Comparative Gene Expression Profiling Between Human Cultured
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Use of Genomic Tools to Discover the Cause of Champagne Dilution Coat Color in Horses and to Map the Genetic Cause of Extreme Lordosis in American Saddlebred Horses
University of Kentucky UKnowledge Theses and Dissertations--Veterinary Science Veterinary Science 2014 USE OF GENOMIC TOOLS TO DISCOVER THE CAUSE OF CHAMPAGNE DILUTION COAT COLOR IN HORSES AND TO MAP THE GENETIC CAUSE OF EXTREME LORDOSIS IN AMERICAN SADDLEBRED HORSES Deborah G. Cook University of Kentucky, [email protected] Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Cook, Deborah G., "USE OF GENOMIC TOOLS TO DISCOVER THE CAUSE OF CHAMPAGNE DILUTION COAT COLOR IN HORSES AND TO MAP THE GENETIC CAUSE OF EXTREME LORDOSIS IN AMERICAN SADDLEBRED HORSES" (2014). Theses and Dissertations--Veterinary Science. 15. https://uknowledge.uky.edu/gluck_etds/15 This Doctoral Dissertation is brought to you for free and open access by the Veterinary Science at UKnowledge. It has been accepted for inclusion in Theses and Dissertations--Veterinary Science by an authorized administrator of UKnowledge. For more information, please contact [email protected]. STUDENT AGREEMENT: I represent that my thesis or dissertation and abstract are my original work. Proper attribution has been given to all outside sources. I understand that I am solely responsible for obtaining any needed copyright permissions. I have obtained needed written permission statement(s) from the owner(s) of each third-party copyrighted matter to be included in my work, allowing electronic distribution (if such use is not permitted by the fair use doctrine) which will be submitted to UKnowledge as Additional File. I hereby grant to The University of Kentucky and its agents the irrevocable, non-exclusive, and royalty-free license to archive and make accessible my work in whole or in part in all forms of media, now or hereafter known. -
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. -
Genome Analysis and Knowledge
Dahary et al. BMC Medical Genomics (2019) 12:200 https://doi.org/10.1186/s12920-019-0647-8 SOFTWARE Open Access Genome analysis and knowledge-driven variant interpretation with TGex Dvir Dahary1*, Yaron Golan1, Yaron Mazor1, Ofer Zelig1, Ruth Barshir2, Michal Twik2, Tsippi Iny Stein2, Guy Rosner3,4, Revital Kariv3,4, Fei Chen5, Qiang Zhang5, Yiping Shen5,6,7, Marilyn Safran2, Doron Lancet2* and Simon Fishilevich2* Abstract Background: The clinical genetics revolution ushers in great opportunities, accompanied by significant challenges. The fundamental mission in clinical genetics is to analyze genomes, and to identify the most relevant genetic variations underlying a patient’s phenotypes and symptoms. The adoption of Whole Genome Sequencing requires novel capacities for interpretation of non-coding variants. Results: We present TGex, the Translational Genomics expert, a novel genome variation analysis and interpretation platform, with remarkable exome analysis capacities and a pioneering approach of non-coding variants interpretation. TGex’s main strength is combining state-of-the-art variant filtering with knowledge-driven analysis made possible by VarElect, our highly effective gene-phenotype interpretation tool. VarElect leverages the widely used GeneCards knowledgebase, which integrates information from > 150 automatically-mined data sources. Access to such a comprehensive data compendium also facilitates TGex’s broad variant annotation, supporting evidence exploration, and decision making. TGex has an interactive, user-friendly, and easy adaptive interface, ACMG compliance, and an automated reporting system. Beyond comprehensive whole exome sequence capabilities, TGex encompasses innovative non-coding variants interpretation, towards the goal of maximal exploitation of whole genome sequence analyses in the clinical genetics practice. This is enabled by GeneCards’ recently developed GeneHancer, a novel integrative and fully annotated database of human enhancers and promoters. -
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. -
Sarcomeric Deficits Underlie MYBPC1-Associated Myopathy with Myogenic Tremor
Sarcomeric deficits underlie MYBPC1-associated myopathy with myogenic tremor Janelle Geist Hauserman, … , Christopher Ward, Aikaterini Kontrogianni- Konstantopoulos JCI Insight. 2021. https://doi.org/10.1172/jci.insight.147612. Research In-Press Preview Cell biology Muscle biology Myosin Binding Protein-C slow (sMyBP-C) comprises a subfamily of cytoskeletal proteins encoded byM YBPC1 that is expressed in skeletal muscles where it contributes to myosin thick filament stabilization and actomyosin cross-bridge regulation. Recently, our group described the causal association of dominant missense pathogenic variants in MYBPC1 with an early-onset myopathy characterized by generalized muscle weakness, hypotonia, dysmorphia, skeletal deformities, and myogenic tremor occurring in the absence of neuropathy. To mechanistically interrogate the etiologies of this MYBPC1-associated myopathy in vivo, we generated a knock-in mouse model carrying the E248K pathogenic variant. Using a battery of phenotypic, behavioral, and physiological measurements spanning neonatal to young adult life, we find that heterozygous E248K mice faithfully recapitulate the onset and progression of generalized myopathy, tremor occurrence, and skeletal deformities seen in human carriers. Moreover, using a combination of biochemical, ultrastructural, and contractile assessments at the level of the tissue, cell, and myofilaments, we show that the loss-of- function phenotype observed in mutant muscles is primarily driven by disordered and misaligned sarcomeres containing fragmented -
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). -
Transcriptome and DNA Methylation Analyses of the Molecular Mechanisms Underlying with Longissimus Dorsi Muscles at Different Stages of Development in the Polled Yak
G C A T T A C G G C A T genes Article Transcriptome and DNA Methylation Analyses of the Molecular Mechanisms Underlying with Longissimus dorsi Muscles at Different Stages of Development in the Polled Yak Xiaoming Ma 1,2, Congjun Jia 1,2, Min Chu 1,2, Donghai Fu 1,2 , Qinhui Lei 1,2, Xuezhi Ding 1,2, Xiaoyun Wu 1,2, Xian Guo 1,2, Jie Pei 1,2, Pengjia Bao 1,2, Ping Yan 1,* and Chunnian Liang 2,* 1 Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; [email protected] (X.M.); [email protected] (C.J.); [email protected] (M.C.); [email protected] (D.F.); [email protected] (Q.L.); [email protected] (X.D.); [email protected] (X.W.); [email protected] (X.G.); [email protected] (J.P.); [email protected] (P.B.) 2 Key Laboratory for Yak Genetics, Breeding, and Reproduction Engineering of Gansu Province, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China * Correspondence: [email protected] (P.Y.); [email protected] (C.L.); Tel.: +86-0931-2115288 (P.Y.); +86-0931-2115271 (C.L.) Received: 29 October 2019; Accepted: 21 November 2019; Published: 26 November 2019 Abstract: DNA methylation modifications are implicated in many biological processes. As the most common epigenetic mechanism DNA methylation also affects muscle growth and development. The majority of previous studies have focused on different varieties of yak, but little is known about the epigenetic regulation mechanisms in different age groups of animals. -
Supplemental Information
Supplemental information Dissection of the genomic structure of the miR-183/96/182 gene. Previously, we showed that the miR-183/96/182 cluster is an intergenic miRNA cluster, located in a ~60-kb interval between the genes encoding nuclear respiratory factor-1 (Nrf1) and ubiquitin-conjugating enzyme E2H (Ube2h) on mouse chr6qA3.3 (1). To start to uncover the genomic structure of the miR- 183/96/182 gene, we first studied genomic features around miR-183/96/182 in the UCSC genome browser (http://genome.UCSC.edu/), and identified two CpG islands 3.4-6.5 kb 5’ of pre-miR-183, the most 5’ miRNA of the cluster (Fig. 1A; Fig. S1 and Seq. S1). A cDNA clone, AK044220, located at 3.2-4.6 kb 5’ to pre-miR-183, encompasses the second CpG island (Fig. 1A; Fig. S1). We hypothesized that this cDNA clone was derived from 5’ exon(s) of the primary transcript of the miR-183/96/182 gene, as CpG islands are often associated with promoters (2). Supporting this hypothesis, multiple expressed sequences detected by gene-trap clones, including clone D016D06 (3, 4), were co-localized with the cDNA clone AK044220 (Fig. 1A; Fig. S1). Clone D016D06, deposited by the German GeneTrap Consortium (GGTC) (http://tikus.gsf.de) (3, 4), was derived from insertion of a retroviral construct, rFlpROSAβgeo in 129S2 ES cells (Fig. 1A and C). The rFlpROSAβgeo construct carries a promoterless reporter gene, the β−geo cassette - an in-frame fusion of the β-galactosidase and neomycin resistance (Neor) gene (5), with a splicing acceptor (SA) immediately upstream, and a polyA signal downstream of the β−geo cassette (Fig. -
Variation in Protein Coding Genes Identifies Information
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. 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. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. 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. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
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. -
Gene Expression During Normal and FSHD Myogenesis Tsumagari Et Al
Gene expression during normal and FSHD myogenesis Tsumagari et al. Tsumagari et al. BMC Medical Genomics 2011, 4:67 http://www.biomedcentral.com/1755-8794/4/67 (27 September 2011) Tsumagari et al. BMC Medical Genomics 2011, 4:67 http://www.biomedcentral.com/1755-8794/4/67 RESEARCHARTICLE Open Access Gene expression during normal and FSHD myogenesis Koji Tsumagari1, Shao-Chi Chang1, Michelle Lacey2,3, Carl Baribault2,3, Sridar V Chittur4, Janet Sowden5, Rabi Tawil5, Gregory E Crawford6 and Melanie Ehrlich1,3* Abstract Background: Facioscapulohumeral muscular dystrophy (FSHD) is a dominant disease linked to contraction of an array of tandem 3.3-kb repeats (D4Z4) at 4q35. Within each repeat unit is a gene, DUX4, that can encode a protein containing two homeodomains. A DUX4 transcript derived from the last repeat unit in a contracted array is associated with pathogenesis but it is unclear how. Methods: Using exon-based microarrays, the expression profiles of myogenic precursor cells were determined. Both undifferentiated myoblasts and myoblasts differentiated to myotubes derived from FSHD patients and controls were studied after immunocytochemical verification of the quality of the cultures. To further our understanding of FSHD and normal myogenesis, the expression profiles obtained were compared to those of 19 non-muscle cell types analyzed by identical methods. Results: Many of the ~17,000 examined genes were differentially expressed (> 2-fold, p < 0.01) in control myoblasts or myotubes vs. non-muscle cells (2185 and 3006, respectively) or in FSHD vs. control myoblasts or myotubes (295 and 797, respectively). Surprisingly, despite the morphologically normal differentiation of FSHD myoblasts to myotubes, most of the disease-related dysregulation was seen as dampening of normal myogenesis- specific expression changes, including in genes for muscle structure, mitochondrial function, stress responses, and signal transduction.