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CD56+ T-Cells in Relation to Cytomegalovirus in Healthy Subjects and Kidney Transplant Patients
CD56+ T-cells in Relation to Cytomegalovirus in Healthy Subjects and Kidney Transplant Patients Institute of Infection and Global Health Department of Clinical Infection, Microbiology and Immunology Thesis submitted in accordance with the requirements of the University of Liverpool for the degree of Doctor in Philosophy by Mazen Mohammed Almehmadi December 2014 - 1 - Abstract Human T cells expressing CD56 are capable of tumour cell lysis following activation with interleukin-2 but their role in viral immunity has been less well studied. The work described in this thesis aimed to investigate CD56+ T-cells in relation to cytomegalovirus infection in healthy subjects and kidney transplant patients (KTPs). Proportions of CD56+ T cells were found to be highly significantly increased in healthy cytomegalovirus-seropositive (CMV+) compared to cytomegalovirus-seronegative (CMV-) subjects (8.38% ± 0.33 versus 3.29%± 0.33; P < 0.0001). In donor CMV-/recipient CMV- (D-/R-)- KTPs levels of CD56+ T cells were 1.9% ±0.35 versus 5.42% ±1.01 in D+/R- patients and 5.11% ±0.69 in R+ patients (P 0.0247 and < 0.0001 respectively). CD56+ T cells in both healthy CMV+ subjects and KTPs expressed markers of effector memory- RA T-cells (TEMRA) while in healthy CMV- subjects and D-/R- KTPs the phenotype was predominantly that of naïve T-cells. Other surface markers, CD8, CD4, CD58, CD57, CD94 and NKG2C were expressed by a significantly higher proportion of CD56+ T-cells in healthy CMV+ than CMV- subjects. Functional studies showed levels of pro-inflammatory cytokines IFN-γ and TNF-α, as well as granzyme B and CD107a were significantly higher in CD56+ T-cells from CMV+ than CMV- subjects following stimulation with CMV antigens. -
The Development of the Mammalian Central
Chapter 5: GRIPE is a novel gene that may assume different roles during development and in adulthood Chapter 5: GRIPE is a novel gene that may assume different roles during development and in adulthood 5.1. Characterisation of GRIPE and E12 mRNA expression during development As a first step towards describing the expression of GRIPE and E12 during neurodevelopment, Northern analysis was conducted to acquire a global pattern of mRNA expression. To do this, embryonic and adult tissues were harvested for RNA isolation as described (Section 2.2). Northern hybridisation was then performed using radiolabelled GRIPE and E12 cDNAs as probes, and these results are shown in Fig. 5.1. A single band of approximately 8kb is detected in all samples; this was the only signal detected in all northern hybridisations performed with different GRIPE cDNA and cRNA probes (see Appendix 10.2). It would appear that GRIPE mRNA is abundant in the head and trunk at e11.5, and levels decrease during embryogenesis, and are undetectable by e17.5. In the adult, GRIPE is abundant in the brain, but is undetectable in the placenta and kidney. However, further RT-PCR experiments (see Figs 6.8 and 6.13) indicate that GRIPE is weakly detectable in these tissues, but not in the kidney. Northern analysis of E12 mRNA shows high levels of expression at e11.5, with a gradual decrease during embryogenesis, and is almost undetectable by e17.5. Further, E12 is undetectable in adult brain, placenta and kidney. These observations demonstrate a coincidence in expression of GRIPE and E12 mRNA during embryogenesis, but not in the adult; where E12 mRNA is undetectable. -
Comparative Transcriptome Profiling of the Human and Mouse Dorsal Root Ganglia: an RNA-Seq-Based Resource for Pain and Sensory Neuroscience Research
bioRxiv preprint doi: https://doi.org/10.1101/165431; this version posted October 13, 2017. 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. Title: Comparative transcriptome profiling of the human and mouse dorsal root ganglia: An RNA-seq-based resource for pain and sensory neuroscience research Short Title: Human and mouse DRG comparative transcriptomics Pradipta Ray 1, 2 #, Andrew Torck 1 , Lilyana Quigley 1, Andi Wangzhou 1, Matthew Neiman 1, Chandranshu Rao 1, Tiffany Lam 1, Ji-Young Kim 1, Tae Hoon Kim 2, Michael Q. Zhang 2, Gregory Dussor 1 and Theodore J. Price 1, # 1 The University of Texas at Dallas, School of Behavioral and Brain Sciences 2 The University of Texas at Dallas, Department of Biological Sciences # Corresponding authors Theodore J Price Pradipta Ray School of Behavioral and Brain Sciences School of Behavioral and Brain Sciences The University of Texas at Dallas The University of Texas at Dallas BSB 14.102G BSB 10.608 800 W Campbell Rd 800 W Campbell Rd Richardson TX 75080 Richardson TX 75080 972-883-4311 972-883-7262 [email protected] [email protected] Number of pages: 27 Number of figures: 9 Number of tables: 8 Supplementary Figures: 4 Supplementary Files: 6 Word count: Abstract = 219; Introduction = 457; Discussion = 1094 Conflict of interest: The authors declare no conflicts of interest Patient anonymity and informed consent: Informed consent for human tissue sources were obtained by Anabios, Inc. (San Diego, CA). Human studies: This work was approved by The University of Texas at Dallas Institutional Review Board (MR 15-237). -
Supplementary Table 1
Supplementary Table 1. 492 genes are unique to 0 h post-heat timepoint. The name, p-value, fold change, location and family of each gene are indicated. Genes were filtered for an absolute value log2 ration 1.5 and a significance value of p ≤ 0.05. Symbol p-value Log Gene Name Location Family Ratio ABCA13 1.87E-02 3.292 ATP-binding cassette, sub-family unknown transporter A (ABC1), member 13 ABCB1 1.93E-02 −1.819 ATP-binding cassette, sub-family Plasma transporter B (MDR/TAP), member 1 Membrane ABCC3 2.83E-02 2.016 ATP-binding cassette, sub-family Plasma transporter C (CFTR/MRP), member 3 Membrane ABHD6 7.79E-03 −2.717 abhydrolase domain containing 6 Cytoplasm enzyme ACAT1 4.10E-02 3.009 acetyl-CoA acetyltransferase 1 Cytoplasm enzyme ACBD4 2.66E-03 1.722 acyl-CoA binding domain unknown other containing 4 ACSL5 1.86E-02 −2.876 acyl-CoA synthetase long-chain Cytoplasm enzyme family member 5 ADAM23 3.33E-02 −3.008 ADAM metallopeptidase domain Plasma peptidase 23 Membrane ADAM29 5.58E-03 3.463 ADAM metallopeptidase domain Plasma peptidase 29 Membrane ADAMTS17 2.67E-04 3.051 ADAM metallopeptidase with Extracellular other thrombospondin type 1 motif, 17 Space ADCYAP1R1 1.20E-02 1.848 adenylate cyclase activating Plasma G-protein polypeptide 1 (pituitary) receptor Membrane coupled type I receptor ADH6 (includes 4.02E-02 −1.845 alcohol dehydrogenase 6 (class Cytoplasm enzyme EG:130) V) AHSA2 1.54E-04 −1.6 AHA1, activator of heat shock unknown other 90kDa protein ATPase homolog 2 (yeast) AK5 3.32E-02 1.658 adenylate kinase 5 Cytoplasm kinase AK7 -
Download Ppis for Each Single Seed, Thus Obtaining Each Seed’S Interactome (Ferrari Et Al., 2018)
bioRxiv preprint doi: https://doi.org/10.1101/2021.01.14.425874; this version posted January 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 4.0 International license. Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias Nikoleta Vavouraki1,2, James E. Tomkins1, Eleanna Kara3, Henry Houlden3, John Hardy4, Marcus J. Tindall2,5, Patrick A. Lewis1,4,6, Claudia Manzoni1,7* Author Affiliations 1: Department of Pharmacy, University of Reading, Reading, RG6 6AH, United Kingdom 2: Department of Mathematics and Statistics, University of Reading, Reading, RG6 6AH, United Kingdom 3: Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, WC1N 3BG, United Kingdom 4: Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, WC1N 3BG, United Kingdom 5: Institute of Cardiovascular and Metabolic Research, University of Reading, Reading, RG6 6AS, United Kingdom 6: Department of Comparative Biomedical Sciences, Royal Veterinary College, London, NW1 0TU, United Kingdom 7: School of Pharmacy, University College London, London, WC1N 1AX, United Kingdom *Corresponding author: [email protected] Abstract The Hereditary Spastic Paraplegias are a group of neurodegenerative diseases characterized by spasticity and weakness in the lower body. Despite the identification of causative mutations in over 70 genes, the molecular aetiology remains unclear. Due to the combination of genetic diversity and variable clinical presentation, the Hereditary Spastic Paraplegias are a strong candidate for protein- protein interaction network analysis as a tool to understand disease mechanism(s) and to aid functional stratification of phenotypes. -
Epi)Genomics Data in the 3T9mycer, Eµ-Myc and Tet-MYC
PhD degree in Molecular Medicine (curriculum in Computational Biology) European School of Molecular Medicine (SEMM), University of Milan and University of Naples “Federico II” An integrative approach to identify binding partners of Myc using (epi)genomics data in the 3T9MycER, Eµ-myc and tet-MYC systems Pranami Bora Center for Genomic Science of IIT@SEMM, Milan Matricola n. R10337 Supervisor: Dr. Bruno Amati Center for Genomic Science of IIT@SEMM, Milan Added Supervisor: Dr. Marco Morelli Center for Genomic Science of IIT@SEMM, Milan Anno accademico 2016-2017 Table of Contents List of abbreviations ...................................................................................................... 4 Figures index ................................................................................................................. 5 Abstract ........................................................................................................................ 8 Chapter 1 ..................................................................................................................... 10 Introduction ................................................................................................................. 10 1.1 Epigenetics ............................................................................................................... 10 1.1.1 Acetylation and methylation ..................................................................................... 10 1.1.2 Transcription factors ................................................................................................. -
Novel Genes Upregulated When NOTCH Signalling Is Disrupted During Hypothalamic Development Ratié Et Al
Novel genes upregulated when NOTCH signalling is disrupted during hypothalamic development Ratié et al. Ratié et al. Neural Development 2013, 8:25 http://www.neuraldevelopment.com/content/8/1/25 Ratié et al. Neural Development 2013, 8:25 http://www.neuraldevelopment.com/content/8/1/25 RESEARCH ARTICLE Open Access Novel genes upregulated when NOTCH signalling is disrupted during hypothalamic development Leslie Ratié1, Michelle Ware1, Frédérique Barloy-Hubler1,2, Hélène Romé1, Isabelle Gicquel1, Christèle Dubourg1,3, Véronique David1,3 and Valérie Dupé1* Abstract Background: The generation of diverse neuronal types and subtypes from multipotent progenitors during development is crucial for assembling functional neural circuits in the adult central nervous system. It is well known that the Notch signalling pathway through the inhibition of proneural genes is a key regulator of neurogenesis in the vertebrate central nervous system. However, the role of Notch during hypothalamus formation along with its downstream effectors remains poorly defined. Results: Here, we have transiently blocked Notch activity in chick embryos and used global gene expression analysis to provide evidence that Notch signalling modulates the generation of neurons in the early developing hypothalamus by lateral inhibition. Most importantly, we have taken advantage of this model to identify novel targets of Notch signalling, such as Tagln3 and Chga, which were expressed in hypothalamic neuronal nuclei. Conclusions: These data give essential advances into the early generation of neurons in the hypothalamus. We demonstrate that inhibition of Notch signalling during early development of the hypothalamus enhances expression of several new markers. These genes must be considered as important new targets of the Notch/proneural network. -
Variation in Protein Coding Genes Identifies Information Flow
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. 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. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. 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. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
Bioinformatics Analysis of Hereditary Disease Gene Set to Identify Key Modulators of Myocardial Remodeling During Heart Regeneration in Zebrafish
EPiC Series in Computing Volume 70, 2020, Pages 226{237 Proceedings of the 12th International Conference on Bioinformatics and Computational Biology Bioinformatics analysis of hereditary disease gene set to identify key modulators of myocardial remodeling during heart regeneration in zebrafish Lawrence Yu-Min Liu1,2, Zih-Yin Lai1, Min-Hsuan Lin1, Yu Shih3 and Yung-Jen Chuang1 1 Department of Medical Science & Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, 30013, Taiwan 2 Division of Cardiology, Department of Internal Medicine, Hsinchu Mackay Memorial Hospital, Hsinchu, 30071, Taiwan 3 Interdisciplinary Program of Life Science, National Tsing Hua University, Hsinchu, 30013, Taiwan [email protected], [email protected], [email protected], [email protected], [email protected] Abstract Unlike mammals, adult zebrafish hearts retain a remarkable capacity to regenerate after injury. Since regeneration shares many common molecular pathways with embryonic development, we investigated myocardial remodeling genes and pathways by performing a comparative transcriptomic analysis of zebrafish heart regeneration using a set of known human hereditary heart disease genes related to myocardial hypertrophy during development. We cross-matched human hypertrophic cardiomyopathy-associated genes with a time-course microarray dataset of adult zebrafish heart regeneration. Genes in the expression profiles that were highly elevated in the early phases of myocardial repair and remodeling after injury in zebrafish were identified. These genes were further analyzed with web-based bioinformatics tools to construct a regulatory network revealing potential transcription factors and their upstream receptors. In silico functional analysis of these genes showed that they are involved in cardiomyocyte proliferation and differentiation, angiogenesis, and inflammation-related pathways. -
Positional Specificity of Different Transcription Factor Classes Within Enhancers
Positional specificity of different transcription factor classes within enhancers Sharon R. Grossmana,b,c, Jesse Engreitza, John P. Raya, Tung H. Nguyena, Nir Hacohena,d, and Eric S. Landera,b,e,1 aBroad Institute of MIT and Harvard, Cambridge, MA 02142; bDepartment of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139; cProgram in Health Sciences and Technology, Harvard Medical School, Boston, MA 02215; dCancer Research, Massachusetts General Hospital, Boston, MA 02114; and eDepartment of Systems Biology, Harvard Medical School, Boston, MA 02215 Contributed by Eric S. Lander, June 19, 2018 (sent for review March 26, 2018; reviewed by Gioacchino Natoli and Alexander Stark) Gene expression is controlled by sequence-specific transcription type-restricted enhancers (active in <50% of the cell types) and factors (TFs), which bind to regulatory sequences in DNA. TF ubiquitous enhancers (active in >90% of the cell types) (SI Ap- binding occurs in nucleosome-depleted regions of DNA (NDRs), pendix, Fig. S1C). which generally encompass regions with lengths similar to those We next sought to infer functional TF-binding sites within the protected by nucleosomes. However, less is known about where active regulatory elements. In a recent study (5), we found that within these regions specific TFs tend to be found. Here, we char- TF binding is strongly correlated with the quantitative DNA acterize the positional bias of inferred binding sites for 103 TFs accessibility of a region. Furthermore, the TF motifs associated within ∼500,000 NDRs across 47 cell types. We find that distinct with enhancer activity in reporter assays in a cell type corre- classes of TFs display different binding preferences: Some tend to sponded closely to those that are most enriched in the genomic have binding sites toward the edges, some toward the center, and sequences of active regulatory elements in that cell type (5). -
Peripheral Nerve Single-Cell Analysis Identifies Mesenchymal Ligands That Promote Axonal Growth
Research Article: New Research Development Peripheral Nerve Single-Cell Analysis Identifies Mesenchymal Ligands that Promote Axonal Growth Jeremy S. Toma,1 Konstantina Karamboulas,1,ª Matthew J. Carr,1,2,ª Adelaida Kolaj,1,3 Scott A. Yuzwa,1 Neemat Mahmud,1,3 Mekayla A. Storer,1 David R. Kaplan,1,2,4 and Freda D. Miller1,2,3,4 https://doi.org/10.1523/ENEURO.0066-20.2020 1Program in Neurosciences and Mental Health, Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada, 2Institute of Medical Sciences University of Toronto, Toronto, Ontario M5G 1A8, Canada, 3Department of Physiology, University of Toronto, Toronto, Ontario M5G 1A8, Canada, and 4Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada Abstract Peripheral nerves provide a supportive growth environment for developing and regenerating axons and are es- sential for maintenance and repair of many non-neural tissues. This capacity has largely been ascribed to paracrine factors secreted by nerve-resident Schwann cells. Here, we used single-cell transcriptional profiling to identify ligands made by different injured rodent nerve cell types and have combined this with cell-surface mass spectrometry to computationally model potential paracrine interactions with peripheral neurons. These analyses show that peripheral nerves make many ligands predicted to act on peripheral and CNS neurons, in- cluding known and previously uncharacterized ligands. While Schwann cells are an important ligand source within injured nerves, more than half of the predicted ligands are made by nerve-resident mesenchymal cells, including the endoneurial cells most closely associated with peripheral axons. At least three of these mesen- chymal ligands, ANGPT1, CCL11, and VEGFC, promote growth when locally applied on sympathetic axons. -
GREAT, a Functional Enrichment Approach and Tool for Interpretation of Genome-Wide Cis-Regulatory Datasets
GREAT, a functional enrichment approach and tool for interpretation of genome-wide cis-regulatory datasets Cory Y. McLean1, Dave Bristor1,2, Michael Hiller2, Shoa L. Clarke3, Bruce T. Schaar2, Craig B. Lowe4, Aaron M. Wenger1, and Gill Bejerano1,2 1Department of Computer Science, Stanford University, Stanford, CA 94305, USA 2Department of Developmental Biology, Stanford University, Stanford, CA 94305, USA 3Department of Genetics, Stanford University, Stanford, CA 94305, USA 4Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA Correspondence should be addressed to G.B. ([email protected]). 04/01/10 Contents SUPPLEMENTARY NOTES . 2 SUPPLEMENTARY REFERENCES . 10 SUPPLEMENTARY FIGURES . 13 SUPPLEMENTARY TABLES . 17 Nature Biotechnology: doi: 10.1038/nbt.1630 1 Supplement SUPPLEMENTARY NOTES Ontologies supported GREAT assimilates knowledge from 20 separate ontologies containing biological knowledge about gene func- tions, phenotype and disease associations, biological pathways, gene expression data, presence of regulatory motifs, and gene families (Supplementary Table 1). Statistics for each ontology list the total number of terms in the ontology that are currently tested by GREAT, the number of genes annotated with one or more terms in the ontology, and the number of direct associations between ontology terms and genes (Sup- plementary Tables 2 and 3). Some ontologies contain parent/child relationships between terms expressed as a directed acyclic graph; general terms within these ontologies inherit genes that are only labeled with more specific child terms as indirect associations. To increase statistical power (by reducing the multiple hypothesis correction factor), GREAT does not test any general term whose associated gene list is identical to the associated gene list of a more specific child term.