Abundancy of Polymorphic CGG Repeats in the Human Genome Suggest a Broad Involvement in Neurological Disease Dale J
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SAN TA C RUZ BI OTEC HNOL OG Y, INC . DIP2A (L-16): sc-67555 BACKGROUND APPLICATIONS DIP2A (Disco-interacting protein 2 homolog A), also known as DIP2, is a 1,571 DIP2A (L-16) is recommended for detection of DIP2A of human origin by amino acid nuclear protein. It is one of three human homologs (DIP2A, DIP2B Western Blotting (starting dilution 1:200, dilution range 1:100-1:1000), and DIP2C) of the Drosophila dip2 (disconnected-interacting protein 2) protein. immunoprecipitation [1-2 µg per 100-500 µg of total protein (1 ml of cell In Drosophila , dip2 interacts with disco, a protein required for neuronal con - lysate)], immunofluorescence (starting dilution 1:50, dilution range 1:50- nections in the visual systems of larvae and adults. The closest vertebrate 1:500) and solid phase ELISA (starting dilution 1:30, dilution range 1:30- homologs to disco are the basonuclin genes. In mice, DIP2 homologs show 1:3000). restricted expression to the brain. This suggests that, similar to the function DIP2A (L-16) is also recommended for detection of DIP2A, also designated of Drosphila dip2, vertebrate DIP2 homologs may play a role in the develop - Disco-interacting protein 2 homolog A, in additional species, including ment of the nervous system. Expressed ubiquitously with highest expression canine. in the brain, DIP2A is thought to function in signaling throughout the central nervous system by providing positional clues for axon patterning and pathfind - Suitable for use as control antibody for DIP2A siRNA (h): sc-62212, DIP2A ing . Four isoforms of DIP2A exist due to alternative splicing events. -
Bayesian Hierarchical Modeling of High-Throughput Genomic Data with Applications to Cancer Bioinformatics and Stem Cell Differentiation
BAYESIAN HIERARCHICAL MODELING OF HIGH-THROUGHPUT GENOMIC DATA WITH APPLICATIONS TO CANCER BIOINFORMATICS AND STEM CELL DIFFERENTIATION by Keegan D. Korthauer A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Statistics) at the UNIVERSITY OF WISCONSIN–MADISON 2015 Date of final oral examination: 05/04/15 The dissertation is approved by the following members of the Final Oral Committee: Christina Kendziorski, Professor, Biostatistics and Medical Informatics Michael A. Newton, Professor, Statistics Sunduz Kele¸s,Professor, Biostatistics and Medical Informatics Sijian Wang, Associate Professor, Biostatistics and Medical Informatics Michael N. Gould, Professor, Oncology © Copyright by Keegan D. Korthauer 2015 All Rights Reserved i in memory of my grandparents Ma and Pa FL Grandma and John ii ACKNOWLEDGMENTS First and foremost, I am deeply grateful to my thesis advisor Christina Kendziorski for her invaluable advice, enthusiastic support, and unending patience throughout my time at UW-Madison. She has provided sound wisdom on everything from methodological principles to the intricacies of academic research. I especially appreciate that she has always encouraged me to eke out my own path and I attribute a great deal of credit to her for the successes I have achieved thus far. I also owe special thanks to my committee member Professor Michael Newton, who guided me through one of my first collaborative research experiences and has continued to provide key advice on my thesis research. I am also indebted to the other members of my thesis committee, Professor Sunduz Kele¸s,Professor Sijian Wang, and Professor Michael Gould, whose valuable comments, questions, and suggestions have greatly improved this dissertation. -
Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino-Like Mouse Models: Tracking the Role of the Hairless Gene
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 5-2006 Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino-like Mouse Models: Tracking the Role of the Hairless Gene Yutao Liu University of Tennessee - Knoxville Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Life Sciences Commons Recommended Citation Liu, Yutao, "Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino- like Mouse Models: Tracking the Role of the Hairless Gene. " PhD diss., University of Tennessee, 2006. https://trace.tennessee.edu/utk_graddiss/1824 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Yutao Liu entitled "Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino-like Mouse Models: Tracking the Role of the Hairless Gene." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Life Sciences. Brynn H. Voy, Major Professor We have read this dissertation and recommend its acceptance: Naima Moustaid-Moussa, Yisong Wang, Rogert Hettich Accepted for the Council: Carolyn R. -
AFF3 Upregulation Mediates Tamoxifen Resistance in Breast
Shi et al. Journal of Experimental & Clinical Cancer Research (2018) 37:254 https://doi.org/10.1186/s13046-018-0928-7 RESEARCH Open Access AFF3 upregulation mediates tamoxifen resistance in breast cancers Yawei Shi1†, Yang Zhao2†, Yunjian Zhang1, NiJiati AiErken3, Nan Shao1, Runyi Ye1, Ying Lin1* and Shenming Wang1* Abstract Background: Although tamoxifen is a highly effective drug for treating estrogen receptor–positive (ER+) breast cancer, nearly all patients with metastasis with initially responsive tumors eventually relapse, and die from acquired drug resistance. Unfortunately, few molecular mediators of tamoxifen resistance have been described. Here, we describe AFF3 (AF4/FMR2 family member 3), which encodes a nuclear protein with transactivation potential that confers tamoxifen resistance and enables estrogen-independent growth. Methods: We investigated AFF3 expression in breast cancer cells and in clinical breast cancer specimens with western blot and Real-time PCR. We also examined the effects of AFF3 knockdown and overexpression on breast cancer cells using luciferase, tetrazolium, colony formation, and anchorage-independent growth assays in vitro and with nude mouse xenografting in vivo. Results: AFF3 was overexpressed in tamoxifen-resistant tumors. AFF3 overexpression in breast cancer cells resulted in tamoxifen resistance, whereas RNA interference–mediated gene knockdown reversed this phenotype. Furthermore, AFF3 upregulation led to estrogen-independent growth in the xenograft assays. Mechanistic investigations revealed that AFF3 overexpression activated the ER signaling pathway and transcriptionally upregulated a subset of ER-regulated genes. Clinical analysis showed that increased AFF3 expression in ER+ breast tumors was associated with worse overall survival. Conclusions: These studies establish AFF3 as a key mediator of estrogen-independent growth and tamoxifen resistance and as a potential novel diagnostic and therapeutic target. -
Coupling of Spliceosome Complexity to Intron Diversity
bioRxiv preprint doi: https://doi.org/10.1101/2021.03.19.436190; this version posted March 20, 2021. 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-NC-ND 4.0 International license. Coupling of spliceosome complexity to intron diversity Jade Sales-Lee1, Daniela S. Perry1, Bradley A. Bowser2, Jolene K. Diedrich3, Beiduo Rao1, Irene Beusch1, John R. Yates III3, Scott W. Roy4,6, and Hiten D. Madhani1,6,7 1Dept. of Biochemistry and Biophysics University of California – San Francisco San Francisco, CA 94158 2Dept. of Molecular and Cellular Biology University of California - Merced Merced, CA 95343 3Department of Molecular Medicine The Scripps Research Institute, La Jolla, CA 92037 4Dept. of Biology San Francisco State University San Francisco, CA 94132 5Chan-Zuckerberg Biohub San Francisco, CA 94158 6Corresponding authors: [email protected], [email protected] 7Lead Contact 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.03.19.436190; this version posted March 20, 2021. 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-NC-ND 4.0 International license. SUMMARY We determined that over 40 spliceosomal proteins are conserved between many fungal species and humans but were lost during the evolution of S. cerevisiae, an intron-poor yeast with unusually rigid splicing signals. We analyzed null mutations in a subset of these factors, most of which had not been investigated previously, in the intron-rich yeast Cryptococcus neoformans. -
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. -
Neurodegenerative Triplet Repeat Expansion Disorders
The Pharma Innovation Journal 2018; 7(11): 34-40 ISSN (E): 2277- 7695 ISSN (P): 2349-8242 NAAS Rating: 5.03 Neurodegenerative triplet repeat expansion disorders: TPI 2018; 7(11): 34-40 © 2018 TPI A review www.thepharmajournal.com Received: 15-09-2018 Accepted: 20-10-2018 Mitesh Patel, RK Patel, Tushar Chauhan, Jigar Suthar and Sanjay Dave Mitesh Patel Genetics Group of Gujarat Abstract Diagnostic Centre, Mehsana, Epigenetic alterations are the major causes of triplet repeat expansion. The repetitive DNA expands of its Gujarat, India normal length results in sever neurodegenerative conditions. The common types of triplet repeat expansion (TNE) disorders are: Huntington disease, Friedreich ataxia, myotonic dystrophy, SBMA and RK Patel SCA1 out of which Huntington disease, SBMA and SCA1 are categorized as a poly glutamine disorder Sandor Animal Biogenics Pvt. due to the repeat of CAG. In contrast, the friedreich ataxia is occurred due to expansion of the GAA Ltd., Hyderabad, Telangana, whereas myotonic dystrophy is due to the expansion of CTG. The triplet disease follows the mechanism India of anticipation in which the onset of the disease increases with age. Conclusively, no clear mechanism can explain the origin of the disease. The pre mutation can be expanded in full mutation in successive Tushar Chauhan Genetics Group of Gujarat generations and the number of repeats increased with each generation. TNE can observe in both somatic Diagnostic Centre, Mehsana, as well as germ line tissues. Gujarat, India Keywords: triplet repeat expansion disorder, trinucleotide repeats, Huntington disease, SBMA, SCA1, Jigar Suthar friedreich ataxia Genetics Group of Gujarat Diagnostic Centre, Mehsana, Introduction Gujarat, India Since the early 1990s, a new class of molecular disease has been characterized based upon the Sanjay Dave presence of unstable and abnormal expansions of DNA-triplets (Trinucleotides). -
Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database. -
Loss of DIP2C in RKO Cells Stimulates Changes in DNA
Larsson et al. BMC Cancer (2017) 17:487 DOI 10.1186/s12885-017-3472-5 RESEARCH ARTICLE Open Access Loss of DIP2C in RKO cells stimulates changes in DNA methylation and epithelial- mesenchymal transition Chatarina Larsson1, Muhammad Akhtar Ali1,2, Tatjana Pandzic1, Anders M. Lindroth3, Liqun He1,4 and Tobias Sjöblom1* Abstract Background: The disco-interacting protein 2 homolog C (DIP2C) gene is an uncharacterized gene found mutated in a subset of breast and lung cancers. To understand the role of DIP2C in tumour development we studied the gene in human cancer cells. Methods: We engineered human DIP2C knockout cells by genome editing in cancer cells. The growth properties of the engineered cells were characterised and transcriptome and methylation analyses were carried out to identify pathways deregulated by inactivation of DIP2C. Effects on cell death pathways and epithelial-mesenchymal transition traits were studied based on the results from expression profiling. Results: Knockout of DIP2C in RKO cells resulted in cell enlargement and growth retardation. Expression profiling revealed 780 genes for which the expression level was affected by the loss of DIP2C, including the tumour-suppressor encoding CDKN2A gene, the epithelial-mesenchymal transition (EMT) regulator-encoding ZEB1,andCD44 and CD24 that encode breast cancer stem cell markers. Analysis of DNA methylation showed more than 30,000 sites affected by differential methylation, the majority of which were hypomethylated following loss of DIP2C. Changes in DNA methylation at promoter regions were strongly correlated to changes in gene expression, and genes involved with EMT and cell death were enriched among the differentially regulated genes. -
Downloads/ (Accessed on 17 January 2020)
cells Review Novel Approaches for Identifying the Molecular Background of Schizophrenia Arkadiy K. Golov 1,2,*, Nikolay V. Kondratyev 1 , George P. Kostyuk 3 and Vera E. Golimbet 1 1 Mental Health Research Center, 34 Kashirskoye shosse, 115522 Moscow, Russian; [email protected] (N.V.K.); [email protected] (V.E.G.) 2 Institute of Gene Biology, Russian Academy of Sciences, 34/5 Vavilova Street, 119334 Moscow, Russian 3 Alekseev Psychiatric Clinical Hospital No. 1, 2 Zagorodnoye shosse, 115191 Moscow, Russian; [email protected] * Correspondence: [email protected] Received: 5 November 2019; Accepted: 16 January 2020; Published: 18 January 2020 Abstract: Recent advances in psychiatric genetics have led to the discovery of dozens of genomic loci associated with schizophrenia. However, a gap exists between the detection of genetic associations and understanding the underlying molecular mechanisms. This review describes the basic approaches used in the so-called post-GWAS studies to generate biological interpretation of the existing population genetic data, including both molecular (creation and analysis of knockout animals, exploration of the transcriptional effects of common variants in human brain cells) and computational (fine-mapping of causal variability, gene set enrichment analysis, partitioned heritability analysis) methods. The results of the crucial studies, in which these approaches were used to uncover the molecular and neurobiological basis of the disease, are also reported. Keywords: schizophrenia; GWAS; causal genetic variants; enhancers; brain epigenomics; genome/epigenome editing 1. Introduction Schizophrenia is a severe mental illness that affects between 0.5% and 0.7% of the human population [1]. Both environmental and genetic factors are thought to be involved in its pathogenesis, with genetic factors playing a key role in disease risk, as the heritability of schizophrenia is estimated to be 70–85% [2,3]. -
WES Gene Package Multiple Congenital Anomalie.Xlsx
Whole Exome Sequencing Gene package Multiple congenital anomalie, version 5, 1‐2‐2018 Technical information DNA was enriched using Agilent SureSelect Clinical Research Exome V2 capture and paired‐end sequenced on the Illumina platform (outsourced). The aim is to obtain 8.1 Giga base pairs per exome with a mapped fraction of 0.99. The average coverage of the exome is ~50x. Duplicate reads are excluded. Data are demultiplexed with bcl2fastq Conversion Software from Illumina. Reads are mapped to the genome using the BWA‐MEM algorithm (reference: http://bio‐bwa.sourceforge.net/). Variant detection is performed by the Genome Analysis Toolkit HaplotypeCaller (reference: http://www.broadinstitute.org/gatk/). The detected variants are filtered and annotated with Cartagenia software and classified with Alamut Visual. It is not excluded that pathogenic mutations are being missed using this technology. At this moment, there is not enough information about the sensitivity of this technique with respect to the detection of deletions and duplications of more than 5 nucleotides and of somatic mosaic mutations (all types of sequence changes). HGNC approved Phenotype description including OMIM phenotype ID(s) OMIM median depth % covered % covered % covered gene symbol gene ID >10x >20x >30x A4GALT [Blood group, P1Pk system, P(2) phenotype], 111400 607922 101 100 100 99 [Blood group, P1Pk system, p phenotype], 111400 NOR polyagglutination syndrome, 111400 AAAS Achalasia‐addisonianism‐alacrimia syndrome, 231550 605378 73 100 100 100 AAGAB Keratoderma, palmoplantar, -
Supplementary Materials
Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine