Mouse Tfdp2 Knockout Project (CRISPR/Cas9)
Total Page：16
File Type：pdf, Size：1020Kb
Load more
Recommended publications
-
Differential Expression Profile Prioritization of Positional Candidate Glaucoma Genes the GLC1C Locus
LABORATORY SCIENCES Differential Expression Profile Prioritization of Positional Candidate Glaucoma Genes The GLC1C Locus Frank W. Rozsa, PhD; Kathleen M. Scott, BS; Hemant Pawar, PhD; John R. Samples, MD; Mary K. Wirtz, PhD; Julia E. Richards, PhD Objectives: To develop and apply a model for priori- est because of moderate expression and changes in tization of candidate glaucoma genes. expression. Transcription factor ZBTB38 emerges as an interesting candidate gene because of the overall expres- Methods: This Affymetrix GeneChip (Affymetrix, Santa sion level, differential expression, and function. Clara, Calif) study of gene expression in primary cul- ture human trabecular meshwork cells uses a positional Conclusions: Only1geneintheGLC1C interval fits our differential expression profile model for prioritization of model for differential expression under multiple glau- candidate genes within the GLC1C genetic inclusion in- coma risk conditions. The use of multiple prioritization terval. models resulted in filtering 7 candidate genes of higher interest out of the 41 known genes in the region. Results: Sixteen genes were expressed under all condi- tions within the GLC1C interval. TMEM22 was the only Clinical Relevance: This study identified a small sub- gene within the interval with differential expression in set of genes that are most likely to harbor mutations that the same direction under both conditions tested. Two cause glaucoma linked to GLC1C. genes, ATP1B3 and COPB2, are of interest in the con- text of a protein-misfolding model for candidate selec- tion. SLC25A36, PCCB, and FNDC6 are of lesser inter- Arch Ophthalmol. 2007;125:117-127 IGH PREVALENCE AND PO- identification of additional GLC1C fami- tential for severe out- lies7,18-20 who provide optimal samples for come combine to make screening candidate genes for muta- adult-onset primary tions.7,18,20 The existence of 2 distinct open-angle glaucoma GLC1C haplotypes suggests that muta- (POAG) a significant public health prob- tions will not be limited to rare descen- H1 lem. -
Predicting Gene Ontology Biological Process from Temporal Gene Expression Patterns Astrid Lægreid,1,4 Torgeir R
Methods Predicting Gene Ontology Biological Process From Temporal Gene Expression Patterns Astrid Lægreid,1,4 Torgeir R. Hvidsten,2 Herman Midelfart,2 Jan Komorowski,2,3,4 and Arne K. Sandvik1 1Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, N-7489 Trondheim, Norway; 2Department of Information and Computer Science, Norwegian University of Science and Technology, N-7491 Trondheim, Norway; 3The Linnaeus Centre for Bioinformatics, Uppsala University, SE-751 24 Uppsala, Sweden The aim of the present study was to generate hypotheses on the involvement of uncharacterized genes in biological processes. To this end,supervised learning was used to analyz e microarray-derived time-series gene expression data. Our method was objectively evaluated on known genes using cross-validation and provided high-precision Gene Ontology biological process classifications for 211 of the 213 uncharacterized genes in the data set used. In addition,new roles in biological process were hypothesi zed for known genes. Our method uses biological knowledge expressed by Gene Ontology and generates a rule model associating this knowledge with minimal characteristic features of temporal gene expression profiles. This model allows learning and classification of multiple biological process roles for each gene and can predict participation of genes in a biological process even though the genes of this class exhibit a wide variety of gene expression profiles including inverse coregulation. A considerable number of the hypothesized new roles for known genes were confirmed by literature search. In addition,many biological process roles hypothesi zed for uncharacterized genes were found to agree with assumptions based on homology information. -
Differential Expression Profile Prioritization of Positional Candidate Glaucoma Genes the GLC1C Locus
LABORATORY SCIENCES Differential Expression Profile Prioritization of Positional Candidate Glaucoma Genes The GLC1C Locus Frank W. Rozsa, PhD; Kathleen M. Scott, BS; Hemant Pawar, PhD; John R. Samples, MD; Mary K. Wirtz, PhD; Julia E. Richards, PhD Objectives: To develop and apply a model for priori- est because of moderate expression and changes in tization of candidate glaucoma genes. expression. Transcription factor ZBTB38 emerges as an interesting candidate gene because of the overall expres- Methods: This Affymetrix GeneChip (Affymetrix, Santa sion level, differential expression, and function. Clara, Calif) study of gene expression in primary cul- ture human trabecular meshwork cells uses a positional Conclusions: Only1geneintheGLC1C interval fits our differential expression profile model for prioritization of model for differential expression under multiple glau- candidate genes within the GLC1C genetic inclusion in- coma risk conditions. The use of multiple prioritization terval. models resulted in filtering 7 candidate genes of higher interest out of the 41 known genes in the region. Results: Sixteen genes were expressed under all condi- tions within the GLC1C interval. TMEM22 was the only Clinical Relevance: This study identified a small sub- gene within the interval with differential expression in set of genes that are most likely to harbor mutations that the same direction under both conditions tested. Two cause glaucoma linked to GLC1C. genes, ATP1B3 and COPB2, are of interest in the con- text of a protein-misfolding model for candidate selec- tion. SLC25A36, PCCB, and FNDC6 are of lesser inter- Arch Ophthalmol. 2007;125:117-127 IGH PREVALENCE AND PO- identification of additional GLC1C fami- tential for severe out- lies7,18-20 who provide optimal samples for come combine to make screening candidate genes for muta- adult-onset primary tions.7,18,20 The existence of 2 distinct open-angle glaucoma GLC1C haplotypes suggests that muta- (POAG) a significant public health prob- tions will not be limited to rare descen- H1 lem. -
Rank Aggregation Via the Cross Entropy Algorithm
Finding cancer genes through meta-analysis of of the individual microarray studies were hybridized with the A®ymetrix GeneChip Human Genome HG-U133A and record expression levels for 22,283 Probe IDs. To make our ¯nal results meaningful and comprehensive at the same time, we decided to focus on microarray experiments that study di®erent types of cancer in humans. The goal of our meta-analysis is to identify genetic factors which are common across di®erent types and stages of cancer. For that purpose, we have selected 20 di®erent cancer-related microarray experiments which are included in the contest meta-dataset and have explicit cell type groupings necessary for detecting di®erentially expressed genes. Here, we list the selected experiment IDs along with the number of samples in each experiment in the parenthesis: E-MEXP-72 (20), E-MEXP-83 (22), E-MEXP-76 (17), E-MEXP-97 (24), E-MEXP-121 (30), E-MEXP-149 (20), E-MEXP-231 (58), E-MEXP-353 (96), E-TABM-26 (57), E-MEXP-669 (24), GSE4475 (221), GSE1456 (159), GSE5090 (17), GSE1420 (24), GSE1577 (29), GSE1729 (43), GSE2485 (18), GSE2603 (21), GSE3585 (12), GSE4127 (29). The total number of selected arrays is 941 (about 1/6 of the overall number of samples in the meta-dataset). One can refer to ArrayExpress database which provides public access to the microarray data from these experiments (http://www.ebi.ac.uk/arrayexpress/ ). 3 Methodology The proposed meta-analysis approach to microarray data is a two-step procedure: 1. Individual Analysis. By analyzing each microarray dataset individually, a set of \interesting" genes (top-50 Probe IDs) that exhibit the largest di®erences in terms of expression values between the groups is obtained for each dataset. -
Predicting Tissue-Specific Enhancers in the Human Genome
Downloaded from genome.cshlp.org on October 1, 2021 - Published by Cold Spring Harbor Laboratory Press Methods Predicting tissue-specific enhancers in the human genome Len A. Pennacchio,1,2 Gabriela G. Loots,3 Marcelo A. Nobrega,4 and Ivan Ovcharenko2,5,6 1Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA; 2U.S. Department of Energy, Joint Genome Institute, Walnut Creek, California 94598, USA; 3Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, California 94550, USA; 4Department of Human Genetics, University of Chicago, Chicago, Illinois 60637, USA; 5Computation Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA Determining how transcriptional regulatory signals are encoded in vertebrate genomes is essential for understanding the origins of multicellular complexity; yet the genetic code of vertebrate gene regulation remains poorly understood. In an attempt to elucidate this code, we synergistically combined genome-wide gene-expression profiling, vertebrate genome comparisons, and transcription factor binding-site analysis to define sequence signatures characteristic of candidate tissue-specific enhancers in the human genome. We applied this strategy to microarray-based gene expression profiles from 79 human tissues and identified 7187 candidate enhancers that defined their flanking gene expression, the majority of which were located outside of known promoters. We cross-validated this method for its ability to de novo predict tissue-specific gene expression and confirmed its reliability in 57 of the 79 available human tissues, with an average precision in enhancer recognition ranging from 32% to 63% and a sensitivity of 47%. We used the sequence signatures identified by this approach to successfully assign tissue-specific predictions to ∼328,000 human–mouse conserved noncoding elements in the human genome. -
Gene Expression Differences Associated with Human Papillomavirus Status in Head and Neck Squamous Cell Carcinoma Robbertj.C
Human Cancer Biology Gene Expression Differences Associated with Human Papillomavirus Status in Head and Neck Squamous Cell Carcinoma RobbertJ.C. Slebos,1, 2 Yajun Yi, 7 Kim Ely,3 Jesse Carter,8 Amy Evjen,1Xueqiong Zhang,4 Yu Shyr,4 Barbara M. Murphy,8 Anthony J. Cmelak,5 Brian B. Burkey,2 James L. Netterville,2 Shawn Levy,6 Wendell G. Yarbrough,1, 2 and Christine H. Chung8 Abstract Human papillomavirus (HPV) is associated with a subset of head and neck squamous cell carcinoma (HNSCC). Between 15% and 35% of HNSCCs harbor HPV DNA. Demographic and exposure differences between HPV-positive (HPV+) and negative (HPVÀ) HNSCCs suggest that HPV + tumors may constitute a subclass with different biology, whereas clinical differences have also been observed. Gene expression profiles of HPV+ and HPVÀ tumors were compared with further exploration of the biological effect of HPV in HNSCC. Thirty-six HNSCC tumors were analyzed using Affymetrix Human 133U Plus 2.0 GeneChip and for HPV by PCR and real-time PCR. Eight of 36 (22%) tumors were positive for HPV subtype 16. Statistical analysis using Significance Analysis of Microarrays based on HPV status as a supervising variable resulted in a list of 91genes that were differentially expressed with statistical significance. Results for a subset of these genes were verified by real-time PCR. Genes highly expressed in HPV+ samples included cell cycle regulators (p16INK4A, p18, and CDC7) and transcription factors (TAF7L, RFC4, RPA2, andTFDP2).The microarray data were also investigated by mapping genes by chromosomal loca- tion (DIGMAP).A large number of genes on chromosome 3q24-qter had high levels of expression in HPV+ tumors. -
Genome Wide Association of Chronic Kidney Disease Progression: the CRIC Study (Author List and Affiliations Listed at End of Document)
SUPPLEMENTARY MATERIALS Genome Wide Association of Chronic Kidney Disease Progression: The CRIC Study (Author list and affiliations listed at end of document) Genotyping information page 2 Molecular pathway analysis information page 3 Replication cohort acknowledgments page 4 Supplementary Table 1. AA top hit region gene function page 5-6 Supplementary Table 2. EA top hit region gene function page 7 Supplementary Table 3. GSA pathway results page 8 Supplementary Table 4. Number of molecular interaction based on top candidate gene molecular networks page 9 Supplementary Table 5. Results of top gene marker association in AA, based on EA derived candidate gene regions page 10 Supplementary Table 6. Results of top gene marker association in EA, based on AA derived candidate gene regions page 11 Supplementary Table 7. EA Candidate SNP look up page 12 Supplementary Table 8. AA Candidate SNP look up page 13 Supplementary Table 9. Replication cohorts page 14 Supplementary Table 10. Replication cohort study characteristics page 15 Supplementary Figure 1a-b. Boxplot of eGFR decline in AA and EA page 16 Supplementary Figure 2a-l. Regional association plot of candidate SNPs identified in AA groups pages 17-22 Supplementary Figure 3a-f. Regional association plot of candidate SNPs identified in EA groups pages 23-25 Supplementary Figure 4. Molecular Interaction network of candidate genes for renal, cardiovascular and immunological diseases pages 26-27 Supplementary Figure 5. Molecular Interaction network of candidate genes for renal diseases pages 28-29 Supplementary Figure 6. ARRDC4 LD map page 30 Author list and affiliations page 31 1 Supplemental Materials Genotyping Genotyping was performed on a total of 3,635 CRIC participants who provided specific consent for investigations of inherited genetics (of a total of 3,939 CRIC participants). -
Supplementary Data
Supplementary Figure 1 Supplementary Figure 2 CCR-10-3244.R1 Supplementary Figure Legends Supplementary Figure 1. B-Myb is overexpressed in primary AML blasts and B-CLL cells. Baseline B-Myb mRNA levels were determined by quantitative RT-PCR, after normalization to the level of housekeeping gene, in primary B-CLL (n=10) and AML (n=5) patient samples, and in normal CD19+ (n=5) and CD34+ (n=4) cell preparations. Each sample was determined in triplicate. Horizontal bars are median, upper and lower edges of box are 75th and 25th percentiles, lines extending from box are 10th and 90th percentiles. Supplementary Figure 2. Cytotoxicity by Nutlin-3 and Chlorambucil used alone or in combination in leukemic cells. The p53wild-type EHEB and SKW6.4 cells lines, and the p53mutated BJAB cell line were exposed to Nutlin-3 or Chlorambucil used either alone or in combination. (Nutl.+Chlor.). In A, upon treatment with Nutlin-3 or Chlorambucil, used either alone (both at 10 μM) or in combination (Nutl.+Chlor.), induction of apoptosis was quantitatively evaluated by Annexin V/PI staining, while E2F1 and pRb protein levels were analyzed by Western blot. Tubulin staining is shown as loading control. The average combination index (CI) values (analyzed by the method of Chou and Talalay) for effects of Chlorambucil+Nutlin-3 on cell viability are shown. ED indicates effect dose. In B, levels of B-Myb and E2F1 mRNA were analyzed by quantitative RT- PCR. Results are expressed as fold of B-Myb and E2F1 modulation in cells treated for 24 hours as indicated, with respect to the control untreated cultures set to 1 (hatched line). -
Functional Genomic Annotation of Genetic Risk Loci Highlights Inflammation and Epithelial Biology Networks in CKD
BASIC RESEARCH www.jasn.org Functional Genomic Annotation of Genetic Risk Loci Highlights Inflammation and Epithelial Biology Networks in CKD Nora Ledo, Yi-An Ko, Ae-Seo Deok Park, Hyun-Mi Kang, Sang-Youb Han, Peter Choi, and Katalin Susztak Renal Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania ABSTRACT Genome-wide association studies (GWASs) have identified multiple loci associated with the risk of CKD. Almost all risk variants are localized to the noncoding region of the genome; therefore, the role of these variants in CKD development is largely unknown. We hypothesized that polymorphisms alter transcription factor binding, thereby influencing the expression of nearby genes. Here, we examined the regulation of transcripts in the vicinity of CKD-associated polymorphisms in control and diseased human kidney samples and used systems biology approaches to identify potentially causal genes for prioritization. We interro- gated the expression and regulation of 226 transcripts in the vicinity of 44 single nucleotide polymorphisms using RNA sequencing and gene expression arrays from 95 microdissected control and diseased tubule samples and 51 glomerular samples. Gene expression analysis from 41 tubule samples served for external validation. 92 transcripts in the tubule compartment and 34 transcripts in glomeruli showed statistically significant correlation with eGFR. Many novel genes, including ACSM2A/2B, FAM47E, and PLXDC1, were identified. We observed that the expression of multiple genes in the vicinity of any single CKD risk allele correlated with renal function, potentially indicating that genetic variants influence multiple transcripts. Network analysis of GFR-correlating transcripts highlighted two major clusters; a positive correlation with epithelial and vascular functions and an inverse correlation with inflammatory gene cluster. -
Chromatin Conformation Links Distal Target Genes to CKD Loci
BASIC RESEARCH www.jasn.org Chromatin Conformation Links Distal Target Genes to CKD Loci Maarten M. Brandt,1 Claartje A. Meddens,2,3 Laura Louzao-Martinez,4 Noortje A.M. van den Dungen,5,6 Nico R. Lansu,2,3,6 Edward E.S. Nieuwenhuis,2 Dirk J. Duncker,1 Marianne C. Verhaar,4 Jaap A. Joles,4 Michal Mokry,2,3,6 and Caroline Cheng1,4 1Experimental Cardiology, Department of Cardiology, Thoraxcenter Erasmus University Medical Center, Rotterdam, The Netherlands; and 2Department of Pediatrics, Wilhelmina Children’s Hospital, 3Regenerative Medicine Center Utrecht, Department of Pediatrics, 4Department of Nephrology and Hypertension, Division of Internal Medicine and Dermatology, 5Department of Cardiology, Division Heart and Lungs, and 6Epigenomics Facility, Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands ABSTRACT Genome-wide association studies (GWASs) have identified many genetic risk factors for CKD. However, linking common variants to genes that are causal for CKD etiology remains challenging. By adapting self-transcribing active regulatory region sequencing, we evaluated the effect of genetic variation on DNA regulatory elements (DREs). Variants in linkage with the CKD-associated single-nucleotide polymorphism rs11959928 were shown to affect DRE function, illustrating that genes regulated by DREs colocalizing with CKD-associated variation can be dysregulated and therefore, considered as CKD candidate genes. To identify target genes of these DREs, we used circular chro- mosome conformation capture (4C) sequencing on glomerular endothelial cells and renal tubular epithelial cells. Our 4C analyses revealed interactions of CKD-associated susceptibility regions with the transcriptional start sites of 304 target genes. Overlap with multiple databases confirmed that many of these target genes are involved in kidney homeostasis. -
Dendritic Cell Maturation Transcription Factor E2F1 Suppresses
Transcription Factor E2F1 Suppresses Dendritic Cell Maturation Fang Fang, Yan Wang, Rui Li, Ying Zhao, Yang Guo, Ming Jiang, Jie Sun, Yang Ma, Zijia Ren, Zhigang Tian, Feng This information is current as Wei, De Yang and Weihua Xiao of September 29, 2021. J Immunol 2010; 184:6084-6091; Prepublished online 26 April 2010; doi: 10.4049/jimmunol.0902561 http://www.jimmunol.org/content/184/11/6084 Downloaded from References This article cites 40 articles, 21 of which you can access for free at: http://www.jimmunol.org/content/184/11/6084.full#ref-list-1 http://www.jimmunol.org/ Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication by guest on September 29, 2021 *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2010 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Transcription Factor E2F1 Suppresses Dendritic Cell Maturation Fang Fang,*,1 Yan Wang,*,1 Rui Li,* Ying Zhao,* Yang Guo,* Ming Jiang,* Jie Sun,* Yang Ma,* Zijia Ren,* Zhigang Tian,* Feng Wei,† De Yang,†,‡ and Weihua Xiao* Transcription factor E2F1 has been largely studied as a promoter of S-phase transition in the cell cycle and as a regulator of ap- optosis. -
1 Supplementary Information for Acetylated Histone H3K56 Interacts
Supplementary Information for Acetylated histone H3K56 interacts with Oct4 to promote mouse embryonic stem cell pluripotency Table of contents Supplementary Figures 1-4 and Figure Legends Supplementary Methods Cell culture Plasmid construction and transfection ChIP-Sequencing ChIP-Seq data analysis K-means clustering Co-immunoprecipitation assay In vivo peptide pull-down assay Flag-immunoprecipitation assay In vitro peptide pull-down assay Mononucleosome immunoprecipitation Western blot Quantitative PCR Gel mobility shift assay Supplementary Tables 1-8 Supplementary References 1 Supplementary Figures and Legends 0 1 %&'() %&'() *(+, *(+, !"#$ !"#$ -./01&" -./01&" 023 ()*+ 023 ,'-+ . / %&'() %&'() *(+, *(+, !"#$ !"#$ -./01&" -./01&" 023 !"#$% 023 !$&"' Supplementary Figure 1. The distribution of ChIP-Seq signals for NSO and H3K56ac at Cluster 1 regions. (A-D) Enrichment patterns of Nanog, Sox2 and Oct4 (NSO) and H3K56ac at Oct4 (also known as Pou5f1) (A), Klf4 (B), Nanog (C), and Nodal (D) gene loci are shown by University of California, Santa Cruz (UCSC) genome browser. 2 !"#$%&$"'($)*+($,- . F&G-(%5+# F&G-(%5+: F&G-(%5+; F&G-(%5+2 2!! 2!! 2!! 2!! ;!! ;!! ;!! ;!! :!! :!! :!! :!! #!! #!! #!! #!! A50B)&%+0B+C;D"E'/ <05='&>)%,+?*%5'@%+ ! ! ! ! 92 9: ! : 2 92 9: ! : 2 92 9: ! : 2 92 9: ! : 2 $%&'()*%+,)-('./%+(0+ $%&'()*%+,)-('./%+(0+ $%&'()*%+,)-('./%+(0+ $%&'()*%+,)-('./%+(0+ 1/(2+3%'4+/%.(%5-+6478 1/(2+3%'4+/%.(%5-+6478 1/(2+3%'4+/%.(%5-+6478 1/(2+3%'4+/%.(%5-+6478 / F&G-(%5+# F&G-(%5+: F&G-(%5+; F&G-(%5+2 #"! #"! #"! #"! #!! #!! #!! #!!