The PHF21B Gene Is Associated with Major Depression and Modulates the Stress Response
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KRBA1 CRISPR/Cas9 KO Plasmid (M): Sc-429589
SANTA CRUZ BIOTECHNOLOGY, INC. KRBA1 CRISPR/Cas9 KO Plasmid (m): sc-429589 BACKGROUND APPLICATIONS The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and KRBA1 CRISPR/Cas9 KO Plasmid (m) is recommended for the disruption of CRISPR-associated protein (Cas9) system is an adaptive immune response gene expression in mouse cells. defense mechanism used by archea and bacteria for the degradation of for- eign genetic material (4,6). This mechanism can be repurposed for other 20 nt non-coding RNA sequence: guides Cas9 functions, including genomic engineering for mammalian systems, such as to a specific target location in the genomic DNA gene knockout (KO) (1,2,3,5). CRISPR/Cas9 KO Plasmid products enable the U6 promoter: drives gRNA scaffold: helps Cas9 identification and cleavage of specific genes by utilizing guide RNA (gRNA) expression of gRNA bind to target DNA sequences derived from the Genome-scale CRISPR Knock-Out (GeCKO) v2 library developed in the Zhang Laboratory at the Broad Institute (3,5). Termination signal Green Fluorescent Protein: to visually REFERENCES verify transfection CRISPR/Cas9 Knockout Plasmid CBh (chicken β-Actin 1. Cong, L., et al. 2013. Multiplex genome engineering using CRISPR/Cas hybrid) promoter: drives systems. Science 339: 819-823. 2A peptide: expression of Cas9 allows production of both Cas9 and GFP from the 2. Mali, P., et al. 2013. RNA-guided human genome engineering via Cas9. same CBh promoter Science 339: 823-826. Nuclear localization signal 3. Ran, F.A., et al. 2013. Genome engineering using the CRISPR-Cas9 system. Nuclear localization signal SpCas9 ribonuclease Nat. Protoc. 8: 2281-2308. -
Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis 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 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. -
Mouse Rabac1 Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Rabac1 Knockout Project (CRISPR/Cas9) Objective: To create a Rabac1 knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Rabac1 gene (NCBI Reference Sequence: NM_010261 ; Ensembl: ENSMUSG00000003380 ) is located on Mouse chromosome 7. 5 exons are identified, with the ATG start codon in exon 1 and the TAA stop codon in exon 5 (Transcript: ENSMUST00000076961). Exon 1~5 will be selected as target site. Cas9 and gRNA will be co-injected into fertilized eggs for KO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 1 starts from about 0.18% of the coding region. Exon 1~5 covers 100.0% of the coding region. The size of effective KO region: ~2657 bp. The KO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 2 3 4 5 Legends Exon of mouse Rabac1 Knockout region Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot (up) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section upstream of start codon is aligned with itself to determine if there are tandem repeats. No significant tandem repeat is found in the dot plot matrix. So this region is suitable for PCR screening or sequencing analysis. Overview of the Dot Plot (down) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section downstream of stop codon is aligned with itself to determine if there are tandem repeats. -
Topological Scoring of Protein Interaction Networks
bioRxiv preprint doi: https://doi.org/10.1101/438408; this version posted October 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Topological Scoring of Protein Interaction Networks Mihaela E. Sardiu1, Joshua M. Gilmore1,2, Brad D. Groppe1,3, Arnob Dutta1,4, Laurence Florens1, and Michael P. Washburn1,5‡ 1Stowers Institute for Medical Research, Kansas City, MO 64110 U.S.A. 2Current Address: Boehringer Ingelheim Vetmedica, St. Joseph, MO 64506 U.S.A. 3Current Address: Thermo Fisher Scientific, Waltham, MA 02451, U.S.A. 4Current Address: Department of Cell and Molecular Biology, University of Rhode Island, 287 CBLS, 120 Flagg Road, Kingston, RI 02881. 5 Department of Pathology and Laboratory Medicine, The University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, Kansas 66160, USA ‡To whom correspondence should be addressed: Michael Washburn, Ph.D. Stowers Institute for Medical Research 1000 E. 50th St. Kansas City, MO 64110 Phone: 816-926-4457 E-mail: [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/438408; this version posted October 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract It remains a significant challenge to define individual protein associations within networks where an individual protein can directly interact with other proteins and/or be part of large complexes, which contain functional modules. Here we demonstrate the topological scoring (TopS) algorithm for the analysis of quantitative proteomic analyses of affinity purifications. -
Data Sheet 160-3P
Rudolf-Wissell-Str. 28a Background 37079 Göttingen, Germany Phone: +49 551-50556-0 Homer is a scaffolding protein of the post synaptic density (PSD) and enriched at excitatory synapses. Fax: +49 551-50556-384 The protein binds metabotropic glutamate receptors, TRPC1, proteins of the Shank family and others. E-mail: [email protected] By aggregating these proteins into clusters, Homer was suggested to organize distinct signalling Web: www.sysy.com domains. Homer 3 Three isoforms, Homer 1, 2 and 3 have been described. Each of these isoforms is subject to alternative splicing yielding the splice variants a, b, c, d. Cat.No. 160-3P; control protein, 100 µg protein (lyophilized) Data Sheet Selected General References Homer2 and Homer3 interact with amyloid precursor protein and inhibit Abeta production. Parisiadou L, Bethani I, Michaki V, Krousti K, Rapti G, Efthimiopoulos S Reconstitution/ 100 µg lyophilized protein. For reconstitution add 100 µl H2O to get a 1mg/ml Neurobiology of disease (2008) 303: 353-64. Storage solution in MBS. Then aliquot and store at -20°C until use. Differential expression of Homer family proteins in the developing mouse brain. For detailed information, see back of the data sheet. Shiraishi Y, Mizutani A, Yuasa S, Mikoshiba K, Furuichi T The Journal of comparative neurology (2004) 4734: 582-99. Immunogen Recombinant protein corresponding to AA 1 to 177 from rat Homer3 (UniProt Id: Q9Z2X5) Molecular characterisation of two structurally distinct groups of human homers, generated by extensive alternative splicing. Soloviev MM, Ciruela F, Chan WY, McIlhinney RA Recommended Optimal concentrations should be determined by the end-user. -
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-Wide Rnai Screening Identifies Human Proteins with A
RESOURCES Genome-wide RNAi screening identifies human proteins with a regulatory function in the early secretory pathway Jeremy C. Simpson1,7, Brigitte Joggerst2, Vibor Laketa2, Fatima Verissimo2, Cihan Cetin2, Holger Erfle2,6, Mariana G. Bexiga1, Vasanth R. Singan1, Jean-Karim Hériché3, Beate Neumann3, Alvaro Mateos2, Jonathon Blake4, Stephanie Bechtel5, Vladimir Benes4, Stefan Wiemann5, Jan Ellenberg2,3 and Rainer Pepperkok2,7 The secretory pathway in mammalian cells has evolved to facilitate the transfer of cargo molecules to internal and cell surface membranes. Use of automated microscopy-based genome-wide RNA interference screens in cultured human cells allowed us to identify 554 proteins influencing secretion. Cloning, fluorescent-tagging and subcellular localization analysis of 179 of these proteins revealed that more than two-thirds localize to either the cytoplasm or membranes of the secretory and endocytic pathways. The depletion of 143 of them resulted in perturbations in the organization of the COPII and/or COPI vesicular coat complexes of the early secretory pathway, or the morphology of the Golgi complex. Network analyses revealed a so far unappreciated link between early secretory pathway function, small GTP-binding protein regulation, actin cytoskeleton organization and EGF-receptor-mediated signalling. This work provides an important resource for an integrative understanding of global cellular organization and regulation of the secretory pathway in mammalian cells. Within higher eukaryotic cells membrane traffic pathways connect the Extensive efforts over many years have revealed a significant number various membrane-bounded organelles, thereby ensuring that they of regulators associated with the secretory pathway. Early biochemical retain the correct complement of proteins and lipids to maintain approaches to identify individual machinery components have started cellular homeostasis. -
Mapping of Quantitative Trait Loci for Milk Yield Traits on Bovine Chromosome 5 in the Fleckvieh Cattle
From the Department of Veterinary Sciences Faculty of Veterinary Medicine Ludwig-Maximilians-Universität München Arbeit angefertigt unter der Leitung von Univ. Prof. Dr. Dr. habil. Martin Förster Mapping of Quantitative Trait Loci for Milk Yield Traits on Bovine Chromosome 5 in the Fleckvieh Cattle Inaugural–Dissertation For the attainment of Doctor Degree in Veterinary Medicine From the Faculty of Veterinary Medicine of the Ludwig-Maximilians-Universität München by Ashraf Fathy Said Awad from Sharkia- Egypt München 2011 Gedruckt mit Genehmigung der Tierärztlichen Fakultät der Ludwig–Maximilians–Universität München Dekan: Univ. Prof. Dr. Braun Berichterstatter: Univ. Prof. Dr. Dr. habil Förster Korreferent: Univ. Prof. Dr. Mansfeld Tag der Promotion: 12. February 2011 This work is dedicated to My Parents, my wife and my lovely daughters; Sama, Shaza, Hana CONTENTS CONTENTS ABBREVIATION……………………………………………………………… IV CHAPTER 1: GENERAL INTRODUCTION……………………………….. 1 CHAPTER 2: REVIEW OF LITERATURE………………………………… 3 2.1. DNA Markers……………………………………………………….. 3 2.1.1. Microsatellites………………………………………………………….. 3 2.1.2. Single Nucleotide Polymorphism (SNPs)…………………………… 4 2.2. Mapping of Quantitative Trait Loci (QTL)…………………….. 5 2.2.1. QTL Mapping Designs………………………………………………... 6 2.2.1.1. Daughter Design………………………………………………... 6 2.2.1.2. Granddaughter Design………………………………………… 7 2.2.1.3. Complex Pedigree Design…………………………………….. 9 2.2.2. QTL Mapping Strategies……………………………………………… 10 2.2.2.1. Candidate Gene Approach……………………………………. 10 2.2.2.2. Genome Scan Approach……………………………………… 11 2.3. Principles of Linkage Mapping…………………………………. 12 2.4. QTL Fine Mapping………………………………………………… 14 2.4.1. Linkage Disequilibrium……………………………………………… 15 2.4.2. Combined Linkage Disequilibrium and Linkage (LDL) Mapping… 17 2.5. Identification of Candidate Genes……………………………… 18 2.6. -
The Genetic Basis of Dupuytren's Disease Gloria Sue Yale School of Medicine, [email protected]
Yale University EliScholar – A Digital Platform for Scholarly Publishing at Yale Yale Medicine Thesis Digital Library School of Medicine January 2014 The Genetic Basis Of Dupuytren's Disease Gloria Sue Yale School of Medicine, [email protected] Follow this and additional works at: http://elischolar.library.yale.edu/ymtdl Recommended Citation Sue, Gloria, "The Genetic Basis Of Dupuytren's Disease" (2014). Yale Medicine Thesis Digital Library. 1926. http://elischolar.library.yale.edu/ymtdl/1926 This Open Access Thesis is brought to you for free and open access by the School of Medicine at EliScholar – A Digital Platform for Scholarly Publishing at Yale. It has been accepted for inclusion in Yale Medicine Thesis Digital Library by an authorized administrator of EliScholar – A Digital Platform for Scholarly Publishing at Yale. For more information, please contact [email protected]. The Genetic Basis of Dupuytren’s Disease A Thesis Submitted to the Yale University School of Medicine In Partial Fulfillment of the Requirements for the Degree of Doctor of Medicine by Gloria R. Sue 2014 THE GENETIC BASIS OF DUPUYTREN’S DISEASE. Gloria R. Sue, Deepak Narayan. Section of Plastic and Reconstructive Surgery, Department of Surgery, Yale University School of Medicine, New Haven, CT. Dupuytren’s disease is a common heritable connective tissue disorder of poorly understood etiology. It is thought that oxidative stress pathways may play a critical role in the development of Dupuytren’s disease, given the various disease associations that have been observed. We sought to sequence the mitochondrial and nuclear genomes of patients affected with Dupuytren’s disease using next-generation sequencing technology to potentially identify genes of potential pathogenetic interest. -
Supplementary Methods
Heterogeneous Contribution of Microdeletions in the Development of Common Generalized and Focal epilepsies. SUPPLEMENTARY METHODS Epilepsy subtype extended description. Genetic Gereralized Epilepsy (GGE): Features unprovoked tonic and/or clonic seizures, originated inconsistently at some focal point within the brain that rapidly generalizes engaging bilateral distributed spikes and waves discharges on the electroencephalogram. This generalization can include cortical and sub cortical structures but not necessarily the entire cortex[1]. GGE is the most common group of epilepsies accounting for 20% of all cases[2]. It is characterized by an age-related onset and a strong familial aggregation and heritability which allows the assumption of a genetic cause. Although genetic associations have been identified, a broad spectrum of causes is acknowledged and remains largely unsolved [3]. Rolandic Epilepsy (RE): Commonly known also as Benign Epilepsy with Centrotemporal Spikes (BECTS), hallmarks early onset diagnosis (mean onset = 7 years old) with brief, focal hemifacial or oropharyngeal sensorimotor seizures alongside speech arrest and secondarily generalized tonic– clonic seizures, which mainly occur during sleep[4]. Rolandic epilepsy features a broad spectrum of less benign related syndromes called atypical Rolandic epilepsy (ARE), including benign partial epilepsy (ABPE), Landau–Kleffner syndrome(LKS) and epileptic encephalopathy with continuous spike-and-waves during sleep (CSWSS)[5]. Together they are the most common childhood epilepsy with a prevalence of 0.2–0.73/1000 (i.e. _1/2500)[6]. Adult Focal Epilepsy (AFE). Focal epilepsy is characterized by sporadic events of seizures originated within a specific brain region and restricted to one hemisphere. Although they can exhibit more than one network of wave discharges on the electroencephalogram, and different degrees of spreading, they feature a consistent site of origin. -
Statistical and Bioinformatic Analysis of Hemimethylation Patterns in Non-Small Cell Lung Cancer
Statistical and Bioinformatic Analysis of Hemimethylation Patterns in Non-Small Cell Lung Cancer Shuying Sun ( [email protected] ) Texas State University San Marcos https://orcid.org/0000-0003-3974-6996 Austin Zane Texas A&M University College Station Carolyn Fulton Schreiner University Jasmine Philipoom Case Western Reserve University Research article Keywords: Methylation, Hemimethylation, Lung Cancer, Bioinformatics, Epigenetics Posted Date: October 12th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-17794/v2 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Version of Record: A version of this preprint was published on March 12th, 2021. See the published version at https://doi.org/10.1186/s12885-021-07990-7. Page 1/29 Abstract Background: DNA methylation is an epigenetic event involving the addition of a methyl-group to a cytosine-guanine base pair (i.e., CpG site). It is associated with different cancers. Our research focuses on studying non- small cell lung cancer hemimethylation, which refers to methylation occurring on only one of the two DNA strands. Many studies often assume that methylation occurs on both DNA strands at a CpG site. However, recent publications show the existence of hemimethylation and its signicant impact. Therefore, it is important to identify cancer hemimethylation patterns. Methods: In this paper, we use the Wilcoxon signed rank test to identify hemimethylated CpG sites based on publicly available non-small cell lung cancer methylation sequencing data. We then identify two types of hemimethylated CpG clusters, regular and polarity clusters, and genes with large numbers of hemimethylated sites. -
Brain Region-Dependent Gene Networks Associated with Selective Breeding for Increased Voluntary Wheel-Running Behavior
UC Riverside UC Riverside Previously Published Works Title Brain region-dependent gene networks associated with selective breeding for increased voluntary wheel-running behavior. Permalink https://escholarship.org/uc/item/8c49g8fd Journal PloS one, 13(8) ISSN 1932-6203 Authors Zhang, Pan Rhodes, Justin S Garland, Theodore et al. Publication Date 2018 DOI 10.1371/journal.pone.0201773 Peer reviewed eScholarship.org Powered by the California Digital Library University of California RESEARCH ARTICLE Brain region-dependent gene networks associated with selective breeding for increased voluntary wheel-running behavior Pan Zhang1,2, Justin S. Rhodes3,4, Theodore Garland, Jr.5, Sam D. Perez3, Bruce R. Southey2, Sandra L. Rodriguez-Zas2,6,7* 1 Illinois Informatics Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America, 2 Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United a1111111111 States of America, 3 Beckman Institute for Advanced Science and Technology, Urbana, IL, United States of a1111111111 America, 4 Center for Nutrition, Learning and Memory, University of Illinois at Urbana-Champaign, Urbana, a1111111111 IL, United States of America, 5 Department of Evolution, Ecology, and Organismal Biology, University of a1111111111 California, Riverside, CA, United States of America, 6 Department of Statistics, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America, 7 Carle Woese Institute for Genomic Biology, a1111111111 University of Illinois at Urbana-Champaign, Urbana, IL, United States of America * [email protected] OPEN ACCESS Abstract Citation: Zhang P, Rhodes JS, Garland T, Jr., Perez SD, Southey BR, Rodriguez-Zas SL (2018) Brain Mouse lines selectively bred for high voluntary wheel-running behavior are helpful models region-dependent gene networks associated with for uncovering gene networks associated with increased motivation for physical activity and selective breeding for increased voluntary wheel- other reward-dependent behaviors.