Mouse Nrip2 Conditional Knockout Project (CRISPR/Cas9)

Total Page:16

File Type:pdf, Size:1020Kb

Mouse Nrip2 Conditional Knockout Project (CRISPR/Cas9) https://www.alphaknockout.com Mouse Nrip2 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Nrip2 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Nrip2 gene (NCBI Reference Sequence: NM_021717 ; Ensembl: ENSMUSG00000001520 ) is located on Mouse chromosome 6. 6 exons are identified, with the ATG start codon in exon 1 and the TGA stop codon in exon 6 (Transcript: ENSMUST00000001561). Exon 2~3 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Nrip2 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-359I11 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 2 starts from about 27.66% of the coding region. The knockout of Exon 2~3 will result in frameshift of the gene. The size of intron 1 for 5'-loxP site insertion: 4633 bp, and the size of intron 3 for 3'-loxP site insertion: 781 bp. The size of effective cKO region: ~2138 bp. The cKO 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 6 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Nrip2 Homology arm cKO region loxP site Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region 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 GC Content Distribution Window size: 300 bp Sequence 12 Summary: Full Length(8638bp) | A(25.69% 2219) | C(24.54% 2120) | T(25.8% 2229) | G(23.96% 2070) Note: The sequence of homologous arms and cKO region is analyzed to determine the GC content. No significant high GC-content region is found. So this region is suitable for PCR screening or sequencing analysis. Page 3 of 8 https://www.alphaknockout.com BLAT Search Results (up) QUERY SCORE START END QSIZE IDENTITY CHROM STRAND START END SPAN ----------------------------------------------------------------------------------------------- browser details YourSeq 3000 1 3000 3000 100.0% chr6 + 128401686 128404685 3000 browser details YourSeq 127 1887 2349 3000 79.2% chr10 + 42428797 42429187 391 browser details YourSeq 125 944 1253 3000 83.1% chr4 - 123575354 123575633 280 browser details YourSeq 120 984 1238 3000 89.4% chr2 - 156866337 156890792 24456 browser details YourSeq 114 947 1248 3000 85.9% chr11 - 101490709 101792646 301938 browser details YourSeq 105 992 1249 3000 90.0% chr2 + 163762466 163763038 573 browser details YourSeq 102 943 1248 3000 82.6% chr6 + 120340094 120340385 292 browser details YourSeq 100 1887 2024 3000 85.3% chr1 - 87361041 87361174 134 browser details YourSeq 95 1887 2201 3000 74.3% chr11 + 89565488 89565639 152 browser details YourSeq 94 945 1099 3000 94.3% chr15 + 26754170 26754325 156 browser details YourSeq 93 1148 1481 3000 89.1% chr13 + 55182882 55183374 493 browser details YourSeq 92 1133 1477 3000 79.7% chr11 - 100617115 100617440 326 browser details YourSeq 92 945 1231 3000 91.9% chr11 - 62498290 62498755 466 browser details YourSeq 91 1456 2007 3000 95.1% chr10 + 92729939 92730859 921 browser details YourSeq 88 1898 2079 3000 83.1% chr2 - 54999435 54999593 159 browser details YourSeq 88 1748 2028 3000 77.4% chr3 + 91800682 91800807 126 browser details YourSeq 85 1897 2024 3000 84.0% chr5 + 122083711 122083832 122 browser details YourSeq 85 1042 1248 3000 93.0% chr5 + 20869268 20869566 299 browser details YourSeq 85 1882 2008 3000 81.1% chr11 + 87714186 87714306 121 browser details YourSeq 84 1900 2035 3000 80.8% chr14 - 32385038 32385163 126 Note: The 3000 bp section upstream of Exon 2 is BLAT searched against the genome. No significant similarity is found. BLAT Search Results (down) QUERY SCORE START END QSIZE IDENTITY CHROM STRAND START END SPAN ----------------------------------------------------------------------------------------------- browser details YourSeq 3000 1 3000 3000 100.0% chr6 + 128406824 128409823 3000 browser details YourSeq 72 294 415 3000 92.1% chr4 + 45955764 45955896 133 browser details YourSeq 68 298 414 3000 90.5% chr14 - 24062999 24063126 128 browser details YourSeq 67 296 423 3000 87.7% chr1 + 59672892 59673037 146 browser details YourSeq 65 303 431 3000 98.6% chr17 + 33636575 33637010 436 browser details YourSeq 63 299 405 3000 92.0% chr18 + 7500657 7500780 124 browser details YourSeq 57 304 412 3000 96.8% chr2 + 105653840 105653972 133 browser details YourSeq 57 289 416 3000 92.7% chr14 + 31723123 31723252 130 browser details YourSeq 55 294 414 3000 89.9% chr2 - 125850014 125850139 126 browser details YourSeq 54 298 405 3000 78.7% chr5 - 72208539 72208629 91 browser details YourSeq 52 288 404 3000 93.6% chrX + 48667826 48667951 126 browser details YourSeq 49 298 396 3000 92.9% chr1 + 10960746 10961056 311 browser details YourSeq 47 294 402 3000 92.8% chr11 - 87732593 87732713 121 browser details YourSeq 47 289 400 3000 72.6% chr10 - 79942735 79942805 71 browser details YourSeq 47 289 404 3000 96.2% chr1 - 34435998 34436113 116 browser details YourSeq 44 290 333 3000 100.0% chr4 - 44050628 44050671 44 browser details YourSeq 44 289 402 3000 90.2% chr11 - 79794851 79794963 113 browser details YourSeq 44 2791 2972 3000 98.0% chr11 - 9332962 9333144 183 browser details YourSeq 43 2345 2516 3000 89.1% chr10 - 69747344 69747809 466 browser details YourSeq 43 289 332 3000 100.0% chr2 + 165977969 165978013 45 Note: The 3000 bp section downstream of Exon 3 is BLAT searched against the genome. No significant similarity is found. Page 4 of 8 https://www.alphaknockout.com Gene and protein information: Nrip2 nuclear receptor interacting protein 2 [ Mus musculus (house mouse) ] Gene ID: 60345, updated on 12-Aug-2019 Gene summary Official Symbol Nrip2 provided by MGI Official Full Name nuclear receptor interacting protein 2 provided by MGI Primary source MGI:MGI:1891884 See related Ensembl:ENSMUSG00000001520 Gene type protein coding RefSeq status VALIDATED Organism Mus musculus Lineage Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi; Mammalia; Eutheria; Euarchontoglires; Glires; Rodentia; Myomorpha; Muroidea; Muridae; Murinae; Mus; Mus Also known as NIX1; AW491344 Expression Broad expression in cerebellum adult (RPKM 9.0), frontal lobe adult (RPKM 3.6) and 18 other tissues See more Orthologs human all Genomic context Location: 6; 6 F3 See Nrip2 in Genome Data Viewer Exon count: 9 Annotation release Status Assembly Chr Location 108 current GRCm38.p6 (GCF_000001635.26) 6 NC_000072.6 (128397818..128409780) Build 37.2 previous assembly MGSCv37 (GCF_000001635.18) 6 NC_000072.5 (128349784..128358953) Chromosome 6 - NC_000072.6 Page 5 of 8 https://www.alphaknockout.com Transcript information: This gene has 6 transcripts Gene: Nrip2 ENSMUSG00000001520 Description nuclear receptor interacting protein 2 [Source:MGI Symbol;Acc:MGI:1891884] Gene Synonyms NIX1 Location Chromosome 6: 128,399,296-128,416,427 forward strand. GRCm38:CM000999.2 About this gene This gene has 6 transcripts (splice variants), 161 orthologues, 4 paralogues and is a member of 1 Ensembl protein family. Transcripts Name Transcript ID bp Protein Translation ID Biotype CCDS UniProt Flags Nrip2- ENSMUST00000001561.11 1893 229aa ENSMUSP00000001561.5 Protein coding CCDS20571 Q9JHR9 TSL:1 201 GENCODE basic APPRIS P1 Nrip2- ENSMUST00000120405.3 1468 270aa ENSMUSP00000113317.1 Protein coding CCDS51917 Q9JHR9 TSL:2 202 GENCODE basic Nrip2- ENSMUST00000123867.7 668 219aa ENSMUSP00000122558.1 Protein coding - E9QAD1 CDS 3' 203 incomplete TSL:5 Nrip2- ENSMUST00000204836.1 572 190aa ENSMUSP00000144750.1 Non stop decay - A0A0N4SUN5 CDS 5' 206 incomplete TSL:5 Nrip2- ENSMUST00000147155.7 774 113aa ENSMUSP00000122305.1 Nonsense mediated - F7BIJ5 CDS 5' 205 decay incomplete TSL:5 Nrip2- ENSMUST00000136631.1 3261 No - Retained intron - - TSL:2 204 protein Page 6 of 8 https://www.alphaknockout.com 37.13 kb Forward strand 128.39Mb 128.40Mb 128.41Mb 128.42Mb Genes Gm44596-201 >protein coding (Comprehensive set... Nrip2-203 >protein coding Nrip2-205 >nonsense mediated decay Nrip2-201 >protein coding Nrip2-202 >protein coding Nrip2-204 >retained intron Nrip2-206 >non stop decay Contigs AC116573.7 > Genes < Gm15862-201lncRNA < Itfg2-201protein coding (Comprehensive set... < Itfg2-208protein coding < Itfg2-202retained intron < Itfg2-203protein coding < Itfg2-206protein coding < Itfg2-209retained intron < Itfg2-210retained intron < Itfg2-205retained intron < Itfg2-207nonsense mediated decay < Itfg2-204nonsense mediated decay Regulatory Build 128.39Mb 128.40Mb 128.41Mb 128.42Mb Reverse strand 37.13 kb Regulation Legend CTCF Enhancer Open Chromatin Promoter Promoter Flank Transcription Factor Binding Site Gene Legend Protein Coding Ensembl protein coding merged Ensembl/Havana Non-Protein Coding RNA gene processed transcript Page 7 of 8 https://www.alphaknockout.com Transcript: ENSMUST00000001561 9.17 kb Forward strand Nrip2-201 >protein coding ENSMUSP00000001... MobiDB lite Low complexity (Seg) Superfamily Aspartic peptidase domain superfamily Pfam Aspartic peptidase, DDI1-type PANTHER PTHR12917:SF17 PTHR12917 Gene3D Aspartic peptidase domain superfamily All sequence SNPs/i... Sequence variants (dbSNP and all other sources) Variant Legend missense variant synonymous variant Scale bar 0 20 40 60 80 100 120 140 160 180 200 229 We wish to acknowledge the following valuable scientific information resources: Ensembl, MGI, NCBI, UCSC. Page 8 of 8.
Recommended publications
  • 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]
  • Open Data for Differential Network Analysis in Glioma
    International Journal of Molecular Sciences Article Open Data for Differential Network Analysis in Glioma , Claire Jean-Quartier * y , Fleur Jeanquartier y and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. y Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 Abstract: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes.
    [Show full text]
  • Module Analysis Using Single-Patient Differential Expression Signatures
    bioRxiv preprint doi: https://doi.org/10.1101/2020.01.05.894931; this version posted January 6, 2020. 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. Module analysis using single-patient differential expression signatures improve the power of association study for Alzheimer's disease Jialan Huang1, Dong Lu1, and Guofeng Meng1,∗ 1Institute of interdisciplinary integrative Medicine Research, shanghai University of Traditional Chinese Medicine, shanghai, China Abstract The causal mechanism of Alzheimer's disease is extremely complex. It usually requires a huge number of samples to achieve a good statistical power in association studies. In this work, we illustrated a different strategy to identify AD risk genes by clustering AD patients into modules based on their single-patient differential expression signatures. Evaluation suggested that our method could enrich AD patients with common clinical manifestations. Applying it to a cohort of only 310 AD patients, we identified 175 AD risk loci at a strict threshold of empirical p < 0:05 while only two loci were identified using all the AD patients. As an evaluation, we collected 23 AD risk genes reported in a recent large-scale meta-analysis and found that 18 of them were re-discovered by association studies using clustered AD patients, while only three of them were re-discovered using all AD patients. Functional annotation suggested that AD associated genetic variants mainly disturbed neuronal/synaptic function.
    [Show full text]
  • Single-Cell Transcriptome Profiling of the Kidney Glomerulus Identifies Key Cell Types and Reactions to Injury
    BASIC RESEARCH www.jasn.org Single-Cell Transcriptome Profiling of the Kidney Glomerulus Identifies Key Cell Types and Reactions to Injury Jun-Jae Chung ,1 Leonard Goldstein ,2 Ying-Jiun J. Chen,2 Jiyeon Lee ,1 Joshua D. Webster,3 Merone Roose-Girma,2 Sharad C. Paudyal,4 Zora Modrusan,2 Anwesha Dey,5 and Andrey S. Shaw1 Due to the number of contributing authors, the affiliations are listed at the end of this article. ABSTRACT Background The glomerulus is a specialized capillary bed that is involved in urine production and BP control. Glomerular injury is a major cause of CKD, which is epidemic and without therapeutic options. Single-cell transcriptomics has radically improved our ability to characterize complex organs, such as the kidney. Cells of the glomerulus, however, have been largely underrepresented in previous single-cell kidney studies due to their paucity and intractability. Methods Single-cell RNA sequencing comprehensively characterized the types of cells in the glomerulus from healthy mice and from four different disease models (nephrotoxic serum nephritis, diabetes, doxo- rubicin toxicity, and CD2AP deficiency). Results Allcelltypesintheglomeruluswereidentified using unsupervised clustering analysis. Novel marker genes and gene signatures of mesangial cells, vascular smooth muscle cells of the afferent and efferent arteri- oles, parietal epithelial cells, and three types of endothelial cells were identified. Analysis of the disease models revealed cell type–specific and injury type–specific responses in the glomerulus, including acute activation of the Hippo pathway in podocytes after nephrotoxic immune injury. Conditional deletion of YAP or TAZ resulted in more severe and prolonged proteinuria in response to injury, as well as worse glomerulosclerosis.
    [Show full text]
  • Nº Ref Uniprot Proteína Péptidos Identificados Por MS/MS 1 P01024
    Document downloaded from http://www.elsevier.es, day 26/09/2021. This copy is for personal use. Any transmission of this document by any media or format is strictly prohibited. Nº Ref Uniprot Proteína Péptidos identificados 1 P01024 CO3_HUMAN Complement C3 OS=Homo sapiens GN=C3 PE=1 SV=2 por 162MS/MS 2 P02751 FINC_HUMAN Fibronectin OS=Homo sapiens GN=FN1 PE=1 SV=4 131 3 P01023 A2MG_HUMAN Alpha-2-macroglobulin OS=Homo sapiens GN=A2M PE=1 SV=3 128 4 P0C0L4 CO4A_HUMAN Complement C4-A OS=Homo sapiens GN=C4A PE=1 SV=1 95 5 P04275 VWF_HUMAN von Willebrand factor OS=Homo sapiens GN=VWF PE=1 SV=4 81 6 P02675 FIBB_HUMAN Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 78 7 P01031 CO5_HUMAN Complement C5 OS=Homo sapiens GN=C5 PE=1 SV=4 66 8 P02768 ALBU_HUMAN Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 66 9 P00450 CERU_HUMAN Ceruloplasmin OS=Homo sapiens GN=CP PE=1 SV=1 64 10 P02671 FIBA_HUMAN Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 58 11 P08603 CFAH_HUMAN Complement factor H OS=Homo sapiens GN=CFH PE=1 SV=4 56 12 P02787 TRFE_HUMAN Serotransferrin OS=Homo sapiens GN=TF PE=1 SV=3 54 13 P00747 PLMN_HUMAN Plasminogen OS=Homo sapiens GN=PLG PE=1 SV=2 48 14 P02679 FIBG_HUMAN Fibrinogen gamma chain OS=Homo sapiens GN=FGG PE=1 SV=3 47 15 P01871 IGHM_HUMAN Ig mu chain C region OS=Homo sapiens GN=IGHM PE=1 SV=3 41 16 P04003 C4BPA_HUMAN C4b-binding protein alpha chain OS=Homo sapiens GN=C4BPA PE=1 SV=2 37 17 Q9Y6R7 FCGBP_HUMAN IgGFc-binding protein OS=Homo sapiens GN=FCGBP PE=1 SV=3 30 18 O43866 CD5L_HUMAN CD5 antigen-like OS=Homo
    [Show full text]
  • Lessons from the Gtex Dataset Tim O. Nieuwenhuis1,2
    bioRxiv preprint doi: https://doi.org/10.1101/602367; this version posted January 2, 2020. 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. 1 Basal Contamination of Sequencing: Lessons from the GTEx dataset 2 3 Tim O. Nieuwenhuis1,2, Stephanie Yang2, Rohan X. Verma1, Vamsee Pillalamarri2, Dan 4 E. Arking2, Avi Z. Rosenberg1, Matthew N. McCall3, Marc K. Halushka1* 5 6 7 1 Department of Pathology, Johns Hopkins University SOM, Baltimore, MD, USA 8 2McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins 9 University SOM, Baltimore, MD, USA 10 3Department of Biostatistics and Computational Biology, University of Rochester 11 Medical Center, Rochester, NY, USA 12 13 14 15 16 Email Addresses: 17 [email protected] 18 [email protected] 19 [email protected] 20 [email protected] 21 [email protected] 22 [email protected] 23 [email protected] 24 25 * Correspondence and address for reprints to: 26 Marc K. Halushka, M.D., Ph.D. 27 Johns Hopkins University School of Medicine 28 Ross Bldg. Rm 632B 29 720 Rutland Avenue 30 Baltimore, MD 21205 31 410-614-8138 (ph) 32 410-502-5862 (fax) 33 [email protected] 34 1 bioRxiv preprint doi: https://doi.org/10.1101/602367; this version posted January 2, 2020. 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.
    [Show full text]
  • Aïda Homs Raubert
    Epigenetic alterations in autism spectrum disorders (ASD) Aïda Homs Raubert DOCTORAL THESIS UPF 2015 THESIS SUPERVISORS Prof. Luis A. Pérez Jurado Dra. Ivon Cuscó Martí DEPARTAMENT DE CIÈNCIES EXPERIMENTALS I DE LA SALUT Als meus pares, a l’Alexandra a l’Agustí i als bessons que vindran iii ACKNOWLEDGEMENTS Aquesta no és nomes la meva tesi, en ella han contribuït moltes persones, tant de l’entorn del parc de recerca, de terres lleidatanes, Berguedanes i fins i tot de l’altra banda de l’Atlàntic. Primer, volia agrair als directors de tesi, al Prof. Luis Pérez Jurado i a la Dra. Ivon Cuscó, tot el temps dedicat a revisar i corregir els raonaments i les paraules en aquesta tesi, ja que sempre han tingut la porta oberta per atendre qualsevol dubte. També per haver-me ensenyat una metodologia, un rigor i un llenguatge científic, on l’entrenament és necessari per assolir els conceptes per la recerca en concret, i pel món de la ciència i la genètica. Gràcies per la dedicació, la paciencia, la feina i energia dipositada. No hagués arribat al mateix port si al laboratori no m’hagués trobat amb persones que m’inspiren. Primer de tot, a les nenes: a la Gabi, la companya de vaixell fins i tot el dia de dipositar la tesi, perquè sobretot ens hem sabut acompanyar i entendre malgrat tenir altres maneres de funcionar, gràcies. També a la Marta i la Cristina, que amb la seva honestedat i bona fe, omplen el laboratori de bones energies, gràcies per ser-hi en tot moment.
    [Show full text]
  • RHNO1 Bidirectional Genes in Cancer
    RESEARCH ARTICLE Co-regulation and function of FOXM1/ RHNO1 bidirectional genes in cancer Carter J Barger1, Linda Chee1, Mustafa Albahrani1, Catalina Munoz-Trujillo1, Lidia Boghean1, Connor Branick1, Kunle Odunsi2, Ronny Drapkin3, Lee Zou4, Adam R Karpf1* 1Eppley Institute for Cancer Research and Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, United States; 2Departments of Gynecologic Oncology, Immunology, and Center for Immunotherapy, Roswell Park Comprehensive Cancer Center, Buffalo, United States; 3Penn Ovarian Cancer Research Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, United States; 4Massachusetts General Hospital Cancer Center, Harvard Medical School, Charlestown, United States Abstract The FOXM1 transcription factor is an oncoprotein and a top biomarker of poor prognosis in human cancer. Overexpression and activation of FOXM1 is frequent in high-grade serous carcinoma (HGSC), the most common and lethal form of human ovarian cancer, and is linked to copy number gains at chromosome 12p13.33. We show that FOXM1 is co-amplified and co- expressed with RHNO1, a gene involved in the ATR-Chk1 signaling pathway that functions in the DNA replication stress response. We demonstrate that FOXM1 and RHNO1 are head-to-head (i.e., bidirectional) genes (BDG) regulated by a bidirectional promoter (BDP) (named F/R-BDP). FOXM1 and RHNO1 each promote oncogenic phenotypes in HGSC cells, including clonogenic growth, DNA homologous recombination repair, and poly-ADP ribosylase inhibitor resistance. FOXM1 and RHNO1 are one of the first examples of oncogenic BDG, and therapeutic targeting of FOXM1/ RHNO1 BDG is a potential therapeutic approach for ovarian and other cancers. *For correspondence: [email protected] Introduction Competing interests: The The forkhead/winged helix domain transcription factor FOXM1 promotes cancer by transactivating authors declare that no genes with oncogenic potential (Halasi and Gartel, 2013a; Kalathil et al., 2020).
    [Show full text]
  • Genome-Wide Association Meta-Analysis Identifies GP2 Gene
    ARTICLE https://doi.org/10.1038/s41467-020-16711-w OPEN Genome-wide association meta-analysis identifies GP2 gene risk variants for pancreatic cancer Yingsong Lin et al.# Pancreatic cancer is the fourth leading cause of cancer-related deaths in Japan. To identify risk loci, we perform a meta-analysis of three genome-wide association studies comprising 2,039 pancreatic cancer patients and 32,592 controls in the Japanese population. Here, we identify 3 fi −8 1234567890():,; (13q12.2, 13q22.1, and 16p12.3) genome-wide signi cant loci (P <5.0×10 ), of which 16p12.3 has not been reported in the Western population. The lead single nucleotide polymorphism (SNP) at 16p12.3 is rs78193826 (odds ratio = 1.46, 95% confidence interval = 1.29-1.66, P = 4.28 × 10−9), an Asian-specific, nonsynonymous glycoprotein 2 (GP2) gene variant. Associa- tions between selected GP2 gene variants and pancreatic cancer are replicated in 10,822 additional cases and controls of East Asian origin. Functional analyses using cell lines provide supporting evidence of the effect of rs78193826 on KRAS activity. These findings suggest that GP2 gene variants are probably associated with pancreatic cancer susceptibility in populations of East Asian ancestry. #A list of authors and their affiliations appears at the end of the paper. NATURE COMMUNICATIONS | (2020) 11:3175 | https://doi.org/10.1038/s41467-020-16711-w | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-16711-w ith 35,390 related deaths in 2018, pancreatic cancer is populations12. Therefore, the role of common susceptibility loci the fourth leading cause of cancer deaths in Japan, in East Asian populations remains uncertain and needs further W 1 after lung, colorectal, and stomach cancers .
    [Show full text]
  • Genomic Approach in Idiopathic Intellectual Disability Maria De Fátima E Costa Torres
    ESTUDOS DE 8 01 PDPGM 2 CICLO Genomic approach in idiopathic intellectual disability Maria de Fátima e Costa Torres D Autor. Maria de Fátima e Costa Torres D.ICBAS 2018 Genomic approach in idiopathic intellectual disability Genomic approach in idiopathic intellectual disability Maria de Fátima e Costa Torres SEDE ADMINISTRATIVA INSTITUTO DE CIÊNCIAS BIOMÉDICAS ABEL SALAZAR FACULDADE DE MEDICINA MARIA DE FÁTIMA E COSTA TORRES GENOMIC APPROACH IN IDIOPATHIC INTELLECTUAL DISABILITY Tese de Candidatura ao grau de Doutor em Patologia e Genética Molecular, submetida ao Instituto de Ciências Biomédicas Abel Salazar da Universidade do Porto Orientadora – Doutora Patrícia Espinheira de Sá Maciel Categoria – Professora Associada Afiliação – Escola de Medicina e Ciências da Saúde da Universidade do Minho Coorientadora – Doutora Maria da Purificação Valenzuela Sampaio Tavares Categoria – Professora Catedrática Afiliação – Faculdade de Medicina Dentária da Universidade do Porto Coorientadora – Doutora Filipa Abreu Gomes de Carvalho Categoria – Professora Auxiliar com Agregação Afiliação – Faculdade de Medicina da Universidade do Porto DECLARAÇÃO Dissertação/Tese Identificação do autor Nome completo _Maria de Fátima e Costa Torres_ N.º de identificação civil _07718822 N.º de estudante __ 198600524___ Email institucional [email protected] OU: [email protected] _ Email alternativo [email protected] _ Tlf/Tlm _918197020_ Ciclo de estudos (Mestrado/Doutoramento) _Patologia e Genética Molecular__ Faculdade/Instituto _Instituto de Ciências
    [Show full text]
  • Table S1. 103 Ferroptosis-Related Genes Retrieved from the Genecards
    Table S1. 103 ferroptosis-related genes retrieved from the GeneCards. Gene Symbol Description Category GPX4 Glutathione Peroxidase 4 Protein Coding AIFM2 Apoptosis Inducing Factor Mitochondria Associated 2 Protein Coding TP53 Tumor Protein P53 Protein Coding ACSL4 Acyl-CoA Synthetase Long Chain Family Member 4 Protein Coding SLC7A11 Solute Carrier Family 7 Member 11 Protein Coding VDAC2 Voltage Dependent Anion Channel 2 Protein Coding VDAC3 Voltage Dependent Anion Channel 3 Protein Coding ATG5 Autophagy Related 5 Protein Coding ATG7 Autophagy Related 7 Protein Coding NCOA4 Nuclear Receptor Coactivator 4 Protein Coding HMOX1 Heme Oxygenase 1 Protein Coding SLC3A2 Solute Carrier Family 3 Member 2 Protein Coding ALOX15 Arachidonate 15-Lipoxygenase Protein Coding BECN1 Beclin 1 Protein Coding PRKAA1 Protein Kinase AMP-Activated Catalytic Subunit Alpha 1 Protein Coding SAT1 Spermidine/Spermine N1-Acetyltransferase 1 Protein Coding NF2 Neurofibromin 2 Protein Coding YAP1 Yes1 Associated Transcriptional Regulator Protein Coding FTH1 Ferritin Heavy Chain 1 Protein Coding TF Transferrin Protein Coding TFRC Transferrin Receptor Protein Coding FTL Ferritin Light Chain Protein Coding CYBB Cytochrome B-245 Beta Chain Protein Coding GSS Glutathione Synthetase Protein Coding CP Ceruloplasmin Protein Coding PRNP Prion Protein Protein Coding SLC11A2 Solute Carrier Family 11 Member 2 Protein Coding SLC40A1 Solute Carrier Family 40 Member 1 Protein Coding STEAP3 STEAP3 Metalloreductase Protein Coding ACSL1 Acyl-CoA Synthetase Long Chain Family Member 1 Protein
    [Show full text]
  • A CRISPR Screen to Identify Combination Therapies of Cytotoxic
    CRISPRi Screens to Identify Combination Therapies for the Improved Treatment of Ovarian Cancer By Erika Daphne Handly B.S. Chemical Engineering Brigham Young University, 2014 Submitted to the Department of Biological Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biological Engineering at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY February 2021 © 2020 Massachusetts Institute of Technology. All rights reserved. Signature of author………………………………………………………………………………… Erika Handly Department of Biological Engineering February 2021 Certified by………………………………………………………………………………………… Michael Yaffe Director MIT Center for Precision Cancer Medicine Department of Biological Engineering and Biology Thesis Supervisor Accepted by………………………………………………………………………………………... Katharina Ribbeck Professor of Biological Engineering Chair of Graduate Program, Department of Biological Engineering Thesis Committee members Michael T. Hemann, Ph.D. Associate Professor of Biology Massachusetts Institute of Technology Douglas A. Lauffenburger, Ph.D. (Chair) Ford Professor of Biological Engineering, Chemical Engineering, and Biology Massachusetts Institute of Technology Michael B. Yaffe, M.D., Ph.D. (Thesis Supervisor) David H. Koch Professor of Science Prof. of Biology and Biological Engineering Massachusetts Institute of Technology 2 CRISPRi Screens to Identify Combination Therapies for the Improved Treatment of Ovarian Cancer By Erika Daphne Handly B.S. Chemical Engineering Brigham Young University, 2014 Submitted to the Department of Biological Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biological Engineering ABSTRACT Ovarian cancer is the fifth leading cause of cancer death for women in the United States, with only modest improvements in patient survival in the past few decades. Standard-of-care consists of surgical debulking followed by a combination of platinum and taxane agents, but relapse and resistance frequently occur.
    [Show full text]