DOCSLIB.ORG

Mouse Ccnl2 Conditional Knockout Project (CRISPR/Cas9)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Mouse Ccnl2 Conditional Knockout Project (CRISPR/Cas9) https://www.alphaknockout.com Mouse Ccnl2 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Ccnl2 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Ccnl2 gene (NCBI Reference Sequence: NM_207678 ; Ensembl: ENSMUSG00000029068 ) is located on Mouse chromosome 4. 11 exons are identified, with the ATG start codon in exon 1 and the TGA stop codon in exon 11 (Transcript: ENSMUST00000030944). Exon 5 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Ccnl2 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-128M14 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 5 starts from about 37.9% of the coding region. The knockout of Exon 5 will result in frameshift of the gene. The size of intron 4 for 5'-loxP site insertion: 2559 bp, and the size of intron 5 for 3'-loxP site insertion: 2341 bp. The size of effective cKO region: ~565 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' 1 5 11 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Ccnl2 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(7065bp) | A(25.14% 1776) | C(20.3% 1434) | T(32.29% 2281) | G(22.28% 1574) 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% chr4 + 155814674 155817673 3000 browser details YourSeq 183 1060 1371 3000 93.0% chr1 + 157420933 157421408 476 browser details YourSeq 170 1060 1415 3000 87.0% chr4 + 59056491 59056745 255 browser details YourSeq 157 1218 1417 3000 96.5% chr4 - 125980535 125980862 328 browser details YourSeq 148 2011 2334 3000 89.8% chr4 - 129277829 129278160 332 browser details YourSeq 147 1218 1749 3000 83.1% chr5 - 88681912 88682199 288 browser details YourSeq 144 1117 1372 3000 93.9% chr4 - 59799680 59800019 340 browser details YourSeq 139 1218 1421 3000 87.7% chr11 - 23466118 23466283 166 browser details YourSeq 139 1216 1416 3000 87.5% chr18 + 30661921 30662091 171 browser details YourSeq 136 1210 1372 3000 90.0% chr5 - 97553791 97553941 151 browser details YourSeq 135 1218 1372 3000 91.7% chr2 - 48034009 48034152 144 browser details YourSeq 134 1218 1372 3000 91.6% chr10 - 110791040 110791182 143 browser details YourSeq 134 2010 2415 3000 86.5% chr1 - 86050154 86050691 538 browser details YourSeq 133 1218 1395 3000 89.3% chr12 - 73971049 73971188 140 browser details YourSeq 132 1210 1370 3000 89.8% chr13 - 67676823 67676970 148 browser details YourSeq 132 1218 1372 3000 91.1% chr11 - 107183042 107183188 147 browser details YourSeq 131 1218 1409 3000 86.5% chr17 - 9257618 9257768 151 browser details YourSeq 131 1216 1372 3000 91.0% chr1 + 3385056 3385202 147 browser details YourSeq 128 1216 1372 3000 92.8% chr10 + 56425956 56426107 152 browser details YourSeq 128 1218 1418 3000 86.4% chr1 + 178257376 178257532 157 Note: The 3000 bp section upstream of Exon 5 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% chr4 + 155818239 155821238 3000 browser details YourSeq 68 2928 3000 3000 93.0% chr5 - 77224010 77224080 71 browser details YourSeq 66 2929 3000 3000 97.2% chr7 + 99310784 99310865 82 browser details YourSeq 65 2923 3000 3000 94.5% chr13 - 62696378 62696462 85 browser details YourSeq 63 2928 3000 3000 97.1% chr11 - 5537545 5537619 75 browser details YourSeq 62 2936 3000 3000 98.5% chr2 - 70518320 70518385 66 browser details YourSeq 62 2932 3000 3000 98.5% chr2 + 5029107 5029177 71 browser details YourSeq 61 2926 3000 3000 97.1% chr9 + 53778142 53778220 79 browser details YourSeq 61 2935 3000 3000 97.0% chr1 + 167452831 167452897 67 browser details YourSeq 60 2930 3000 3000 92.9% chr9 - 70637471 70637548 78 browser details YourSeq 60 2931 3000 3000 98.4% chr15 + 79894210 79894279 70 browser details YourSeq 59 2931 3000 3000 96.9% chr2 + 34792765 34792835 71 browser details YourSeq 58 2928 3000 3000 98.4% chr19 - 33404721 33404793 73 browser details YourSeq 58 2928 3000 3000 95.4% chr10 + 4393738 4393811 74 browser details YourSeq 57 2928 3000 3000 98.4% chr9 - 62326546 62326620 75 browser details YourSeq 57 2929 3000 3000 93.9% chr15 - 41086641 41086714 74 browser details YourSeq 56 2936 3000 3000 98.3% chr14 - 28360627 28360692 66 browser details YourSeq 55 2934 3000 3000 95.1% chr6 + 149321173 149321241 69 browser details YourSeq 55 2935 3000 3000 96.7% chr5 + 96540051 96540117 67 browser details YourSeq 55 2935 3000 3000 93.0% chr2 + 59638031 59638094 64 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: Ccnl2 cyclin L2 [ Mus musculus (house mouse) ] Gene ID: 56036, updated on 12-Aug-2019 Gene summary Official Symbol Ccnl2 provided by MGI Official Full Name cyclin L2 provided by MGI Primary source MGI:MGI:1927119 See related Ensembl:ENSMUSG00000029068 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 Pcee; SB138; Ania-6b; AA409936; AW261738; HCLA-150; 1700010A01Rik; 1810019L15Rik; 2010319M22Rik Expression Ubiquitous expression in limb E14.5 (RPKM 65.5), bladder adult (RPKM 45.8) and 28 other tissues See more Orthologs human all Genomic context Location: 4 E2; 4 87.47 cM See Ccnl2 in Genome Data Viewer Exon count: 11 Annotation release Status Assembly Chr Location 108 current GRCm38.p6 (GCF_000001635.26) 4 NC_000070.6 (155810219..155824543) Build 37.2 previous assembly MGSCv37 (GCF_000001635.18) 4 NC_000070.5 (155186598..155198652) Chromosome 4 - NC_000070.6 Page 5 of 7 https://www.alphaknockout.com Transcript information: This gene has 5 transcripts Gene: Ccnl2 ENSMUSG00000029068 Description cyclin L2 [Source:MGI Symbol;Acc:MGI:1927119] Gene Synonyms 1700010A01Rik, 1810019L15Rik, 2010319M22Rik, Pcee, ania-6b Location Chromosome 4: 155,812,489-155,824,543 forward strand. GRCm38:CM000997.2 About this gene This gene has 5 transcripts (splice variants), 162 orthologues, 6 paralogues and is a member of 1 Ensembl protein family. Transcripts Name Transcript ID bp Protein Translation ID Biotype CCDS UniProt Flags Ccnl2- ENSMUST00000030944.10 2442 518aa ENSMUSP00000030944.4 Protein coding CCDS19042 Q9JJA7 TSL:1 201 GENCODE basic APPRIS P1 Ccnl2- ENSMUST00000126346.1 1894 93aa ENSMUSP00000116000.1 Nonsense mediated - F6RXL0 CDS 5' 202 decay incomplete TSL:5 Ccnl2- ENSMUST00000139066.7 4220 No - Retained intron - - TSL:1 205 protein Ccnl2- ENSMUST00000129850.7 3411 No - Retained intron - - TSL:1 203 protein Ccnl2- ENSMUST00000136370.1 833 No - Retained intron - - TSL:1 204 protein 32.05 kb Forward strand 155.81Mb 155.82Mb 155.83Mb Genes (Comprehensive set... Mrpl20-205 >lncRNA Ccnl2-201 >protein coding Aurkaip1-206 >retained intron Mrpl20-201 >protein coding Ccnl2-205 >retained intron Aurkaip1-205 >protein coding Mrpl20-206 >protein coding Ccnl2-203 >retained intron Aurkaip1-201 >protein coding Mrpl20-203 >nonsense mediated decay Ccnl2-204 >retained intron Aurkaip1-203 >protein coding Mrpl20-202 >protein coding Ccnl2-202 >nonsense mediated decay Aurkaip1-204 >lncRNA Mrpl20-204 >lncRNA Aurkaip1-202 >protein coding Contigs AL670236.9 > Regulatory Build 155.81Mb 155.82Mb 155.83Mb Reverse strand 32.05 kb Regulation Legend CTCF Open Chromatin Promoter Promoter Flank Gene Legend Protein Coding merged Ensembl/Havana Ensembl protein coding Non-Protein Coding processed transcript RNA gene Page 6 of 7 https://www.alphaknockout.com Transcript: ENSMUST00000030944 12.05 kb Forward strand Ccnl2-201 >protein coding protein_pic We wish to acknowledge the following valuable scientific information resources: Ensembl, MGI, NCBI, UCSC. Page 7 of 7.
Load more

Recommended publications
  • Brain-Specific Knock-Out of Hypoxia-Inducible Factor-1Α
    The Journal of Neuroscience, April 20, 2005 • 25(16):4099–4107 • 4099 Neurobiology of Disease Brain-Specific Knock-Out of Hypoxia-Inducible Factor-1␣ Reduces Rather Than Increases Hypoxic–Ischemic Damage Rob Helton,1* Jiankun Cui,2* John R. Scheel,1* Julie A. Ellison,1 Chris Ames,1 Claire Gibson,2 Barbara Blouw,3 Ling Ouyang,1 Ioannis Dragatsis,4 Scott Zeitlin,5 Randall S. Johnson,3 Stuart A. Lipton,2 and Carrolee Barlow1 1Laboratory of Genetics, The Salk Institute for Biological Studies, and 2Center for Neuroscience and Aging, The Burnham Institute, La Jolla, California 92037, 3Molecular Biology Section, Division of Biology, University of California, San Diego, La Jolla, California 92093, 4Department of Physiology, The University of Tennessee, Health Science Center, Memphis, Tennessee 38163, and 5Department of Neuroscience, University of Virginia School of Medicine, Charlottesville, Virginia 22908 ␣ ␣ Hypoxia-inducible factor-1 (HIF-1 ) plays an essential role in cellular and systemic O2 homeostasis by regulating the expression of genes important in glycolysis, erythropoiesis, angiogenesis, and catecholamine metabolism. It is also believed to be a key component of the cellular response to hypoxia and ischemia under pathophysiological conditions, such as stroke. To clarify the function of HIF-1␣ in the brain, we exposed adult mice with late-stage brain deletion of HIF-1␣ to hypoxic injuries. Contrary to expectations, the brains from the HIF-1␣-deficient mice were protected from hypoxia-induced cell death. These surprising findings suggest that decreas- ing the level of HIF-1␣ can be neuroprotective. Gene chip expression analysis revealed that, contrary to expectations, the majority of hypoxia-dependent gene-expression changes were unaltered, whereas a specific downregulation of apoptotic genes was observed in the HIF-1␣-deficient mice.
    [Show full text]
  • Gene Expression Responses to DNA Damage Are Altered in Human Aging and in Werner Syndrome
    Oncogene (2005) 24, 5026–5042 & 2005 Nature Publishing Group All rights reserved 0950-9232/05 $30.00 www.nature.com/onc Gene expression responses to DNA damage are altered in human aging and in Werner Syndrome Kasper J Kyng1,2, Alfred May1, Tinna Stevnsner2, Kevin G Becker3, Steen Klvra˚ 4 and Vilhelm A Bohr*,1 1Laboratory of Molecular Gerontology, National Institute on Aging, National Institutes of Health, 5600 Nathan Shock Drive, Baltimore, MD 21224, USA; 2Danish Center for Molecular Gerontology, Department of Molecular Biology, University of Aarhus, DK-8000 Aarhus C, Denmark; 3Gene Expression and Genomics Unit, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA; 4Institute for Human Genetics, University of Aarhus, Denmark The accumulation of DNA damage and mutations is syndromes, caused by heritable mutations inactivating considered a major cause of cancer and aging. While it is proteins that sense or repair DNA damage, which known that DNA damage can affect changes in gene accelerate some but not all signs of normal aging (Hasty expression, transcriptional regulation after DNA damage et al., 2003). Age is associated withan increase in is poorly understood. We characterized the expression of susceptibility to various forms of stress, and sporadic 6912 genes in human primary fibroblasts after exposure to reports suggest that an age-related decrease in DNA three different kinds of cellular stress that introduces repair may increase the susceptibility of cells to agents DNA damage: 4-nitroquinoline-1-oxide (4NQO), c-irra- causing DNA damage. Reduced base excision repair has diation, or UV-irradiation. Each type of stress elicited been demonstrated in nuclear extracts from aged human damage specific gene expression changes of up to 10-fold.
    [Show full text]
  • Comparative Transcriptomics Reveals Similarities and Differences
    Seifert et al. BMC Cancer (2015) 15:952 DOI 10.1186/s12885-015-1939-9 RESEARCH ARTICLE Open Access Comparative transcriptomics reveals similarities and differences between astrocytoma grades Michael Seifert1,2,5*, Martin Garbe1, Betty Friedrich1,3, Michel Mittelbronn4 and Barbara Klink5,6,7 Abstract Background: Astrocytomas are the most common primary brain tumors distinguished into four histological grades. Molecular analyses of individual astrocytoma grades have revealed detailed insights into genetic, transcriptomic and epigenetic alterations. This provides an excellent basis to identify similarities and differences between astrocytoma grades. Methods: We utilized public omics data of all four astrocytoma grades focusing on pilocytic astrocytomas (PA I), diffuse astrocytomas (AS II), anaplastic astrocytomas (AS III) and glioblastomas (GBM IV) to identify similarities and differences using well-established bioinformatics and systems biology approaches. We further validated the expression and localization of Ang2 involved in angiogenesis using immunohistochemistry. Results: Our analyses show similarities and differences between astrocytoma grades at the level of individual genes, signaling pathways and regulatory networks. We identified many differentially expressed genes that were either exclusively observed in a specific astrocytoma grade or commonly affected in specific subsets of astrocytoma grades in comparison to normal brain. Further, the number of differentially expressed genes generally increased with the astrocytoma grade with one major exception. The cytokine receptor pathway showed nearly the same number of differentially expressed genes in PA I and GBM IV and was further characterized by a significant overlap of commonly altered genes and an exclusive enrichment of overexpressed cancer genes in GBM IV. Additional analyses revealed a strong exclusive overexpression of CX3CL1 (fractalkine) and its receptor CX3CR1 in PA I possibly contributing to the absence of invasive growth.
    [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]
  • CCNL2 (NM 030937) Human Tagged ORF Clone Product Data
    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 RC213824L1 CCNL2 (NM_030937) Human Tagged ORF Clone Product data: Product Type: Expression Plasmids Product Name: CCNL2 (NM_030937) Human Tagged ORF Clone Tag: Myc-DDK Symbol: CCNL2 Synonyms: ANIA-6B; CCNM; CCNS; HCLA-ISO; HLA-ISO; PCEE; SB138 Vector: pLenti-C-Myc-DDK (PS100064) E. coli Selection: Chloramphenicol (34 ug/mL) Cell Selection: None ORF Nucleotide The ORF insert of this clone is exactly the same as(RC213824). Sequence: Restriction Sites: SgfI-MluI Cloning Scheme: ACCN: NM_030937 ORF Size: 1560 bp 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 CCNL2 (NM_030937) Human Tagged ORF Clone – RC213824L1 OTI Disclaimer: The molecular sequence of this clone aligns with the gene accession number as a point of reference only. However, individual transcript sequences of the same gene can differ through naturally occurring variations (e.g. polymorphisms), each with its own valid existence. This clone is substantially in agreement with the reference, but a complete review of all prevailing variants is recommended prior to use. More info OTI Annotation: This clone was engineered to express the complete ORF with an expression tag. Expression varies depending on the nature of the gene. RefSeq: NM_030937.3 RefSeq Size: 3107 bp RefSeq ORF: 1563 bp Locus ID: 81669 UniProt ID: Q96S94 Protein Families: Druggable Genome MW: 58 kDa Gene Summary: The protein encoded by this gene belongs to the cyclin family.
    [Show full text]
  • Agricultural University of Athens
    ΓΕΩΠΟΝΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ ΣΧΟΛΗ ΕΠΙΣΤΗΜΩΝ ΤΩΝ ΖΩΩΝ ΤΜΗΜΑ ΕΠΙΣΤΗΜΗΣ ΖΩΙΚΗΣ ΠΑΡΑΓΩΓΗΣ ΕΡΓΑΣΤΗΡΙΟ ΓΕΝΙΚΗΣ ΚΑΙ ΕΙΔΙΚΗΣ ΖΩΟΤΕΧΝΙΑΣ ΔΙΔΑΚΤΟΡΙΚΗ ΔΙΑΤΡΙΒΗ Εντοπισμός γονιδιωματικών περιοχών και δικτύων γονιδίων που επηρεάζουν παραγωγικές και αναπαραγωγικές ιδιότητες σε πληθυσμούς κρεοπαραγωγικών ορνιθίων ΕΙΡΗΝΗ Κ. ΤΑΡΣΑΝΗ ΕΠΙΒΛΕΠΩΝ ΚΑΘΗΓΗΤΗΣ: ΑΝΤΩΝΙΟΣ ΚΟΜΙΝΑΚΗΣ ΑΘΗΝΑ 2020 ΔΙΔΑΚΤΟΡΙΚΗ ΔΙΑΤΡΙΒΗ Εντοπισμός γονιδιωματικών περιοχών και δικτύων γονιδίων που επηρεάζουν παραγωγικές και αναπαραγωγικές ιδιότητες σε πληθυσμούς κρεοπαραγωγικών ορνιθίων Genome-wide association analysis and gene network analysis for (re)production traits in commercial broilers ΕΙΡΗΝΗ Κ. ΤΑΡΣΑΝΗ ΕΠΙΒΛΕΠΩΝ ΚΑΘΗΓΗΤΗΣ: ΑΝΤΩΝΙΟΣ ΚΟΜΙΝΑΚΗΣ Τριμελής Επιτροπή: Aντώνιος Κομινάκης (Αν. Καθ. ΓΠΑ) Ανδρέας Κράνης (Eρευν. B, Παν. Εδιμβούργου) Αριάδνη Χάγερ (Επ. Καθ. ΓΠΑ) Επταμελής εξεταστική επιτροπή: Aντώνιος Κομινάκης (Αν. Καθ. ΓΠΑ) Ανδρέας Κράνης (Eρευν. B, Παν. Εδιμβούργου) Αριάδνη Χάγερ (Επ. Καθ. ΓΠΑ) Πηνελόπη Μπεμπέλη (Καθ. ΓΠΑ) Δημήτριος Βλαχάκης (Επ. Καθ. ΓΠΑ) Ευάγγελος Ζωίδης (Επ.Καθ. ΓΠΑ) Γεώργιος Θεοδώρου (Επ.Καθ. ΓΠΑ) 2 Εντοπισμός γονιδιωματικών περιοχών και δικτύων γονιδίων που επηρεάζουν παραγωγικές και αναπαραγωγικές ιδιότητες σε πληθυσμούς κρεοπαραγωγικών ορνιθίων Περίληψη Σκοπός της παρούσας διδακτορικής διατριβής ήταν ο εντοπισμός γενετικών δεικτών και υποψηφίων γονιδίων που εμπλέκονται στο γενετικό έλεγχο δύο τυπικών πολυγονιδιακών ιδιοτήτων σε κρεοπαραγωγικά ορνίθια. Μία ιδιότητα σχετίζεται με την ανάπτυξη (σωματικό βάρος στις 35 ημέρες, ΣΒ) και η άλλη με την αναπαραγωγική
    [Show full text]
  • Analysis of Genetic Association Using Hierarchical Clustering and Cluster Validation Indices
    Analysis of genetic association using Hierarchical Clustering and Cluster Validation Indices Inti A. Pagnuco1,2,3, Juan I. Pastore1,2,3, Guillermo Abras1, Marcel Brun1,2, and Virginia L. Ballarin1 1Digital Image Processing Lab, ICyTE, UNMdP, 2Department of Mathematics, School of Engineering, UNMdP, 3CONICET Abstract It is usually assumed that co-expressed genes suggest co-regulation in the underlying regulatory network. Determining sets of co-expressed genes is an important task, based on some criteria of similarity. This task is usually performed by clustering algorithms, where the genes are clustered into meaningful groups based on their expression values in a set of experiment. In this work we propose a method to find sets of co-expressed genes, based on cluster validation indices as a measure of similarity for individual gene groups, and a combination of variants of hierarchical clustering to generate the candidate groups. We evaluated its ability to retrieve significant sets on simulated correlated and real genomics data, where the performance is measured based on its detection ability of co-regulated sets against a full search. Additionally, we analyzed the quality of the best ranked groups using an online bioinformatics tool that provides network information for the selected genes. 1 Introduction approach, while still providing, as shown in the results, a New technologies for genomic analysis measure the good approach to the optimal results. expression of thousand of genes simultaneously. An The Matlab code is available from https://subversion. important goal in some studies is to find sets of assembla.com/svn/pdilab-gaa/tags/Tag.1.1.1 co-expressed genes, to study co-regulation and biological In the next section we present a) an introduction to functions.
    [Show full text]
  • ZBTB33 Is Mutated in Clonal Hematopoiesis and Myelodysplastic Syndromes and Impacts RNA Splicing
    RESEARCH ARTICLE ZBTB33 Is Mutated in Clonal Hematopoiesis and Myelodysplastic Syndromes and Impacts RNA Splicing Ellen M. Beauchamp1,2, Matthew Leventhal1,2, Elsa Bernard3, Emma R. Hoppe4,5,6, Gabriele Todisco7,8, Maria Creignou8, Anna Gallì7, Cecilia A. Castellano1,2, Marie McConkey1,2, Akansha Tarun1,2, Waihay Wong1,2, Monica Schenone2, Caroline Stanclift2, Benjamin Tanenbaum2, Edyta Malolepsza2, Björn Nilsson1,2,9, Alexander G. Bick2,10,11, Joshua S. Weinstock12, Mendy Miller2, Abhishek Niroula1,2, Andrew Dunford2, Amaro Taylor-Weiner2, Timothy Wood2, Alex Barbera2, Shankara Anand2; Bruce M. Psaty13,14, Pinkal Desai15, Michael H. Cho16,17, Andrew D. Johnson18, Ruth Loos19,20; for the NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; Daniel G. MacArthur2,21,22,23, Monkol Lek2,21,24; for the Exome Aggregation Consortium, Donna S. Neuberg25, Kasper Lage2,26, Steven A. Carr2, Eva Hellstrom-Lindberg8, Luca Malcovati7, Elli Papaemmanuil3, Chip Stewart2, Gad Getz2,27,28, Robert K. Bradley4,5,6, Siddhartha Jaiswal29, and Benjamin L. Ebert1,2,30 Downloaded from https://bloodcancerdiscov.aacrjournals.org by guest on September 30, 2021. Copyright 2021 American Copyright 2021 by AssociationAmerican for Association Cancer Research. for Cancer Research. ABSTRACT Clonal hematopoiesis results from somatic mutations in cancer driver genes in hematopoietic stem cells. We sought to identify novel drivers of clonal expansion using an unbiased analysis of sequencing data from 84,683 persons and identified common mutations in the 5-methylcytosine reader ZBTB33 as well as in YLPM1, SRCAP, and ZNF318. We also identified these mutations at low frequency in patients with myelodysplastic syndrome. Zbtb33-edited mouse hematopoietic stem and progenitor cells exhibited a competitive advantage in vivo and increased genome-wide intron retention.
    [Show full text]
  • CDK11 Loss Induces Cell Cycle Dysfunction and Death of BRAF and NRAS Melanoma Cells
    pharmaceuticals Article CDK11 Loss Induces Cell Cycle Dysfunction and Death of BRAF and NRAS Melanoma Cells Rehana L. Ahmed 1,2, Daniel P. Shaughnessy 3, Todd P. Knutson 4,5 , Rachel I. Vogel 2,6 , Khalil Ahmed 2,3,4,7, Betsy T. Kren 2,3 and Janeen H. Trembley 2,3,4,* 1 Department of Dermatology, University of Minnesota, Minneapolis, MN 55455, USA; [email protected] 2 Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA; [email protected] (R.I.V.); [email protected] (K.A.); [email protected] (B.T.K.) 3 Research Service, Minneapolis VA Health Care System, Minneapolis, MN 55417, USA; [email protected] 4 Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA; [email protected] 5 Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA 6 Department of Obstetrics, Gynecology and Women’s Health, University of Minnesota, Minneapolis, MN 55455, USA 7 Department of Urology, University of Minnesota, Minneapolis, MN 55455, USA * Correspondence: [email protected] Received: 15 February 2019; Accepted: 28 March 2019; Published: 2 April 2019 Abstract: Cyclin dependent kinase 11 (CDK11) is a protein kinase that regulates RNA transcription, pre-mRNA splicing, mitosis, and cell death. Targeting of CDK11 expression levels is effective in the experimental treatment of breast and other cancers, but these data are lacking in melanoma. To understand CDK11 function in melanoma, we evaluated protein and RNA levels of CDK11, Cyclin L1 and Cyclin L2 in benign melanocytes and BRAF- as well as NRAS-mutant melanoma cell lines. We investigated the effectiveness of reducing expression of this survival kinase using RNA interference on viability, clonal survival, and tumorsphere formation in melanoma cell lines.
    [Show full text]
  • CDK11 Loss Induces Cell Cycle Dysfunction and Death of BRAF and NRAS Melanoma Cells
    Article CDK11 Loss Induces Cell Cycle Dysfunction and Death of BRAF and NRAS Melanoma Cells Rehana L. Ahmed 1,2, Daniel P. Shaughnessy 3, Todd P. Knutson 4,5, Rachel I. Vogel 2,6, Khalil Ahmed 2,3,4,7, Betsy T. Kren 2,3 and Janeen H. Trembley 2,3,4,* 1 Department of Dermatology, University of Minnesota, Minneapolis, MN 55455, USA; [email protected] 2 Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA 3 Research Service, Minneapolis VA Health Care System, Minneapolis, MN 55417, USA; [email protected] (D.P.S); [email protected] (K.A.); [email protected] (B.T.K.); [email protected] (J.H.T.) 4 Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA; [email protected] 5 Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA 6 Department of Obstetrics, Gynecology and Women’s Health, University of Minnesota, Minneapolis, MN 55455, USA.; [email protected] (R.I.V.) 7 Department of Urology, University of Minnesota, Minneapolis, MN 55455, USA * Correspondence: [email protected] Received: 15 February 2019; Accepted: 24 March 2019; Published: 2 April 2019 Abstract: Cyclin dependent kinase 11 (CDK11) is a protein kinase that regulates RNA transcription, pre-mRNA splicing, mitosis, and cell death. Targeting of CDK11 expression levels is effective in the experimental treatment of breast and other cancers, but these data are lacking in melanoma. To understand CDK11 function in melanoma, we evaluated protein and RNA levels of CDK11, Cyclin L1 and Cyclin L2 in benign melanocytes and BRAF- as well as NRAS-mutant melanoma cell lines.
    [Show full text]
  • Mouse SNP Miner: an Annotated Database of Mouse Functional Single Nucleotide Polymorphisms Eli Reuveni*1, Vasily E Ramensky2 and Cornelius Gross1
    BMC Genomics BioMed Central Database Open Access Mouse SNP Miner: an annotated database of mouse functional single nucleotide polymorphisms Eli Reuveni*1, Vasily E Ramensky2 and Cornelius Gross1 Address: 1Mouse Biology Unit, EMBL, Via Ramarini 32, 00016 Monterotondo, Italy and 2Engelhardt Institute of Molecular Biology, Vavilova 32, 119991 Moscow, Russia Email: Eli Reuveni* - [email protected]; Vasily E Ramensky - [email protected]; Cornelius Gross - [email protected] * Corresponding author Published: 21 January 2007 Received: 4 August 2006 Accepted: 21 January 2007 BMC Genomics 2007, 8:24 doi:10.1186/1471-2164-8-24 This article is available from: http://www.biomedcentral.com/1471-2164/8/24 © 2007 Reuveni et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: The mapping of quantitative trait loci in rat and mouse has been extremely successful in identifying chromosomal regions associated with human disease-related phenotypes. However, identifying the specific phenotype-causing DNA sequence variations within a quantitative trait locus has been much more difficult. The recent availability of genomic sequence from several mouse inbred strains (including C57BL/6J, 129X1/SvJ, 129S1/SvImJ, A/J, and DBA/2J) has made it possible to catalog DNA sequence differences within a quantitative trait locus derived from crosses between these strains. However, even for well-defined quantitative trait loci (<10 Mb) the identification of candidate functional DNA sequence changes remains challenging due to the high density of sequence variation between strains.
    [Show full text]
  • Spatial Maps of Prostate Cancer Transcriptomes Reveal an Unexplored Landscape of Heterogeneity
    ARTICLE DOI: 10.1038/s41467-018-04724-5 OPEN Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity Emelie Berglund1, Jonas Maaskola1, Niklas Schultz2, Stefanie Friedrich3, Maja Marklund1, Joseph Bergenstråhle1, Firas Tarish2, Anna Tanoglidi4, Sanja Vickovic 1, Ludvig Larsson1, Fredrik Salmeń1, Christoph Ogris3, Karolina Wallenborg2, Jens Lagergren5, Patrik Ståhl1, Erik Sonnhammer3, Thomas Helleday2 & Joakim Lundeberg 1 1234567890():,; Intra-tumor heterogeneity is one of the biggest challenges in cancer treatment today. Here we investigate tissue-wide gene expression heterogeneity throughout a multifocal prostate cancer using the spatial transcriptomics (ST) technology. Utilizing a novel approach for deconvolution, we analyze the transcriptomes of nearly 6750 tissue regions and extract distinct expression profiles for the different tissue components, such as stroma, normal and PIN glands, immune cells and cancer. We distinguish healthy and diseased areas and thereby provide insight into gene expression changes during the progression of prostate cancer. Compared to pathologist annotations, we delineate the extent of cancer foci more accurately, interestingly without link to histological changes. We identify gene expression gradients in stroma adjacent to tumor regions that allow for re-stratification of the tumor micro- environment. The establishment of these profiles is the first step towards an unbiased view of prostate cancer and can serve as a dictionary for future studies. 1 Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology (KTH), Science for Life Laboratory, Tomtebodavägen 23, Solna 17165, Sweden. 2 Department of Oncology-Pathology, Karolinska Institutet (KI), Science for Life Laboratory, Tomtebodavägen 23, Solna 17165, Sweden. 3 Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Tomtebodavägen 23, Solna 17165, Sweden.
    [Show full text]