Gene-Based Nonparametric Testing of Interactions Using Distance Correlation Coefficient in Case-Control Association Studies
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Chuanxiong Rhizoma Compound on HIF-VEGF Pathway and Cerebral Ischemia-Reperfusion Injury’S Biological Network Based on Systematic Pharmacology
ORIGINAL RESEARCH published: 25 June 2021 doi: 10.3389/fphar.2021.601846 Exploring the Regulatory Mechanism of Hedysarum Multijugum Maxim.-Chuanxiong Rhizoma Compound on HIF-VEGF Pathway and Cerebral Ischemia-Reperfusion Injury’s Biological Network Based on Systematic Pharmacology Kailin Yang 1†, Liuting Zeng 1†, Anqi Ge 2†, Yi Chen 1†, Shanshan Wang 1†, Xiaofei Zhu 1,3† and Jinwen Ge 1,4* Edited by: 1 Takashi Sato, Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of 2 Tokyo University of Pharmacy and Life Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China, Galactophore Department, The First 3 Sciences, Japan Hospital of Hunan University of Chinese Medicine, Changsha, China, School of Graduate, Central South University, Changsha, China, 4Shaoyang University, Shaoyang, China Reviewed by: Hui Zhao, Capital Medical University, China Background: Clinical research found that Hedysarum Multijugum Maxim.-Chuanxiong Maria Luisa Del Moral, fi University of Jaén, Spain Rhizoma Compound (HCC) has de nite curative effect on cerebral ischemic diseases, *Correspondence: such as ischemic stroke and cerebral ischemia-reperfusion injury (CIR). However, its Jinwen Ge mechanism for treating cerebral ischemia is still not fully explained. [email protected] †These authors share first authorship Methods: The traditional Chinese medicine related database were utilized to obtain the components of HCC. The Pharmmapper were used to predict HCC’s potential targets. Specialty section: The CIR genes were obtained from Genecards and OMIM and the protein-protein This article was submitted to interaction (PPI) data of HCC’s targets and IS genes were obtained from String Ethnopharmacology, a section of the journal database. -
Agricultural University of Athens
ΓΕΩΠΟΝΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ ΣΧΟΛΗ ΕΠΙΣΤΗΜΩΝ ΤΩΝ ΖΩΩΝ ΤΜΗΜΑ ΕΠΙΣΤΗΜΗΣ ΖΩΙΚΗΣ ΠΑΡΑΓΩΓΗΣ ΕΡΓΑΣΤΗΡΙΟ ΓΕΝΙΚΗΣ ΚΑΙ ΕΙΔΙΚΗΣ ΖΩΟΤΕΧΝΙΑΣ ΔΙΔΑΚΤΟΡΙΚΗ ΔΙΑΤΡΙΒΗ Εντοπισμός γονιδιωματικών περιοχών και δικτύων γονιδίων που επηρεάζουν παραγωγικές και αναπαραγωγικές ιδιότητες σε πληθυσμούς κρεοπαραγωγικών ορνιθίων ΕΙΡΗΝΗ Κ. ΤΑΡΣΑΝΗ ΕΠΙΒΛΕΠΩΝ ΚΑΘΗΓΗΤΗΣ: ΑΝΤΩΝΙΟΣ ΚΟΜΙΝΑΚΗΣ ΑΘΗΝΑ 2020 ΔΙΔΑΚΤΟΡΙΚΗ ΔΙΑΤΡΙΒΗ Εντοπισμός γονιδιωματικών περιοχών και δικτύων γονιδίων που επηρεάζουν παραγωγικές και αναπαραγωγικές ιδιότητες σε πληθυσμούς κρεοπαραγωγικών ορνιθίων Genome-wide association analysis and gene network analysis for (re)production traits in commercial broilers ΕΙΡΗΝΗ Κ. ΤΑΡΣΑΝΗ ΕΠΙΒΛΕΠΩΝ ΚΑΘΗΓΗΤΗΣ: ΑΝΤΩΝΙΟΣ ΚΟΜΙΝΑΚΗΣ Τριμελής Επιτροπή: Aντώνιος Κομινάκης (Αν. Καθ. ΓΠΑ) Ανδρέας Κράνης (Eρευν. B, Παν. Εδιμβούργου) Αριάδνη Χάγερ (Επ. Καθ. ΓΠΑ) Επταμελής εξεταστική επιτροπή: Aντώνιος Κομινάκης (Αν. Καθ. ΓΠΑ) Ανδρέας Κράνης (Eρευν. B, Παν. Εδιμβούργου) Αριάδνη Χάγερ (Επ. Καθ. ΓΠΑ) Πηνελόπη Μπεμπέλη (Καθ. ΓΠΑ) Δημήτριος Βλαχάκης (Επ. Καθ. ΓΠΑ) Ευάγγελος Ζωίδης (Επ.Καθ. ΓΠΑ) Γεώργιος Θεοδώρου (Επ.Καθ. ΓΠΑ) 2 Εντοπισμός γονιδιωματικών περιοχών και δικτύων γονιδίων που επηρεάζουν παραγωγικές και αναπαραγωγικές ιδιότητες σε πληθυσμούς κρεοπαραγωγικών ορνιθίων Περίληψη Σκοπός της παρούσας διδακτορικής διατριβής ήταν ο εντοπισμός γενετικών δεικτών και υποψηφίων γονιδίων που εμπλέκονται στο γενετικό έλεγχο δύο τυπικών πολυγονιδιακών ιδιοτήτων σε κρεοπαραγωγικά ορνίθια. Μία ιδιότητα σχετίζεται με την ανάπτυξη (σωματικό βάρος στις 35 ημέρες, ΣΒ) και η άλλη με την αναπαραγωγική -
Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection
Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ...................................................... -
Identification, Expression and Variation of the GNPDA2 Gene, and Its Association with Body Weight and Fatness Traits in Chicken
Identification, expression and variation of the GNPDA2 gene, and its association with body weight and fatness traits in chicken Hongjia Ouyang, Huan Zhang, Weimin Li, Sisi Liang, Endashaw Jebessa, Bahareldin A. Abdalla and Qinghua Nie Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, China Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, Guangzhou, China ABSTRACT Background. The GNPDA2 (glucosamine-6-phosphate deaminase 2) gene is a member of Glucosamine-6-phosphate (GlcN6P) deaminase subfamily, which encoded an allosteric enzyme of GlcN6P. Genome-wide association studies (GWAS) have shown that variations of human GNPDA2 are associated with body mass index and obesity risk, but its function and metabolic implications remain to be elucidated.The object of this study was to characterize the gene structure, expression, and biological functions of GNPDA2 in chickens. Methods. Variant transcripts of chicken GNPDA2 and their expression were investi- gated using rapid amplification of cDNA ends (RACE) system and real-time quantita- tive PCR technology. We detected the GNPDA2 expression in hypothalamic, adipose, and liver tissue of Xinghua chickens with fasting and high-glucose-fat diet treatments, and performed association analysis of variations of GNPDA2 with productive traits in chicken. The function of GNPDA2 was further studied by overexpression and small interfering RNA (siRNA) methods in chicken preadipocytes. Results. Four chicken GNPDA2 transcripts (cGNPDA2-a∼cGNPDA2-d) were identified in this study. The complete transcript GNPDA2-a was predominantly Submitted 3 March 2016 expressed in adipose tissue (subcutaneous fat and abdominal fat), hypothalamus, and Accepted 23 May 2016 Published 15 June 2016 duodenum. -
Genetic Investigation in Non-Obstructive Azoospermia: from the X Chromosome to The
DOTTORATO DI RICERCA IN SCIENZE BIOMEDICHE CICLO XXIX COORDINATORE Prof. Persio dello Sbarba Genetic investigation in non-obstructive azoospermia: from the X chromosome to the whole exome Settore Scientifico Disciplinare MED/13 Dottorando Tutore Dott. Antoni Riera-Escamilla Prof. Csilla Gabriella Krausz _______________________________ _____________________________ (firma) (firma) Coordinatore Prof. Carlo Maria Rotella _______________________________ (firma) Anni 2014/2016 A la meva família Agraïments El resultat d’aquesta tesis és fruit d’un esforç i treball continu però no hauria estat el mateix sense la col·laboració i l’ajuda de molta gent. Segurament em deixaré molta gent a qui donar les gràcies però aquest són els que em venen a la ment ara mateix. En primer lloc voldria agrair a la Dra. Csilla Krausz por haberme acogido con los brazos abiertos desde el primer día que llegué a Florencia, por enseñarme, por su incansable ayuda y por contagiarme de esta pasión por la investigación que nunca se le apaga. Voldria agrair també al Dr. Rafael Oliva per haver fet possible el REPROTRAIN, per haver-me ensenyat moltíssim durant l’època del màster i per acceptar revisar aquesta tesis. Alhora voldria agrair a la Dra. Willy Baarends, thank you for your priceless help and for reviewing this thesis. També voldria donar les gràcies a la Dra. Elisabet Ars per acollir-me al seu laboratori a Barcelona, per ajudar-me sempre que en tot el què li he demanat i fer-me tocar de peus a terra. Ringrazio a tutto il gruppo di Firenze, che dal primo giorno mi sono trovato come a casa. -
Comparison Between Timelines of Transcriptional Regulation in Mammals, Birds, and Teleost Fish Somitogenesis
RESEARCH ARTICLE Comparison between Timelines of Transcriptional Regulation in Mammals, Birds, and Teleost Fish Somitogenesis Bernard Fongang, Andrzej Kudlicki* Department of Biochemistry and Molecular Biology, Sealy Center for Molecular Medicine, Institute for Translational Sciences, University of Texas Medical Branch, 301 University Blvd, Galveston, Texas, USA * [email protected] a11111 Abstract Metameric segmentation of the vertebrate body is established during somitogenesis, when a cyclic spatial pattern of gene expression is created within the mesoderm of the developing OPEN ACCESS embryo. The process involves transcriptional regulation of genes associated with the Wnt, Citation: Fongang B, Kudlicki A (2016) Comparison Notch, and Fgf signaling pathways, each gene is expressed at a specific time during the between Timelines of Transcriptional Regulation in somite cycle. Comparative genomics, including analysis of expression timelines may reveal Mammals, Birds, and Teleost Fish Somitogenesis. the underlying regulatory modules and their causal relations, explaining the nature and ori- PLoS ONE 11(5): e0155802. doi:10.1371/journal. gin of the segmentation mechanism. Using a deconvolution approach, we computationally pone.0155802 reconstruct and compare the precise timelines of expression during somitogenesis in Editor: Barbara Jennings, Oxford Brookes University, chicken and zebrafish. The result constitutes a resource that may be used for inferring pos- UNITED KINGDOM sible causal relations between genes and subsequent pathways. While the sets of regulated Received: February 29, 2016 genes and expression profiles vary between different species, notable similarities exist Accepted: May 4, 2016 between the temporal organization of the pathways involved in the somite clock in chick and Published: May 18, 2016 mouse, with certain aspects (as the phase of expression of Notch genes) conserved also in Copyright: © 2016 Fongang, Kudlicki. -
HHS Public Access Author Manuscript
HHS Public Access Author manuscript Author Manuscript Author ManuscriptNat Genet Author Manuscript. Author manuscript; Author Manuscript available in PMC 2010 March 15. Published in final edited form as: Nat Genet. 2009 April ; 41(4): 415–423. doi:10.1038/ng.325. Validation of Candidate Causal Genes for Abdominal Obesity Which Affect Shared Metabolic Pathways and Networks Xia Yang1, Joshua L. Deignan1, Hongxiu Qi1, Jun Zhu2, Su Qian3, Judy Zhong2, Gevork Torosyan4, Sana Majid4, Brie Falkard4, Robert R. Kleinhanz2, Jenny Karlsson6, Lawrence W. Castellani1, Sheena Mumick3, Kai Wang2, Tao Xie2, Michael Coon2, Chunsheng Zhang2, Daria Estrada-Smith4, Charles R. Farber1, Susanna S. Wang4, Atila Van Nas4, Anatole Ghazalpour4, Bin Zhang2, Douglas J. MacNeil3, John R. Lamb2, Katrina M. Dipple4, Marc L. Reitman5, Margarete Mehrabian1, Pek Y. Lum2, Eric E. Schadt2, Aldons J. Lusis1,4, and Thomas A. Drake6 1Department of Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, California, 90095, USA 2Rosetta Inpharmatics, LLC, a Wholly Owned Subsidiary of Merck & Co. Inc., Seattle, Washington, 98109, USA 3Department of Metabolic Disorders, Merck & Co. Inc., Rahway, New Jersey, 07065, USA 4Department of Human Genetics, David Geffen School of Medicine at UCLA, University of, California, Los Angeles, California, 90095, USA 5Department of Clinical Pharmacology, Merck & Co. Inc., Rahway, New Jersey, 07065, USA 6Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at, UCLA, University of California, Los Angeles, California, 90095, USA Abstract A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. -
Qualifying Exam Study Guide and Examples
Department of Human Genetics PhD Qualifying Exam & MS Comprehensive Exam Preparation Materials Updated August 31, 2019 The doctoral Qualifying Exam and Master’s Comprehensive Exam begins with an oral presentation by the student of a research article from the primary literature. The paper serves as a starting point for an oral examination of the student’s understanding of the paper and knowledge of the field. Students will be required to demonstrate expertise in their chosen areas of concentration, which may include deeper knowledge than covered in the required coursework. In addition to mastery in their specific area, students will be required to demonstrate broad knowledge of the field of Human Genetics as a whole. Students will be responsible for showcasing their ability to utilize knowledge and skills obtained through coursework and research experiences to inform their interpretation, synthesis, and evaluation of the contemporary peer-reviewed literature. The following list of knowledge requirements that students are expected to master may be used to assist in preparing for the qualifying exam. Likewise, course objectives for the core Human Genetics curriculum are provided below and may serve as an outline of topics to assist in students’ preparations. Material covered in additional courses relevant to an individual student’s academic experience and trajectory may also be germane. Lastly, example qualifying exam questions pertaining to specific example papers are provided. Please be aware that some questions raised during the oral examination may not have clear or objective “right” answers, but may instead reflect current uncertainties in the field of human genetics. In these cases, students may be expected to provide thoughtful discussion regarding prevailing hypotheses or themes, demonstrate knowledge of ambiguities in the scientific evidence or limitations of our current knowledge, and propose new experiments that could yield insight into the question at hand. -
A Data Integration Approach to Prioritize Disease-Associated Genes 1 1,2, Daniel S
bioRxiv preprint doi: https://doi.org/10.1101/011569; this version posted December 11, 2014. 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. 1 Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes 1 1;2; Daniel S. Himmelstein , Sergio E. Baranzini ∗ 1 Biological & Medical Informatics, UCSF, San Francisco, California, USA 2 Neurology, UCSF, San Francisco, California, USA ∗ E-mail: [email protected] 1 Abstract 2 The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease- 3 associated variants. Two important derivations will be the translation of this information into a multiscale 4 understanding of pathogenic variants, and leveraging existing data to increase the power of existing and 5 future studies through prioritization. We explore edge prediction on heterogeneous networks|graphs 6 with multiple node and edge types|for accomplishing both tasks. First we constructed a network 7 with 18 node types|genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures 8 database) collections|and 19 edge types from high-throughput publicly-available resources. From this 9 network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology 10 between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the 11 probability of association between each protein-coding gene and each of 29 well-studied complex diseases.