PLATFORM ABSTRACTS Abstract Abstract Numbers Numbers Tuesday 31 Advances and References in Genomic Technology Room 210 #196–#204 2
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The Genetics of Nontraditional Glycemic Biomarkers of Type 2 Diabetes
THE GENETICS OF NONTRADITIONAL GLYCEMIC BIOMARKERS OF TYPE 2 DIABETES by Stephanie J. Loomis, MPH A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland May, 2018 © 2018 Stephanie J. Loomis All rights reserved Abstract Type 2 diabetes is a major public health problem that affects over 10% of the US adult population. It is associated with substantially increased risks of mortality and serious clinical outcomes such as heart disease, stroke, kidney disease and retinopathy. Diabetes is defined by hyperglycemia, or elevated glucose concentrations in the blood, which are commonly measured by fasting glucose and hemoglobin A1c (HbA1c), but these have limitations. As a result, nontraditional glycemic biomarkers, fructosamine, glycated albumin and 1,5-anhydroglucitol (1,5-AG) are gaining interest. While it is established that genetics play a role in type 2 diabetes, fasting glucose, and HbA1c, the genetics of fructosamine, glycated albumin, and 1,5-AG have not been well explored. This dissertation sought to determine the amount of variation in each biomarker due to genetics through heritability estimation, and to determine the specific genetic variants associated with each biomarker though genome wide association study (GWAS) analysis, multivariate phenotype analysis, and exome sequencing analysis. Heritability estimates showed a substantial portion of fructosamine, glycated albumin and 1,5-AG variation was due to genetics, which is likely comprised of both common and rare variants. GWAS identified common variants associated with fructosamine and glycated albumin including a known diabetes variant and a likely nonglycemic variant. Exome sequencing did not identify variants associated with fructosamine and glycated albumin, but multivariate phenotype analysis identified a potentially interesting region in a gene that alters bilirubin levels that may affect fructosamine in a nonglycemic manner. -
Isyte: Integrated Systems Tool for Eye Gene Discovery
Lens iSyTE: Integrated Systems Tool for Eye Gene Discovery Salil A. Lachke,1,2,3,4 Joshua W. K. Ho,1,4,5 Gregory V. Kryukov,1,4,6 Daniel J. O’Connell,1 Anton Aboukhalil,1,7 Martha L. Bulyk,1,8,9 Peter J. Park,1,5,10 and Richard L. Maas1 PURPOSE. To facilitate the identification of genes associated ther investigation. Extension of this approach to other ocular with cataract and other ocular defects, the authors developed tissue components will facilitate eye disease gene discovery. and validated a computational tool termed iSyTE (integrated (Invest Ophthalmol Vis Sci. 2012;53:1617–1627) DOI: Systems Tool for Eye gene discovery; http://bioinformatics. 10.1167/iovs.11-8839 udel.edu/Research/iSyTE). iSyTE uses a mouse embryonic lens gene expression data set as a bioinformatics filter to select candidate genes from human or mouse genomic regions impli- ven with the advent of high-throughput sequencing, the cated in disease and to prioritize them for further mutational Ediscovery of genes associated with congenital birth defects and functional analyses. such as eye defects remains a challenge. We sought to develop METHODS. Microarray gene expression profiles were obtained a straightforward experimental approach that could facilitate for microdissected embryonic mouse lens at three key devel- the identification of candidate genes for developmental disor- opmental time points in the transition from the embryonic day ders, and, as proof-of-principle, we chose defects involving the (E)10.5 stage of lens placode invagination to E12.5 lens primary ocular lens. Opacification of the lens results in cataract, a leading cause of blindness that affects 77 million persons and fiber cell differentiation. -
NDUFAF1 Antibody
Efficient Professional Protein and Antibody Platforms NDUFAF1 Antibody Basic information: Catalog No.: UPA63763 Source: Rabbit Size: 50ul/100ul Clonality: monoclonal Concentration: 1mg/ml Isotype: Rabbit IgG Purification: Protein A purified. Useful Information: WB:1:1000 ICC:1:50-1:200 Applications: IHC:1:50-1:200 FC:1:50-1:100 Reactivity: Human Specificity: This antibody recognizes NDUFAF1 protein. Immunogen: Synthetic peptide within C terminal human NDUFAF1. This gene encodes a complex I assembly factor protein. Complex I (NADH-ubiquinone oxidoreductase) catalyzes the transfer of electrons from NADH to ubiquinone (coenzyme Q) in the first step of the mitochondrial respiratory chain, resulting in the translocation of protons across the inner mitochondrial membrane. The encoded protein is required for assembly of complex I, and mutations in this gene are a cause of mitochondrial complex I deficiency. Alternatively spliced transcript variants have been observed for Description: this gene, and a pseudogene of this gene is located on the long arm of chromosome 19. Part of the mitochondrial complex I assembly (MCIA) com- plex. The complex comprises at least TMEM126B, NDUFAF1, ECSIT, and ACAD9. Interacts with ECSIT. Interacts with ACAD9. At early stages of com- plex I assembly, it is found in intermediate subcomplexes that contain dif- ferent subunits including NDUFB6, NDUFA6, NDUFA9, NDUFS3, NDUFS7, ND1, ND2 and ND3 Uniprot: Q9Y375 Human BiowMW: 38 kDa Buffer: 1*TBS (pH7.4), 1%BSA, 50%Glycerol. Preservative: 0.05% Sodium Azide. Storage: Store at 4°C short term and -20°C long term. Avoid freeze-thaw cycles. Note: For research use only, not for use in diagnostic procedure. -
Molecular Mechanism of ACAD9 in Mitochondrial Respiratory Complex 1 Assembly
bioRxiv preprint doi: https://doi.org/10.1101/2021.01.07.425795; this version posted January 9, 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. Molecular mechanism of ACAD9 in mitochondrial respiratory complex 1 assembly Chuanwu Xia1, Baoying Lou1, Zhuji Fu1, Al-Walid Mohsen2, Jerry Vockley2, and Jung-Ja P. Kim1 1Department of Biochemistry, Medical College of Wisconsin, Milwaukee, Wisconsin, 53226, USA 2Department of Pediatrics, University of Pittsburgh School of Medicine, University of Pittsburgh, Children’s Hospital of Pittsburgh of UPMC, Pittsburgh, PA 15224, USA Abstract ACAD9 belongs to the acyl-CoA dehydrogenase family, which catalyzes the α-β dehydrogenation of fatty acyl-CoA thioesters. Thus, it is involved in fatty acid β-oxidation (FAO). However, it is now known that the primary function of ACAD9 is as an essential chaperone for mitochondrial respiratory complex 1 assembly. ACAD9 interacts with ECSIT and NDUFAF1, forming the mitochondrial complex 1 assembly (MCIA) complex. Although the role of MCIA in the complex 1 assembly pathway is well studied, little is known about the molecular mechanism of the interactions among these three assembly factors. Our current studies reveal that when ECSIT interacts with ACAD9, the flavoenzyme loses the FAD cofactor and consequently loses its FAO activity, demonstrating that the two roles of ACAD9 are not compatible. ACAD9 binds to the carboxy-terminal half (C-ECSIT), and NDUFAF1 binds to the amino-terminal half of ECSIT. Although the binary complex of ACAD9 with ECSIT or with C-ECSIT is unstable and aggregates easily, the ternary complex of ACAD9-ECSIT-NDUFAF1 (i.e., the MCIA complex) is soluble and extremely stable. -
PLATFORM ABSTRACTS Abstract Abstract Numbers Numbers Tuesday, November 6 41
American Society of Human Genetics 62nd Annual Meeting November 6–10, 2012 San Francisco, California PLATFORM ABSTRACTS Abstract Abstract Numbers Numbers Tuesday, November 6 41. Genes Underlying Neurological Disease Room 134 #196–#204 2. 4:30–6:30pm: Plenary Abstract 42. Cancer Genetics III: Common Presentations Hall D #1–#6 Variants Ballroom 104 #205–#213 43. Genetics of Craniofacial and Wednesday, November 7 Musculoskeletal Disorders Room 124 #214–#222 10:30am–12:45 pm: Concurrent Platform Session A (11–19): 44. Tools for Phenotype Analysis Room 132 #223–#231 11. Genetics of Autism Spectrum 45. Therapy of Genetic Disorders Room 130 #232–#240 Disorders Hall D #7–#15 46. Pharmacogenetics: From Discovery 12. New Methods for Big Data Ballroom 103 #16–#24 to Implementation Room 123 #241–#249 13. Cancer Genetics I: Rare Variants Room 135 #25–#33 14. Quantitation and Measurement of Friday, November 9 Regulatory Oversight by the Cell Room 134 #34–#42 8:00am–10:15am: Concurrent Platform Session D (47–55): 15. New Loci for Obesity, Diabetes, and 47. Structural and Regulatory Genomic Related Traits Ballroom 104 #43–#51 Variation Hall D #250–#258 16. Neuromuscular Disease and 48. Neuropsychiatric Disorders Ballroom 103 #259–#267 Deafness Room 124 #52–#60 49. Common Variants, Rare Variants, 17. Chromosomes and Disease Room 132 #61–#69 and Everything in-Between Room 135 #268–#276 18. Prenatal and Perinatal Genetics Room 130 #70–#78 50. Population Genetics Genome-Wide Room 134 #277–#285 19. Vascular and Congenital Heart 51. Endless Forms Most Beautiful: Disease Room 123 #79–#87 Variant Discovery in Genomic Data Ballroom 104 #286–#294 52. -
Pharmacogenomics and Nutrigenomics: Synergies and Differences
European Journal of Clinical Nutrition (2007) 61, 567–574 & 2007 Nature Publishing Group All rights reserved 0954-3007/07 $30.00 www.nature.com/ejcn REVIEW Pharmacogenomics and nutrigenomics: synergies and differences D Ghosh, MA Skinner and WA Laing The Horticulture and Food Research Institute of New Zealand Ltd, Auckland, New Zealand The success of the Human Genome Project and the spectacular development of broad genomics tools have catalyzed a new era in both medicine and nutrition. The terms pharmacogenomics and nutrigenomics are relatively new. Both have grown out of their genetic forbears as large-scale genomics technologies have been developed in the last decade. The aim of both disciplines is to individualize or personalize medicine and food and nutrition, and ultimately health, by tailoring the drug or the food to the individual genotype. This review article provides an overview of synergies and differences between these two potentially powerful science areas. Individual genetic variation is the common factor on which both pharmacogenomics and nutrigenomics are based. Each human is genetically (including epigenetics) unique and phenotypically distinct. One of the expectations of both technologies is that a wide range of gene variants and related single-nucleotide polymorphism will be identified as to their importance in health status, validated and incorporated into genotype based strategies for the optimization of health and the prevention of disease. Pharmacogenomics requires rigorous genomic testing that will be regulated and analyzed by professionals and acted on by medical practitioners. As further information is obtained on the importance of the interaction of food and the human genotype in disease prevention and health, pharmacogenomics can provide an opportunity driver for nutrigenomics. -
Protein Interaction Network of Alternatively Spliced Isoforms from Brain Links Genetic Risk Factors for Autism
ARTICLE Received 24 Aug 2013 | Accepted 14 Mar 2014 | Published 11 Apr 2014 DOI: 10.1038/ncomms4650 OPEN Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism Roser Corominas1,*, Xinping Yang2,3,*, Guan Ning Lin1,*, Shuli Kang1,*, Yun Shen2,3, Lila Ghamsari2,3,w, Martin Broly2,3, Maria Rodriguez2,3, Stanley Tam2,3, Shelly A. Trigg2,3,w, Changyu Fan2,3, Song Yi2,3, Murat Tasan4, Irma Lemmens5, Xingyan Kuang6, Nan Zhao6, Dheeraj Malhotra7, Jacob J. Michaelson7,w, Vladimir Vacic8, Michael A. Calderwood2,3, Frederick P. Roth2,3,4, Jan Tavernier5, Steve Horvath9, Kourosh Salehi-Ashtiani2,3,w, Dmitry Korkin6, Jonathan Sebat7, David E. Hill2,3, Tong Hao2,3, Marc Vidal2,3 & Lilia M. Iakoucheva1 Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases. -
Parallel Molecular Evolution in Pathways, Genes, and Sites in High-Elevation Hummingbirds Revealed by Comparative Transcriptomics
GBE Parallel Molecular Evolution in Pathways, Genes, and Sites in High-Elevation Hummingbirds Revealed by Comparative Transcriptomics Marisa C.W. Lim1,*, Christopher C. Witt2, Catherine H. Graham1,3,andLilianaM.Davalos 1,4 1Department of Ecology and Evolution, Stony Brook University 2 Museum of Southwestern Biology and Department of Biology, University of New Mexico Downloaded from https://academic.oup.com/gbe/article-abstract/11/6/1552/5494706 by guest on 08 June 2019 3Swiss Federal Research Institute (WSL), Birmensdorf, Switzerland 4Consortium for Inter-Disciplinary Environmental Research, Stony Brook University *Corresponding author: E-mail: [email protected]. Accepted: May 12, 2019 Data deposition: The raw read data have been deposited in the NCBI Sequence Read Archive under BioProject: PRJNA543673, BioSample: SAMN11774663-SAMN11774674, SRA Study: SRP198856. All scripts used for analyses are available on Dryad: doi:10.5061/dryad.v961mb4. Abstract High-elevation organisms experience shared environmental challenges that include low oxygen availability, cold temperatures, and intense ultraviolet radiation. Consequently, repeated evolution of the same genetic mechanisms may occur across high-elevation taxa. To test this prediction, we investigated the extent to which the same biochemical pathways, genes, or sites were subject to parallel molecular evolution for 12 Andean hummingbird species (family: Trochilidae) representing several independent transitions to high elevation across the phylogeny. Across high-elevation species, we discovered parallel evolution for several pathways and genes with evidence of positive selection. In particular, positively selected genes were frequently part of cellular respiration, metabolism, or cell death pathways. To further examine the role of elevation in our analyses, we compared results for low- and high-elevation species and tested different thresholds for defining elevation categories. -
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. -
Inborn Errors of Metabolism Test Requisition
LABORATORY OF GENETICS AND GENOMICS Mailing Address: For local courier service and/or inquiries, please contact 513-636-4474 • Fax: 513-636-4373 3333 Burnet Avenue, Room R1042 www.cincinnatichildrens.org/moleculargenetics • Email: [email protected] Cincinnati, OH 45229 INBORN ERRORS OF METABOLISM TEST REQUISITION All Information Must Be Completed Before Sample Can Be Processed PATIENT INFORMATION ETHNIC/RACIAL BACKGROUND (Choose All) Patient Name: ___________________ , ___________________ , ________ European American (White) African-American (Black) Last First MI Native American or Alaskan Asian-American Address: ____________________________________________________ Pacific Islander Ashkenazi Jewish ancestry ____________________________________________________ Latino-Hispanic _____________________________________________ Home Phone: ________________________________________________ (specify country/region of origin) MR# __________________ Date of Birth ________ / ________ / _______ Other ____________________________________________________ (specify country/region of origin) Gender: Male Female BILLING INFORMATION (Choose ONE method of payment) o REFERRING INSTITUTION o COMMERCIAL INSURANCE* Insurance can only be billed if requested at the time of service. Institution: ____________________________________________________ Policy Holder Name: _____________________________________________ Address: _____________________________________________________ Gender: ________________ Date of Birth ________ / ________ / _______ -
SUPPLEMENTARY DATA Principal Investigators and Acknowledgment
SUPPLEMENTARY DATA Principal Investigators and Acknowledgment Hispanic Community Health Study/Study of Latinos (HSHC/SOL) Robert Kaplan, Sylvia Wassertheil-Smoller (Bronx); Martha L. Daviglus, Aida L. Giachello (Chicago); Neil Schneiderman, David Lee, Leopoldo Raij, John Ryan (Miami); Greg Talavera, John Elder, Matthew Allison, Michael Criqui (San Deigo). The authors thank the staff and participants of HCHS/SOL for their important contributions. A complete list of staff and investigators has been provided by Sorlie P., et al. in Ann Epidemiol. 2010 ;20:642-649 and is also available on the study website http://www.cscc.unc.edu/hchs/. The baseline examination of the Hispanic Community Health Study/Study of Latinos was carried out as a collaborative study supported by contracts from the National Heart, Lung, and Blood Institute (NHLBI) to the University of North Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), Northwestern University (N01-HC65236), and San Diego State University (N01-HC65237). The following Institutes/Centers/Offices contributed to the HCHS/SOL first funding period through a transfer of funds to the NHLBI: National Institute on Minority Health and Health Disparities, the National Institute of Deafness and Other Communications Disorders, the National Institute of Dental and Craniofacial Research, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Neurological Disorders and Stroke, and the NIH Office of Dietary Supplements. The Genetic Analysis Center at the University of Washington was supported by NHLBI and NIDCR contracts (HHSN268201300005C AM03 and MOD03). Genotyping efforts were supported by NHLBI HSN 26220/20054C, NCATS CTSI grant UL1TR000123, and NIDDK Diabetes Research Center (DRC) grant DK063491. -
Genes Investigated
BabyNEXTTM EXTENDED Investigated genes and associated diseases Gene Disease OMIM OMIM Condition RUSP gene Disease ABCC8 Familial hyperinsulinism 600509 256450 Metabolic disorder - ABCC8-related Inborn error of amino acid metabolism ABCD1 Adrenoleukodystrophy 300371 300100 Miscellaneous RUSP multisystem (C) * diseases ABCD4 Methylmalonic aciduria and 603214 614857 Metabolic disorder - homocystinuria, cblJ type Inborn error of amino acid metabolism ACAD8 Isobutyryl-CoA 604773 611283 Metabolic Disorder - RUSP dehydrogenase deficiency Inborn error of (S) ** organic acid metabolism ACAD9 acyl-CoA dehydrogenase-9 611103 611126 Metabolic Disorder - (ACAD9) deficiency Inborn error of fatty acid metabolism ACADM Acyl-CoA dehydrogenase, 607008 201450 Metabolic Disorder - RUSP medium chain, deficiency of Inborn error of fatty (C) acid metabolism ACADS Acyl-CoA dehydrogenase, 606885 201470 Metabolic Disorder - RUSP short-chain, deficiency of Inborn error of fatty (S) acid metabolism ACADSB 2-methylbutyrylglycinuria 600301 610006 Metabolic Disorder - RUSP Inborn error of (S) organic acid metabolism ACADVL very long-chain acyl-CoA 609575 201475 Metabolic Disorder - RUSP dehydrogenase deficiency Inborn error of fatty (C) acid metabolism ACAT1 Alpha-methylacetoacetic 607809 203750 Metabolic Disorder - RUSP aciduria Inborn error of (C) organic acid metabolism ACSF3 Combined malonic and 614245 614265 Metabolic Disorder - methylmalonic aciduria Inborn error of organic acid metabolism 1 ADA Severe combined 608958 102700 Primary RUSP immunodeficiency due