DOCSLIB.ORG

Mouse Net1 Knockout Project (CRISPR/Cas9)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Mouse Net1 Knockout Project (CRISPR/Cas9) https://www.alphaknockout.com Mouse Net1 Knockout Project (CRISPR/Cas9) Objective: To create a Net1 knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Net1 gene (NCBI Reference Sequence: NM_019671 ; Ensembl: ENSMUSG00000021215 ) is located on Mouse chromosome 13. 12 exons are identified, with the ATG start codon in exon 1 and the TAA stop codon in exon 12 (Transcript: ENSMUST00000091853). Exon 4~11 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: Mice homozygous for a knock-out allele exhibit delayed mammary gland development during puberty associated with slower ductal extension, reduced ductal branching and epithelial cell proliferation, disorganized myoepithelial and ductal epithelial cells, and increased collagen deposition. Exon 4 starts from about 14.34% of the coding region. Exon 4~11 covers 63.25% of the coding region. The size of effective KO region: ~4176 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 4 5 6 7 8 9 10 11 12 Legends Exon of mouse Net1 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 4 is aligned with itself to determine if there are tandem repeats. Tandem repeats are found in the dot plot matrix. The gRNA site is selected outside of these tandem repeats. Overview of the Dot Plot (down) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 329 bp section downstream of Exon 11 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. Page 3 of 9 https://www.alphaknockout.com Overview of the GC Content Distribution (up) Window size: 300 bp Sequence 12 Summary: Full Length(2000bp) | A(24.05% 481) | C(20.2% 404) | T(35.2% 704) | G(20.55% 411) Note: The 2000 bp section upstream of Exon 4 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. Overview of the GC Content Distribution (down) Window size: 300 bp Sequence 12 Summary: Full Length(329bp) | A(30.4% 100) | C(23.4% 77) | T(25.84% 85) | G(20.36% 67) Note: The 329 bp section downstream of Exon 11 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 4 of 9 https://www.alphaknockout.com BLAT Search Results (up) QUERY SCORE START END QSIZE IDENTITY CHROM STRAND START END SPAN -------------------------------------------------------------------------------------------------------------- browser details YourSeq 2000 1 2000 2000 100.0% chr13 - 3888979 3890978 2000 browser details YourSeq 109 253 372 2000 96.7% chr5 - 118481305 118481805 501 browser details YourSeq 103 255 372 2000 94.1% chr14 + 63304167 63304285 119 browser details YourSeq 102 251 372 2000 92.2% chr10 + 34288866 34288985 120 browser details YourSeq 101 257 372 2000 94.0% chr3 - 43126832 43126959 128 browser details YourSeq 99 267 373 2000 96.3% chr10 - 120670536 120670642 107 browser details YourSeq 95 263 372 2000 93.7% chr8 - 13274889 13275000 112 browser details YourSeq 82 266 372 2000 92.8% chr5_JH584299_random + 428270 428377 108 browser details YourSeq 80 281 372 2000 92.1% chr2 + 84726331 84726420 90 browser details YourSeq 76 269 367 2000 92.3% chr17 + 18152722 18152839 118 browser details YourSeq 68 270 375 2000 85.4% chr11 - 103155336 103155439 104 browser details YourSeq 66 279 370 2000 93.5% chr11 - 55375829 55375921 93 browser details YourSeq 61 277 365 2000 84.3% chr18 - 63678910 63678998 89 browser details YourSeq 54 250 320 2000 84.1% chr2 + 165008665 165008733 69 browser details YourSeq 51 274 338 2000 89.3% chr19 - 3772372 3772436 65 browser details YourSeq 50 274 334 2000 84.3% chr7 + 82372262 82372318 57 browser details YourSeq 47 1206 1300 2000 94.3% chr13 + 111613365 111613471 107 browser details YourSeq 46 250 314 2000 86.0% chr9 - 58243953 58244018 66 browser details YourSeq 46 1226 1320 2000 92.6% chr6 + 83076522 83076856 335 browser details YourSeq 44 1227 1319 2000 85.3% chr1 - 4763682 4763785 104 Note: The 2000 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 329 1 329 329 100.0% chr13 - 3884474 3884802 329 browser details YourSeq 45 74 154 329 92.4% chr10 - 4050861 4267602 216742 browser details YourSeq 40 55 110 329 83.4% chr9 + 57545695 57545749 55 browser details YourSeq 36 72 110 329 97.5% chr11 + 77010165 77010205 41 browser details YourSeq 35 62 103 329 81.6% chr12 - 76116567 76116604 38 browser details YourSeq 33 69 110 329 97.2% chr11 - 85301579 85301630 52 browser details YourSeq 33 72 125 329 74.3% chr6 + 143102421 143102462 42 browser details YourSeq 31 61 95 329 94.3% chr2 - 121258046 121258080 35 browser details YourSeq 30 73 112 329 91.5% chr9 + 121310835 121310877 43 browser details YourSeq 30 64 95 329 96.9% chr6 + 83658663 83658694 32 browser details YourSeq 30 31 95 329 96.9% chr10 + 94139314 94139412 99 browser details YourSeq 29 76 112 329 90.4% chr11 - 19868731 19868766 36 browser details YourSeq 29 69 101 329 94.0% chr5 + 123637741 123637773 33 browser details YourSeq 28 77 107 329 96.7% chr9 - 54597909 54597942 34 browser details YourSeq 28 79 107 329 100.0% chr1 - 180681271 180681314 44 browser details YourSeq 27 69 95 329 100.0% chr12 - 18509495 18509521 27 browser details YourSeq 27 69 95 329 100.0% chr11 - 51686155 51686181 27 browser details YourSeq 27 74 110 329 96.6% chr10 - 61166919 61166955 37 browser details YourSeq 27 69 103 329 88.6% chr1 + 175531927 175531961 35 browser details YourSeq 25 67 93 329 96.3% chr9 - 22000866 22000892 27 Note: The 329 bp section downstream of Exon 11 is BLAT searched against the genome. No significant similarity is found. Page 5 of 9 https://www.alphaknockout.com Gene and protein information: Net1 neuroepithelial cell transforming gene 1 [ Mus musculus (house mouse) ] Gene ID: 56349, updated on 24-Sep-2019 Gene summary Official Symbol Net1 provided by MGI Official Full Name neuroepithelial cell transforming gene 1 provided by MGI Primary source MGI:MGI:1927138 See related Ensembl:ENSMUSG00000021215 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 Net1a; mNET1 Expression Ubiquitous expression in large intestine adult (RPKM 25.9), limb E14.5 (RPKM 18.9) and 28 other tissues See more Orthologs human all Genomic context Location: 13; 13 A1 See Net1 in Genome Data Viewer Exon count: 14 Annotation release Status Assembly Chr Location 108 current GRCm38.p6 (GCF_000001635.26) 13 NC_000079.6 (3882018..3918220, complement) Build 37.2 previous assembly MGSCv37 (GCF_000001635.18) 13 NC_000079.5 (3881807..3917466, complement) Chromosome 13 - NC_000079.6 Page 6 of 9 https://www.alphaknockout.com Transcript information: This gene has 7 transcripts Gene: Net1 ENSMUSG00000021215 Description neuroepithelial cell transforming gene 1 [Source:MGI Symbol;Acc:MGI:1927138] Gene Synonyms 0610025H04Rik, 9530071N24Rik, Net1 homolog, mNET1 Location Chromosome 13: 3,882,018-3,918,220 reverse strand. GRCm38:CM001006.2 About this gene This gene has 7 transcripts (splice variants), 256 orthologues, 8 paralogues, is a member of 1 Ensembl protein family and is associated with 4 phenotypes. Transcripts Name Transcript ID bp Protein Translation ID Biotype CCDS UniProt Flags Net1-202 ENSMUST00000099946.5 3871 541aa ENSMUSP00000097529.4 Protein coding CCDS36583 Q3USZ7 Q9Z206 TSL:1 GENCODE basic APPRIS ALT2 Net1-201 ENSMUST00000091853.11 2680 595aa ENSMUSP00000089464.4 Protein coding CCDS26216 Q9Z206 TSL:1 GENCODE basic APPRIS P3 Net1-207 ENSMUST00000223258.1 597 72aa ENSMUSP00000152333.1 Protein coding - A0A1Y7VJ80 TSL:1 GENCODE basic Net1-206 ENSMUST00000222504.1 447 44aa ENSMUSP00000152173.1 Protein coding - A0A1Y7VKV6 TSL:3 GENCODE basic Net1-203 ENSMUST00000220887.1 763 No protein - Retained intron - - TSL:5 Net1-204 ENSMUST00000222017.1 569 No protein - Retained intron - - TSL:NA Net1-205 ENSMUST00000222442.1 519 No protein - Retained intron - - TSL:3 Page 7 of 9 https://www.alphaknockout.com 56.20 kb Forward strand 3.88Mb 3.89Mb 3.90Mb 3.91Mb 3.92Mb Genes Tubal3-202 >lncRNA (Comprehensive set... Tubal3-201 >protein coding Contigs AC139323.4 > Genes (Comprehensive set... < Net1-202protein coding < Gm47813-201TEC < Net1-207protein coding < Net1-206protein coding < Net1-201protein coding < Net1-203retained intron < Net1-204retained intron < Net1-205retained intron Regulatory Build 3.88Mb 3.89Mb 3.90Mb 3.91Mb 3.92Mb Reverse strand 56.20 kb Regulation Legend CTCF Enhancer Open Chromatin Promoter Promoter Flank Transcription Factor Binding Site Gene Legend Protein Coding merged Ensembl/Havana Ensembl protein coding Non-Protein Coding RNA gene processed transcript Page 8 of 9 https://www.alphaknockout.com Transcript: ENSMUST00000091853 < Net1-201protein coding Reverse strand 34.89 kb ENSMUSP00000089... MobiDB lite Low complexity (Seg) Superfamily Dbl homology (DH) domain superfamily SSF50729 SMART Dbl homology (DH) domain Pleckstrin homology domain Pfam Dbl homology (DH) domain Pleckstrin homology domain PROSITE profiles Dbl homology (DH) domain Pleckstrin homology domain PROSITE patterns Guanine-nucleotide dissociation stimulator, CDC24, conserved site PANTHER PTHR46006:SF4 PTHR46006 Gene3D Dbl homology (DH) domain superfamily PH-like domain superfamily CDD Dbl homology (DH) domain Net1, PH domain All sequence SNPs/i..
Load more

Recommended publications
  • Analysis of Gene Expression Data for Gene Ontology
    ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins.
    [Show full text]
  • Chromosome 10, Frequently Lost in Human Melanoma, Encodes Multiple Tumor-Suppressive Functions
    Published OnlineFirst January 22, 2014; DOI: 10.1158/0008-5472.CAN-13-1446 Cancer Tumor and Stem Cell Biology Research Chromosome 10, Frequently Lost in Human Melanoma, Encodes Multiple Tumor-Suppressive Functions Lawrence N. Kwong and Lynda Chin Abstract Although many DNA aberrations in melanoma have been well characterized, including focal amplification and deletions of oncogenes and tumor suppressors, broad regions of chromosomal gain and loss are less well understood. One possibility is that these broad events are a consequence of collateral damage from targeting single loci. Another possibility is that the loss of large regions permits the simultaneous repression of multiple tumor suppressors by broadly decreasing the resident gene dosage and expression. Here, we test this hypothesis in a targeted fashion using RNA interference to suppress multiple candidate residents in broad regions of loss. We find that loss of chromosome regions 6q, 10, and 11q21-ter is correlated with broadly decreased expression of most resident genes and that multiple resident genes impacted by broad regional loss of chromosome 10 are tumor suppressors capable of affecting tumor growth and/or invasion. We also provide additional functional support for Ablim1 as a novel tumor suppressor. Our results support the hypothesis that multiple cancer genes are targeted by regional chromosome copy number aberrations. Cancer Res; 74(6); 1814–21. Ó2014 AACR. Introduction mas (7). In contrast, the observed approximately 60% of CDKN2A/B Increasingly high-resolution genomic studies have estab- deletions on chromosome 9p are focal. These data PTEN lished that recurrent focal deletions and amplifications in suggest that although is a major driver of chromosome cancer can selectively target specific oncogenes and tumor 10 loss, other genes may also be targeted for inactivation.
    [Show full text]
  • 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.
    [Show full text]
  • Identification and in Silico Bioinformatics Analysis of PR10 Proteins in Cashew Nut
    Received: 17 October 2019 Revised: 13 March 2020 Accepted: 18 March 2020 DOI: 10.1002/pro.3856 ARTICLE Identification and in silico bioinformatics analysis of PR10 proteins in cashew nut Shanna Bastiaan-Net1 | Maria C. Pina-Pérez2 | Bas J. W. Dekkers3 | Adrie H. Westphal4 | Antoine H. P. America5 | Renata M. C. Ariëns1 | Nicolette W. de Jong6 | Harry J. Wichers1 | Jurriaan J. Mes1 1Wageningen Food and Biobased Research, Wageningen University and Research, Wageningen, The Netherlands 2Institute of Life Technologies, HES-SO Valais-Wallis, Sion, Switzerland 3Wageningen Seed Lab, Laboratory of Plant Physiology, Wageningen University, Wageningen, The Netherlands 4Biochemistry, Wageningen University and Research, Wageningen, The Netherlands 5Wageningen Plant Research, Wageningen University and Research, Wageningen, The Netherlands 6Allergology, Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands Correspondence Shanna Bastiaan-Net, Wageningen Food Abstract and Biobased Research, Bornse Proteins from cashew nut can elicit mild to severe allergic reactions. Three Weilanden 9, 6708 WG Wageningen, The allergenic proteins have already been identified, and it is expected that addi- Netherlands. Email: [email protected] tional allergens are present in cashew nut. pathogenesis-related protein 10 (PR10) allergens from pollen have been found to elicit similar allergic reac- Funding information tions as those from nuts and seeds. Therefore, we investigated the presence of Minsiterio de Educacion, Cultura y Deporte (MECD), Grant/Award Number: PR10 genes in cashew nut. Using RNA-seq analysis, we were able to identify CAS17/00051; Netherlands Organisation several PR10-like transcripts in cashew nut and clone six putative PR10 genes. for Health Research and Development In addition, PR10 protein expression in raw cashew nuts was confirmed by (ZonMw), Grant/Award Number: 435003012; Technology Foundation STW, immunoblotting and liquid chromatography–mass spectrometry (LC–MS/MS) Grant/Award Number: 11868 analyses.
    [Show full text]
  • Enhancer Rnas: Transcriptional Regulators and Workmates of Namirnas in Myogenesis
    Odame et al. Cell Mol Biol Lett (2021) 26:4 https://doi.org/10.1186/s11658-021-00248-x Cellular & Molecular Biology Letters REVIEW Open Access Enhancer RNAs: transcriptional regulators and workmates of NamiRNAs in myogenesis Emmanuel Odame , Yuan Chen, Shuailong Zheng, Dinghui Dai, Bismark Kyei, Siyuan Zhan, Jiaxue Cao, Jiazhong Guo, Tao Zhong, Linjie Wang, Li Li* and Hongping Zhang* *Correspondence: [email protected]; zhp@sicau. Abstract edu.cn miRNAs are well known to be gene repressors. A newly identifed class of miRNAs Farm Animal Genetic Resources Exploration termed nuclear activating miRNAs (NamiRNAs), transcribed from miRNA loci that and Innovation Key exhibit enhancer features, promote gene expression via binding to the promoter and Laboratory of Sichuan enhancer marker regions of the target genes. Meanwhile, activated enhancers pro- Province, College of Animal Science and Technology, duce endogenous non-coding RNAs (named enhancer RNAs, eRNAs) to activate gene Sichuan Agricultural expression. During chromatin looping, transcribed eRNAs interact with NamiRNAs University, Chengdu 611130, through enhancer-promoter interaction to perform similar functions. Here, we review China the functional diferences and similarities between eRNAs and NamiRNAs in myogen- esis and disease. We also propose models demonstrating their mutual mechanism and function. We conclude that eRNAs are active molecules, transcriptional regulators, and partners of NamiRNAs, rather than mere RNAs produced during enhancer activation. Keywords: Enhancer RNA, NamiRNAs, MicroRNA, Myogenesis, Transcriptional regulator Introduction Te identifcation of lin-4 miRNA in Caenorhabditis elegans in 1993 [1] triggered research to discover and understand small microRNAs’ (miRNAs) mechanisms. Recently, some miRNAs are reported to activate target genes during transcription via base pairing to the 3ʹ or 5ʹ untranslated regions (3ʹ or 5ʹ UTRs), the promoter [2], and the enhancer regions [3].
    [Show full text]
  • Identification of Key Genes and Pathways in Pancreatic Cancer
    G C A T T A C G G C A T genes Article Identification of Key Genes and Pathways in Pancreatic Cancer Gene Expression Profile by Integrative Analysis Wenzong Lu * , Ning Li and Fuyuan Liao Department of Biomedical Engineering, College of Electronic and Information Engineering, Xi’an Technological University, Xi’an 710021, China * Correspondence: [email protected]; Tel.: +86-29-86173358 Received: 6 July 2019; Accepted: 7 August 2019; Published: 13 August 2019 Abstract: Background: Pancreatic cancer is one of the malignant tumors that threaten human health. Methods: The gene expression profiles of GSE15471, GSE19650, GSE32676 and GSE71989 were downloaded from the gene expression omnibus database including pancreatic cancer and normal samples. The differentially expressed genes between the two types of samples were identified with the Limma package using R language. The gene ontology functional and pathway enrichment analyses of differentially-expressed genes were performed by the DAVID software followed by the construction of a protein–protein interaction network. Hub gene identification was performed by the plug-in cytoHubba in cytoscape software, and the reliability and survival analysis of hub genes was carried out in The Cancer Genome Atlas gene expression data. Results: The 138 differentially expressed genes were significantly enriched in biological processes including cell migration, cell adhesion and several pathways, mainly associated with extracellular matrix-receptor interaction and focal adhesion pathway in pancreatic cancer. The top hub genes, namely thrombospondin 1, DNA topoisomerase II alpha, syndecan 1, maternal embryonic leucine zipper kinase and proto-oncogene receptor tyrosine kinase Met were identified from the protein–protein interaction network.
    [Show full text]
  • Deep Sequencing Reveals New Aspects of Progesterone Receptor Signaling in Breast Cancer Cells
    Deep Sequencing Reveals New Aspects of Progesterone Receptor Signaling in Breast Cancer Cells Anastasia Kougioumtzi1, Panayiotis Tsaparas2, Angeliki Magklara3,4* 1 Department of Biological Applications and Technologies, University of Ioannina, Ioannina, Greece, 2 Department of Computer Science and Engineering, University of Ioannina, Ioannina, Greece, 3 Laboratory of Clinical Chemistry, School of Medicine, University of Ioannina, Ioannina, Greece, 4 Foundation of Research and Technology- Hellas, Institute of Molecular Biology & Biotechnology, Department of Biomedical Research, Ioannina, Greece Abstract Despite the pleiotropic effects of the progesterone receptor in breast cancer, the molecular mechanisms in play remain largely unknown. To gain a global view of the PR-orchestrated networks, we used next-generation sequencing to determine the progestin-regulated transcriptome in T47D breast cancer cells. We identify a large number of PR target genes involved in critical cellular programs, such as regulation of transcription, apoptosis, cell motion and angiogenesis. Integration of the transcriptomic data with the PR-binding profiling of hormonally treated cells identifies numerous components of the small- GTPases signaling pathways as direct PR targets. Progestin-induced deregulation of the small GTPases may contribute to the PR’s role in mammary tumorigenesis. Transcript expression analysis reveals significant expression changes of specific transcript variants in response to the extracellular hormonal stimulus. Using the NET1 gene as an example, we show that the PR can dictate alternative promoter usage leading to the upregulation of an isoform that may play a role in metastatic breast cancer. Future studies should aim to characterize these selectively regulated variants and evaluate their clinical utility in prognosis and targeted therapy of hormonally responsive breast tumors.
    [Show full text]
  • Cell Cycle Arrest Through Indirect Transcriptional Repression by P53: I Have a DREAM
    Cell Death and Differentiation (2018) 25, 114–132 Official journal of the Cell Death Differentiation Association OPEN www.nature.com/cdd Review Cell cycle arrest through indirect transcriptional repression by p53: I have a DREAM Kurt Engeland1 Activation of the p53 tumor suppressor can lead to cell cycle arrest. The key mechanism of p53-mediated arrest is transcriptional downregulation of many cell cycle genes. In recent years it has become evident that p53-dependent repression is controlled by the p53–p21–DREAM–E2F/CHR pathway (p53–DREAM pathway). DREAM is a transcriptional repressor that binds to E2F or CHR promoter sites. Gene regulation and deregulation by DREAM shares many mechanistic characteristics with the retinoblastoma pRB tumor suppressor that acts through E2F elements. However, because of its binding to E2F and CHR elements, DREAM regulates a larger set of target genes leading to regulatory functions distinct from pRB/E2F. The p53–DREAM pathway controls more than 250 mostly cell cycle-associated genes. The functional spectrum of these pathway targets spans from the G1 phase to the end of mitosis. Consequently, through downregulating the expression of gene products which are essential for progression through the cell cycle, the p53–DREAM pathway participates in the control of all checkpoints from DNA synthesis to cytokinesis including G1/S, G2/M and spindle assembly checkpoints. Therefore, defects in the p53–DREAM pathway contribute to a general loss of checkpoint control. Furthermore, deregulation of DREAM target genes promotes chromosomal instability and aneuploidy of cancer cells. Also, DREAM regulation is abrogated by the human papilloma virus HPV E7 protein linking the p53–DREAM pathway to carcinogenesis by HPV.Another feature of the pathway is that it downregulates many genes involved in DNA repair and telomere maintenance as well as Fanconi anemia.
    [Show full text]
  • UNIVERSITY of CALIFORNIA, SAN DIEGO Measuring
    UNIVERSITY OF CALIFORNIA, SAN DIEGO Measuring and Correlating Blood and Brain Gene Expression Levels: Assays, Inbred Mouse Strain Comparisons, and Applications to Human Disease Assessment A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Biomedical Sciences by Mary Elizabeth Winn Committee in charge: Professor Nicholas J Schork, Chair Professor Gene Yeo, Co-Chair Professor Eric Courchesne Professor Ron Kuczenski Professor Sanford Shattil 2011 Copyright Mary Elizabeth Winn, 2011 All rights reserved. 2 The dissertation of Mary Elizabeth Winn is approved, and it is acceptable in quality and form for publication on microfilm and electronically: Co-Chair Chair University of California, San Diego 2011 iii DEDICATION To my parents, Dennis E. Winn II and Ann M. Winn, to my siblings, Jessica A. Winn and Stephen J. Winn, and to all who have supported me throughout this journey. iv TABLE OF CONTENTS Signature Page iii Dedication iv Table of Contents v List of Figures viii List of Tables x Acknowledgements xiii Vita xvi Abstract of Dissertation xix Chapter 1 Introduction and Background 1 INTRODUCTION 2 Translational Genomics, Genome-wide Expression Analysis, and Biomarker Discovery 2 Neuropsychiatric Diseases, Tissue Accessibility and Blood-based Gene Expression 4 Mouse Models of Human Disease 5 Microarray Gene Expression Profiling and Globin Reduction 7 Finding and Accessible Surrogate Tissue for Neural Tissue 9 Genetic Background Effect Analysis 11 SPECIFIC AIMS 12 ENUMERATION OF CHAPTERS
    [Show full text]
  • Microarray Analysis of Differential Gene Expression Profile in Peripheral Blood Cells of Patients with Human Essential Hypertension Melvin T
    Int. J. Med. Sci. 2011, 8 168 International Journal of Medical Sciences 2011; 8(2):168-179 Research Paper Microarray Analysis of Differential Gene Expression Profile in Peripheral Blood Cells of Patients with Human Essential Hypertension Melvin T. Korkor1, Fan Bo Meng1, Shen Yang Xing2, Mu Chun Zhang3, Jin Rui Guo1, Xiao Xue Zhu1, Ping Yang1 1. Department of Internal Medicine and Cardiology, China-Japan Union Hospital, Norman Bethune College of Medicine, Jilin University, China, 2. College of Animal Science and veterinary medicine, Jilin University, China 3. Department of Urology, China-Japan Union Hospital, Norman Bethune College of Medicine, Jilin University, China Corresponding author: Ping Yang, Ph.D, Department of Internal Medicine and Cardiology, China-Japan Union Hospital, Norman Bethune College of Medicine, Jilin University, China. Tel: 0086-431-84995091; Fax: 0086-431-84995091; E-mail: [email protected] © Ivyspring International Publisher. This is an open-access article distributed under the terms of the Creative Commons License (http://creativecommons.org/ licenses/by-nc-nd/3.0/). Reproduction is permitted for personal, noncommercial use, provided that the article is in whole, unmodified, and properly cited. Received: 2010.10.31; Accepted: 2011.02.21; Published: 2011.02.27 Abstract The polygenic nature of essential hypertension and its dependence on environmental factors pose a challenge for biomedical research. We hypothesized that the analysis of gene ex- pression profiles from peripheral blood cells would distinguish patients with hypertension from normotensives. In order to test this, total RNA from peripheral blood cells was isolated. RNA was reversed-transcribed and labeled and gene expression analyzed using significance Analysis Microarrays (Stanford University, CA, USA).
    [Show full text]
  • DGCR5 Is Activated by PAX5 and Promotes Pancreatic Cancer Via
    Int. J. Biol. Sci. 2021, Vol. 17 498 Ivyspring International Publisher International Journal of Biological Sciences 2021; 17(2): 498-513. doi: 10.7150/ijbs.55636 Research Paper DGCR5 is activated by PAX5 and promotes pancreatic cancer via targeting miR-3163/TOP2A and activating Wnt/β-catenin pathway Shi-lei Liu1,2*, Chen Cai1,2*, Zi-yi Yang1,2*, Zi-you Wu1, Xiang-song Wu1,2, Xue-feng Wang1,2, Ping Dong1,2, Wei Gong1,2 1. Department of General Surgery, Xinhua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai 200092, China 2. Shanghai Key Laboratory of Biliary Tract Disease Research, No. 1665 Kongjiang Road, Shanghai 200092, China *These authors contributed equally to this work. Corresponding authors: Ping Dong, Department of General Surgery, Xinhua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, No.1665 Kongjiang Road, Shanghai 200092, China. Tel/Fax: +86-21-25077875; E-mail: [email protected]. Wei Gong, Department of General Surgery, Xinhua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, No.1665 Kongjiang Road, Shanghai 200092, China. Tel/Fax: +86-21-25077885; E-mail: [email protected] © The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions. Received: 2020.11.08; Accepted: 2020.12.22; Published: 2021.01.01 Abstract Long noncoding RNA DiGeorge syndrome critical region gene 5 (DGCR5) has been shown to be highly associated with cancer development.
    [Show full text]
  • Genomic Sequence Comparisons
    Harvard-MIT Division of Health Sciences and Technology HST.508: Genomics and Computational Biology Protein2: Last week's take home lessons • Separation of proteins & peptides • Protein localization & complexes • Peptide identification (MS/MS) – Database searching & sequencing. • Protein quantitation – Absolute & relative • Protein modifications & crosslinking • Protein - metabolite quantitation 1 Net1: Today's story & goals • Macroscopic continuous concentration rates – Cooperativity & Hill coefficients – Bistability • Mesoscopic discrete molecular numbers – Approximate & exact stochastic • Chromosome Copy Number Control • Flux balance optimization – Universal stoichiometric matrix – Genomic sequence comparisons 2 Networks Why model? Red blood cell metabolism Enzyme kinetics (Pro2) Cell division cycle Checkpoints (RNA2) Plasmid Copy No. Control Single molecules Phage λ switch Stochastic bistability Comparative metabolism Genomic connections Circadian rhythm Long time delays E. coli chemotaxis Adaptive, spatial effects also, all have large genetic & kinetic datasets. 3 Types of interaction models Quantum Electrodynamics subatomic Quantum mechanics electron clouds Molecular mechanics spherical atoms (101Pro1) Master equations stochastic single molecules (Net1) Phenomenological rates ODE Concentration & time (C,t) Flux Balance dCik/dt optima steady state (Net1) Thermodynamic models dCik/dt = 0 k reversible reactions Steady State ΣdCik/dt = 0 (sum k reactions) Metabolic Control Analysis d(dCik/dt)/dCj (i = chem.species) Spatially inhomogenous models dCi/dx Increasing scope, decreasing resolution 4 In vivo & (classical) in vitro 1) "Most measurements in enzyme kinetics are based on initial rate measurements, where only the substrate is present… enzymes in cells operate in the presence of their products" Fell p.54 (Pub) (http://www.amazon.com/exec/obidos/ASIN/185578047X/) 2) Enzymes & substrates are closer to equimolar than in classical in vitro experiments.
    [Show full text]