Gene Expression Patterns Induced by HPV-16 L1 Virus-Like Particles in Leukocytes from Vaccine Recipients
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Integrative Genomic and Epigenomic Analyses Identified IRAK1 As a Novel Target for Chronic Inflammation-Driven Prostate Tumorigenesis
bioRxiv preprint doi: https://doi.org/10.1101/2021.06.16.447920; this version posted June 16, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. Integrative genomic and epigenomic analyses identified IRAK1 as a novel target for chronic inflammation-driven prostate tumorigenesis Saheed Oluwasina Oseni1,*, Olayinka Adebayo2, Adeyinka Adebayo3, Alexander Kwakye4, Mirjana Pavlovic5, Waseem Asghar5, James Hartmann1, Gregg B. Fields6, and James Kumi-Diaka1 Affiliations 1 Department of Biological Sciences, Florida Atlantic University, Florida, USA 2 Morehouse School of Medicine, Atlanta, Georgia, USA 3 Georgia Institute of Technology, Atlanta, Georgia, USA 4 College of Medicine, Florida Atlantic University, Florida, USA 5 Department of Computer and Electrical Engineering, Florida Atlantic University, Florida, USA 6 Department of Chemistry & Biochemistry and I-HEALTH, Florida Atlantic University, Florida, USA Corresponding Author: [email protected] (S.O.O) Running Title: Chronic inflammation signaling in prostate tumorigenesis bioRxiv preprint doi: https://doi.org/10.1101/2021.06.16.447920; this version posted June 16, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. Abstract The impacts of many inflammatory genes in prostate tumorigenesis remain understudied despite the increasing evidence that associates chronic inflammation with prostate cancer (PCa) initiation, progression, and therapy resistance. -
PALM-IST: Pathway Assembly from Literature Mining
www.nature.com/scientificreports OPEN PALM-IST: Pathway Assembly from Literature Mining - an Information Search Tool Received: 06 September 2014 Accepted: 18 February 2015 Sapan Mandloi & Saikat Chakrabarti Published: 19 May 2015 Manual curation of biomedical literature has become extremely tedious process due to its exponential growth in recent years. To extract meaningful information from such large and unstructured text, newer and more efficient mining tool is required. Here, we introduce PALM-IST, a computational platform that not only allows users to explore biomedical abstracts using keyword based text mining but also extracts biological entity (e.g., gene/protein, drug, disease, biological processes, cellular component, etc.) information from the extracted text and subsequently mines various databases to provide their comprehensive inter-relation (e.g., interaction, expression, etc.). PALM-IST constructs protein interaction network and pathway information data relevant to the text search using multiple data mining tools and assembles them to create a meta-interaction network. It also analyzes scientific collaboration by extraction and creation of “co-authorship network,” for a given search context. Hence, this useful combination of literature and data mining provided in PALM- IST can be used to extract novel protein-protein interaction (PPI), to generate meta-pathways and further to identify key crosstalk and bottleneck proteins. PALM-IST is available at www.hpppi.iicb. res.in/ctm. Biomedical research has shifted from studying individual gene and protein to complete biological sys- tem. In today’s age of “big data biology” embraced with overwhelming amount of publication records and of high-throughput data, automated and efficient text and data mining platforms are absolutely essential to access and present biological information into more computable and comprehensible form1–3. -
The Effect of Temperature Adaptation on the Ubiquitin–Proteasome Pathway in Notothenioid Fishes Anne E
© 2017. Published by The Company of Biologists Ltd | Journal of Experimental Biology (2017) 220, 369-378 doi:10.1242/jeb.145946 RESEARCH ARTICLE The effect of temperature adaptation on the ubiquitin–proteasome pathway in notothenioid fishes Anne E. Todgham1,*, Timothy A. Crombie2 and Gretchen E. Hofmann3 ABSTRACT proliferation to compensate for the effects of low temperature on ’ There is an accumulating body of evidence suggesting that the sub- aerobic metabolism (Johnston, 1989; O Brien et al., 2003; zero Antarctic marine environment places physiological constraints Guderley, 2004). Recently, there has been an accumulating body – on protein homeostasis. Levels of ubiquitin (Ub)-conjugated proteins, of literature to suggest that protein homeostasis the maintenance of – 20S proteasome activity and mRNA expression of many proteins a functional protein pool has been highly impacted by evolution involved in both the Ub tagging of damaged proteins as well as the under these cold and stable conditions. different complexes of the 26S proteasome were measured to Maintaining protein homeostasis is a fundamental physiological examine whether there is thermal compensation of the Ub– process, reflecting a dynamic balance in synthetic and degradation proteasome pathway in Antarctic fishes to better understand the processes. There are numerous lines of evidence to suggest efficiency of the protein degradation machinery in polar species. Both temperature compensation of protein synthesis in Antarctic Antarctic (Trematomus bernacchii, Pagothenia borchgrevinki)and invertebrates (Whiteley et al., 1996; Marsh et al., 2001; Robertson non-Antarctic (Notothenia angustata, Bovichtus variegatus) et al., 2001; Fraser et al., 2002) and fish (Storch et al., 2005). In notothenioids were included in this study to investigate the zoarcid fishes, it has been demonstrated that Antarctic eelpouts mechanisms of cold adaptation of this pathway in polar species. -
TGF-Β1 Signaling Targets Metastasis-Associated Protein 1, a New Effector in Epithelial Cells
Oncogene (2011) 30, 2230–2241 & 2011 Macmillan Publishers Limited All rights reserved 0950-9232/11 www.nature.com/onc ORIGINAL ARTICLE TGF-b1 signaling targets metastasis-associated protein 1, a new effector in epithelial cells SB Pakala1, K Singh1,3, SDN Reddy1, K Ohshiro1, D-Q Li1, L Mishra2 and R Kumar1 1Department of Biochemistry and Molecular Biology and Institute of Coregulator Biology, The George Washington University Medical Center, Washington, DC, USA and 2Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, TX, USA In spite of a large number of transforming growth factor b1 gene chromatin in response to upstream signals. The (TGF-b1)-regulated genes, the nature of its targets with TGF-b1-signaling is largely mediated by Smad proteins roles in transformation continues to be poorly understood. (Massague et al., 2005) where Smad2 and Smad3 are Here, we discovered that TGF-b1 stimulates transcription phosphorylated by TGF-b1-receptors and associate with of metastasis-associated protein 1 (MTA1), a dual master the common mediator Smad4, which translocates to the coregulator, in epithelial cells, and that MTA1 status is a nucleus to participate in the expression of TGF-b1-target determinant of TGF-b1-induced epithelial-to-mesenchymal genes (Deckers et al., 2006). Previous studies have shown transition (EMT) phenotypes. In addition, we found that that CUTL1, also known as CDP (CCAAT displacement MTA1/polymerase II/activator protein-1 (AP-1) co-activator protein), a target of TGF-b1, is needed for its short-term complex interacts with the FosB-gene chromatin and stimu- effects of TGF-b1 on cell motility involving Smad4- lates its transcription, and FosB in turn, utilizes FosB/histone dependent pathway (Michl et al.,2005). -
Identification of Long-Lived Synaptic Proteins by Proteomic Analysis Of
Identification of long-lived synaptic proteins by PNAS PLUS proteomic analysis of synaptosome protein turnover Seok Heoa,1, Graham H. Dieringa,1, Chan Hyun Nab,c,d,e,1, Raja Sekhar Nirujogib,c, Julia L. Bachmana, Akhilesh Pandeyb,c,f,g,h, and Richard L. Huganira,i,2 aSolomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205; bDepartment of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD 21205; cMcKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205; dDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205; eInstitute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205; fDepartment of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21205; gDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21205; hManipal Academy of Higher Education, Manipal 576104, Karnataka, India; and iKavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21205 Contributed by Richard L. Huganir, February 28, 2018 (sent for review December 4, 2017; reviewed by Stephen J. Moss and Angus C. Nairn) Memory formation is believed to result from changes in synapse the eye and cartilage, respectively, last for decades. Components strength and structure. While memories may persist for the lifetime of the nuclear pore complex of nondividing cells and some his- of an organism, the proteins and lipids that make up synapses tones have also been shown to last for years (12–15). The ex- undergo constant turnover with lifetimes from minutes to days. The treme stability of some of these proteins is obviously critical for molecular basis for memory maintenance may rely on a subset of the structural integrity of relatively metabolically inactive regions long-lived proteins (LLPs). -
Silencing of Phosphoinositide-Specific
ANTICANCER RESEARCH 34: 4069-4076 (2014) Silencing of Phosphoinositide-specific Phospholipase C ε Remodulates the Expression of the Phosphoinositide Signal Transduction Pathway in Human Osteosarcoma Cell Lines VINCENZA RITA LO VASCO1, MARTINA LEOPIZZI2, DANIELA STOPPOLONI3 and CARLO DELLA ROCCA2 Departments of 1Sense Organs , 2Medicine and Surgery Sciences and Biotechnologies and 3Biochemistry Sciences “A. Rossi Fanelli”, Sapienza University, Rome, Italy Abstract. Background: Ezrin, a member of the signal transduction pathway (5). The reduction of PIP2 ezrin–radixin–moesin family, is involved in the metastatic induces ezrin dissociation from the plasma membrane (6). spread of osteosarcoma. Ezrin binds phosphatydil inositol-4,5- The levels of PIP2 are regulated by the PI-specific bisphosphate (PIP2), a crucial molecule of the phospholipase C (PI-PLC) family (7), constituting thirteen phosphoinositide signal transduction pathway. PIP2 levels are enzymes divided into six sub-families on the basis of amino regulated by phosphoinositide-specific phospholipase C (PI- acid sequence, domain structure, mechanism of recruitment PLC) enzymes. PI-PLCε isoform, a well-characterized direct and tissue distribution (7-15). PI-PLCε, a direct effector of effector of rat sarcoma (RAS), is at a unique convergence RAS (14-15), might be the point of convergence for the point for the broad range of signaling pathways that promote broad range of signalling pathways that promote the RAS GTPase-mediated signalling. Materials and Methods. By RASGTPase-mediated signalling (16). using molecular biology methods and microscopic analyses, In previous studies, we suggested a relationship between we analyzed the expression of ezrin and PLC genes after PI-PLC expression and ezrin (17-18). -
Hematopoietic Differentiation
Hematopoietic Differentiation A number of lineages of mature cells are derived by a series of steps from a common stem cell precursor. Myeloid cells including neutrophils and monocytes are important for early defense against infection and because of the occurrence of myeloid leukemias. Myeloid differentiation and activation offers a favorable system for experimental investigation. Some Models of Gene Expression in Myeloid Cells 1. Compare resting neutrophils with neutrophils exposed for two hours to either. – A. Non-pathogenic E. coli. – B. Yersinia pestis D27 (plague bacillus). – C. Non-pathogenic Yersinia D28. 2. Induced differentiation of MPRO promyelocytic cells. NeutrophilsNeutrophils Major frontline defense cells against invading pathogens. – Constitute 70% of the total blood leukocytes. – Terminally differentiated professional phagocytic cells with an antibacterial arsenal. – Use both oxygen dependent and oxygen independent killing mechanisms. Play a central role in 2000X mag inflammation. Distribution of IL-8 transcripts on neutrophils (in situ hybridization) A combination of two Cy3 (red)- A labeled oligonucleotides complementary to IL-8 transcripts were used for in situ hybridization with neutrophils incubated in the absence (A) or presence (B) of ten E. coli K12 organisms per neutrophil. BB Methods of Analysis 1. mRNA analysis. – A. 3’ end restriction fragment gel display. – B. Affymetrix 60k or 42k oligonucleotide arrays. 2. Protein analysis. 2D gel electrophoresis using a variety of ph ranges for isoelectric focusing. MALDI-TOF identification of proteins. Representative Segments of Display Gels of cDNA Fragments: (Left) Neutrophils and Monocytes Exposed to E. coli for 2 H; (Right) Neutrophils Exposed to Various Bacteria for 2 H Ec: E. coli K12; Yp (KIM5): Y. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
A Multistep Bioinformatic Approach Detects Putative Regulatory
BMC Bioinformatics BioMed Central Research article Open Access A multistep bioinformatic approach detects putative regulatory elements in gene promoters Stefania Bortoluzzi1, Alessandro Coppe1, Andrea Bisognin1, Cinzia Pizzi2 and Gian Antonio Danieli*1 Address: 1Department of Biology, University of Padova – Via Bassi 58/B, 35131, Padova, Italy and 2Department of Information Engineering, University of Padova – Via Gradenigo 6/B, 35131, Padova, Italy Email: Stefania Bortoluzzi - [email protected]; Alessandro Coppe - [email protected]; Andrea Bisognin - [email protected]; Cinzia Pizzi - [email protected]; Gian Antonio Danieli* - [email protected] * Corresponding author Published: 18 May 2005 Received: 12 November 2004 Accepted: 18 May 2005 BMC Bioinformatics 2005, 6:121 doi:10.1186/1471-2105-6-121 This article is available from: http://www.biomedcentral.com/1471-2105/6/121 © 2005 Bortoluzzi et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Searching for approximate patterns in large promoter sequences frequently produces an exceedingly high numbers of results. Our aim was to exploit biological knowledge for definition of a sheltered search space and of appropriate search parameters, in order to develop a method for identification of a tractable number of sequence motifs. Results: Novel software (COOP) was developed for extraction of sequence motifs, based on clustering of exact or approximate patterns according to the frequency of their overlapping occurrences. -
Study of the G Protein Nucleolar 2 Value in Liver Hepatocellular Carcinoma Treatment and Prognosis
Hindawi BioMed Research International Volume 2021, Article ID 4873678, 12 pages https://doi.org/10.1155/2021/4873678 Research Article Study of the G Protein Nucleolar 2 Value in Liver Hepatocellular Carcinoma Treatment and Prognosis Yiwei Dong,1 Qianqian Cai,2 Lisheng Fu,1 Haojie Liu,1 Mingzhe Ma,3 and Xingzhong Wu 1 1Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Key Lab of Glycoconjugate Research, Ministry of Public Health, Shanghai 200032, China 2Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China 3Department of Gastric Surgery, Fudan University Shanghai Cancer Center, Shanghai 200025, China Correspondence should be addressed to Xingzhong Wu; [email protected] Received 10 May 2021; Accepted 29 June 2021; Published 20 July 2021 Academic Editor: Tao Huang Copyright © 2021 Yiwei Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. LIHC (liver hepatocellular carcinoma) mostly occurs in patients with chronic liver disease. It is primarily induced by a vicious cycle of liver injury, inflammation, and regeneration that usually last for decades. The G protein nucleolar 2 (GNL2), as a protein- encoding gene, is also known as NGP1, Nog2, Nug2, Ngp-1, and HUMAUANTIG. Few reports are shown towards the specific biological function of GNL2. Meanwhile, it is still unclear whether it is related to the pathogenesis of carcinoma up to date. Here, our study attempts to validate the role and function of GNL2 in LIHC via multiple databases and functional assays. -
Role of Phospholipases in Adrenal Steroidogenesis
229 1 W B BOLLAG Phospholipases in adrenal 229:1 R29–R41 Review steroidogenesis Role of phospholipases in adrenal steroidogenesis Wendy B Bollag Correspondence should be addressed Charlie Norwood VA Medical Center, One Freedom Way, Augusta, GA, USA to W B Bollag Department of Physiology, Medical College of Georgia, Augusta University (formerly Georgia Regents Email University), Augusta, GA, USA [email protected] Abstract Phospholipases are lipid-metabolizing enzymes that hydrolyze phospholipids. In some Key Words cases, their activity results in remodeling of lipids and/or allows the synthesis of other f adrenal cortex lipids. In other cases, however, and of interest to the topic of adrenal steroidogenesis, f angiotensin phospholipases produce second messengers that modify the function of a cell. In this f intracellular signaling review, the enzymatic reactions, products, and effectors of three phospholipases, f phospholipids phospholipase C, phospholipase D, and phospholipase A2, are discussed. Although f signal transduction much data have been obtained concerning the role of phospholipases C and D in regulating adrenal steroid hormone production, there are still many gaps in our knowledge. Furthermore, little is known about the involvement of phospholipase A2, Endocrinology perhaps, in part, because this enzyme comprises a large family of related enzymes of that are differentially regulated and with different functions. This review presents the evidence supporting the role of each of these phospholipases in steroidogenesis in the Journal Journal of Endocrinology adrenal cortex. (2016) 229, R1–R13 Introduction associated GTP-binding protein exchanges a bound GDP for a GTP. The G protein with GTP bound can then Phospholipids serve a structural function in the cell in that activate the enzyme, phospholipase C (PLC), that cleaves they form the lipid bilayer that maintains cell integrity. -
Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database.