PECONPI: a Novel Software for Uncovering Pathogenic Copy
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Genetic Variation Across the Human Olfactory Receptor Repertoire Alters Odor Perception
bioRxiv preprint doi: https://doi.org/10.1101/212431; this version posted November 1, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 4.0 International license. Genetic variation across the human olfactory receptor repertoire alters odor perception Casey Trimmer1,*, Andreas Keller2, Nicolle R. Murphy1, Lindsey L. Snyder1, Jason R. Willer3, Maira Nagai4,5, Nicholas Katsanis3, Leslie B. Vosshall2,6,7, Hiroaki Matsunami4,8, and Joel D. Mainland1,9 1Monell Chemical Senses Center, Philadelphia, Pennsylvania, USA 2Laboratory of Neurogenetics and Behavior, The Rockefeller University, New York, New York, USA 3Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, USA 4Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, USA 5Department of Biochemistry, University of Sao Paulo, Sao Paulo, Brazil 6Howard Hughes Medical Institute, New York, New York, USA 7Kavli Neural Systems Institute, New York, New York, USA 8Department of Neurobiology and Duke Institute for Brain Sciences, Duke University Medical Center, Durham, North Carolina, USA 9Department of Neuroscience, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA *[email protected] ABSTRACT The human olfactory receptor repertoire is characterized by an abundance of genetic variation that affects receptor response, but the perceptual effects of this variation are unclear. To address this issue, we sequenced the OR repertoire in 332 individuals and examined the relationship between genetic variation and 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity. -
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. -
Identification of Potential Markers for Type 2 Diabetes Mellitus Via Bioinformatics Analysis
1868 MOLECULAR MEDICINE REPORTS 22: 1868-1882, 2020 Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis YANA LU1, YIHANG LI1, GUANG LI1* and HAITAO LU2* 1Key Laboratory of Dai and Southern Medicine of Xishuangbanna Dai Autonomous Prefecture, Yunnan Branch, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Jinghong, Yunnan 666100; 2Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, P.R. China Received March 20, 2019; Accepted January 20, 2020 DOI: 10.3892/mmr.2020.11281 Abstract. Type 2 diabetes mellitus (T2DM) is a multifactorial and cell proliferation’. These candidate genes were also involved in multigenetic disease, and its pathogenesis is complex and largely different signaling pathways associated with ‘PI3K/Akt signaling unknown. In the present study, microarray data (GSE201966) of pathway’, ‘Rap1 signaling pathway’, ‘Ras signaling pathway’ β-cell enriched tissue obtained by laser capture microdissection and ‘MAPK signaling pathway’, which are highly associated were downloaded, including 10 control and 10 type 2 diabetic with the development of T2DM. Furthermore, a microRNA subjects. A comprehensive bioinformatics analysis of microarray (miR)-target gene regulatory network and a transcription data in the context of protein-protein interaction (PPI) networks factor-target gene regulatory network were constructed based was employed, combined with subcellular location information on miRNet and NetworkAnalyst databases, respectively. to mine the potential candidate genes for T2DM and provide Notably, hsa-miR‑192-5p, hsa-miR‑124-5p and hsa-miR‑335-5p further insight on the possible mechanisms involved. First, appeared to be involved in T2DM by potentially regulating the differential analysis screened 108 differentially expressed expression of various candidate genes, including procollagen genes. -
Genome-Wide Linkage and Admixture Mapping of Type 2 Diabetes In
ORIGINAL ARTICLE Genome-Wide Linkage and Admixture Mapping of Type 2 Diabetes in African American Families From the American Diabetes Association GENNID (Genetics of NIDDM) Study Cohort Steven C. Elbein,1,2 Swapan K. Das,1,2 D. Michael Hallman,3 Craig L. Hanis,3 and Sandra J. Hasstedt4 OBJECTIVE—We used a single nucleotide polymorphism (SNP) map in a large cohort of 580 African American families to identify regions linked to type 2 diabetes, age of type 2 diabetes ype 2 diabetes is marked by a clear genetic diagnosis, and BMI. propensity, a high concordance in identical twins, tendencies for both diabetes and age of RESEARCH DESIGN AND METHODS—After removing outli- onset to be familial (1), and marked differences ers and problematic samples, we conducted linkage analysis T in prevalence among ethnic groups (2). Despite consider- using 5,914 SNPs in 1,344 individuals from 530 families. Linkage analysis was conducted using variance components for type 2 able evidence for a genetic predisposition, unraveling the diabetes, age of type 2 diabetes diagnosis, and BMI and nonpara- genetic etiology has been daunting, with few confirmed metric linkage analyses. Ordered subset analyses were con- genes identified from genome-wide linkage scans. Recent ducted ranking on age of type 2 diabetes diagnosis, BMI, waist successes with genome-wide association scans (3) have circumference, waist-to-hip ratio, and amount of European ad- greatly increased the number of confirmed genetic loci, mixture. Admixture mapping was conducted using 4,486 markers but these successes have been limited primarily to Cauca- not in linkage disequilibrium. -
The Adaptation and Changes of Titin System Following Exercise Training Hassan Farhadi 1 and Mir Hojjat Mosavineghad 2
Available online a t www.pelagiaresearchlibrary.com Pelagia Research Library European Journal of Experimental Biology, 2012, 2 (4):1256-1260 ISSN: 2248 –9215 CODEN (USA): EJEBAU The adaptation and changes of titin system following exercise training Hassan Farhadi 1 and Mir Hojjat Mosavineghad 2 1Department of Physical Education and Sport Sciences, Ahar branch, Islamic Azad University, Ahar, Iran 2Department of Physical Education and Sport Sciences, khoy branch, Islamic Azad University, khoy, Iran _____________________________________________________________________________________________ ABSTRACT Titin is a large structural protein in muscle that has the ability to store elastic energy. The specific structure of titin and its elastic nature has recently become better understood. In addition, the utilization of stored elastic energy in human movement and the significant contribution of not only tendon but muscle tissue itself to this process has been re-evaluated. In this study, we reviewed the effects of exercise training on titin system (titin expression and titin- complex proteins). Keywords: Titin, titin-complex proteins, exercise training _________________________________________________________________________________________ The giant sarcomeric protein titin is 3-4 MDa and spans the sarcomere from Z-line to M-line. Titin was first discovered in 1979 by Wang et. al. (1), who initially thought that titin was a collection of polypeptides that formed one large protein. Although titin’s physical identification eluded researchers for many years, probably due to its susceptibility to proteolytic cleavage, many scientists, including Earnest Starling and A.F. Huxley, posited its existence (2). Starling and Huxley modeled their theories on the premise that something within striated muscle was regulating passive properties. This is now known to be the role of titin, which is one peptide, encoded by a single gene, TTN (2). -
Supplementary Figure S4
18DCIS 18IDC Supplementary FigureS4 22DCIS 22IDC C D B A E (0.77) (0.78) 16DCIS 14DCIS 28DCIS 16IDC 28IDC (0.43) (0.49) 0 ADAMTS12 (p.E1469K) 14IDC ERBB2, LASP1,CDK12( CCNE1 ( NUTM2B SDHC,FCGR2B,PBX1,TPR( CD1D, B4GALT3, BCL9, FLG,NUP21OL,TPM3,TDRD10,RIT1,LMNA,PRCC,NTRK1 0 ADAMTS16 (p.E67K) (0.67) (0.89) (0.54) 0 ARHGEF38 (p.P179Hfs*29) 0 ATG9B (p.P823S) (0.68) (1.0) ARID5B, CCDC6 CCNE1, TSHZ3,CEP89 CREB3L2,TRIM24 BRAF, EGFR (7p11); 0 ABRACL (p.R35H) 0 CATSPER1 (p.P152H) 0 ADAMTS18 (p.Y799C) 19q12 0 CCDC88C (p.X1371_splice) (0) 0 ADRA1A (p.P327L) (10q22.3) 0 CCNF (p.D637N) −4 −2 −4 −2 0 AKAP4 (p.G454A) 0 CDYL (p.Y353Lfs*5) −4 −2 Log2 Ratio Log2 Ratio −4 −2 Log2 Ratio Log2 Ratio 0 2 4 0 2 4 0 ARID2 (p.R1068H) 0 COL27A1 (p.G646E) 0 2 4 0 2 4 2 EDRF1 (p.E521K) 0 ARPP21 (p.P791L) ) 0 DDX11 (p.E78K) 2 GPR101, p.A174V 0 ARPP21 (p.P791T) 0 DMGDH (p.W606C) 5 ANP32B, p.G237S 16IDC (Ploidy:2.01) 16DCIS (Ploidy:2.02) 14IDC (Ploidy:2.01) 14DCIS (Ploidy:2.9) -3 -2 -1 -3 -2 -1 -3 -2 -1 -3 -2 -1 -3 -2 -1 -3 -2 -1 Log Ratio Log Ratio Log Ratio Log Ratio 12DCIS 0 ASPM (p.S222T) Log Ratio Log Ratio 0 FMN2 (p.G941A) 20 1 2 3 2 0 1 2 3 2 ERBB3 (p.D297Y) 2 0 1 2 3 20 1 2 3 0 ATRX (p.L1276I) 20 1 2 3 2 0 1 2 3 0 GALNT18 (p.F92L) 2 MAPK4, p.H147Y 0 GALNTL6 (p.E236K) 5 C11orf1, p.Y53C (10q21.2); 0 ATRX (p.R1401W) PIK3CA, p.H1047R 28IDC (Ploidy:2.0) 28DCIS (Ploidy:2.0) 22IDC (Ploidy:3.7) 22DCIS (Ploidy:4.1) 18IDC (Ploidy:3.9) 18DCIS (Ploidy:2.3) 17q12 0 HCFC1 (p.S2025C) 2 LCMT1 (p.S34A) 0 ATXN7L2 (p.X453_splice) SPEN, p.P677Lfs*13 CBFB 1 2 3 4 5 6 7 8 9 10 11 -
LETTER Doi:10.1038/Nature09515
LETTER doi:10.1038/nature09515 Distant metastasis occurs late during the genetic evolution of pancreatic cancer Shinichi Yachida1*, Siaˆn Jones2*, Ivana Bozic3, Tibor Antal3,4, Rebecca Leary2, Baojin Fu1, Mihoko Kamiyama1, Ralph H. Hruban1,5, James R. Eshleman1, Martin A. Nowak3, Victor E. Velculescu2, Kenneth W. Kinzler2, Bert Vogelstein2 & Christine A. Iacobuzio-Donahue1,5,6 Metastasis, the dissemination and growth of neoplastic cells in an were present in the primary pancreatic tumours from which the meta- organ distinct from that in which they originated1,2, is the most stases arose. A small number of these samples of interest were cell lines common cause of death in cancer patients. This is particularly true or xenografts, similar to the index lesions, whereas the majority were for pancreatic cancers, where most patients are diagnosed with fresh-frozen tissues that contained admixed neoplastic, stromal, metastatic disease and few show a sustained response to chemo- inflammatory, endothelial and normal epithelial cells (Fig. 1a). Each therapy or radiation therapy3. Whether the dismal prognosis of tissue sample was therefore microdissected to minimize contaminat- patients with pancreatic cancer compared to patients with other ing non-neoplastic elements before purifying DNA. types of cancer is a result of late diagnosis or early dissemination of Two categories of mutations were identified (Fig. 1b). The first and disease to distant organs is not known. Here we rely on data gen- largest category corresponded to those mutations present in all samples erated by sequencing the genomes of seven pancreatic cancer meta- from a given patient (‘founder’ mutations, mean of 64%, range 48–83% stases to evaluate the clonal relationships among primary and of all mutations per patient; Fig. -
A Population-Specific Major Allele Reference Genome from the United
Edith Cowan University Research Online ECU Publications Post 2013 2021 A population-specific major allele efr erence genome from the United Arab Emirates population Gihan Daw Elbait Andreas Henschel Guan K. Tay Edith Cowan University Habiba S. Al Safar Follow this and additional works at: https://ro.ecu.edu.au/ecuworkspost2013 Part of the Life Sciences Commons, and the Medicine and Health Sciences Commons 10.3389/fgene.2021.660428 Elbait, G. D., Henschel, A., Tay, G. K., & Al Safar, H. S. (2021). A population-specific major allele reference genome from the United Arab Emirates population. Frontiers in Genetics, 12, article 660428. https://doi.org/10.3389/ fgene.2021.660428 This Journal Article is posted at Research Online. https://ro.ecu.edu.au/ecuworkspost2013/10373 fgene-12-660428 April 19, 2021 Time: 16:18 # 1 ORIGINAL RESEARCH published: 23 April 2021 doi: 10.3389/fgene.2021.660428 A Population-Specific Major Allele Reference Genome From The United Arab Emirates Population Gihan Daw Elbait1†, Andreas Henschel1,2†, Guan K. Tay1,3,4,5 and Habiba S. Al Safar1,3,6* 1 Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates, 2 Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates, 3 Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates, 4 Division of Psychiatry, Faculty of Health and Medical Sciences, The University of Western Australia, Crawley, WA, Australia, 5 School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia, 6 Department of Genetics and Molecular Biology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates The ethnic composition of the population of a country contributes to the uniqueness of each national DNA sequencing project and, ideally, individual reference genomes are required to reduce the confounding nature of ethnic bias. -
Crosstalk in Competing Endogenous RNA Network Reveals the Complex Molecular Mechanism Underlying Lung Cancer
www.impactjournals.com/oncotarget/ Oncotarget, 2017, Vol. 8, (No. 53), pp: 91270-91280 Research Paper Crosstalk in competing endogenous RNA network reveals the complex molecular mechanism underlying lung cancer Xiang Jin1, Yinghui Guan1, Hui Sheng1 and Yang Liu1 1Department of Respiration, The First Hospital of Jilin University, Changchun City, Jilin Province, 130021, China Correspondence to: Yinghui Guan, email: [email protected] Keywords: crosstalk, ceRNA network, lung cancer, molecular mechanism, circRNA-seq Received: March 29, 2017 Accepted: July 16, 2017 Published: August 24, 2017 Copyright: Jin et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT We investigated the transcriptional mechanism underlying lung cancer development. RNA sequencing analysis was performed on blood samples from lung cancer cases and healthy controls. Differentially expressed microRNAs (miRNAs), circular RNAs (circRNAs), mRNAs (genes), and long non-coding RNAs (lncRNA) were identified, followed by pathway enrichment analysis. Based on miRNA target interactions, a competing endogenous network was established and significant nodes were screened. Differentially expressed transcriptional factors were retrieved from the TRRUST database and the transcriptional factor regulatory network was constructed. The expression of 59 miRNAs, 18,306 genes,232 lncRNAs, and 292 circRNAs were greatly altered in patients with lung cancer. miRNAs were closely associated with cancer-related pathways, such as pathways in cancer, colorectal cancer, and transcriptional misregulation in cancer. Two novel pathways, olfactory transduction and neuroactive ligand-receptor interactions, were significantly enriched by differentially expressed genes. -
Rabbit Anti-OR13C9 Polyclonal Antibody - Middle Region
Catalog: OM105521 Scan to get more validated information Rabbit anti-OR13C9 polyclonal antibody - middle region Catalog: OM105521 100ug Product profile Product name Rabbit anti-OR13C9 polyclonal antibody - middle region Antibody Type Primary Antibodies Immunogen The immunogen for anti-OR13C9 antibody: synthetic peptide directed towards the middle region of hum an OR13C9 Key Feature Clonality Polyclonal Isotype IgG Host Species Rabbit Tested Applications WB Species Reactivity Dog Human Concentration 1 mg/ml Purification Affinity purified Target Information Gene Symbol OR13C9 Gene Synonyms OR37L; OR9-13 Gene Full Name Olfactory receptor, family 13, subfamily C, member 9 Gene Summary Olfactory receptors interact with odorant molecules in the nose, to initiate a neuronal response that trigg ers the perception of a smell. The olfactory receptor proteins are members of a large family of G-protein- coupled receptors (GPCR) arising from single coding-exon genes. Olfactory receptors share a 7-transme mbrane domain structure with many neurotransmitter and hormone receptors and are responsible for the recognition and G protein-mediated transduction of odorant signals. The olfactory receptor gene family i s the largest in the genome. The nomenclature assigned to the olfactory receptor genes and proteins for this organism is independent of other organisms. Alternative Names OR37L, OR9-13 Alternative Names OR37L, OR9-13 Molecular Weight(MW) 36kDa Sequence 318 amino acids Database Links Entrez Gene 286362 SwissProt ID Q8NGS9 Protein Accession NP_001001956 Application Western blot 0.2-1 ug/ml ELISA Titer: 1:312500 Positive Control: HepG2 cell lysate Application Notes WB:1:500~1:2000 Notes:Optimal dilutions/concentrations should be determined by the researcher. -
Supplementary Table 1
Supplemental Table 1. Genes that were increased or decreased more than two-fold in anti- FGFR2IIIc monoclonal antibody-treated cells Fold change Gene Symbol Description HCT-15 LoVo NKD1 Protein naked cuticle homolog 1 (Naked-1) (hNkd1) 24.74 2.18 (hNkd) [Source: UniProtKB/Swiss-Prot; Acc: Q969G9] [ENST00000268459] SAA1 Homo sapiens serum amyloid A1 (SAA1), transcript 21.09 3.84 variant 1, mRNA [NM_000331] LRRC18 Homo sapiens leucine-rich repeat containing 18 13.70 4.60 (LRRC18), mRNA [NM_001006939] ABCB5 Homo sapiens ATP-binding cassette, sub-family B 11.78 2.54 (MDR/TAP), member 5 (ABCB5), transcript variant 2, mRNA [NM_178559] CXCL1 Homo sapiens chemokine (C-X-C motif) ligand 1 10.61 7.05 (melanoma growth-stimulating activity, alpha) (CXCL1), mRNA [NM_001511] SERPINA9 Homo sapiens serpin peptidase inhibitor, clade A 9.63 4.14 (alpha-1 antiproteinase, antitrypsin), member 9 (SERPINA9), transcript variant A, mRNA [NM_175739] LCN2 Homo sapiens lipocalin 2 (LCN2), mRNA 9.00 4.78 [NM_005564] HS6ST3 Homo sapiens heparan sulfate 6-O-sulfotransferase 3 8.53 2.70 (HS6ST3), mRNA [NM_153456] CXCL3 Homo sapiens chemokine (C-X-C motif) ligand 3 7.89 4.55 (CXCL3), mRNA [NM_002090] IL8 Homo sapiens interleukin 8 (IL8), mRNA 7.34 9.61 [NM_000584] CXorf18 PREDICTED: Homo sapiens chromosome X open 6.55 2.06 reading frame 18 (CXorf18), miscRNA [XR_040313] BEST1 Homo sapiens bestrophin 1 (BEST1), transcript variant 6.54 2.43 1, mRNA [NM_004183] CXCL2 Homo sapiens chemokine (C-X-C motif) ligand 2 6.39 5.52 (CXCL2), mRNA [NM_002089] USH2A Homo sapiens Usher -
Misexpression of Cancer/Testis (Ct) Genes in Tumor Cells and the Potential Role of Dream Complex and the Retinoblastoma Protein Rb in Soma-To-Germline Transformation
Michigan Technological University Digital Commons @ Michigan Tech Dissertations, Master's Theses and Master's Reports 2019 MISEXPRESSION OF CANCER/TESTIS (CT) GENES IN TUMOR CELLS AND THE POTENTIAL ROLE OF DREAM COMPLEX AND THE RETINOBLASTOMA PROTEIN RB IN SOMA-TO-GERMLINE TRANSFORMATION SABHA M. ALHEWAT Michigan Technological University, [email protected] Copyright 2019 SABHA M. ALHEWAT Recommended Citation ALHEWAT, SABHA M., "MISEXPRESSION OF CANCER/TESTIS (CT) GENES IN TUMOR CELLS AND THE POTENTIAL ROLE OF DREAM COMPLEX AND THE RETINOBLASTOMA PROTEIN RB IN SOMA-TO- GERMLINE TRANSFORMATION", Open Access Master's Thesis, Michigan Technological University, 2019. https://doi.org/10.37099/mtu.dc.etdr/933 Follow this and additional works at: https://digitalcommons.mtu.edu/etdr Part of the Cancer Biology Commons, and the Cell Biology Commons MISEXPRESSION OF CANCER/TESTIS (CT) GENES IN TUMOR CELLS AND THE POTENTIAL ROLE OF DREAM COMPLEX AND THE RETINOBLASTOMA PROTEIN RB IN SOMA-TO-GERMLINE TRANSFORMATION By Sabha Salem Alhewati A THESIS Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE In Biological Sciences MICHIGAN TECHNOLOGICAL UNIVERSITY 2019 © 2019 Sabha Alhewati This thesis has been approved in partial fulfillment of the requirements for the Degree of MASTER OF SCIENCE in Biological Sciences. Department of Biological Sciences Thesis Advisor: Paul Goetsch. Committee Member: Ebenezer Tumban. Committee Member: Zhiying Shan. Department Chair: Chandrashekhar Joshi. Table of Contents List of figures .......................................................................................................................v