DOCSLIB.ORG

Anwendung Der Mikroarray-Technologie Für Eine Globale Charakterisierung Der Spermatogenese Des Menschen Und Seiner Pathologien

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Anwendung Der Mikroarray-Technologie Für Eine Globale Charakterisierung Der Spermatogenese Des Menschen Und Seiner Pathologien Anwendung der Mikroarray-Technologie für eine globale Charakterisierung der Spermatogenese des Menschen und seiner Pathologien Dissertation zur Erlangung des Doktorgrades der Naturwissenschaften im Fachbereich Chemie der Universität Hamburg vorgelegt von CAROLINE FEIG Hamburg 2007 Die vorliegende Arbeit wurde in der Abteilung für Andrologie des Universitätsklinikums Hamburg-Eppendorf durchgeführt. 1. Dissertationsgutachter: Herr Prof. Dr. Ulrich Hahn Abteilung für Biochemie und Molekularbiologie Martin-Luther-King-Platz 6 20146 Hamburg 2. Dissertationsgutachterin: Frau Prof. Dr. Christiane Kirchhoff Abteilung für Andrologie Universitätsklinikum Hamburg-Eppendorf Martinistrasse 52 20246 Hamburg Tag der Disputation: 11. Mai 2007 Inhaltsverzeichnis I Inhaltsverzeichnis ................................................................................................ I Abkürzungsverzeichnis ..................................................................................... V 1 Einleitung ............................................................................................................ 1 1.1 Das Problem der männlichen Infertilität................................................................. 1 1.2 Die Organisation des Hodens.................................................................................. 3 1.3 Die Spermatogenese................................................................................................ 4 1.3.1 Phasen der Spermatogenese .................................................................................... 4 1.3.2 Synchronisation der spermatogenetischen Stadien ................................................. 6 1.4 Hormonelle Kontrolle der Spermatogenese............................................................ 8 1.5 Genexpression im Testis ......................................................................................... 9 1.6 Mikroarray-Technologie ...................................................................................... 12 1.6.1 Das Prinzip............................................................................................................ 12 1.6.2 Verschiedene Mikroarray-Plattformen.................................................................. 14 1.6.2.1 Aufbau und Herstellung der Affymetrix GeneChips ® .......................................... 14 1.6.2.2 Aufbau und Herstellung der CodeLink Bioarrays ................................................ 17 1.6.3 Die Mikroarray-Technologie in der Untersuchung multifaktorieller Krankheiten ........................................................................................................... 18 1.6.4 Klassifizierung unterschiedlicher Testispathologien als eine notwendige Voraussetzung für globale Genexpressionsstudien............................................... 20 1.7 Zielsetzung der Arbeit........................................................................................... 23 2 Material und Methoden .................................................................... 25 2.1 Material ................................................................................................................. 25 2.1.1 Chemikalien .......................................................................................................... 25 2.1.2 Verbrauchsmaterialien .......................................................................................... 26 2.1.3 Geräte .................................................................................................................... 26 2.1.4 Software und Internetadressen .............................................................................. 27 2.1.5 Reaktions-Kits ....................................................................................................... 28 2.1.6 Puffer und Lösungen............................................................................................. 28 2.1.6.1 Allgemeine Lösungen ........................................................................................... 28 2.1.6.2 Für Mikroarray-Experimente verwendete Lösungen ............................................ 29 2.1.6.2.1 Affymetrix-Plattform ............................................................................................ 29 2.1.6.2.2 CodeLink-Plattform .............................................................................................. 30 Inhaltsverzeichnis II 2.1.7 Molekulargewichtsmarker..................................................................................... 30 2.1.8 Oligonukleotide..................................................................................................... 30 2.1.8.1 Primer für Referenzgene ....................................................................................... 31 2.1.8.2 Primer für die Mikroarray-Validierung ausgewählter Gene ................................. 31 2.1.9 Humanes Gewebematerial..................................................................................... 33 2.2 Methoden............................................................................................................... 33 2.2.1 Molekularbiologische Methoden........................................................................... 33 2.2.1.1 Isolierung von Gesamt-RNA aus humanen Gewebeproben ................................. 33 2.2.1.2 Photometrische Konzentrationsbestimmung......................................................... 34 2.2.1.2.1 Photometer Ultrospec 3000................................................................................... 34 2.2.1.2.2 NanoDrop ND-1000-Spektrophotometer.............................................................. 34 2.2.1.3 Agarose-GITC-Gelelektrophorese zur Überprüfung von Gesamt-........................... RNA-Extraktion .................................................................................................... 34 2.2.1.4 Bioanalyzer-Messung............................................................................................ 35 2.2.1.5 cDNA-Synthese..................................................................................................... 36 2.2.1.6 Real-time RT-PCR mit dem LightCycler TM .......................................................... 36 2.2.2 Mikroarray-Analyse .............................................................................................. 40 2.2.2.1 Genereller Ablauf eines Mikroarray-Experimentes .............................................. 40 2.2.2.2 Allgemeine Informationen zur Auswertung von Mikroarray-Daten..................... 41 2.2.2.3 Affymetrix-Plattform (Santa Clara, USA) ............................................................ 43 2.2.2.3.1 cRNA-Herstellung................................................................................................. 43 2.2.2.3.2 Hybridisierung und Waschen der Mikroarrays ..................................................... 44 2.2.2.3.3 Scannen der Mikroarrays ...................................................................................... 45 2.2.2.3.4 Qualitätskontrolle.................................................................................................. 46 2.2.2.3.5 Normalisierung der Daten und Hintergrundfilterung............................................ 46 2.2.2.3.6 Identifizierung differentiell exprimierter Gene ..................................................... 47 2.2.2.3.7 Clusteranalyse der Proben..................................................................................... 47 2.2.2.3.8 Clustering von Genen mit ähnlichen Expressionsprofilen.................................... 48 2.2.2.3.9 Funktionelle Annotierung der PAM-Cluster......................................................... 49 2.2.2.4 CodeLink-System (GE Healthcare, Piscataway, NJ)............................................ 50 2.2.2.4.1 cRNA-Herstellung................................................................................................. 50 2.2.2.4.2 Hybridisierung und Waschen der Mikroarrays ..................................................... 50 2.2.2.4.3 Scannen der Mikroarrays ...................................................................................... 51 2.2.2.4.4 Hintergrundfilterung und Normalisierung der Daten............................................ 51 Inhaltsverzeichnis III 2.2.2.4.5 Identifizierung differentiell exprimierter Gene ..................................................... 52 2.2.2.4.6 Clusteranalyse der Proben..................................................................................... 52 2.2.2.4.7 Clustering von Genen mit ähnlichen Expressionsprofilen.................................... 52 3 Ergebnisse .......................................................................................... 54 3.1 Etablierung eines „molekularen Archivs“ individueller humaner Testisbiopsien........................................................................................................ 54 3.1.1 Einführung............................................................................................................. 54 3.1.2 RNA-Qualität ........................................................................................................ 54 3.2 Auswahl geeigneter Biopsien für die globale Genexpressionsanalyse des humanen Testis...................................................................................................... 59 3.3 Verteilungsmuster der spermatogenetischen Aktivität im Testis.......................... 64 3.4 Qualitätskontrolle der Arrays vor der Normalisierung.........................................
Load more

Recommended publications
  • Genetic Analysis of Retinopathy in Type 1 Diabetes
    Genetic Analysis of Retinopathy in Type 1 Diabetes by Sayed Mohsen Hosseini A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Institute of Medical Science University of Toronto © Copyright by S. Mohsen Hosseini 2014 Genetic Analysis of Retinopathy in Type 1 Diabetes Sayed Mohsen Hosseini Doctor of Philosophy Institute of Medical Science University of Toronto 2014 Abstract Diabetic retinopathy (DR) is a leading cause of blindness worldwide. Several lines of evidence suggest a genetic contribution to the risk of DR; however, no genetic variant has shown convincing association with DR in genome-wide association studies (GWAS). To identify common polymorphisms associated with DR, meta-GWAS were performed in three type 1 diabetes cohorts of White subjects: Diabetes Complications and Control Trial (DCCT, n=1304), Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR, n=603) and Renin-Angiotensin System Study (RASS, n=239). Severe (SDR) and mild (MDR) retinopathy outcomes were defined based on repeated fundus photographs in each study graded for retinopathy severity on the Early Treatment Diabetic Retinopathy Study (ETDRS) scale. Multivariable models accounted for glycemia (measured by A1C), diabetes duration and other relevant covariates in the association analyses of additive genotypes with SDR and MDR. Fixed-effects meta- analysis was used to combine the results of GWAS performed separately in WESDR, ii RASS and subgroups of DCCT, defined by cohort and treatment group. Top association signals were prioritized for replication, based on previous supporting knowledge from the literature, followed by replication in three independent white T1D studies: Genesis-GeneDiab (n=502), Steno (n=936) and FinnDiane (n=2194).
    [Show full text]
  • Seq2pathway Vignette
    seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway.
    [Show full text]
  • 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.
    [Show full text]
  • Downloaded from [266]
    Patterns of DNA methylation on the human X chromosome and use in analyzing X-chromosome inactivation by Allison Marie Cotton B.Sc., The University of Guelph, 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate Studies (Medical Genetics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) January 2012 © Allison Marie Cotton, 2012 Abstract The process of X-chromosome inactivation achieves dosage compensation between mammalian males and females. In females one X chromosome is transcriptionally silenced through a variety of epigenetic modifications including DNA methylation. Most X-linked genes are subject to X-chromosome inactivation and only expressed from the active X chromosome. On the inactive X chromosome, the CpG island promoters of genes subject to X-chromosome inactivation are methylated in their promoter regions, while genes which escape from X- chromosome inactivation have unmethylated CpG island promoters on both the active and inactive X chromosomes. The first objective of this thesis was to determine if the DNA methylation of CpG island promoters could be used to accurately predict X chromosome inactivation status. The second objective was to use DNA methylation to predict X-chromosome inactivation status in a variety of tissues. A comparison of blood, muscle, kidney and neural tissues revealed tissue-specific X-chromosome inactivation, in which 12% of genes escaped from X-chromosome inactivation in some, but not all, tissues. X-linked DNA methylation analysis of placental tissues predicted four times higher escape from X-chromosome inactivation than in any other tissue. Despite the hypomethylation of repetitive elements on both the X chromosome and the autosomes, no changes were detected in the frequency or intensity of placental Cot-1 holes.
    [Show full text]
  • Aberrant Promoter Methylation and Tumor Suppressive Activity of the DFNA5 Gene in Colorectal Carcinoma
    Oncogene (2008) 27, 3624–3634 & 2008 Nature Publishing Group All rights reserved 0950-9232/08 $30.00 www.nature.com/onc ORIGINAL ARTICLE Aberrant promoter methylation and tumor suppressive activity of the DFNA5 gene in colorectal carcinoma MS Kim1, X Chang1, K Yamashita1, JK Nagpal1, JH Baek2,GWu3, B Trink1, EA Ratovitski1, M Mori4 and D Sidransky1 1Department of Otolaryngology, Head and Neck Cancer Research Division, Johns Hopkins University, Baltimore, MD, USA; 2Department of Genetic Medicine, Institute of Cell Engineering, Johns Hopkins University, Baltimore, MD, USA; 3Karmanos Cancer Institute, Department of Pathology, Wayne State University, Detroit, MI, USA and 4Department of Surgical Oncology, Medical Institute of Bioregulation, Kyushu University, Tsurumibaru, Beppu, Japan To identify novel methylated gene promoters, we com- Introduction pared differential RNA expression profiles of colorectal cancer (CRC) cell lines with or without treatment of Aberrant gene expression is a characteristic of human 5-aza-20-deoxycytidine (5-aza-dC). Out of 1776 genes cancers, and changes in DNA methylation status can that were initially ‘absent (that is, silenced)’ by gene have profound effects on the expression of genes. Tumor expression array analysis, we selected 163 genes that were suppressor genes (TSGs) display both genetic and increased after 5-aza-dC treatment in at least two of three epigenetic inactivation in human tumors, and the CRC cell lines. The microarray results were confirmed by transcriptional silencing of TSGs has established hy- Reverse Transcription–PCR, and CpG island of the gene permethylation as a common mechanism for loss of promoters were amplified and sequenced for examination TSG function in human cancer (Herman, 1999).
    [Show full text]
  • Role of Amylase in Ovarian Cancer Mai Mohamed University of South Florida, [email protected]
    University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School July 2017 Role of Amylase in Ovarian Cancer Mai Mohamed University of South Florida, [email protected] Follow this and additional works at: http://scholarcommons.usf.edu/etd Part of the Pathology Commons Scholar Commons Citation Mohamed, Mai, "Role of Amylase in Ovarian Cancer" (2017). Graduate Theses and Dissertations. http://scholarcommons.usf.edu/etd/6907 This Dissertation is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Role of Amylase in Ovarian Cancer by Mai Mohamed A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Pathology and Cell Biology Morsani College of Medicine University of South Florida Major Professor: Patricia Kruk, Ph.D. Paula C. Bickford, Ph.D. Meera Nanjundan, Ph.D. Marzenna Wiranowska, Ph.D. Lauri Wright, Ph.D. Date of Approval: June 29, 2017 Keywords: ovarian cancer, amylase, computational analyses, glycocalyx, cellular invasion Copyright © 2017, Mai Mohamed Dedication This dissertation is dedicated to my parents, Ahmed and Fatma, who have always stressed the importance of education, and, throughout my education, have been my strongest source of encouragement and support. They always believed in me and I am eternally grateful to them. I would also like to thank my brothers, Mohamed and Hussien, and my sister, Mariam. I would also like to thank my husband, Ahmed.
    [Show full text]
  • Chip-Seq Annotation and Visualization How to Add Biological Meaning to Peaks
    ChIP-seq Annotation and Visualization How to add biological meaning to peaks M. Defrance, C. Herrmann, D. Puthier, M. Thomas-Chollier, S Le Gras, J van Helden Our data in the context Custom track uploded by the user (here ESR1 peaks in siGATA3 context) public UCSC annotation/data tracks Typical questions - What are the genes associated to the peaks? ChIP-seq peaks - Are some genomic categories over-represented? - Are some functional categories over-represented? - Are the peaks close to the TSS, …? ChIP-seq peaks Annotation Visualisation Enrichment profiles Annotated peaks Genomic & functional Average Profile near TSS Average Profile near TTS Annotation Genomic location Relation to CpG island 0.30 1.0 2500 0.8 chr start end Gene 0.25 chr15 65294195 65295186 0.6 chrX 19635923 19638359 Chst7 Average Profile Average Profile Average 3000 chr8 33993863 33995559 1500 0.4 0.20 chr10 114236977 114239326 Trhde # of regions # of regions 0.2 Distribution of Peak Heights chrX 69515082 69516482 Gabre 500 chr4 49857142 49858913 Grin3a 1000 −3000 −2000 −1000 0 1000 2000 3000 −3000 −2000 −1000 0 1000 2000 3000 0 5 10 15 chr16 7352861 7353410 Rbfox1 0 0 Relative Distance to TSS (bp) Relative Distance to TTS (bp) chr7 64764156 64765421 Gabra5 ChIP Regions (Peaks) over Chromosomes Average Gene Profile chrX 83436881 83438330 Nr0b1 CGI Shore Distant chr10 120288598 120289143 Msrb3 Multiple 1 Promoter Intergenic 2 chr5 67446361 67446855 Limch1 Gene Body 3 0.8 4 5 6 7 0.6 8 9 Average Profile Average 10 0.4 11 12 Chromosome 13 0.2 14 15 −1000 0 1000 2000 3000 4000 16 Upstream (bp), 3000 bp of Meta−gene, Downstream (bp) 17 18 19 Average Concatenated Exon Profile Average Concatenated Intron Profile X Y 0.0e+00 5.0e+07 1.0e+08 1.5e+08 2.0e+08 1.0 Chromosome Size (bp) 1.0 0.8 0.8 0.6 0.6 Average Profile Average Profile Average 0.4 0.4 0.2 0.2 0 20 40 60 80 100 0 20 40 60 80 100 Relative Location (%) Relative Location (%) Distribution of Peak Heights 0 5 10 15 ChIP Regions (Peaks) over Chromosomes 1 2 3 4 5 6 7 8 9 10 11 12 Chromosome W.Huang et al.
    [Show full text]
  • Anti-CST8 Polyclonal Antibody Cat: K002485P Summary
    Anti-CST8 Polyclonal Antibody Cat: K002485P Summary: 【Product name】: Anti-CST8 antibody 【Source】: Rabbit 【Isotype】: IgG 【Species reactivity】: Human Mouse 【Swiss Prot】: O60676 【Gene ID】: 10047 【Calculated】: MW:16kDa 【Observed】: MW:28kDa 【Purification】: Affinity purification 【Tested applications】: WB, IF 【Recommended dilution】: WB 1:500-2000. IF 1:50-200. 【WB Positive sample】: Mouse testis,Mouse liver 【Subcellular location】: Secreted 【Immunogen】: Recombinant protein of human CST8 【Storage】: Shipped at 4°C. Upon delivery aliquot and store at -20°C Background: The cystatin superfamily encompasses proteins that contain multiple cystatin-like sequences. Some of the members are active cysteine protease inhibitors, while others have lost or perhaps never acquired this inhibitory activity. There are three inhibitory families in the superfamily, including the type 1 cystatins (stefins), type 2 cystatins and the kininogens. The type 2 cystatin proteins are a class of cysteine proteinase inhibitors found in a variety of human fluids and secretions. The cystatin locus on chromosome 20 contains the majority of the type 2 cystatin genes and pseudogenes. This gene is located in the cystatin locus and encodes a protein similar to type 2 cystatins. The encoded protein exhibits highly tissue-specific expression in the reproductive tract, suggesting implicit roles in reproduction. Alternative splicing results in multiple transcript variants. Sales:[email protected] For research purposes only. Tech:[email protected] Please visit www.solarbio.com for a more product information Verified picture Western blot analysis with CST8 antibody diluted at 1:1000 Sales:[email protected] For research purposes only. Tech:[email protected] Please visit www.solarbio.com for a more product information.
    [Show full text]
  • CST8 Polyclonal Antibody Gene Summary: the Cystatin Superfamily Encompasses Proteins That Contain Multiple Cystatin-Like Sequences
    CST8 polyclonal antibody Gene Summary: The cystatin superfamily encompasses proteins that contain multiple cystatin-like sequences. Catalog Number: PAB19022 Some of the members are active cysteine protease inhibitors, while others have lost or perhaps never Regulatory Status: For research use only (RUO) acquired this inhibitory activity. There are three inhibitory families in the superfamily, including the type 1 cystatins Product Description: Goat polyclonal antibody raised (stefins), type 2 cystatins and the kininogens. The type 2 against synthetic peptide of CST8. cystatin proteins are a class of cysteine proteinase inhibitors found in a variety of human fluids and Immunogen: A synthetic peptide corresponding to secretions. The cystatin locus on chromosome 20 amino acids 100-112 at internal region of human CST8. contains the majority of the type 2 cystatin genes and pseudogenes. This gene is located in the cystatin locus Sequence: STNEICAIQENSK and encodes a protein similar to type 2 cystatins. The Host: Goat protein exhibits highly tissue-specific expression in the reproductive tract, suggesting implicit roles in Theoretical MW (kDa): 23 reproduction. Alternative splicing identified in mouse is suggested in human based on EST evidence but the Reactivity: Human full-length nature of putative variants has not been determined. [provided by RefSeq] Applications: ELISA, WB-Tr (See our web site product page for detailed applications information) Protocols: See our web site at http://www.abnova.com/support/protocols.asp or product page for detailed protocols Form: Liquid Purification: Antigen affinity purification Concentration: 0.5 mg/mL Recommend Usage: ELISA (1:16000) Western Blot (1-3 ug/mL) The optimal working dilution should be determined by the end user.
    [Show full text]
  • The DNA Sequence and Comparative Analysis of Human Chromosome 20
    articles The DNA sequence and comparative analysis of human chromosome 20 P. Deloukas, L. H. Matthews, J. Ashurst, J. Burton, J. G. R. Gilbert, M. Jones, G. Stavrides, J. P. Almeida, A. K. Babbage, C. L. Bagguley, J. Bailey, K. F. Barlow, K. N. Bates, L. M. Beard, D. M. Beare, O. P. Beasley, C. P. Bird, S. E. Blakey, A. M. Bridgeman, A. J. Brown, D. Buck, W. Burrill, A. P. Butler, C. Carder, N. P. Carter, J. C. Chapman, M. Clamp, G. Clark, L. N. Clark, S. Y. Clark, C. M. Clee, S. Clegg, V. E. Cobley, R. E. Collier, R. Connor, N. R. Corby, A. Coulson, G. J. Coville, R. Deadman, P. Dhami, M. Dunn, A. G. Ellington, J. A. Frankland, A. Fraser, L. French, P. Garner, D. V. Grafham, C. Grif®ths, M. N. D. Grif®ths, R. Gwilliam, R. E. Hall, S. Hammond, J. L. Harley, P. D. Heath, S. Ho, J. L. Holden, P. J. Howden, E. Huckle, A. R. Hunt, S. E. Hunt, K. Jekosch, C. M. Johnson, D. Johnson, M. P. Kay, A. M. Kimberley, A. King, A. Knights, G. K. Laird, S. Lawlor, M. H. Lehvaslaiho, M. Leversha, C. Lloyd, D. M. Lloyd, J. D. Lovell, V. L. Marsh, S. L. Martin, L. J. McConnachie, K. McLay, A. A. McMurray, S. Milne, D. Mistry, M. J. F. Moore, J. C. Mullikin, T. Nickerson, K. Oliver, A. Parker, R. Patel, T. A. V. Pearce, A. I. Peck, B. J. C. T. Phillimore, S. R. Prathalingam, R. W. Plumb, H. Ramsay, C. M.
    [Show full text]
  • Identification of Differentially Expressed Gene After Femoral Fracture Via Microarray Profiling
    Hindawi Publishing Corporation International Journal of Genomics Volume 2014, Article ID 208751, 9 pages http://dx.doi.org/10.1155/2014/208751 Research Article Identification of Differentially Expressed Gene after Femoral Fracture via Microarray Profiling Donggen Zhong Department of Physical Education, Jiangxi University of Finance and Economics, Nanchang 330013, China Correspondence should be addressed to Donggen Zhong; [email protected] Received 15 March 2014; Revised 8 May 2014; Accepted 18 May 2014; Published 8 July 2014 Academic Editor: Henry Heng Copyright © 2014 Donggen Zhong. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We aimed to investigate differentially expressed genes (DEGs) in different stages after femoral fracture based on rat models, providing the basis for the treatment of sport-related fractures. Gene expression data GSE3298 was downloaded from Gene Expression Omnibus (GEO), including 16 chips. All femoral fracture samples were classified into earlier fracture stage and later fracture stage. Total 87 DEGs simultaneously occurred in two stages, of which 4 genes showed opposite expression tendency. Out of the 4 genes, Rest and Cst8 were hub nodes in protein-protein interaction (PPI) network. The GO (Gene Ontology) function enrichment analysis verified that nutrition supply related genes were enriched in the earlier stage and neuron growth related genes were enriched in the later stage. Calcium signaling pathway was the most significant pathway in earlier stage; in later stage, DEGs were enriched into 2 neurodevelopment-related pathways. Analysis of Pearson’s correlation coefficient showed that a total of 3,300 genes were significantly associated with fracture time, none of which was overlapped with identified DEGs.
    [Show full text]
  • Exploring the Pharmacological Mechanism of Quercetin-Resveratrol Combination for Polycystic Ovary Syndrome
    www.nature.com/scientificreports Corrected: Publisher Correction OPEN Exploring the Pharmacological Mechanism of Quercetin- Resveratrol Combination for Polycystic Ovary Syndrome: A Systematic Pharmacological Strategy-Based Research Kailin Yang1,2,6, Liuting Zeng 3,6*, Tingting Bao3,4,6, Zhiyong Long5 & Bing Jin3* Resveratrol and quercetin have efects on polycystic ovary syndrome (PCOS). Hence, resveratrol combined with quercetin may have better efects on it. However, because of the limitations in animal and human experiments, the pharmacological and molecular mechanism of quercetin-resveratrol combination (QRC) remains to be clarifed. In this research, a systematic pharmacological approach comprising multiple compound target collection, multiple potential target prediction, and network analysis was used for comparing the characteristic of resveratrol, quercetin and QRC, and exploring the mechanism of QRC. After that, four networks were constructed and analyzed: (1) compound-compound target network; (2) compound-potential target network; (3) QRC-PCOS PPI network; (4) QRC-PCOS- other human proteins (protein-protein interaction) PPI network. Through GO and pathway enrichment analysis, it can be found that three compounds focus on diferent biological processes and pathways; and it seems that QRC combines the characteristics of resveratrol and quercetin. The in-depth study of QRC further showed more PCOS-related biological processes and pathways. Hence, this research not only ofers clues to the researcher who is interested in comparing the diferences among resveratrol, quercetin and QRC, but also provides hints for the researcher who wants to explore QRC’s various synergies and its pharmacological and molecular mechanism. Polycystic ovary syndrome (PCOS) is one of the most common female endocrine diseases characterized by hyperandrogenism, menstrual disorders and infertility.
    [Show full text]