DOCSLIB.ORG

Mouse Pgm1 Conditional Knockout Project (CRISPR/Cas9)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

https://www.alphaknockout.com Mouse Pgm1 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Pgm1 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Pgm1 gene (NCBI Reference Sequence: None ; Ensembl: ENSMUSG00000025791 ) is located on Mouse chromosome 5. 11 exons are identified, with the ATG start codon in exon 1 and the TAG stop codon in exon 11 (Transcript: ENSMUST00000102783). Exon 4~5 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Pgm1 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-283K16 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 4 starts from about 35.11% of the coding region. The knockout of Exon 4~5 will result in frameshift of the gene. The size of intron 3 for 5'-loxP site insertion: 1327 bp, and the size of intron 5 for 3'-loxP site insertion: 2193 bp. The size of effective cKO region: ~1848 bp. The cKO region does not have any other known gene. Page 1 of 7 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 5 1 9 2 3 8 4 7 6 5 4 6 11 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Pgm1 Exon of mouse Hnrnpd Homology arm cKO region loxP site Page 2 of 7 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(8348bp) | A(34.42% 2873) | C(20.81% 1737) | T(29.24% 2441) | G(15.54% 1297) 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 7 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% chr5 + 99960269 99963268 3000 browser details YourSeq 134 1575 2038 3000 89.2% chr5 + 100035390 100035832 443 browser details YourSeq 24 1288 1316 3000 81.5% chr19 + 7440405 7440431 27 Note: The 3000 bp section upstream of Exon 4 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% chr5 + 99965117 99968116 3000 browser details YourSeq 175 1029 2272 3000 83.5% chrX + 162845828 162846036 209 browser details YourSeq 153 1230 1419 3000 88.6% chr4 - 150183022 150183199 178 browser details YourSeq 153 1242 1424 3000 92.8% chr3 + 71657980 71658164 185 browser details YourSeq 149 1246 1430 3000 90.9% chr8 + 126914116 126914299 184 browser details YourSeq 148 1237 1427 3000 86.2% chr13 - 14041591 14041772 182 browser details YourSeq 139 1255 1419 3000 89.2% chrX - 52255919 52256075 157 browser details YourSeq 138 1234 1424 3000 86.9% chr13 - 55173541 55173722 182 browser details YourSeq 138 1246 1410 3000 89.9% chr12 + 83608802 83608960 159 browser details YourSeq 137 1246 1414 3000 88.7% chr7 + 127265205 127265366 162 browser details YourSeq 137 1249 1411 3000 89.9% chr1 + 164414245 164414403 159 browser details YourSeq 136 1232 1411 3000 87.6% chr3 - 150861202 150861374 173 browser details YourSeq 136 1244 1412 3000 89.3% chr2 + 155530054 155530218 165 browser details YourSeq 135 1246 1414 3000 90.8% chr7 - 22059364 22059533 170 browser details YourSeq 135 1246 1414 3000 87.6% chr15 + 52211045 52211206 162 browser details YourSeq 132 1246 1423 3000 84.8% chr11 - 26421138 26421304 167 browser details YourSeq 132 1246 1424 3000 93.0% chr1 - 142534884 142535064 181 browser details YourSeq 132 1255 1424 3000 87.3% chr8 + 123051152 123051318 167 browser details YourSeq 132 1249 1414 3000 87.5% chr1 + 81036634 81036795 162 browser details YourSeq 131 1245 1407 3000 90.4% chr5 + 122116550 122116711 162 Note: The 3000 bp section downstream of Exon 5 is BLAT searched against the genome. No significant similarity is found. Page 4 of 7 https://www.alphaknockout.com Gene and protein information: Pgm1 phosphoglucomutase 1 [ Mus musculus (house mouse) ] Gene ID: 72157, updated on 10-Oct-2019 Gene summary Official Symbol Pgm1 provided by MGI Official Full Name phosphoglucomutase 1 provided by MGI Primary source MGI:MGI:97565 See related Ensembl:ENSMUSG00000025791 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 Pgm2; Pgm-2; Pgm1a; AA407108; AI098105; 2610020G18Rik Annotation Note: The name of this locus, Pgm1, was recently updated by Mouse Genomic Informatics to be consistent with information mammalian orthologs identified as Pgm1. Note that this gene is frequently referred to as Pgm2 in the literature. [02 Oct Expression 2019] Orthologs Ubiquitous expression in liver E18 (RPKM 86.8), mammary gland adult (RPKM 70.6) and 27 other tissues See more human all Genomic context Location: 4 C6; 4 45.71 cM See Pgm1 in Genome Data Viewer Exon count: 11 Annotation release Status Assembly Chr Location 108 current GRCm38.p6 (GCF_000001635.26) 4 NC_000070.6 (99929451..99987294) Build 37.2 previous assembly MGSCv37 (GCF_000001635.18) 4 NC_000070.5 (99602056..99659899) Chromosome 4 - NC_000070.6 Page 5 of 7 https://www.alphaknockout.com Transcript information: This gene has 2 transcripts Gene: Pgm1 ENSMUSG00000025791 Description phosphoglucomutase 1 [Source:MGI Symbol;Acc:MGI:97565] Gene Synonyms 2610020G18Rik, Pgm-2, Pgm1a, Pgm2 Location Chromosome 4: 99,929,414-99,987,294 forward strand. GRCm38:CM000997.2 View alleles of this gene on alternative sequences About this gene This gene has 2 transcripts (splice variants), 1 gene allele, 198 orthologues, 5 paralogues and is a member of 1 Ensembl protein family. Transcripts Name Transcript ID bp Protein Translation ID Biotype CCDS UniProt Flags Pgm1-201 ENSMUST00000058351.15 2371 562aa ENSMUSP00000061227.9 Protein coding CCDS18388 Q9D0F9 TSL:1 GENCODE basic APPRIS P2 Pgm1-202 ENSMUST00000102783.4 2523 580aa ENSMUSP00000099844.4 Protein coding - A2CEK3 TSL:1 GENCODE basic APPRIS ALT1 77.88 kb Forward strand Genes (Comprehensive set... Pgm1-201 >protein coding Pgm1-202 >protein coding Contigs CR536609.3 > Genes < Itgb3bp-203lncRNA (Comprehensive set... Regulatory Build Reverse strand 77.88 kb Gene Legend Protein Coding Ensembl protein coding merged Ensembl/Havana Non-Protein Coding RNA gene Regulation Legend CTCF Enhancer Open Chromatin Promoter Promoter Flank Page 6 of 7 https://www.alphaknockout.com Transcript: ENSMUST00000102783 31.55 kb Forward strand Pgm1-202 >protein coding ENSMUSP00000099... Low complexity (Seg) Superfamily Alpha-D-phosphohexomutase, C-terminal domain superfamily Alpha-D-phosphohexomutase, alpha/beta/alpha I/II/III Prints Alpha-D-phosphohexomutase superfamily Pfam Alpha-D-phosphohexomutase, alpha/beta/alpha domain I Alpha-D-phosphohexomutase, alpha/beta/alpha domain III Alpha-D-phosphohexomutase, alpha/beta/alpha domain II Alpha-D-phosphohexomutase, C-terminal PROSITE patterns Alpha-D-phosphohexomutase, conserved site PANTHER PTHR22573 PTHR22573:SF55 Gene3D 3.40.120.10 3.30.310.50 CDD cd03085 All sequence SNPs/i... Sequence variants (dbSNP and all other sources) Variant Legend missense variant splice region variant synonymous variant Scale bar 0 60 120 180 240 300 360 420 480 580 We wish to acknowledge the following valuable scientific information resources: Ensembl, MGI, NCBI, UCSC. Page 7 of 7.
Recommended publications
  • ARTICLE Doi:10.1038/Nature10523

    ARTICLE Doi:10.1038/Nature10523

    ARTICLE doi:10.1038/nature10523 Spatio-temporal transcriptome of the human brain Hyo Jung Kang1*, Yuka Imamura Kawasawa1*, Feng Cheng1*, Ying Zhu1*, Xuming Xu1*, Mingfeng Li1*, Andre´ M. M. Sousa1,2, Mihovil Pletikos1,3, Kyle A. Meyer1, Goran Sedmak1,3, Tobias Guennel4, Yurae Shin1, Matthew B. Johnson1,Zˇeljka Krsnik1, Simone Mayer1,5, Sofia Fertuzinhos1, Sheila Umlauf6, Steven N. Lisgo7, Alexander Vortmeyer8, Daniel R. Weinberger9, Shrikant Mane6, Thomas M. Hyde9,10, Anita Huttner8, Mark Reimers4, Joel E. Kleinman9 & Nenad Sˇestan1 Brain development and function depend on the precise regulation of gene expression. However, our understanding of the complexity and dynamics of the transcriptome of the human brain is incomplete. Here we report the generation and analysis of exon-level transcriptome and associated genotyping data, representing males and females of different ethnicities, from multiple brain regions and neocortical areas of developing and adult post-mortem human brains. We found that 86 per cent of the genes analysed were expressed, and that 90 per cent of these were differentially regulated at the whole-transcript or exon level across brain regions and/or time. The majority of these spatio-temporal differences were detected before birth, with subsequent increases in the similarity among regional transcriptomes. The transcriptome is organized into distinct co-expression networks, and shows sex-biased gene expression and exon usage. We also profiled trajectories of genes associated with neurobiological categories and diseases, and identified associations between single nucleotide polymorphisms and gene expression. This study provides a comprehensive data set on the human brain transcriptome and insights into the transcriptional foundations of human neurodevelopment.
  • Growth and Molecular Profile of Lung Cancer Cells Expressing Ectopic LKB1: Down-Regulation of the Phosphatidylinositol 3؅-Phosphate Kinase/PTEN Pathway1

    Growth and Molecular Profile of Lung Cancer Cells Expressing Ectopic LKB1: Down-Regulation of the Phosphatidylinositol 3؅-Phosphate Kinase/PTEN Pathway1

    [CANCER RESEARCH 63, 1382–1388, March 15, 2003] Growth and Molecular Profile of Lung Cancer Cells Expressing Ectopic LKB1: Down-Regulation of the Phosphatidylinositol 3؅-Phosphate Kinase/PTEN Pathway1 Ana I. Jimenez, Paloma Fernandez, Orlando Dominguez, Ana Dopazo, and Montserrat Sanchez-Cespedes2 Molecular Pathology Program [A. I. J., P. F., M. S-C.], Genomics Unit [O. D.], and Microarray Analysis Unit [A. D.], Spanish National Cancer Center, 28029 Madrid, Spain ABSTRACT the cell cycle in G1 (8, 9). However, the intrinsic mechanism by which LKB1 activity is regulated in cells and how it leads to the suppression Germ-line mutations in LKB1 gene cause the Peutz-Jeghers syndrome of cell growth is still unknown. It has been proposed that growth (PJS), a genetic disease with increased risk of malignancies. Recently, suppression by LKB1 is mediated through p21 in a p53-dependent LKB1-inactivating mutations have been identified in one-third of sporadic lung adenocarcinomas, indicating that LKB1 gene inactivation is critical in mechanism (7). In addition, it has been observed that LKB1 binds to tumors other than those of the PJS syndrome. However, the in vivo brahma-related gene 1 protein (BRG1) and this interaction is required substrates of LKB1 and its role in cancer development have not been for BRG1-induced growth arrest (10). Similar to what happens in the completely elucidated. Here we show that overexpression of wild-type PJS, Lkb1 heterozygous knockout mice show gastrointestinal hamar- LKB1 protein in A549 lung adenocarcinomas cells leads to cell-growth tomatous polyposis and frequent hepatocellular carcinomas (11, 12). suppression. To examine changes in gene expression profiles subsequent to Interestingly, the hamartomas, but not the malignant tumors, arising in exogenous wild-type LKB1 in A549 cells, we used cDNA microarrays.
  • Supplementary Materials

    Supplementary Materials

    Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine
  • Literature Mining Sustains and Enhances Knowledge Discovery from Omic Studies

    Literature Mining Sustains and Enhances Knowledge Discovery from Omic Studies

    LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES by Rick Matthew Jordan B.S. Biology, University of Pittsburgh, 1996 M.S. Molecular Biology/Biotechnology, East Carolina University, 2001 M.S. Biomedical Informatics, University of Pittsburgh, 2005 Submitted to the Graduate Faculty of School of Medicine in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2016 UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE This dissertation was presented by Rick Matthew Jordan It was defended on December 2, 2015 and approved by Shyam Visweswaran, M.D., Ph.D., Associate Professor Rebecca Jacobson, M.D., M.S., Professor Songjian Lu, Ph.D., Assistant Professor Dissertation Advisor: Vanathi Gopalakrishnan, Ph.D., Associate Professor ii Copyright © by Rick Matthew Jordan 2016 iii LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES Rick Matthew Jordan, M.S. University of Pittsburgh, 2016 Genomic, proteomic and other experimentally generated data from studies of biological systems aiming to discover disease biomarkers are currently analyzed without sufficient supporting evidence from the literature due to complexities associated with automated processing. Extracting prior knowledge about markers associated with biological sample types and disease states from the literature is tedious, and little research has been performed to understand how to use this knowledge to inform the generation of classification models from ‘omic’ data. Using pathway analysis methods to better understand the underlying biology of complex diseases such as breast and lung cancers is state-of-the-art. However, the problem of how to combine literature- mining evidence with pathway analysis evidence is an open problem in biomedical informatics research.
  • ITGB3BP (NM 014288) Human Recombinant Protein – TP300064

    ITGB3BP (NM 014288) Human Recombinant Protein – TP300064

    OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for TP300064 ITGB3BP (NM_014288) Human Recombinant Protein Product data: Product Type: Recombinant Proteins Description: Recombinant protein of human integrin beta 3 binding protein (beta3-endonexin) (ITGB3BP) Species: Human Expression Host: HEK293T Tag: C-Myc/DDK Predicted MW: 20 kDa Concentration: >50 ug/mL as determined by microplate BCA method Purity: > 80% as determined by SDS-PAGE and Coomassie blue staining Buffer: 25 mM Tris.HCl, pH 7.3, 100 mM glycine, 10% glycerol Preparation: Recombinant protein was captured through anti-DDK affinity column followed by conventional chromatography steps. Storage: Store at -80°C. Stability: Stable for 12 months from the date of receipt of the product under proper storage and handling conditions. Avoid repeated freeze-thaw cycles. RefSeq: NP_055103 Locus ID: 23421 UniProt ID: Q13352 RefSeq Size: 1019 Cytogenetics: 1p31.3 RefSeq ORF: 531 Synonyms: CENP-R; CENPR; HSU37139; NRIF3; TAP20 This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 2 ITGB3BP (NM_014288) Human Recombinant Protein – TP300064 Summary: This gene encodes a transcriptional coregulator that binds to and enhances the activity of members of the nuclear receptor families, thyroid hormone receptors and retinoid X receptors. This protein also acts as a corepressor of NF-kappaB-dependent signaling. This protein induces apoptosis in breast cancer cells through a caspase 2-mediated signaling pathway.
  • A High-Throughput Approach to Uncover Novel Roles of APOBEC2, a Functional Orphan of the AID/APOBEC Family

    A High-Throughput Approach to Uncover Novel Roles of APOBEC2, a Functional Orphan of the AID/APOBEC Family

    Rockefeller University Digital Commons @ RU Student Theses and Dissertations 2018 A High-Throughput Approach to Uncover Novel Roles of APOBEC2, a Functional Orphan of the AID/APOBEC Family Linda Molla Follow this and additional works at: https://digitalcommons.rockefeller.edu/ student_theses_and_dissertations Part of the Life Sciences Commons A HIGH-THROUGHPUT APPROACH TO UNCOVER NOVEL ROLES OF APOBEC2, A FUNCTIONAL ORPHAN OF THE AID/APOBEC FAMILY A Thesis Presented to the Faculty of The Rockefeller University in Partial Fulfillment of the Requirements for the degree of Doctor of Philosophy by Linda Molla June 2018 © Copyright by Linda Molla 2018 A HIGH-THROUGHPUT APPROACH TO UNCOVER NOVEL ROLES OF APOBEC2, A FUNCTIONAL ORPHAN OF THE AID/APOBEC FAMILY Linda Molla, Ph.D. The Rockefeller University 2018 APOBEC2 is a member of the AID/APOBEC cytidine deaminase family of proteins. Unlike most of AID/APOBEC, however, APOBEC2’s function remains elusive. Previous research has implicated APOBEC2 in diverse organisms and cellular processes such as muscle biology (in Mus musculus), regeneration (in Danio rerio), and development (in Xenopus laevis). APOBEC2 has also been implicated in cancer. However the enzymatic activity, substrate or physiological target(s) of APOBEC2 are unknown. For this thesis, I have combined Next Generation Sequencing (NGS) techniques with state-of-the-art molecular biology to determine the physiological targets of APOBEC2. Using a cell culture muscle differentiation system, and RNA sequencing (RNA-Seq) by polyA capture, I demonstrated that unlike the AID/APOBEC family member APOBEC1, APOBEC2 is not an RNA editor. Using the same system combined with enhanced Reduced Representation Bisulfite Sequencing (eRRBS) analyses I showed that, unlike the AID/APOBEC family member AID, APOBEC2 does not act as a 5-methyl-C deaminase.
  • The Genetic Program of Pancreatic Beta-Cell Replication in Vivo

    The Genetic Program of Pancreatic Beta-Cell Replication in Vivo

    Page 1 of 65 Diabetes The genetic program of pancreatic beta-cell replication in vivo Agnes Klochendler1, Inbal Caspi2, Noa Corem1, Maya Moran3, Oriel Friedlich1, Sharona Elgavish4, Yuval Nevo4, Aharon Helman1, Benjamin Glaser5, Amir Eden3, Shalev Itzkovitz2, Yuval Dor1,* 1Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem 91120, Israel 2Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel. 3Department of Cell and Developmental Biology, The Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel 4Info-CORE, Bioinformatics Unit of the I-CORE Computation Center, The Hebrew University and Hadassah, The Institute for Medical Research Israel- Canada, The Hebrew University-Hadassah Medical School, Jerusalem 91120, Israel 5Endocrinology and Metabolism Service, Department of Internal Medicine, Hadassah-Hebrew University Medical Center, Jerusalem 91120, Israel *Correspondence: [email protected] Running title: The genetic program of pancreatic β-cell replication 1 Diabetes Publish Ahead of Print, published online March 18, 2016 Diabetes Page 2 of 65 Abstract The molecular program underlying infrequent replication of pancreatic beta- cells remains largely inaccessible. Using transgenic mice expressing GFP in cycling cells we sorted live, replicating beta-cells and determined their transcriptome. Replicating beta-cells upregulate hundreds of proliferation- related genes, along with many novel putative cell cycle components. Strikingly, genes involved in beta-cell functions, namely glucose sensing and insulin secretion were repressed. Further studies using single molecule RNA in situ hybridization revealed that in fact, replicating beta-cells double the amount of RNA for most genes, but this upregulation excludes genes involved in beta-cell function.
  • Computational Simulations to Predict Creatine Kinase-Associated Factors: Protein-Protein Interaction Studies of Brain and Muscle Types of Creatine Kinases

    Computational Simulations to Predict Creatine Kinase-Associated Factors: Protein-Protein Interaction Studies of Brain and Muscle Types of Creatine Kinases

    SAGE-Hindawi Access to Research Enzyme Research Volume 2011, Article ID 328249, 12 pages doi:10.4061/2011/328249 Research Article Computational Simulations to Predict Creatine Kinase-Associated Factors: Protein-Protein Interaction Studies of Brain and Muscle Types of Creatine Kinases Wei-Jiang Hu,1 Sheng-Mei Zhou,2 Joshua SungWoo Yang,3, 4 and Fan-Guo Meng1 1 Zhejiang Provincial Key Laboratory of Applied Enzymology, Yangtze Delta Region Institute of Tsinghua University, Jiaxing 314006, China 2 College of Biology and Chemical Engineering, Jiaxing University, Jiaxing 314001, China 3 Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience & Biotechnology (KRIBB), Daejeon 305-806, Republic of Korea 4 Department of Bioinformatics, University of Sciences & Technology, Daejeon 205-305, Republic of Korea Correspondence should be addressed to Fan-Guo Meng, [email protected] Received 17 May 2011; Accepted 26 May 2011 Academic Editor: Jun-Mo Yang Copyright © 2011 Wei-Jiang Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Creatine kinase (CK; EC 2.7.3.2) is related to several skin diseases such as psoriasis and dermatomyositis. CK is important in skin energy homeostasis because it catalyzes the reversible transfer of a phosphoryl group from MgATP to creatine. In this study, we predicted CK binding proteins via the use of bioinformatic tools such as protein-protein interaction (PPI) mappings and suggest the putative hub proteins for CK interactions. We obtained 123 proteins for brain type CK and 85 proteins for muscle type CK in the interaction networks.
  • System Approaches Reveal the Molecular Networks Involved in Neural Stem Cell Differentiation

    System Approaches Reveal the Molecular Networks Involved in Neural Stem Cell Differentiation

    Protein Cell 2012, 3(3): 213–224 DOI 10.1007/s13238-012-0014-4 Protein & Cell RESEARCH ARTICLE System approaches reveal the molecular networks involved in neural stem cell differentiation Kai Wang1,5*, Haifeng Wang2,5*, Jiao Wang2,5*, Yuqiong Xie1,5, Jun Chen3,4, Huang Yan1,5, Zengrong Liu5, Tieqiao Wen1,5 1 Laboratory of Molecular Neurobiology, School of Life Sciences, Shanghai University, Shanghai 200444, China 2 School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China 3 College of Life Science, Wuhan University, Wuhan 430072, China 4 College of Chemical Engineering and Technology, Wuhan University of Science and Technology, Wuhan 430081, China 5 Institute of Systems Biology, Shanghai University, Shanghai 200444, China Correspondence: [email protected] Received December 20, 2011 Accepted January 12, 2012 ABSTRACT INTRODUCTION The self-renewal and multipotent potentials in neural Neural stem cells (NSCs) are central nervous system (CNS) stem cells (NSCs) maintain the normal physiological originated multipotent stem cells, which possess self-renewal functions of central nervous system (CNS). The ab- potential and have the ability to differentiate into astrocytes, normal differentiation of NSCs would lead to CNS dis- oligodendrocytes (OLs) and neurons along with the devel- orders. However, the mechanisms of how NSCs differ- opment time course (Temple, 2001). They play essential entiate into astrocytes, oligodendrocytes (OLs) and roles in repairing and maintaining normal CNS cells. Besides, neurons are still unclear, which is mainly due to the tissue engineering by normal NSCs transplantation also complexity of differentiation processes and the limita- enables the clinical therapeutic applications for CNS regres- tion of the cell separation method.
  • Figure S1. Endogenous MIR45

    Figure S1. Endogenous MIR45

    Figure S1. Endogenous MIR452 and VEGFA expression in CRC tissues and cell lines. (A) The expression of MIR452 was validated using 10 CRC tissue samples and matched normal colon tissue samples. miRNA levels were normalized to colon-specific RNU48. Values are presented as the fold- change in tumor tissue relative levels (ΔΔCT) to normal tissue. (B) The relative endogenous MIR452 expression levels in six CRC cell lines. The data are presented as a fold change in HT29, Caco2, HCT116, LoVo, and SW48 cells relative to SW480 cells. This experiment was performed as two independent experiments, each carried out in triplicate. (C) MIR452 expression level analysis by qRT- PCR for MIR452 transfection efficiency in Caco2 and SW48 cells. (D) The relative endogenous VEGFA expression levels in five CRC cell lines. The data are presented as a fold change in HT29, Caco2, HCT116, or SW48 cells relative to SW480 cells. This experiment was performed three independent experiments, each carried out in duplicate. Table S1. The putative target genes of MIR452 identified and predicted by the microarray analysis from the MIR452 overexpressed cells. Symbol Definition Accession Homo sapiens acyl-CoA thioesterase 8 (ACOT8), transcript ACOT8 NM_005469.2 variant 1, mRNA. Homo sapiens ARP6 actin-related protein 6 homolog (yeast) ACTR6 NM_022496.3 (ACTR6), mRNA. ADI1 Homo sapiens acireductone dioxygenase 1 (ADI1), mRNA. NM_018269.1 Homo sapiens aftiphilin (AFTPH), transcript variant 1, AFTPH NM_203437.2 mRNA. AHNAK2 Homo sapiens AHNAK nucleoprotein 2 (AHNAK2), mRNA. NM_138420.2 Homo sapiens A kinase (PRKA) anchor protein 7 (AKAP7), AKAP7 NM_004842.2 transcript variant alpha, mRNA. Homo sapiens anaphase promoting complex subunit 13 ANAPC13 NM_015391.2 (ANAPC13), mRNA.
  • Mouse Itgb3bp Knockout Project (CRISPR/Cas9)

    Mouse Itgb3bp Knockout Project (CRISPR/Cas9)

    https://www.alphaknockout.com Mouse Itgb3bp Knockout Project (CRISPR/Cas9) Objective: To create a Itgb3bp knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Itgb3bp gene (NCBI Reference Sequence: NM_026348 ; Ensembl: ENSMUSG00000028549 ) is located on Mouse chromosome 4. 9 exons are identified, with the ATG start codon in exon 1 and the TGA stop codon in exon 8 (Transcript: ENSMUST00000146258). Exon 3~4 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 3 starts from about 9.28% of the coding region. Exon 3~4 covers 38.45% of the coding region. The size of effective KO region: ~3537 bp. The KO region does not have any other known gene. Page 1 of 9 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 3 4 9 Legends Exon of mouse Itgb3bp Knockout region Page 2 of 9 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 Exon 3 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 Exon 4 is aligned with itself to determine if there are tandem repeats.
  • A Microrna Program Regulates the Balance Between Cardiomyocyte Hyperplasia and Hypertrophy and Stimulates Cardiac Regeneration

    A Microrna Program Regulates the Balance Between Cardiomyocyte Hyperplasia and Hypertrophy and Stimulates Cardiac Regeneration

    ARTICLE https://doi.org/10.1038/s41467-021-25211-4 OPEN A microRNA program regulates the balance between cardiomyocyte hyperplasia and hypertrophy and stimulates cardiac regeneration Andrea Raso1,12, Ellen Dirkx1,12, Vasco Sampaio-Pinto1,2, Hamid el Azzouzi1,3, Ryan J. Cubero 4,5, Daniel W. Sorensen6, Lara Ottaviani1, Servé Olieslagers1, Manon M. Huibers 7, Roel de Weger7, Sailay Siddiqi8, Silvia Moimas 9, Consuelo Torrini9, Lorena Zentillin9, Luca Braga9, Diana S. Nascimento 2, Paula A. da Costa Martins 1,10, Jop H. van Berlo 6, Serena Zacchigna 8, Mauro Giacca 9,11 & ✉ Leon J. De Windt 1 1234567890():,; Myocardial regeneration is restricted to early postnatal life, when mammalian cardiomyo- cytes still retain the ability to proliferate. The molecular cues that induce cell cycle arrest of neonatal cardiomyocytes towards terminally differentiated adult heart muscle cells remain obscure. Here we report that the miR-106b~25 cluster is higher expressed in the early postnatal myocardium and decreases in expression towards adulthood, especially under conditions of overload, and orchestrates the transition of cardiomyocyte hyperplasia towards cell cycle arrest and hypertrophy by virtue of its targetome. In line, gene delivery of miR- 106b~25 to the mouse heart provokes cardiomyocyte proliferation by targeting a network of negative cell cycle regulators including E2f5, Cdkn1c, Ccne1 and Wee1. Conversely, gene- targeted miR-106b~25 null mice display spontaneous hypertrophic remodeling and exag- gerated remodeling to overload by derepression of the prohypertrophic transcription factors Hand2 and Mef2d. Taking advantage of the regulatory function of miR-106b~25 on cardio- myocyte hyperplasia and hypertrophy, viral gene delivery of miR-106b~25 provokes nearly complete regeneration of the adult myocardium after ischemic injury.