Rgs7 (NM 011880) Mouse Tagged ORF Clone Product Data
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G Protein-Coupled Receptors
G PROTEIN-COUPLED RECEPTORS Overview:- The completion of the Human Genome Project allowed the identification of a large family of proteins with a common motif of seven groups of 20-24 hydrophobic amino acids arranged as α-helices. Approximately 800 of these seven transmembrane (7TM) receptors have been identified of which over 300 are non-olfactory receptors (see Frederikson et al., 2003; Lagerstrom and Schioth, 2008). Subdivision on the basis of sequence homology allows the definition of rhodopsin, secretin, adhesion, glutamate and Frizzled receptor families. NC-IUPHAR recognizes Classes A, B, and C, which equate to the rhodopsin, secretin, and glutamate receptor families. The nomenclature of 7TM receptors is commonly used interchangeably with G protein-coupled receptors (GPCR), although the former nomenclature recognises signalling of 7TM receptors through pathways not involving G proteins. For example, adiponectin and membrane progestin receptors have some sequence homology to 7TM receptors but signal independently of G-proteins and appear to reside in membranes in an inverted fashion compared to conventional GPCR. Additionally, the NPR-C natriuretic peptide receptor has a single transmembrane domain structure, but appears to couple to G proteins to generate cellular responses. The 300+ non-olfactory GPCR are the targets for the majority of drugs in clinical usage (Overington et al., 2006), although only a minority of these receptors are exploited therapeutically. Signalling through GPCR is enacted by the activation of heterotrimeric GTP-binding proteins (G proteins), made up of α, β and γ subunits, where the α and βγ subunits are responsible for signalling. The α subunit (tabulated below) allows definition of one series of signalling cascades and allows grouping of GPCRs to suggest common cellular, tissue and behavioural responses. -
Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses
medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract Sporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened. -
Screening of Potential Genes and Transcription Factors Of
ANIMAL STUDY e-ISSN 1643-3750 © Med Sci Monit, 2018; 24: 503-510 DOI: 10.12659/MSM.907445 Received: 2017.10.08 Accepted: 2018.01.01 Screening of Potential Genes and Transcription Published: 2018.01.25 Factors of Postoperative Cognitive Dysfunction via Bioinformatics Methods Authors’ Contribution: ABE 1 Yafeng Wang 1 Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical Study Design A AB 1 Ailan Huang University, Nanning, Guangxi, P.R. China Data Collection B 2 Department of Gynecology, People’s Hospital of Guangxi Zhuang Autonomous Statistical Analysis C BEF 1 Lixia Gan Region, The First Affiliated Hospital of Guangxi Medical University, Nanning, Data Interpretation D BCF 1 Yanli Bao Guangxi, P.R. China Manuscript Preparation E BDF 1 Weilin Zhu 3 Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical Literature Search F University, Nanning, Guangxi, P.R. China Funds Collection G EF 1 Yanyan Hu AE 1 Li Ma CF 2 Shiyang Wei DE 3 Yuyan Lan Corresponding Author: Yafeng Wang, e-mail: [email protected] Source of support: Departmental sources Background: The aim of this study was to explore the potential genes and transcription factors involved in postoperative cognitive dysfunction (POCD) via bioinformatics analysis. Material/Methods: GSE95070 miRNA expression profiles were downloaded from Gene Expression Omnibus database, which in- cluded five hippocampal tissues from POCD mice and controls. Moreover, the differentially expressed miRNAs (DEMs) between the two groups were identified. In addition, the target genes of DEMs were predicted using Targetscan 7.1, followed by protein-protein interaction (PPI) network construction, functional enrichment anal- ysis, pathway analysis, and prediction of transcription factors (TFs) targeting the potential targets. -
Multi-Functionality of Proteins Involved in GPCR and G Protein Signaling: Making Sense of Structure–Function Continuum with In
Cellular and Molecular Life Sciences (2019) 76:4461–4492 https://doi.org/10.1007/s00018-019-03276-1 Cellular andMolecular Life Sciences REVIEW Multi‑functionality of proteins involved in GPCR and G protein signaling: making sense of structure–function continuum with intrinsic disorder‑based proteoforms Alexander V. Fonin1 · April L. Darling2 · Irina M. Kuznetsova1 · Konstantin K. Turoverov1,3 · Vladimir N. Uversky2,4 Received: 5 August 2019 / Revised: 5 August 2019 / Accepted: 12 August 2019 / Published online: 19 August 2019 © Springer Nature Switzerland AG 2019 Abstract GPCR–G protein signaling system recognizes a multitude of extracellular ligands and triggers a variety of intracellular signal- ing cascades in response. In humans, this system includes more than 800 various GPCRs and a large set of heterotrimeric G proteins. Complexity of this system goes far beyond a multitude of pair-wise ligand–GPCR and GPCR–G protein interactions. In fact, one GPCR can recognize more than one extracellular signal and interact with more than one G protein. Furthermore, one ligand can activate more than one GPCR, and multiple GPCRs can couple to the same G protein. This defnes an intricate multifunctionality of this important signaling system. Here, we show that the multifunctionality of GPCR–G protein system represents an illustrative example of the protein structure–function continuum, where structures of the involved proteins represent a complex mosaic of diferently folded regions (foldons, non-foldons, unfoldons, semi-foldons, and inducible foldons). The functionality of resulting highly dynamic conformational ensembles is fne-tuned by various post-translational modifcations and alternative splicing, and such ensembles can undergo dramatic changes at interaction with their specifc partners. -
Genetic Variability in a Holstein Population Using SNP Markers and Their Use for Monitoring Mating Strategies
https://doi.org/10.22319/rmcp.v10i3.4842 Article Genetic variability in a Holstein population using SNP markers and their use for monitoring mating strategies Kathy Scienski a,b,c Angelo Ialacci c Alessandro Bagnato c Davide Reginelli d Marina Durán-Aguilar e Maria Giuseppina Strillacci c* a Texas A&M University, College Station. Interdisciplinary Program in Genetics. Texas, USA. b Texas A&M University. Department of Animal Science, Texas, USA. c Università degli Studi di Milano. Department of Veterinary Medicine, Via Trentacoste 2, 20134 Milano, Italy. d Università degli Studi di Milano. Azienda Agraria Didattico Sperimentale Angelo Menozzi, Landriano, Pavia, Italy. e Universidad Autónoma de Querétaro. Facultad de Ciencias Naturales. Querétaro. México. * Corresponding author: [email protected] Abstract: As genotyping costs continue to decrease, the demand for genotyping has increased among farmers. In most livestock herds, an important issue is controlling the increase in inbreeding coefficient. While this remains a large motive to genotype, producers are often unaware of the other benefits that genotyping could bring. The aim of this study was to demonstrate that SNP chips could be used as an effective herd management tool by utilizing a population of Italian Holstein-Friesian cattle. After filtering, the total number 643 Rev Mex Cienc Pecu 2019;10(3):643-663 of animals and SNPs retained for analyses were 44 and 27,365, respectively. The principal component analyses (PCA) were able to identify a sire and origin-of-sire effect within the herd, while determining that sires do not influence individual genomic selection index values. The inbreeding coefficients calculated from genotypes (FIS) provided a glimpse into the herd’s heterozygosity and determined that the genetic variability is being well maintained. -
Supplementary Table 1: Differentially Methylated Genes and Functions of the Genes Before/After Treatment with A) Doxorubicin and B) FUMI and in C) Responders Vs
Supplementary Table 1: Differentially methylated genes and functions of the genes before/after treatment with a) doxorubicin and b) FUMI and in c) responders vs. non- responders for doxorubicin and d) FUMI Differentially methylated genes before/after treatment a. Doxo GENE FUNCTION CCL5, CCL8, CCL15, CCL21, CCR1, CD33, IL5, immunoregulatory and inflammatory processes IL8, IL24, IL26, TNFSF11 CCNA1, CCND2, CDKN2A cell cycle regulators ESR1, FGF2, FGF14, FGF18 growth factors WT1, RASSF5, RASSF6 tumor suppressor b. FUMI GENE FUNCTION CCL7, CCL15, CD28, CD33, CD40, CD69, TNFSF18 immunoregulatory and inflammatory processes CCND2, CDKN2A cell cycle regulators IGF2BP1, IGFBP3 growth factors HOXB4, HOXB6, HOXC8 regulation of cell transcription WT1, RASSF6 tumor suppressor Differentially methylated genes in responders vs. non-responders c. Doxo GENE FUNCTION CBR1, CCL4, CCL8, CCR1, CCR7, CD1A, CD1B, immunoregulatory and inflammatory processes CD1D, CD1E, CD33, CD40, IL5, IL8, IL20, IL22, TLR4 CCNA1, CCND2, CDKN2A cell cycle regulators ESR2, ERBB3, FGF11, FGF12, FGF14, FGF17 growth factors WNT4, WNT16, WNT10A implicated in oncogenesis TNFSF12, TNFSF15 apoptosis FOXL1, FOXL2, FOSL1,HOXA2, HOXA7, HOXA11, HOXA13, HOXB4, HOXB6, HOXB8, HOXB9, HOXC8, regulation of cell transcription HOXD8, HOXD9, HOXD11 GSTP1, MGMT DNA repair APC, WT1 tumor suppressor d. FUMI GENE FUNCTION CCL1, CCL3, CCL5,CCL14, CD1B, CD33, CD40, CD69, immunoregulatory and inflammatory IL20, IL32 processes CCNA1, CCND2, CDKN2A cell cycle regulators IGF2BP1, IGFBP3, IGFBP7, EGFR, ESR2,RARB2 -
CENTOGENE's Severe and Early Onset Disorder Gene List
CENTOGENE’s severe and early onset disorder gene list USED IN PRENATAL WES ANALYSIS AND IDENTIFICATION OF “PATHOGENIC” AND “LIKELY PATHOGENIC” CENTOMD® VARIANTS IN NGS PRODUCTS The following gene list shows all genes assessed in prenatal WES tests or analysed for P/LP CentoMD® variants in NGS products after April 1st, 2020. For searching a single gene coverage, just use the search on www.centoportal.com AAAS, AARS1, AARS2, ABAT, ABCA12, ABCA3, ABCB11, ABCB4, ABCB7, ABCC6, ABCC8, ABCC9, ABCD1, ABCD4, ABHD12, ABHD5, ACACA, ACAD9, ACADM, ACADS, ACADVL, ACAN, ACAT1, ACE, ACO2, ACOX1, ACP5, ACSL4, ACTA1, ACTA2, ACTB, ACTG1, ACTL6B, ACTN2, ACVR2B, ACVRL1, ACY1, ADA, ADAM17, ADAMTS2, ADAMTSL2, ADAR, ADARB1, ADAT3, ADCY5, ADGRG1, ADGRG6, ADGRV1, ADK, ADNP, ADPRHL2, ADSL, AFF2, AFG3L2, AGA, AGK, AGL, AGPAT2, AGPS, AGRN, AGT, AGTPBP1, AGTR1, AGXT, AHCY, AHDC1, AHI1, AIFM1, AIMP1, AIPL1, AIRE, AK2, AKR1D1, AKT1, AKT2, AKT3, ALAD, ALDH18A1, ALDH1A3, ALDH3A2, ALDH4A1, ALDH5A1, ALDH6A1, ALDH7A1, ALDOA, ALDOB, ALG1, ALG11, ALG12, ALG13, ALG14, ALG2, ALG3, ALG6, ALG8, ALG9, ALMS1, ALOX12B, ALPL, ALS2, ALX3, ALX4, AMACR, AMER1, AMN, AMPD1, AMPD2, AMT, ANK2, ANK3, ANKH, ANKRD11, ANKS6, ANO10, ANO5, ANOS1, ANTXR1, ANTXR2, AP1B1, AP1S1, AP1S2, AP3B1, AP3B2, AP4B1, AP4E1, AP4M1, AP4S1, APC2, APTX, AR, ARCN1, ARFGEF2, ARG1, ARHGAP31, ARHGDIA, ARHGEF9, ARID1A, ARID1B, ARID2, ARL13B, ARL3, ARL6, ARL6IP1, ARMC4, ARMC9, ARSA, ARSB, ARSL, ARV1, ARX, ASAH1, ASCC1, ASH1L, ASL, ASNS, ASPA, ASPH, ASPM, ASS1, ASXL1, ASXL2, ASXL3, ATAD3A, ATCAY, ATIC, ATL1, ATM, ATOH7, -
GSE50161, (C) GSE66354, (D) GSE74195 and (E) GSE86574
Figure S1. Boxplots of normalized samples in five datasets. (A) GSE25604, (B) GSE50161, (C) GSE66354, (D) GSE74195 and (E) GSE86574. The x‑axes indicate samples, and the y‑axes represent the expression of genes. Figure S2. Volanco plots of DEGs in five datasets. (A) GSE25604, (B) GSE50161, (C) GSE66354, (D) GSE74195 and (E) GSE86574. Red nodes represent upregulated DEGs and green nodes indicate downregulated DEGs. Cut‑off criteria were P<0.05 and |log2 FC|>1. DEGs, differentially expressed genes; FC, fold change; adj.P.Val, adjusted P‑value. Figure S3. Transcription factor‑gene regulatory network constructed using the Cytoscape iRegulion plug‑in. Table SI. Primer sequences for reverse transcription‑quantitative polymerase chain reaction. Genes Sequences hsa‑miR‑124 F: 5'‑ACACTCCAGCTGGGCAGCAGCAATTCATGTTT‑3' R: 5'‑CTCAACTGGTGTCGTGGA‑3' hsa‑miR‑330‑3p F: 5'‑CATGAATTCACTCTCCCCGTTTCTCCCTCTGC‑3' R: 5'‑CCTGCGGCCGCGAGCCGCCCTGTTTGTCTGAG‑3' hsa‑miR‑34a‑5p F: 5'‑TGGCAGTGTCTTAGCTGGTTGT‑3' R: 5'‑GCGAGCACAGAATTAATACGAC‑3' hsa‑miR‑449a F: 5'‑TGCGGTGGCAGTGTATTGTTAGC‑3' R: 5'‑CCAGTGCAGGGTCCGAGGT‑3' CD44 F: 5'‑CGGACACCATGGACAAGTTT‑3' R: 5'‑TGTCAATCCAGTTTCAGCATCA‑3' PCNA F: 5'‑GAACTGGTTCATTCATCTCTATGG‑3' F: 5'‑TGTCACAGACAAGTAATGTCGATAAA‑3' SYT1 F: 5'‑CAATAGCCATAGTCGCAGTCCT‑3' R: 5'‑TGTCAATCCAGTTTCAGCATCA‑3' U6 F: 5'‑GCTTCGGCAGCACATATACTAAAAT‑3' R: 5'‑CGCTTCACGAATTTGCGTGTCAT‑3' GAPDH F: 5'‑GGAAAGCTGTGGCGTGAT‑3' R: 5'‑AAGGTGGAAGAATGGGAGTT‑3' hsa, homo sapiens; miR, microRNA; CD44, CD44 molecule (Indian blood group); PCNA, proliferating cell nuclear antigen; -
Jcem1109.Pdf
JCEM ONLINE Advances in Genetics—Endocrine Research Identification of Gene Expression Profiles Associated With Cortisol Secretion in Adrenocortical Adenomas Hortense Wilmot Roussel,* Delphine Vezzosi,* Marthe Rizk-Rabin, Olivia Barreau, Bruno Ragazzon, Fernande René-Corail, Aurélien de Reynies, Jérôme Bertherat, and Guillaume Assie´ Institut National de la Santé et de la Recherche Médicale Unité 1016 (H.W.R., D.V., M.R.-R., O.B., B.R., Downloaded from https://academic.oup.com/jcem/article/98/6/E1109/2536811 by guest on 28 September 2021 F.R.-C., J.B., G.A.); Institut Cochin, Centre National de la Recherche Scientifique Unité Mixte de Recherche 8104 (H.W.R., D.V., M.R.-R, O.B., B.R., F.R.-C., J.B., G.A.); and Department of Endocrinology (H.W.R., O.B., J.B., G.A.), Reference Center for Rare Adrenal Diseases (J.B.), Assistance Publique Hôpitaux de Paris, Hôpital Cochin, 75014 Paris, France; Faculté de Médecine Paris Descartes (H.W.R., D.V., M.R.-R., O.B., B.R., F.R.-C., J.B., G.A.), Université Paris Descartes, Sorbonne Paris Cité, 75006 Paris, France; Department of Endocrinology (D.V.), Hôpital Larrey, 31480 Toulouse, France; and Plateforme de Bioinformatique (A.d.R.), Carte d’Identité des Tumeurs, Ligue Contre le Cancer, 75013 Paris, France Context: The cortisol secretion of adrenocortical adenomas can be either subtle or overt. The mechanisms leading to the autonomous hypersecretion of cortisol are unknown. Objective: The objective of the study was to identify the gene expression profile associated with the autonomous and excessive cortisol secretion of adrenocortical adenomas. -
Perkinelmer Genomics to Request the Saliva Swab Collection Kit for Patients That Cannot Provide a Blood Sample As Whole Blood Is the Preferred Sample
Autism and Intellectual Disability TRIO Panel Test Code TR002 Test Summary This test analyzes 2429 genes that have been associated with Autism and Intellectual Disability and/or disorders associated with Autism and Intellectual Disability with the analysis being performed as a TRIO Turn-Around-Time (TAT)* 3 - 5 weeks Acceptable Sample Types Whole Blood (EDTA) (Preferred sample type) DNA, Isolated Dried Blood Spots Saliva Acceptable Billing Types Self (patient) Payment Institutional Billing Commercial Insurance Indications for Testing Comprehensive test for patients with intellectual disability or global developmental delays (Moeschler et al 2014 PMID: 25157020). Comprehensive test for individuals with multiple congenital anomalies (Miller et al. 2010 PMID 20466091). Patients with autism/autism spectrum disorders (ASDs). Suspected autosomal recessive condition due to close familial relations Previously negative karyotyping and/or chromosomal microarray results. Test Description This panel analyzes 2429 genes that have been associated with Autism and ID and/or disorders associated with Autism and ID. Both sequencing and deletion/duplication (CNV) analysis will be performed on the coding regions of all genes included (unless otherwise marked). All analysis is performed utilizing Next Generation Sequencing (NGS) technology. CNV analysis is designed to detect the majority of deletions and duplications of three exons or greater in size. Smaller CNV events may also be detected and reported, but additional follow-up testing is recommended if a smaller CNV is suspected. All variants are classified according to ACMG guidelines. Condition Description Autism Spectrum Disorder (ASD) refers to a group of developmental disabilities that are typically associated with challenges of varying severity in the areas of social interaction, communication, and repetitive/restricted behaviors. -
Network-Based Identification of Genetic Factors in Ageing, Lifestyle
bioRxiv preprint doi: https://doi.org/10.1101/482844; this version posted November 29, 2018. 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-ND 4.0 International license. Network-based identification of genetic factors in Ageing, lifestyle and Type 2 Diabetes that Influence in the progression of Alzheimer’s disease 1st Utpala Nanda Chowdhury 2nd Md. Babul Islam Department of Computer Science and Engineering Department of Applied Physics and Electronic Engineering University of Rajshahi University of Rajshahi Rajshahi, Bangladesh Rajshahi, Bangladesh [email protected] [email protected] 3rd Shamim Ahmad 5th Mohammad Ali Moni Department of Computer Science and Engineering Discipline of Biomedical Science, Faculty of Medicine and Health University of Rajshahi The University of Sydney Rajshahi, Bangladesh NSW, Australia shamim [email protected] [email protected] Abstract—Alzheimer’s disease (AD) is an incurable disease, I. INTRODUCTION and the causative risk factors, especially the modifiable ones, are still poorly understood which impedes the effective prevention Alzheimer’s disease (AD) is the most common form of and remedy strategies. We proposed a network-based quanti- tative framework to reveal the complex relationship of various dementia characterized by gradual degeneration in memory, biasing genetic factors for the AD. We analyzed gene expression thinking, language, and learning capacity [1]. Initial signs microarray data from the AD, ageing, severe alcohol consump- begin with the inability of forming recent memories, but tion, type II diabetes, high body fat, high dietary fat, obesity, di- inevitably affect all cognitive functions resulting in complete etary red meat, sedentary lifestyle, smoking, and control datasets. -
Mouse Models of Inherited Retinal Degeneration with Photoreceptor Cell Loss
cells Review Mouse Models of Inherited Retinal Degeneration with Photoreceptor Cell Loss 1, 1, 1 1,2,3 1 Gayle B. Collin y, Navdeep Gogna y, Bo Chang , Nattaya Damkham , Jai Pinkney , Lillian F. Hyde 1, Lisa Stone 1 , Jürgen K. Naggert 1 , Patsy M. Nishina 1,* and Mark P. Krebs 1,* 1 The Jackson Laboratory, Bar Harbor, Maine, ME 04609, USA; [email protected] (G.B.C.); [email protected] (N.G.); [email protected] (B.C.); [email protected] (N.D.); [email protected] (J.P.); [email protected] (L.F.H.); [email protected] (L.S.); [email protected] (J.K.N.) 2 Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand 3 Siriraj Center of Excellence for Stem Cell Research, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand * Correspondence: [email protected] (P.M.N.); [email protected] (M.P.K.); Tel.: +1-207-2886-383 (P.M.N.); +1-207-2886-000 (M.P.K.) These authors contributed equally to this work. y Received: 29 February 2020; Accepted: 7 April 2020; Published: 10 April 2020 Abstract: Inherited retinal degeneration (RD) leads to the impairment or loss of vision in millions of individuals worldwide, most frequently due to the loss of photoreceptor (PR) cells. Animal models, particularly the laboratory mouse, have been used to understand the pathogenic mechanisms that underlie PR cell loss and to explore therapies that may prevent, delay, or reverse RD. Here, we reviewed entries in the Mouse Genome Informatics and PubMed databases to compile a comprehensive list of monogenic mouse models in which PR cell loss is demonstrated.