The Function and Production of Eccdna
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DEPs in osteosarcoma cells comparing to osteoblastic cells Biological Process Protein Percentage of Hits metabolic process (GO:0008152) 29.3 29.3% cellular process (GO:0009987) 20.2 20.2% localization (GO:0051179) 9.4 9.4% biological regulation (GO:0065007) 8 8.0% developmental process (GO:0032502) 7.8 7.8% response to stimulus (GO:0050896) 5.6 5.6% cellular component organization (GO:0071840) 5.6 5.6% multicellular organismal process (GO:0032501) 4.4 4.4% immune system process (GO:0002376) 4.2 4.2% biological adhesion (GO:0022610) 2.7 2.7% apoptotic process (GO:0006915) 1.6 1.6% reproduction (GO:0000003) 0.8 0.8% locomotion (GO:0040011) 0.4 0.4% cell killing (GO:0001906) 0.1 0.1% 100.1% Genes 2179Hits 3870 biological adhesion apoptotic process … reproduction (GO:0000003) , 0.8% (GO:0022610) , 2.7% locomotion (GO:0040011) ,… immune system process cell killing (GO:0001906) , 0.1% (GO:0002376) , 4.2% multicellular organismal process (GO:0032501) , metabolic process 4.4% (GO:0008152) , 29.3% cellular component organization (GO:0071840) , 5.6% response to stimulus (GO:0050896), 5.6% developmental process (GO:0032502) , 7.8% biological regulation (GO:0065007) , 8.0% cellular process (GO:0009987) , 20.2% localization (GO:0051179) , 9. -
A Network Propagation Approach to Prioritize Long Tail Genes in Cancer
bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429983; this version posted February 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. A Network Propagation Approach to Prioritize Long Tail Genes in Cancer Hussein Mohsen1,*, Vignesh Gunasekharan2, Tao Qing2, Sahand Negahban3, Zoltan Szallasi4, Lajos Pusztai2,*, Mark B. Gerstein1,5,6,3,* 1 Computational Biology & Bioinformatics Program, Yale University, New Haven, CT 06511, USA 2 Breast Medical Oncology, Yale School of Medicine, New Haven, CT 06511, USA 3 Department of Statistics & Data Science, Yale University, New Haven, CT 06511, USA 4 Children’s Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA 5 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA 6 Department of Computer Science, Yale University, New Haven, CT 06511, USA * Corresponding author Abstract Introduction. The diversity of genomic alterations in cancer pose challenges to fully understanding the etiologies of the disease. Recent interest in infrequent mutations, in genes that reside in the “long tail” of the mutational distribution, uncovered new genes with significant implication in cancer development. The study of these genes often requires integrative approaches with multiple types of biological data. Network propagation methods have demonstrated high efficacy in uncovering genomic patterns underlying cancer using biological interaction networks. Yet, the majority of these analyses have focused their assessment on detecting known cancer genes or identifying altered subnetworks. -
The Middle Temporal Gyrus Is Transcriptionally Altered in Patients with Alzheimer’S Disease
1 The middle temporal gyrus is transcriptionally altered in patients with Alzheimer’s Disease. 2 1 3 Shahan Mamoor 1Thomas Jefferson School of Law 4 East Islip, NY 11730 [email protected] 5 6 We sought to understand, at the systems level and in an unbiased fashion, how gene 7 expression was most different in the brains of patients with Alzheimer’s Disease (AD) by mining published microarray datasets (1, 2). Comparing global gene expression profiles between 8 patient and control revealed that a set of 84 genes were expressed at significantly different levels in the middle temporal gyrus (MTG) of patients with Alzheimer’s Disease (1, 2). We used 9 computational analyses to classify these genes into known pathways and existing gene sets, 10 and to describe the major differences in the epigenetic marks at the genomic loci of these genes. While a portion of these genes is computationally cognizable as part of a set of genes 11 up-regulated in the brains of patients with AD (3), many other genes in the gene set identified here have not previously been studied in association with AD. Transcriptional repression, both 12 pre- and post-transcription appears to be affected; nearly 40% of these genes are transcriptional 13 targets of MicroRNA-19A/B (miR-19A/B), the zinc finger protein 10 (ZNF10), or of the AP-1 repressor jun dimerization protein 2 (JDP2). 14 15 16 17 18 19 20 21 22 23 24 25 26 Keywords: Alzheimer’s Disease, systems biology of Alzheimer’s Disease, differential gene 27 expression, middle temporal gyrus. -
Identification of the Key Genes and Pathways in Prostate Cancer
ONCOLOGY LETTERS 16: 6663-6669, 2018 Identification of the key genes and pathways in prostate cancer SHUTONG FAN1*, ZUMU LIANG1*, ZHIQIN GAO1, ZHIWEI PAN2, SHAOJIE HAN3, XIAOYING LIU1, CHUNLING ZHAO1, WEIWEI YANG1, ZHIFANG PAN1 and WEIGUO FENG1 1College of Bioscience and Technology, Weifang Medical University, Weifang, Shandong 261053; 2Department of Internal Medicine, Laizhou Development Zone Hospital, Yantai, Shandong 261400; 3Animal Epidemic Prevention and Epidemic Control Center, Changle County Bureau of Animal Health and Production, Weifang, Shandong 262400, P.R. China Received March 5, 2018; Accepted September 17, 2018 DOI: 10.3892/ol.2018.9491 Abstract. Prostate cancer (PCa) is one of the most common Introduction malignancies in men globally. The aim of the present study was to identify the key genes and pathways involved in the Prostate cancer (PCa) is one of the most common malignancies occurrence of PCa. Gene expression profile (GSE55945) in men globally and the second leading cause of cancer was downloaded from Gene Expression Omnibus, and associated mortality in developed countries (1,2). Like other the differentially expressed genes (DEGs) were identified. cancers, PCa is considered to be a disease which caused by Subsequently, Gene ontology analysis, KEGG pathway age, diet and gene aberrations (3). Accumulating evidences analysis and protein-protein interaction (PPI) analysis of have demonstrated that a series of genes and pathways involved DEGs were performed. Finally, the identified key genes were in the occurrence, progression and metastasis of PCa (4). At confirmed by immunohistochemistry. The GO analysis results present, the underlying mechanism of PCa occurrence is still showed that the DEGs were mainly participated in cell cycle, unclear, which limits the diagnosis and therapy. -
Protein Purification Protein Localization in Vivo Fluorescent Imaging Protein Arrays Real Time Imaging Protein Interactions Protein Trafficking Protein Turnover
Overcoming Challenges of Protein Analysis in Mammalian Systems Danette L. Daniels, Ph.D. Current Technologies for Protein Analysis Biochemical/ In Vivo Proteomic Cell Based Animal Analysis Analysis Models Fluorescent proteins Affinity tags Antibodies How about a system applicable to the all approaches that also addresses limitations of current methods? • Minimal interference with protein of interest • Efficient capture/isolation • Detection/real-time imaging • Differential labeling • High Signal/background HaloTag Platform Biochemical/ In Vivo Proteomic Cell Based Animal Analysis Analysis Models Protein purification Protein localization In vivo fluorescent imaging Protein arrays Real time imaging Protein interactions Protein trafficking Protein turnover HaloTag® HaloCHIP™ HaloLink™ HaloTag® Fluorescent Purification Protein:DNA Protein Arrays Pull-Down Ligands HaloTag is a Genetically Engineered Protein Fusion Tag O Functional Protein of Cl O Interest HT + group Protein of Functional HT O O Interest group . A monomeric , 34 kDa, modified bacterial dehalogenase genetically engineered to covalently bind specific, synthetic HaloTag® ligands . Irreversible, covalent attachment of chemical functionalities . Suitable as either N- or C- terminal fusion Mutagenized HaloTag® Protein Enables Covalent HaloTag®-Ligand Complex Hydrolase (DhaA) HaloTag® Catalytic process Facilitated bond formation T r p 1 0 7 T r p 1 0 7 HaloTag®: • 34kDa protein • Monomeric N N H N 4 1 H N A s n - H H • Single change: C l 4 1 A s n C l 2 1 His272Phe for covalent O R O O - C C bond. 3 R O 1 0 6 A s p 1 0 6 A s p O H O H Covalent bond: H H O O • Stable after N C G l u 1 3 0 C G l u 1 3 0 N - O denaturation. -
Novel Driver Strength Index Highlights Important Cancer Genes in TCGA Pancanatlas Patients
medRxiv preprint doi: https://doi.org/10.1101/2021.08.01.21261447; this version posted August 5, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients Aleksey V. Belikov*, Danila V. Otnyukov, Alexey D. Vyatkin and Sergey V. Leonov Laboratory of Innovative Medicine, School of Biological and Medical Physics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Moscow Region, Russia *Corresponding author: [email protected] NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. 1 medRxiv preprint doi: https://doi.org/10.1101/2021.08.01.21261447; this version posted August 5, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Abstract Elucidating crucial driver genes is paramount for understanding the cancer origins and mechanisms of progression, as well as selecting targets for molecular therapy. Cancer genes are usually ranked by the frequency of mutation, which, however, does not necessarily reflect their driver strength. Here we hypothesize that driver strength is higher for genes that are preferentially mutated in patients with few driver mutations overall, because these few mutations should be strong enough to initiate cancer. -
Characterizing Novel Interactions of Transcriptional Repressor Proteins BCL6 & BCL6B
Characterizing Novel Interactions of Transcriptional Repressor Proteins BCL6 & BCL6B by Geoffrey Graham Lundell-Smith A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Biochemistry University of Toronto © Copyright by Geoffrey Lundell-Smith, 2017 Characterizing Novel Interactions of Transcriptional Repression Proteins BCL6 and BCL6B Geoffrey Graham Lundell-Smith Masters of Science Department of Biochemistry University of Toronto 2016 Abstract B-cell Lymphoma 6 (BCL6) and its close homolog BCL6B encode proteins that are members of the BTB-Zinc Finger family of transcription factors. BCL6 plays an important role in regulating the differentiation and proliferation of B-cells during the adaptive immune response, and is also involved in T cell development and inflammation. BCL6 acts by repressing genes involved in DNA damage response during the affinity maturation of immunoglobulins, and the mis- expression of BCL6 can lead to diffuse large B-cell lymphoma. Although BCL6B shares high sequence similarity with BCL6, the functions of BCL6B are not well-characterized. I used BioID, an in vivo proximity-dependent labeling method, to identify novel BCL6 and BCL6B protein interactors and validated a number of these interactions with co-purification experiments. I also examined the evolutionary relationship between BCL6 and BCL6B and identified conserved residues in an important interaction interface that mediates corepressor binding and gene repression. ii Acknowledgments Thank you to my supervisor, Gil Privé for his mentorship, guidance, and advice, and for giving me the opportunity to work in his lab. Thanks to my committee members, Dr. John Rubinstein and Dr. Jeff Lee for their ideas, thoughts, and feedback during my Masters. -
Attachment PDF Icon
Spectrum Name of Protein Count of Peptides Ratio (POL2RA/IgG control) POLR2A_228kdBand POLR2A DNA-directed RNA polymerase II subunit RPB1 197 NOT IN CONTROL IP POLR2A_228kdBand POLR2B DNA-directed RNA polymerase II subunit RPB2 146 NOT IN CONTROL IP POLR2A_228kdBand RPAP2 Isoform 1 of RNA polymerase II-associated protein 2 24 NOT IN CONTROL IP POLR2A_228kdBand POLR2G DNA-directed RNA polymerase II subunit RPB7 23 NOT IN CONTROL IP POLR2A_228kdBand POLR2H DNA-directed RNA polymerases I, II, and III subunit RPABC3 19 NOT IN CONTROL IP POLR2A_228kdBand POLR2C DNA-directed RNA polymerase II subunit RPB3 17 NOT IN CONTROL IP POLR2A_228kdBand POLR2J RPB11a protein 7 NOT IN CONTROL IP POLR2A_228kdBand POLR2E DNA-directed RNA polymerases I, II, and III subunit RPABC1 8 NOT IN CONTROL IP POLR2A_228kdBand POLR2I DNA-directed RNA polymerase II subunit RPB9 9 NOT IN CONTROL IP POLR2A_228kdBand ALMS1 ALMS1 3 NOT IN CONTROL IP POLR2A_228kdBand POLR2D DNA-directed RNA polymerase II subunit RPB4 6 NOT IN CONTROL IP POLR2A_228kdBand GRINL1A;Gcom1 Isoform 12 of Protein GRINL1A 6 NOT IN CONTROL IP POLR2A_228kdBand RECQL5 Isoform Beta of ATP-dependent DNA helicase Q5 3 NOT IN CONTROL IP POLR2A_228kdBand POLR2L DNA-directed RNA polymerases I, II, and III subunit RPABC5 5 NOT IN CONTROL IP POLR2A_228kdBand KRT6A Keratin, type II cytoskeletal 6A 3 NOT IN CONTROL IP POLR2A_228kdBand POLR2K DNA-directed RNA polymerases I, II, and III subunit RPABC4 2 NOT IN CONTROL IP POLR2A_228kdBand RFC4 Replication factor C subunit 4 1 NOT IN CONTROL IP POLR2A_228kdBand RFC2 -
Extrachromosomal Circular DNA, Microdna, Without Canonical Promoters Produce Short Regulatory Rnas That Suppress Gene Expression
bioRxiv preprint doi: https://doi.org/10.1101/535831; this version posted January 31, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Extrachromosomal circular DNA, microDNA, without canonical promoters produce short regulatory RNAs that suppress gene expression Teressa Paulsen, Yoshiyuki Shibata, Pankaj Kumar, Laura Dillon, Anindya Dutta* Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA To whom correspondence should be addressed. Tel: 434-924-2466 Email: [email protected] Present Address: Dr. Anindya Dutta, Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA 1 bioRxiv preprint doi: https://doi.org/10.1101/535831; this version posted January 31, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. ABSTRACT Interest in extrachromosomal circular DNA (eccDNA) molecules has increased recently because of their widespread presence in normal cells across every species ranging from yeast to humans, their increased levels in cancer cells, and their overlap with oncogenic and drug- resistant genes. However, the majority of eccDNA (microDNA) are too small to carry protein coding genes. We have tested functional capabilities of microDNA, by creating artificial microDNA molecules mimicking known microDNA sequences and have discovered that they express functional small regulatory RNA including microRNA and novel si-like RNA. MicroDNA is transcribed in vitro and in vivo independent of a canonical promoter sequence. -
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. -
Supplemental Data.Pdf
Supplementary material -Table of content Supplementary Figures (Fig 1- Fig 6) Supplementary Tables (1-13) Lists of genes belonging to distinct biological processes identified by GREAT analyses to be significantly enriched with UBTF1/2-bound genes Supplementary Table 14 List of the common UBTF1/2 bound genes within +/- 2kb of their TSSs in NIH3T3 and HMECs. Supplementary Table 15 List of gene identified by microarray expression analysis to be differentially regulated following UBTF1/2 knockdown by siRNA Supplementary Table 16 List of UBTF1/2 binding regions overlapping with histone genes in NIH3T3 cells Supplementary Table 17 List of UBTF1/2 binding regions overlapping with histone genes in HMEC Supplementary Table 18 Sequences of short interfering RNA oligonucleotides Supplementary Table 19 qPCR primer sequences for qChIP experiments Supplementary Table 20 qPCR primer sequences for reverse transcription-qPCR Supplementary Table 21 Sequences of primers used in CHART-PCR Supplementary Methods Supplementary Fig 1. (A) ChIP-seq analysis of UBTF1/2 and Pol I (POLR1A) binding across mouse rDNA. UBTF1/2 is enriched at the enhancer and promoter regions and along the entire transcribed portions of rDNA with little if any enrichment in the intergenic spacer (IGS), which separates the rDNA repeats. This enrichment coincides with the distribution of the largest subunit of Pol I (POLR1A) across the rDNA. All sequencing reads were mapped to the published complete sequence of the mouse rDNA repeat (Gene bank accession number: BK000964). The graph represents the frequency of ribosomal sequences enriched in UBTF1/2 and Pol I-ChIPed DNA expressed as fold change over those of input genomic DNA. -
Gene Duplication and Neofunctionalization: POLR3G and POLR3GL
Downloaded from genome.cshlp.org on September 27, 2021 - Published by Cold Spring Harbor Laboratory Press Research Gene duplication and neofunctionalization: POLR3G and POLR3GL Marianne Renaud,1 Viviane Praz,1,2 Erwann Vieu,1,5 Laurence Florens,3 Michael P. Washburn,3,4 Philippe l’Hoˆte,1 and Nouria Hernandez1,6 1Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland; 2Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; 3Stowers Institute for Medical Research, Kansas City, Missouri 64110, USA; 4Department of Pathology and Laboratory Medicine, The University of Kansas Medical Center, Kansas City, Kansas 66160, USA RNA polymerase III (Pol III) occurs in two versions, one containing the POLR3G subunit and the other the closely related POLR3GL subunit. It is not clear whether these two Pol III forms have the same function, in particular whether they recognize the same target genes. We show that the POLR3G and POLR3GL genes arose from a DNA-based gene duplication, probably in a common ancestor of vertebrates. POLR3G- as well as POLR3GL-containing Pol III are present in cultured cell lines and in normal mouse liver, although the relative amounts of the two forms vary, with the POLR3G-containing Pol III relatively more abundant in dividing cells. Genome-wide chromatin immunoprecipitations followed by high-throughput sequencing (ChIP-seq) reveal that both forms of Pol III occupy the same target genes, in very constant proportions within one cell line, suggesting that the two forms of Pol III have a similar function with regard to specificity for target genes. In contrast, the POLR3G promoter—not the POLR3GL promoter—binds the transcription factor MYC, as do all other pro- moters of genes encoding Pol III subunits.