconstructing Protein Networks across Multiple Conditions Arnon Mazza1, Irit Gat-Viks2, Hesso Farhan3, and Roded Sharan1 1 Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel. Email: [email protected]. 2 Dept. of Cell Research and Immunology, Tel Aviv University, Tel Aviv 69978, Israel. 3 Biotechnology Institute Thurgau, University of Konstanz, Unterseestrasse 47, CH-8280 Kreuzlingen, Switzerland. Abstract. The sheer amounts of biological data that are generated in recent years have driven the development of network analysis tools to fa- cilitate the interpretation and representation of these data. A fundamen- tal challenge in this domain is the reconstruction of a protein-protein sub- network that underlies a process of interest from a genome-wide screen of associated genes. Despite intense work in this area, current algorith- mic approaches are largely limited to analyzing a single screen and are, thus, unable to account for information on condition-specific genes, or reveal the dynamics (over time or condition) of the process in question. Here we propose a novel formulation for network reconstruction from multiple-condition data and devise an efficient integer program solution for it. We apply our algorithm to analyze the response to influenza in- fection in humans over time as well as to analyze a pair of ER export related screens in humans. By comparing to an extant, single-condition tool we demonstrate the power of our new approach in integrating data from multiple conditions in a compact and coherent manner, capturing the dynamics of the underlying processes. 1 Introduction With the increasing availability of high-throughput data, network biol- arXiv:1307.7803v1 [q-bio.QM] 30 Jul 2013 ogy has become the method of choice for filtering, interpreting and rep- resenting these data. -
Figure S1. DMD Module Network. the Network Is Formed by 260 Genes from Disgenet and 1101 Interactions from STRING. Red Nodes Are the Five Seed Candidate Genes
Figure S1. DMD module network. The network is formed by 260 genes from DisGeNET and 1101 interactions from STRING. Red nodes are the five seed candidate genes. Figure S2. DMD module network is more connected than a random module of the same size. It is shown the distribution of the largest connected component of 10.000 random modules of the same size of the DMD module network. The green line (x=260) represents the DMD largest connected component, obtaining a z-score=8.9. Figure S3. Shared genes between BMD and DMD signature. A) A meta-analysis of three microarray datasets (GSE3307, GSE13608 and GSE109178) was performed for the identification of differentially expressed genes (DEGs) in BMD muscle biopsies as compared to normal muscle biopsies. Briefly, the GSE13608 dataset included 6 samples of skeletal muscle biopsy from healthy people and 5 samples from BMD patients. Biopsies were taken from either biceps brachii, triceps brachii or deltoid. The GSE3307 dataset included 17 samples of skeletal muscle biopsy from healthy people and 10 samples from BMD patients. The GSE109178 dataset included 14 samples of controls and 11 samples from BMD patients. For both GSE3307 and GSE10917 datasets, biopsies were taken at the time of diagnosis and from the vastus lateralis. For the meta-analysis of GSE13608, GSE3307 and GSE109178, a random effects model of effect size measure was used to integrate gene expression patterns from the two datasets. Genes with an adjusted p value (FDR) < 0.05 and an │effect size│>2 were identified as DEGs and selected for further analysis. A significant number of DEGs (p<0.001) were in common with the DMD signature genes (blue nodes), as determined by a hypergeometric test assessing the significance of the overlap between the BMD DEGs and the number of DMD signature genes B) MCODE analysis of the overlapping genes between BMD DEGs and DMD signature genes. -
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. -
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).