Forensic Ancestry and Phenotype SNP Analysis and Integration with Established Forensic Markers
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Defining the Akt1 Interactome and Its Role in Regulating the Cell Cycle
www.nature.com/scientificreports OPEN Defning the Akt1 interactome and its role in regulating the cell cycle Shweta Duggal1,3, Noor Jailkhani2, Mukul Kumar Midha2, Namita Agrawal3, Kanury V. S. Rao1 & Ajay Kumar1 Received: 13 July 2017 Cell growth and proliferation are two diverse processes yet always linked. Akt1, a serine/threonine Accepted: 8 January 2018 kinase, is a multi-functional protein implicated in regulation of cell growth, survival and proliferation. Published: xx xx xxxx Though it has a role in G1/S progression, the manner by which Akt1 controls cell cycle and blends cell growth with proliferation is not well explored. In this study, we characterize the Akt1 interactome as the cell cycle progresses from G0 to G1/S and G2 phase. For this, Akt1-overexpressing HEK293 cells were subjected to AP-MS. To distinguish between individual cell cycle stages, cells were cultured in the light, medium and heavy labelled SILAC media. We obtained 213 interacting partners of Akt1 from these studies. GO classifcation revealed that a signifcant number of proteins fall into functional classes related to cell growth or cell cycle processes. Of these, 32 proteins showed varying association with Akt1 in diferent cell cycle stages. Further analyses uncovered a subset of proteins showing counteracting efects so as to tune stage-specifc progression through the cycle. Thus, our study provides some novel perspectives on Akt1-mediated regulation of the cell cycle and ofers the framework for a detailed resolution of the downstream cellular mechanisms that are mediated by this kinase. Te mammalian cell cycle consists of an ordered series of events and is a highly coordinated and regulated pro- cess1. -
GENOME GENERATION Glossary
GENOME GENERATION Glossary Chromosome An organism’s DNA is packaged into chromosomes. Humans have 23 pairs of chromosomesincluding one pair of sex chromosomes. Women have two X chromosomes and men have one X and one Y chromosome. Dominant (see also recessive) Genes come in pairs. A dominant form of a gene is the “stronger” version that will be expressed. Therefore if someone has one dominant and one recessive form of a gene, only the characteristics of the dominant form will appear. DNA DNA is the long molecule that contains the genetic instructions for nearly all living things. Two strands of DNA are twisted together into a double helix. The DNA code is made up of four chemical letters (A, C, G and T) which are commonly referred to as bases or nucleotides. Gene A gene is a section of DNA that is the code for a specific biological component, usually a protein. Each gene may have several alternative forms. Each of us has two copies of most of our genes, one copy inherited from each parent. Most of our traits are the result of the combined effects of a number of different genes. Very few traits are the result of just one gene. Genetic sequence The precise order of letters (bases) in a section of DNA. Genome A genome is the complete DNA instructions for an organism. The human genome contains 3 billion DNA letters and approximately 23,000 genes. Genomics Genomics is the study of genomes. This includes not only the DNA sequence itself, but also an understanding of the function and regulation of genes both individually and in combination. -
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. -
CIP29 (SARNP) Rabbit Polyclonal Antibody – TA308582 | Origene
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for TA308582 CIP29 (SARNP) Rabbit Polyclonal Antibody Product data: Product Type: Primary Antibodies Applications: IF, IHC, WB Recommended Dilution: ICC/IF:1:100-1:1000; IHC:1:100-1:1000; WB:1:500-1:3000 Reactivity: Human (Predicted: Bovine, Chicken, Mouse, Rat) Host: Rabbit Isotype: IgG Clonality: Polyclonal Immunogen: Recombinant fragment corresponding to a region within amino acids 4 and 210 of CIP29 (Uniprot ID#P82979) Formulation: 0.1M Tris, 0.1M Glycine, 10% Glycerol (pH7). 0.01% Thimerosal was added as a preservative. Concentration: lot specific Purification: Purified by antigen-affinity chromatography. Conjugation: Unconjugated Storage: Store at -20°C as received. Stability: Stable for 12 months from date of receipt. Predicted Protein Size: 24 kDa Gene Name: SAP domain containing ribonucleoprotein Database Link: NP_149073 Entrez Gene 66118 MouseEntrez Gene 362819 RatEntrez Gene 84324 Human P82979 Background: This gene encodes a protein that is upregulated in response to various cytokines. The encoded protein may play a role in cell cycle progression. A translocation between this gene and the myeloid/lymphoid leukemia gene, resulting in expression of a chimeric protein, has been associated with acute myelomonocytic leukemia. Pseudogenes exist on chromosomes 7 and 8. Alternatively spliced transcript variants have been described. [provided by RefSeq] Synonyms: CIP29; HCC1; HSPC316; THO1 This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 2 CIP29 (SARNP) Rabbit Polyclonal Antibody – TA308582 Note: Seq homology of immunogen across species: Mouse (95%), Chicken (80%), Rat (95%), Bovine (96%) Product images: Sample (30 ug whole cell lysate). -
Snps) Distant from Xenobiotic Response Elements Can Modulate Aryl Hydrocarbon Receptor Function: SNP-Dependent CYP1A1 Induction S
Supplemental material to this article can be found at: http://dmd.aspetjournals.org/content/suppl/2018/07/06/dmd.118.082164.DC1 1521-009X/46/9/1372–1381$35.00 https://doi.org/10.1124/dmd.118.082164 DRUG METABOLISM AND DISPOSITION Drug Metab Dispos 46:1372–1381, September 2018 Copyright ª 2018 by The American Society for Pharmacology and Experimental Therapeutics Single Nucleotide Polymorphisms (SNPs) Distant from Xenobiotic Response Elements Can Modulate Aryl Hydrocarbon Receptor Function: SNP-Dependent CYP1A1 Induction s Duan Liu, Sisi Qin, Balmiki Ray,1 Krishna R. Kalari, Liewei Wang, and Richard M. Weinshilboum Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics (D.L., S.Q., B.R., L.W., R.M.W.) and Division of Biomedical Statistics and Informatics, Department of Health Sciences Research (K.R.K.), Mayo Clinic, Rochester, Minnesota Received April 22, 2018; accepted June 28, 2018 ABSTRACT Downloaded from CYP1A1 expression can be upregulated by the ligand-activated aryl fashion. LCLs with the AA genotype displayed significantly higher hydrocarbon receptor (AHR). Based on prior observations with AHR-XRE binding and CYP1A1 mRNA expression after 3MC estrogen receptors and estrogen response elements, we tested treatment than did those with the GG genotype. Electrophoretic the hypothesis that single-nucleotide polymorphisms (SNPs) map- mobility shift assay (EMSA) showed that oligonucleotides with the ping hundreds of base pairs (bp) from xenobiotic response elements AA genotype displayed higher LCL nuclear extract binding after (XREs) might influence AHR binding and subsequent gene expres- 3MC treatment than did those with the GG genotype, and mass dmd.aspetjournals.org sion. -
Pharmacogenetic Testing: a Tool for Personalized Drug Therapy Optimization
pharmaceutics Review Pharmacogenetic Testing: A Tool for Personalized Drug Therapy Optimization Kristina A. Malsagova 1,* , Tatyana V. Butkova 1 , Arthur T. Kopylov 1 , Alexander A. Izotov 1, Natalia V. Potoldykova 2, Dmitry V. Enikeev 2, Vagarshak Grigoryan 2, Alexander Tarasov 3, Alexander A. Stepanov 1 and Anna L. Kaysheva 1 1 Biobanking Group, Branch of Institute of Biomedical Chemistry “Scientific and Education Center”, 109028 Moscow, Russia; [email protected] (T.V.B.); [email protected] (A.T.K.); [email protected] (A.A.I.); [email protected] (A.A.S.); [email protected] (A.L.K.) 2 Institute of Urology and Reproductive Health, Sechenov University, 119992 Moscow, Russia; [email protected] (N.V.P.); [email protected] (D.V.E.); [email protected] (V.G.) 3 Institute of Linguistics and Intercultural Communication, Sechenov University, 119992 Moscow, Russia; [email protected] * Correspondence: [email protected]; Tel.: +7-499-764-9878 Received: 2 November 2020; Accepted: 17 December 2020; Published: 19 December 2020 Abstract: Pharmacogenomics is a study of how the genome background is associated with drug resistance and how therapy strategy can be modified for a certain person to achieve benefit. The pharmacogenomics (PGx) testing becomes of great opportunity for physicians to make the proper decision regarding each non-trivial patient that does not respond to therapy. Although pharmacogenomics has become of growing interest to the healthcare market during the past five to ten years the exact mechanisms linking the genetic polymorphisms and observable responses to drug therapy are not always clear. Therefore, the success of PGx testing depends on the physician’s ability to understand the obtained results in a standardized way for each particular patient. -
Consequences of Eye Color, Positioning, and Head Movement for Eye-Tracking Data Quality in Infant Research
THE OFFICIAL JOURNAL OF THE INTERNATIONAL CONGRESS OF INFANT STUDIES Infancy, 20(6), 601–633, 2015 Copyright © International Congress of Infant Studies (ICIS) ISSN: 1525-0008 print / 1532-7078 online DOI: 10.1111/infa.12093 Consequences of Eye Color, Positioning, and Head Movement for Eye-Tracking Data Quality in Infant Research Roy S. Hessels Department of Experimental Psychology, Helmholtz Institute, Utrecht University and Department of Developmental Psychology, Utrecht University Richard Andersson Eye Information Group, IT University of Copenhagen and Department of Philosophy & Cognitive Science Lund University Ignace T. C. Hooge Department of Experimental Psychology, Helmholtz Institute Utrecht University Marcus Nystrom€ Humanities Laboratory Lund University Chantal Kemner Department of Experimental Psychology, Helmholtz Institute, Utrecht University and Department of Developmental Psychology, Utrecht University and Brain Center Rudolf Magnus University Medical Centre Utrecht Correspondence should be sent to Roy S. Hessels, Heidelberglaan 1, 3584 CS Utrecht, The Netherlands. E-mail: [email protected] 602 HESSELS ET AL. Eye tracking has become a valuable tool for investigating infant looking behavior over the last decades. However, where eye-tracking methodology and achieving high data quality have received a much attention for adult participants, it is unclear how these results generalize to infant research. This is particularly important as infants behave different from adults in front of the eye tracker. In this study, we investigated whether eye physiology, posi- tioning, and infant behavior affect measures of eye-tracking data quality: accuracy, precision, and data loss. We report that accuracy and precision are lower, and more data loss occurs for infants with bluish eye color com- pared to infants with brownish eye color. -
HIV Genotyping and Phenotyping AHS – M2093
Corporate Medical Policy HIV Genotyping and Phenotyping AHS – M2093 File Name: HIV_genotyping_and_phenotyping Origination: 1/2019 Last CAP Review: 2/2021 Next CAP Review: 2/2022 Last Review: 2/2021 Description of Procedure or Service Description Human immunodeficiency virus (HIV) is an RNA retrovirus that infects human immune cells (specifically CD4 cells) causing progressive deterioration of the immune system ultimately leading to acquired immune deficiency syndrome (AIDS) characterized by susceptibility to opportunistic infections and HIV-related cancers (CDC, 2014). Related Policies Plasma HIV-1 and HIV-2 RNA Quantification for HIV Infection Scientific Background Human immunodeficiency virus (HIV) targets the immune system, eventually hindering the body’s ability to fight infections and diseases. If not treated, an HIV infection may lead to acquired immunodeficiency syndrome (AIDS) which is a condition caused by the virus. There are two main types of HIV: HIV-1 and HIV-2; both are genetically different. HIV-1 is more common and widespread than HIV-2. HIV replicates rapidly; a replication cycle rate of approximately one to two days ensures that after a single year, the virus in an infected individual may be 200 to 300 generations removed from the initial infection-causing virus (Coffin & Swanstrom, 2013). This leads to great genetic diversity of each HIV infection in a single individual. As an RNA retrovirus, HIV requires the use of a reverse transcriptase for replication purposes. A reverse transcriptase is an enzyme which generates complimentary DNA from an RNA template. This enzyme is error-prone with the overall single-step point mutation rate reaching ∼3.4 × 10−5 mutations per base per replication cycle (Mansky & Temin, 1995), leading to approximately one genome in three containing a mutation after each round of replication (some of which confer drug resistance). -
Literature Mining Sustains and Enhances Knowledge Discovery from Omic Studies
LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES by Rick Matthew Jordan B.S. Biology, University of Pittsburgh, 1996 M.S. Molecular Biology/Biotechnology, East Carolina University, 2001 M.S. Biomedical Informatics, University of Pittsburgh, 2005 Submitted to the Graduate Faculty of School of Medicine in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2016 UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE This dissertation was presented by Rick Matthew Jordan It was defended on December 2, 2015 and approved by Shyam Visweswaran, M.D., Ph.D., Associate Professor Rebecca Jacobson, M.D., M.S., Professor Songjian Lu, Ph.D., Assistant Professor Dissertation Advisor: Vanathi Gopalakrishnan, Ph.D., Associate Professor ii Copyright © by Rick Matthew Jordan 2016 iii LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES Rick Matthew Jordan, M.S. University of Pittsburgh, 2016 Genomic, proteomic and other experimentally generated data from studies of biological systems aiming to discover disease biomarkers are currently analyzed without sufficient supporting evidence from the literature due to complexities associated with automated processing. Extracting prior knowledge about markers associated with biological sample types and disease states from the literature is tedious, and little research has been performed to understand how to use this knowledge to inform the generation of classification models from ‘omic’ data. Using pathway analysis methods to better understand the underlying biology of complex diseases such as breast and lung cancers is state-of-the-art. However, the problem of how to combine literature- mining evidence with pathway analysis evidence is an open problem in biomedical informatics research. -
Review of the Current State of Genetic Testing - a Living Resource
Review of the Current State of Genetic Testing - A Living Resource Prepared by Liza Gershony, DVM, PhD and Anita Oberbauer, PhD of the University of California, Davis Editorial input by Leigh Anne Clark, PhD of Clemson University July, 2020 Contents Introduction .................................................................................................................................................. 1 I. The Basics ......................................................................................................................................... 2 II. Modes of Inheritance ....................................................................................................................... 7 a. Mendelian Inheritance and Punnett Squares ................................................................................. 7 b. Non-Mendelian Inheritance ........................................................................................................... 10 III. Genetic Selection and Populations ................................................................................................ 13 IV. Dog Breeds as Populations ............................................................................................................. 15 V. Canine Genetic Tests ...................................................................................................................... 16 a. Direct and Indirect Tests ................................................................................................................ 17 b. Single -
Evaluation of Openarray™ As a Genotyping Method for Forensic DNA Phenotyping and Human Identification
G C A T T A C G G C A T genes Article Evaluation of OpenArray™ as a Genotyping Method for Forensic DNA Phenotyping and Human Identification Michele Ragazzo 1, Giulio Puleri 1, Valeria Errichiello 1, Laura Manzo 1, Laura Luzzi 1 , Saverio Potenza 2 , Claudia Strafella 1,3 , Cristina Peconi 3, Fabio Nicastro 4 , Valerio Caputo 1 and Emiliano Giardina 1,3,* 1 Department of Biomedicine and Prevention, Tor Vergata University of Rome, 00133 Rome, Italy; [email protected] (M.R.); [email protected] (G.P.); [email protected] (V.E.); [email protected] (L.M.); [email protected] (L.L.); [email protected] (C.S.); [email protected] (V.C.) 2 Department of Biomedicine and Prevention, Section of Legal Medicine, Social Security and Forensic Toxicology, University of Rome Tor Vergata, 00133 Rome, Italy; [email protected] 3 Genomic Medicine Laboratory UILDM, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; [email protected] 4 Austech, 00153 Rome, Italy; [email protected] * Correspondence: [email protected] Abstract: A custom plate of OpenArray™ technology was evaluated to test 60 single-nucleotide polymorphisms (SNPs) validated for the prediction of eye color, hair color, and skin pigmentation, and for personal identification. The SNPs were selected from already validated subsets (Hirisplex-s, Precision ID Identity SNP Panel, and ForenSeq DNA Signature Prep Kit). The concordance rate and call rate for every SNP were calculated by analyzing 314 sequenced DNA samples. The sensitivity of the assay was assessed by preparing a dilution series of 10.0, 5.0, 1.0, and 0.5 ng. -
Genotyping-By-Sequencing to Unlock Genetic Diversity and Population Structure in White Yam (Dioscorea Rotundata Poir.)
agronomy Article Genotyping-by-Sequencing to Unlock Genetic Diversity and Population Structure in White Yam (Dioscorea rotundata Poir.) Ranjana Bhattacharjee 1,* , Paterne Agre 1 , Guillaume Bauchet 2, David De Koeyer 3, Antonio Lopez-Montes 1,4, P. Lava Kumar 1 , Michael Abberton 1, Patrick Adebola 5, Asrat Asfaw 5 and Robert Asiedu 1 1 International Institute of Tropical Agriculture, PMB 5320, Ibadan 200001, Nigeria; [email protected] (P.A.); [email protected] (A.L.-M.); [email protected] (P.L.K.); [email protected] (M.A.); [email protected] (R.A.) 2 Boyce Thompson Institute, Cornell University, Ithaca, NY 14853, USA; [email protected] 3 Agriculture and Agri-Food Canada, Fredericton, NB E3B 4Z7, Canada; [email protected] 4 International Trade Center, 29 Independence Avenue, Accra 00233, Ghana 5 International Institute of Tropical Agriculture, PMB 82, Kubuwa, Abuja 901101, Nigeria; [email protected] (P.A.); [email protected] (A.A.) * Correspondence: [email protected] Received: 7 August 2020; Accepted: 14 September 2020; Published: 22 September 2020 Abstract: White yam (Dioscorea rotundata Poir.) is one of the most important tuber crops in West Africa, where it is indigenous and represents the largest repository of biodiversity through several years of domestication, production, consumption, and trade. In this study, the genotyping-by-sequencing (GBS) approach was used to sequence 814 genotypes consisting of genebank landraces, breeding lines, and market varieties to understand the level of genetic diversity and pattern of the population structure among them. The genetic diversity among different genotypes was assessed using three complementary clustering methods, the model-based admixture, discriminant analysis of principal components (DAPC), and phylogenetic tree.