Identification of Gastric Cancer–Related Genes Using a Cdna Microarray Containing Novel Expressed Sequence Tags Expressed in Gastric Cancer Cells
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PARSANA-DISSERTATION-2020.Pdf
DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks. -
Examples of Successful Protein Expression with SUMO Reference Protein Type Family Kda System (Pubmed ID)
Examples of Successful Protein Expression with SUMO Reference Protein Type Family kDa System (PubMed ID) 23 (FGF23), human Growth factor FGF superfamily ~26 E. coli 22249723 SARS coronavirus (SARS-CoV) membrane 3C-like (3CL) protease Viral membrane protein protein 33.8 E. coli 16211506 5′nucleotidase-related apyrase (5′Nuc) Saliva protein (apyrase) 5′nucleotidase-related proteins 65 E. coli 20351782 Acetyl-CoA carboxylase 1 (ACC1) Cytosolic enzyme Family of five biotin-dependent carboxylases ~7 E. coli 22123817 Acetyl-CoA carboxylase 2 (ACC2) BCCP domain Cytosolic enzyme Family of five biotin-dependent carboxylases ~7 E. coli 22123817 Actinohivin (AH) Lectin Anti-HIV lectin of CBM family 13 12.5 E. coli DTIC Allium sativum leaf agglutinin (ASAL) Sugar-binding protein Mannose-binding lectins 25 E. coli 20100526 Extracellular matrix Anosmin protein Marix protein 100 Mammalian 22898776 Antibacterial peptide CM4 (ABP-CM4) Antibacterial peptide Cecropin family of antimicrobial peptides 3.8 E. coli 19582446 peptide from centipede venoms of Scolopendra Antimicrobial peptide scolopin 1 (AMP-scolopin 1) small cationic peptide subspinipes mutilans 2.6 E. coli 24145284 Antitumor-analgesic Antitumor-analgesic peptide (AGAP) peptide Multifunction scorpion peptide 7 E. coli 20945481 Anti-VEGF165 single-chain variable fragment (scFv) Antibody Small antibody-engineered antibody 30 E. coli 18795288 APRIL TNF receptor ligand tumor necrosis factor (TNF) ligand 16 E. coli 24412409 APRIL (A proliferation-inducing ligand, also named TALL- Type II transmembrane 2, TRDL-1 and TNFSF-13a) protein Tumor necrosis factor (TNF) family 27.51 E. coli 22387304 Aprotinin/Basic pancreatic trypsin inhibitor (BPTI) Inhibitor Kunitz-type inhibitor 6.5 E. -
Metallothionein-Protein Interactions
DOI 10.1515/bmc-2012-0049 BioMol Concepts 2013; 4(2): 143–160 Review S í lvia Atrian * and Merc è Capdevila Metallothionein-protein interactions Abstract: Metallothioneins (MTs) are a family of univer- Introduction sal, small proteins, sharing a high cysteine content and an optimal capacity for metal ion coordination. They take Metallothioneins (MTs) are a family of small ( < 10 kDa), part in a plethora of metal ion-related events (from detoxi- extremely heterogeneous proteins, sharing a high cysteine fication to homeostasis, storage, and delivery), in a wide content (15 – 30 % ) that confers them an optimal capacity range of stress responses, and in different pathological for metal ion coordination. After their discovery in horse processes (tumorigenesis, neurodegeneration, and inflam- kidneys by Bert Vallee in 1957 (1) , MTs have been identi- mation). The information on both intracellular and extra- fied and characterized in most prokaryotic and all eukary- cellular interactions of MTs with other proteins is here otic organisms. Besides metal ion detoxification, they comprehensively reviewed. In mammalian kidney, MT1/ have been related to a plethora of physiological events, MT2 interact with megalin and related receptors, and with from the homeostasis, storage, and delivery of physiologi- the transporter transthyretin. Most of the mammalian MT cal metals, to the defense against a wide range of stresses partners identified concern interactions with central nerv- and pathological processes (tumor genesis, neurodegen- ous system (mainly brain) proteins, both through physical eration, inflammation, etc.). It is now a common agree- contact or metal exchange reactions. Physical interactions ment among MT researchers that the ambiguity when mainly involve neuronal secretion multimers. -
Identification of the Binding Partners for Hspb2 and Cryab Reveals
Brigham Young University BYU ScholarsArchive Theses and Dissertations 2013-12-12 Identification of the Binding arP tners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactions and Non- Redundant Roles for Small Heat Shock Proteins Kelsey Murphey Langston Brigham Young University - Provo Follow this and additional works at: https://scholarsarchive.byu.edu/etd Part of the Microbiology Commons BYU ScholarsArchive Citation Langston, Kelsey Murphey, "Identification of the Binding Partners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactions and Non-Redundant Roles for Small Heat Shock Proteins" (2013). Theses and Dissertations. 3822. https://scholarsarchive.byu.edu/etd/3822 This Thesis is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of BYU ScholarsArchive. For more information, please contact [email protected], [email protected]. Identification of the Binding Partners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactions and Non-Redundant Roles for Small Heat Shock Proteins Kelsey Langston A thesis submitted to the faculty of Brigham Young University in partial fulfillment of the requirements for the degree of Master of Science Julianne H. Grose, Chair William R. McCleary Brian Poole Department of Microbiology and Molecular Biology Brigham Young University December 2013 Copyright © 2013 Kelsey Langston All Rights Reserved ABSTRACT Identification of the Binding Partners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactors and Non-Redundant Roles for Small Heat Shock Proteins Kelsey Langston Department of Microbiology and Molecular Biology, BYU Master of Science Small Heat Shock Proteins (sHSP) are molecular chaperones that play protective roles in cell survival and have been shown to possess chaperone activity. -
A Minimal Set of Internal Control Genes for Gene Expression Studies in Head
bioRxiv preprint doi: https://doi.org/10.1101/108381; this version posted February 14, 2017. 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 4.0 International license. 1 A minimal set of internal control genes for gene expression studies in head 2 and neck squamous cell carcinoma. 1 1 1 2 3 Vinayak Palve , Manisha Pareek , Neeraja M Krishnan , Gangotri Siddappa , 2 2 1,3* 4 Amritha Suresh , Moni Abraham Kuriakose and Binay Panda 5 1 6 Ganit Labs, Bio-IT Centre, Institute of Bioinformatics and Applied Biotechnology, 7 Bangalore 560100 2 8 Mazumdar Shaw Cancer Centre and Mazumdar Shaw Centre for Translational 9 Research, Bangalore 560096 3 10 Strand Life Sciences, Bangalore 560024 11 * Corresponding author: [email protected] 12 13 Abstract: 14 Background: Selection of the right reference gene(s) is crucial in the analysis and 15 interpretation of gene expression data. In head and neck cancer, studies evaluating 16 the efficacy of internal reference genes are rare. Here, we present data for a minimal 17 set of candidates as internal control genes for gene expression studies in head and 18 neck cancer. 19 Methods: We analyzed data from multiple sources (in house whole-genome gene 20 expression microarrays, n=21; TCGA RNA-seq, n=42, and published gene 21 expression studies in head and neck tumors from literature) to come up with a set of 22 genes (discovery set) for their stable expression across tumor and normal tissues. -
Harnessing Gene Expression Profiles for the Identification of Ex Vivo Drug
cancers Article Harnessing Gene Expression Profiles for the Identification of Ex Vivo Drug Response Genes in Pediatric Acute Myeloid Leukemia David G.J. Cucchi 1 , Costa Bachas 1 , Marry M. van den Heuvel-Eibrink 2,3, Susan T.C.J.M. Arentsen-Peters 3, Zinia J. Kwidama 1, Gerrit J. Schuurhuis 1, Yehuda G. Assaraf 4, Valérie de Haas 3 , Gertjan J.L. Kaspers 3,5 and Jacqueline Cloos 1,* 1 Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; [email protected] (D.G.J.C.); [email protected] (C.B.); [email protected] (Z.J.K.); [email protected] (G.J.S.) 2 Department of Pediatric Oncology/Hematology, Erasmus MC–Sophia Children’s Hospital, 3015 CN Rotterdam, The Netherlands; [email protected] 3 Princess Máxima Center for Pediatric Oncology, 3584 CS Utrecht, The Netherlands; [email protected] (S.T.C.J.M.A.-P.); [email protected] (V.d.H.); [email protected] (G.J.L.K.) 4 The Fred Wyszkowski Cancer Research, Laboratory, Department of Biology, Technion-Israel Institute of Technology, 3200003 Haifa, Israel; [email protected] 5 Emma’s Children’s Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Pediatric Oncology, 1081 HV Amsterdam, The Netherlands * Correspondence: [email protected] Received: 21 April 2020; Accepted: 12 May 2020; Published: 15 May 2020 Abstract: Novel treatment strategies are of paramount importance to improve clinical outcomes in pediatric AML. Since chemotherapy is likely to remain the cornerstone of curative treatment of AML, insights in the molecular mechanisms that determine its cytotoxic effects could aid further treatment optimization. -
DNA Topoisomerases and Cancer Topoisomerases and TOP Genes in Humans Humans Vs
DNA Topoisomerases Review DNA Topoisomerases And Cancer Topoisomerases and TOP Genes in Humans Humans vs. Escherichia Coli Topoisomerase differences Comparisons Topoisomerase and genomes Top 1 Top1 and Top2 differences Relaxation of DNA Top1 DNA supercoiling DNA supercoiling In the context of chromatin, where the rotation of DNA is constrained, DNA supercoiling (over- and under-twisting and writhe) is readily generated. TOP1 and TOP1mt remove supercoiling by DNA untwisting, acting as “swivelases”, whereas TOP2a and TOP2b remove writhe, acting as “writhases” at DNA crossovers (see TOP2 section). Here are some basic facts concerning DNA supercoiling that are relevant to topoisomerase activity: • Positive supercoiling (Sc+) tightens the DNA helix whereas negative supercoiling (Sc-) facilitates the opening of the duplex and the generation of single-stranded segments. • Nucleosome formation and disassembly absorbs and releases Sc-, respectively. • Polymerases generate Sc+ ahead and Sc- behind their tracks. • Excess of Sc+ arrests DNA tracking enzymes (helicases and polymerases), suppresses transcription elongation and initiation, and destabilizes nucleosomes. • Sc- facilitates DNA melting during the initiation of replication and transcription, D-loop formation and homologous recombination and nucleosome formation. • Excess of Sc- favors the formation of alternative DNA structures (R-loops, guanine quadruplexes, right-handed DNA (Z-DNA), plectonemic structures), which then absorb Sc- upon their formation and attract regulatory proteins. The -
Lung Cancer Signature Biomarkers: Tissue Specific Semantic Similarity
Srivastava et al. BMC Research Notes 2012, 5:617 http://www.biomedcentral.com/1756-0500/5/617 RESEARCH ARTICLE Open Access Lung Cancer Signature Biomarkers: tissue specific semantic similarity based clustering of Digital Differential Display (DDD) data Mousami Srivastava, Pankaj Khurana and Ragumani Sugadev* Abstract Background: The tissue-specific Unigene Sets derived from more than one million expressed sequence tags (ESTs) in the NCBI, GenBank database offers a platform for identifying significantly and differentially expressed tissue-specific genes by in-silico methods. Digital differential display (DDD) rapidly creates transcription profiles based on EST comparisons and numerically calculates, as a fraction of the pool of ESTs, the relative sequence abundance of known and novel genes. However, the process of identifying the most likely tissue for a specific disease in which to search for candidate genes from the pool of differentially expressed genes remains difficult. Therefore, we have used ‘Gene Ontology semantic similarity score’ to measure the GO similarity between gene products of lung tissue-specific candidate genes from control (normal) and disease (cancer) sets. This semantic similarity score matrix based on hierarchical clustering represents in the form of a dendrogram. The dendrogram cluster stability was assessed by multiple bootstrapping. Multiple bootstrapping also computes a p-value for each cluster and corrects the bias of the bootstrap probability. Results: Subsequent hierarchical clustering by the multiple bootstrapping method (α = 0.95) identified seven clusters. The comparative, as well as subtractive, approach revealed a set of 38 biomarkers comprising four distinct lung cancer signature biomarker clusters (panel 1–4). Further gene enrichment analysis of the four panels revealed that each panel represents a set of lung cancer linked metastasis diagnostic biomarkers (panel 1), chemotherapy/ drug resistance biomarkers (panel 2), hypoxia regulated biomarkers (panel 3) and lung extra cellular matrix biomarkers (panel 4). -
Supplementary Table 3 Complete List of RNA-Sequencing Analysis of Gene Expression Changed by ≥ Tenfold Between Xenograft and Cells Cultured in 10%O2
Supplementary Table 3 Complete list of RNA-Sequencing analysis of gene expression changed by ≥ tenfold between xenograft and cells cultured in 10%O2 Expr Log2 Ratio Symbol Entrez Gene Name (culture/xenograft) -7.182 PGM5 phosphoglucomutase 5 -6.883 GPBAR1 G protein-coupled bile acid receptor 1 -6.683 CPVL carboxypeptidase, vitellogenic like -6.398 MTMR9LP myotubularin related protein 9-like, pseudogene -6.131 SCN7A sodium voltage-gated channel alpha subunit 7 -6.115 POPDC2 popeye domain containing 2 -6.014 LGI1 leucine rich glioma inactivated 1 -5.86 SCN1A sodium voltage-gated channel alpha subunit 1 -5.713 C6 complement C6 -5.365 ANGPTL1 angiopoietin like 1 -5.327 TNN tenascin N -5.228 DHRS2 dehydrogenase/reductase 2 leucine rich repeat and fibronectin type III domain -5.115 LRFN2 containing 2 -5.076 FOXO6 forkhead box O6 -5.035 ETNPPL ethanolamine-phosphate phospho-lyase -4.993 MYO15A myosin XVA -4.972 IGF1 insulin like growth factor 1 -4.956 DLG2 discs large MAGUK scaffold protein 2 -4.86 SCML4 sex comb on midleg like 4 (Drosophila) Src homology 2 domain containing transforming -4.816 SHD protein D -4.764 PLP1 proteolipid protein 1 -4.764 TSPAN32 tetraspanin 32 -4.713 N4BP3 NEDD4 binding protein 3 -4.705 MYOC myocilin -4.646 CLEC3B C-type lectin domain family 3 member B -4.646 C7 complement C7 -4.62 TGM2 transglutaminase 2 -4.562 COL9A1 collagen type IX alpha 1 chain -4.55 SOSTDC1 sclerostin domain containing 1 -4.55 OGN osteoglycin -4.505 DAPL1 death associated protein like 1 -4.491 C10orf105 chromosome 10 open reading frame 105 -4.491 -
Physiological Significance and Molecular Genetics of Red Cell Enzymes Involved in the Ribonucleotide Metabolism
No. 10] Proc. Japan Acad., 78, Ser. B (2002) 287 Review Physiological significance and molecular genetics of red cell enzymes involved in the ribonucleotide metabolism By Hitoshi KANNO,*)'t) Hisaichi FUJII,*) and Shiro MIwA**) (Communicated by Takashi SUGIMURA,M. J. A., Nov. 12, 2002) Abstract: At the final maturation process red blood cells (RBC) are enucleated, becoming unable to synthesize nucleic acids as well as proteins. RBCs survive approximately 120 days in circulation using glu- cose as the sole energy source. Most crucial RBC functions depend on ATP to sustain physiological home- ostasis. It is thus quite important that generation of ATP by glycolysis and replenishing of adenine nucleotide pools by the reaction, which is catalyzed by adenylate kinase (AK1). In turn, ribosomal RNA is degraded dur- ing remodeling of reticulocytes, and pyrimidine ribonucleotides become unnecessary for RBC viability. Thus they should be dephosphorylated by pyrimidine 5'-nucleotidase (P5N-I) and finally transported outside RBCs. There have been reported that hereditary deficiency of AK1 and P5N-I may cause shortened RBC life span, i.e. hemolytic anemia. In this review, we summarize physiological importance of these enzymes, which are involved in ribonucleotides metabolism during RBC maturation. Key words: Erythrocyte; reticulocyte; pyrimidine 5'-nucleotidase; adenylate kinase; hemolytic anemia; gene mutations. Introduction. At the final stage of differentiation, breakdown of mitochondria is mediated by erythroid- erythroid cells are enucleated, and organelles such as specific lipoxygenase,3~ whereas abundant ribosomal mitochondria, ribosomes, lysosomes, endoplasmic retic- RNA is biochemically catabolyzed into ribonucleotides by ulum and Golgi apparatus are eliminated or decayed. ribonuclease.4),5) Among them mitochondria and ribosomes still remain for An ATP-dependent proteolytic system in reticulo- a few days after enucleation, i.e. -
Identification and Characterization of Tissue-Resident Memory T Cells in Humans Brahma Vencel Kumar
Identification and characterization of tissue-resident memory T cells in humans Brahma Vencel Kumar Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2018 ©2017 Brahma Vencel Kumar All rights reserved ABSTRACT Identification and characterization of tissue-resident memory T cells in humans Brahma Vencel Kumar Memory T cells are critical for maintaining lifelong immunity by protecting against reinfection with previously encountered pathogens. In recent years, a subset of memory T cells termed tissue-resident memory T cells (TRM) has emerged as the primary mediator of protection at many tissue sites. Numerous studies in mice have demonstrated that TRM accelerate pathogen clearance compared with other subsets of memory T cells. The defining characteristic of TRM is that they are retained within tissues and do not circulate in the blood. The lack of TRM in blood has proved to be a barrier for investigating the role of TRM in healthy humans. As a result, there are many outstanding questions about TRM biology in humans, including which phenotypic markers identify TRM, if TRM represent a unique memory subset, as well as defining transcriptional and functional characteristics of this subset. Through a unique collaboration with the local organ procurement agency, we obtained samples from >15 tissue sites from healthy organ donors of all ages. We found that the surface marker CD69 was expressed by memory CD4 + and CD8 + T cells in multiple tissues including spleen and other lymphoid tissues, lung, and intestines, but not in blood, suggesting that this marker may identify TRM in human tissues. -
Supplementary Data
SUPPLEMENTARY DATA A cyclin D1-dependent transcriptional program predicts clinical outcome in mantle cell lymphoma Santiago Demajo et al. 1 SUPPLEMENTARY DATA INDEX Supplementary Methods p. 3 Supplementary References p. 8 Supplementary Tables (S1 to S5) p. 9 Supplementary Figures (S1 to S15) p. 17 2 SUPPLEMENTARY METHODS Western blot, immunoprecipitation, and qRT-PCR Western blot (WB) analysis was performed as previously described (1), using cyclin D1 (Santa Cruz Biotechnology, sc-753, RRID:AB_2070433) and tubulin (Sigma-Aldrich, T5168, RRID:AB_477579) antibodies. Co-immunoprecipitation assays were performed as described before (2), using cyclin D1 antibody (Santa Cruz Biotechnology, sc-8396, RRID:AB_627344) or control IgG (Santa Cruz Biotechnology, sc-2025, RRID:AB_737182) followed by protein G- magnetic beads (Invitrogen) incubation and elution with Glycine 100mM pH=2.5. Co-IP experiments were performed within five weeks after cell thawing. Cyclin D1 (Santa Cruz Biotechnology, sc-753), E2F4 (Bethyl, A302-134A, RRID:AB_1720353), FOXM1 (Santa Cruz Biotechnology, sc-502, RRID:AB_631523), and CBP (Santa Cruz Biotechnology, sc-7300, RRID:AB_626817) antibodies were used for WB detection. In figure 1A and supplementary figure S2A, the same blot was probed with cyclin D1 and tubulin antibodies by cutting the membrane. In figure 2H, cyclin D1 and CBP blots correspond to the same membrane while E2F4 and FOXM1 blots correspond to an independent membrane. Image acquisition was performed with ImageQuant LAS 4000 mini (GE Healthcare). Image processing and quantification were performed with Multi Gauge software (Fujifilm). For qRT-PCR analysis, cDNA was generated from 1 µg RNA with qScript cDNA Synthesis kit (Quantabio). qRT–PCR reaction was performed using SYBR green (Roche).