Thalamic Transcriptome Screening in Three Psychiatric States
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Supplementary Data
Figure 2S 4 7 A - C 080125 CSCs 080418 CSCs - + IFN-a 48 h + IFN-a 48 h + IFN-a 72 h 6 + IFN-a 72 h 3 5 MRFI 4 2 3 2 1 1 0 0 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 7 B 13 080125 FBS - D 080418 FBS - + IFN-a 48 h 12 + IFN-a 48 h + IFN-a 72 h + IFN-a 72 h 6 080125 FBS 11 10 5 9 8 4 7 6 3 MRFI 5 4 2 3 2 1 1 0 0 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 Molecule Molecule FIGURE 4S FIGURE 5S Panel A Panel B FIGURE 6S A B C D Supplemental Results Table 1S. Modulation by IFN-α of APM in GBM CSC and FBS tumor cell lines. Molecule * Cell line IFN-α‡ HLA β2-m# HLA LMP TAP1 TAP2 class II A A HC§ 2 7 10 080125 CSCs - 1∞ (1) 3 (65) 2 (91) 1 (2) 6 (47) 2 (61) 1 (3) 1 (2) 1 (3) + 2 (81) 11 (80) 13 (99) 1 (3) 8 (88) 4 (91) 1 (2) 1 (3) 2 (68) 080125 FBS - 2 (81) 4 (63) 4 (83) 1 (3) 6 (80) 3 (67) 2 (86) 1 (3) 2 (75) + 2 (99) 14 (90) 7 (97) 5 (75) 7 (100) 6 (98) 2 (90) 1 (4) 3 (87) 080418 CSCs - 2 (51) 1 (1) 1 (3) 2 (47) 2 (83) 2 (54) 1 (4) 1 (2) 1 (3) + 2 (81) 3 (76) 5 (75) 2 (50) 2 (83) 3 (71) 1 (3) 2 (87) 1 (2) 080418 FBS - 1 (3) 3 (70) 2 (88) 1 (4) 3 (87) 2 (76) 1 (3) 1 (3) 1 (2) + 2 (78) 7 (98) 5 (99) 2 (94) 5 (100) 3 (100) 1 (4) 2 (100) 1 (2) 070104 CSCs - 1 (2) 1 (3) 1 (3) 2 (78) 1 (3) 1 (2) 1 (3) 1 (3) 1 (2) + 2 (98) 8 (100) 10 (88) 4 (89) 3 (98) 3 (94) 1 (4) 2 (86) 2 (79) * expression of APM molecules was evaluated by intracellular staining and cytofluorimetric analysis; ‡ cells were treatead or not (+/-) for 72 h with 1000 IU/ml of IFN-α; # β-2 microglobulin; § β-2 microglobulin-free HLA-A heavy chain; ∞ values are indicated as ratio between the mean of fluorescence intensity of cells stained with the selected mAb and that of the negative control; bold values indicate significant MRFI (≥ 2). -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
Stelios Pavlidis3, Matthew Loza3, Fred Baribaud3, Anthony
Supplementary Data Th2 and non-Th2 molecular phenotypes of asthma using sputum transcriptomics in UBIOPRED Chih-Hsi Scott Kuo1.2, Stelios Pavlidis3, Matthew Loza3, Fred Baribaud3, Anthony Rowe3, Iaonnis Pandis2, Ana Sousa4, Julie Corfield5, Ratko Djukanovic6, Rene 7 7 8 2 1† Lutter , Peter J. Sterk , Charles Auffray , Yike Guo , Ian M. Adcock & Kian Fan 1†* # Chung on behalf of the U-BIOPRED consortium project team 1Airways Disease, National Heart & Lung Institute, Imperial College London, & Biomedical Research Unit, Biomedical Research Unit, Royal Brompton & Harefield NHS Trust, London, United Kingdom; 2Department of Computing & Data Science Institute, Imperial College London, United Kingdom; 3Janssen Research and Development, High Wycombe, Buckinghamshire, United Kingdom; 4Respiratory Therapeutic Unit, GSK, Stockley Park, United Kingdom; 5AstraZeneca R&D Molndal, Sweden and Areteva R&D, Nottingham, United Kingdom; 6Faculty of Medicine, Southampton University, Southampton, United Kingdom; 7Faculty of Medicine, University of Amsterdam, Amsterdam, Netherlands; 8European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, France. †Contributed equally #Consortium project team members are listed under Supplementary 1 Materials *To whom correspondence should be addressed: [email protected] 2 List of the U-BIOPRED Consortium project team members Uruj Hoda & Christos Rossios, Airways Disease, National Heart & Lung Institute, Imperial College London, UK & Biomedical Research Unit, Biomedical Research Unit, Royal -
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 Network of Bhlhzip Transcription Factors in Melanoma: Interactions of MITF, TFEB and TFE3
A network of bHLHZip transcription factors in melanoma: Interactions of MITF, TFEB and TFE3 Josué A. Ballesteros Álvarez Thesis for the degree of Philosophiae Doctor January 2019 Net bHLHZip umritunarþátta í sortuæxlum: Samstarf milli MITF, TFEB og TFE3 Josué A. Ballesteros Álvarez Ritgerð til doktorsgráðu Leiðbeinandi/leiðbeinendur: Eiríkur Steingrímsson Doktorsnefnd: Margrét H. Ögmundsdóttir Þórarinn Guðjónsson Jórunn E. Eyfjörð Lars Rönnstrand Janúar 2019 Thesis for a doctoral degree at tHe University of Iceland. All rigHts reserved. No Part of tHis Publication may be reProduced in any form witHout tHe Prior permission of the copyright holder. © Josue A. Ballesteros Álvarez. 2019 ISBN 978-9935-9421-4-2 Printing by HáskólaPrent Reykjavik, Iceland 2019 Ágrip StjórnPróteinin MITF , TFEB, TFE3 og TFEC (stundum nefnd MiT-TFE þættirnir) tilheyra bHLHZip fjölskyldu umritunarþátta sem bindast DNA og stjórna tjáningu gena. MITF er mikilvægt fyrir myndun og starfsemi litfruma en ættingjar þess, TFEB og TFE3, stjórna myndun og starfsemi lysósóma og sjálfsáti. Sjálfsát er líffræðilegt ferli sem gegnir mikilvægu hlutverki í starfsemi fruma en getur einnig haft áHrif á myndun og meðHöndlun sjúkdóma. Í verkefni þessu var samstarf MITF, TFE3 og TFEB Próteinanna skoðað í sortuæxlisfrumum og hvaða áhrif þau Hafa á tjáningu hvers annars. Eins og MITF eru TFEB og TFE3 genin tjáð í sortuæxlisfrumum og sortuæxlum; TFEC er ekki tjáð í þessum frumum og var því ekki skoðað í þessu verkefni. Með notkun sérvirkra hindra var sýnt að boðleiðir hafa áhrif á staðsetningu próteinanna þriggja í sortuæxlisfrumum. Umritunarþættir þessir geta bundist skyldum DNA-bindisetum og haft áhrif á tjáningu gena sem eru nauðsynleg fyrir myndun bæði lýsósóma og melanósóma. -
An Integrated Genomic Analysis of Gene-Function Correlation on Schizophrenia Susceptibility Genes
Journal of Human Genetics (2010) 55, 285–292 & 2010 The Japan Society of Human Genetics All rights reserved 1434-5161/10 $32.00 www.nature.com/jhg ORIGINAL ARTICLE An integrated genomic analysis of gene-function correlation on schizophrenia susceptibility genes Tearina T Chu and Ying Liu Schizophrenia is a highly complex inheritable disease characterized by numerous genetic susceptibility elements, each contributing a modest increase in risk for the disease. Although numerous linkage or association studies have identified a large set of schizophrenia-associated loci, many are controversial. In addition, only a small portion of these loci overlaps with the large cumulative pool of genes that have shown changes of expression in schizophrenia. Here, we applied a genomic gene-function approach to identify susceptibility loci that show direct effect on gene expression, leading to functional abnormalities in schizophrenia. We carried out an integrated analysis by cross-examination of the literature-based susceptibility loci with the schizophrenia-associated expression gene list obtained from our previous microarray study (Journal of Human Genetics (2009) 54: 665–75) using bioinformatic tools, followed by confirmation of gene expression changes using qPCR. We found nine genes (CHGB, SLC18A2, SLC25A27, ESD, C4A/C4B, TCP1, CHL1 and CTNNA2) demonstrate gene-function correlation involving: synapse and neurotransmission; energy metabolism and defense mechanisms; and molecular chaperone and cytoskeleton. Our findings further support the roles of these genes in genetic influence and functional consequences on the development of schizophrenia. It is interesting to note that four of the nine genes are located on chromosome 6, suggesting a special chromosomal vulnerability in schizophrenia. -
Relazione Oncoexome
EXOME Revolution Oncology Diagnostics Relazione Tecnica M M EXOME Revolution Oncology Diagnostics ONCOEXOME Con il termine “cancro” o tumore” ci si riferisce ad un insieme molto eterogeneo di malattie caratteriz- zate da una crescita cellulare svincolata dai normali meccanismi di controllo dell’organismo. Le cellule tumorali infatti invece di morire vivono più a lungo di quanto sarebbe previsto dal codice genetico, con- tinuano a generare ulteriori cellule anomale e possono inoltre invadere (metastatizzando) i tessuti adia- centi. È noto ormai, che il cancro origina da un accumulo di mutazioni nel DNA, cioè di alterazioni in geni che regolano la proliferazione, la sopravvivenza delle cellule, la loro adesione e la loro mobilità. Le mutazioni possono svilupparsi in tempi molto differenti, anche sotto l'influenza di stimoli esterni. Tanto maggiori saranno le anomalie genetiche accumulate, tanto più la cellula neoplastica si discosterà dall’originaria e la neoplasia maligna sarà indifferenziata e priva di controllo divenendo invasiva a scapito dei tessuti dell’organismo. Il tumore benigno può essere considerato la prima tappa di queste alterazioni, tuttavia, molto di frequente, questa tappa viene saltata e si arriva alla malignità senza evidenti segni precursori. Ad oggi, il tumore è la seconda causa di morte in Italia dopo le malattie cardiovascolari e rappresenta il 30% di causa di tutti i decessi. Secondo statistiche americane, quasi la metà di tutti gli uomini e un po’ più di un terzo di tutte le donne svilupperanno un cancro nel corso della loro esistenza. Per tale motivo, diventa sempre più necessario sviluppare nuovi metodi di prevenzione primaria per ridurre il rischio di ammalarsi ed ottenere cosi approcci terapeutici personalizzati. -
Toxicogenomics Responses in the in Vitro Liver : a View on Human Interindividual Variation
Toxicogenomics responses in the in vitro liver : a view on human interindividual variation Citation for published version (APA): Jetten, M. J. A. (2014). Toxicogenomics responses in the in vitro liver : a view on human interindividual variation. Maastricht University. https://doi.org/10.26481/dis.20141205mj Document status and date: Published: 01/01/2014 DOI: 10.26481/dis.20141205mj Document Version: Publisher's PDF, also known as Version of record Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. -
The Role of Inhibitors of Differentiation Proteins ID1 and ID3 in Breast Cancer Metastasis
The role of Inhibitors of Differentiation proteins ID1 and ID3 in breast cancer metastasis Wee Siang Teo A thesis in fulfilment of the requirements for the degree of Doctor of Philosophy St Vincent’s Clinical School, Faculty of Medicine The University of New South Wales Cancer Research Program The Garvan Institute of Medical Research Sydney, Australia March, 2014 THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet Surname or Family name: Teo First name: Wee Siang Abbreviation for degree as given in the University calendar: PhD (Medicine) School: St Vincent’s Clinical School Faculty: Faculty of Medicine Title: The role of Inhibitors of Differentiation proteins ID1 and ID3 in breast cancer metastasis Abstract 350 words maximum: (PLEASE TYPE) Breast cancer is a leading cause of cancer death in women. While locally-confined breast cancer is generally curable, the survival of patients with metastatic breast cancer is very poor. Treatment for metastatic breast cancer is palliative not curative due to the lack of targeted therapies. Metastasis is a complex process that still remains poorly understood, thus a detailed understanding of the biological complexity that underlies breast cancer metastasis is essential in reducing the lethality of this disease. The Inhibitor of Differentiation proteins 1 and 3 (ID1/3) are transcriptional regulators that control many cell fate and developmental processes and are often deregulated in cancer. ID1/3 are required and sufficient for the metastasis of breast cancer in experimental models. However, the mechanisms by which ID1/3 mediate metastasis in breast cancer remain to be determined. Little is known about pathways regulated by ID1/3 in breast cancer as well as their functional role in the multiple steps of metastatic progression. -
Pflugers Final
CORE Metadata, citation and similar papers at core.ac.uk Provided by Serveur académique lausannois A comprehensive analysis of gene expression profiles in distal parts of the mouse renal tubule. Sylvain Pradervand2, Annie Mercier Zuber1, Gabriel Centeno1, Olivier Bonny1,3,4 and Dmitri Firsov1,4 1 - Department of Pharmacology and Toxicology, University of Lausanne, 1005 Lausanne, Switzerland 2 - DNA Array Facility, University of Lausanne, 1015 Lausanne, Switzerland 3 - Service of Nephrology, Lausanne University Hospital, 1005 Lausanne, Switzerland 4 – these two authors have equally contributed to the study to whom correspondence should be addressed: Dmitri FIRSOV Department of Pharmacology and Toxicology, University of Lausanne, 27 rue du Bugnon, 1005 Lausanne, Switzerland Phone: ++ 41-216925406 Fax: ++ 41-216925355 e-mail: [email protected] and Olivier BONNY Department of Pharmacology and Toxicology, University of Lausanne, 27 rue du Bugnon, 1005 Lausanne, Switzerland Phone: ++ 41-216925417 Fax: ++ 41-216925355 e-mail: [email protected] 1 Abstract The distal parts of the renal tubule play a critical role in maintaining homeostasis of extracellular fluids. In this review, we present an in-depth analysis of microarray-based gene expression profiles available for microdissected mouse distal nephron segments, i.e., the distal convoluted tubule (DCT) and the connecting tubule (CNT), and for the cortical portion of the collecting duct (CCD) (Zuber et al., 2009). Classification of expressed transcripts in 14 major functional gene categories demonstrated that all principal proteins involved in maintaining of salt and water balance are represented by highly abundant transcripts. However, a significant number of transcripts belonging, for instance, to categories of G protein-coupled receptors (GPCR) or serine-threonine kinases exhibit high expression levels but remain unassigned to a specific renal function. -
Mouse DNA Repair Primer Library II
Mouse DNA Repair Primer Library II Catalog No: MDRL-2 Supplier: RealTimePrimers Lot No: XXXXX Supplied as: solid Stability: store at -20°C Description Contains 88 primer sets directed against DNA repair genes and 8 housekeeping genes. Provided in a 96-well microplate (20 ul - 10 uM). Perform up to 100 PCR arrays (based on 20 ul assay volume per reaction). Just add cDNA template and SYBR green master mix. Gene List: • POLB Polymerase (Dna Directed), Beta • CHAF1A Chromatin Assembly Factor 1, Subunit • POLG Polymerase (Dna Directed), Gamma A (P150) • POLD1 Polymerase (Dna Directed), Delta 1, • BLM Bloom Syndrome, Recq Helicase-Like Catalytic Subunit 125Kda • WRN Werner Syndrome, Recq Helicase-Like • POLE Polymerase (Dna Directed), Epsilon • RECQL4 Recq Protein-Like 4 • PCNA Proliferating Cell Nuclear Antigen • ATM Ataxia Telangiectasia Mutated • REV3L Rev3-Like, Catalytic Subunit Of Dna • FANCA Fanconi Anemia, Complementation Polymerase Zeta (Yeast) Group A • MAD2L1 Mad2 Mitotic Arrest Deficient-Like 1 • FANCB Fanconi Anemia, Complementation (Yeast) Group B • MAD2L2 Mad2 Mitotic Arrest Deficient-Like 2 • FANCC Fanconi Anemia, Complementation (Yeast) Group C • REV1 REV1 homolog • FANCD2 Fanconi Anemia, Complementation • POLH Polymerase (Dna Directed), Eta Group D2 • POLI Polymerase (Dna Directed) Iota • FANCE Fanconi Anemia, Complementation • POLQ Polymerase (Dna Directed), Theta Group E • POLK Polymerase (Dna Directed) Kappa • FANCF Fanconi Anemia, Complementation • POLL Polymerase (Dna Directed), Lambda Group F • POLM Polymerase (Dna Directed), Mu • FANCG Fanconi Anemia, Complementation • POLN Polymerase (Dna Directed) Nu Group G • FEN1 Flap Structure-Specific Endonuclease 1 • FANCL Fanconi Anemia, Complementation • TREX1 Three Prime Repair Exonuclease 1 Group L • TREX2 Three Prime Repair Exonuclease 2 • DCLRE1A Dna Cross-Link Repair 1A • EXO1 Exonuclease 1 • DCLRE1B Dna Cross-Link Repair 1B • SPO11 Spo11 Meiotic Protein Covalently Bound • RPA4 Replication Protein A4, 30Kda To Dsb Homolog (S. -
ELMO1 Signaling Is a Promoter of Osteoclast Function and Bone Loss ✉ Sanja Arandjelovic 1 , Justin S
ARTICLE https://doi.org/10.1038/s41467-021-25239-6 OPEN ELMO1 signaling is a promoter of osteoclast function and bone loss ✉ Sanja Arandjelovic 1 , Justin S. A. Perry 1, Ming Zhou 2, Adam Ceroi3, Igor Smirnov4, Scott F. Walk1, Laura S. Shankman1, Isabelle Cambré3, Suna Onengut-Gumuscu 5, Dirk Elewaut 3, Thomas P. Conrads 2 & ✉ Kodi S. Ravichandran 1,3 Osteoporosis affects millions worldwide and is often caused by osteoclast induced bone loss. 1234567890():,; Here, we identify the cytoplasmic protein ELMO1 as an important ‘signaling node’ in osteoclasts. We note that ELMO1 SNPs associate with bone abnormalities in humans, and that ELMO1 deletion in mice reduces bone loss in four in vivo models: osteoprotegerin deficiency, ovariectomy, and two types of inflammatory arthritis. Our transcriptomic analyses coupled with CRISPR/Cas9 genetic deletion identify Elmo1 associated regulators of osteoclast function, including cathepsin G and myeloperoxidase. Further, we define the ‘ELMO1 inter- actome’ in osteoclasts via proteomics and reveal proteins required for bone degradation. ELMO1 also contributes to osteoclast sealing zone on bone-like surfaces and distribution of osteoclast-specific proteases. Finally, a 3D structure-based ELMO1 inhibitory peptide reduces bone resorption in wild type osteoclasts. Collectively, we identify ELMO1 as a signaling hub that regulates osteoclast function and bone loss, with relevance to osteoporosis and arthritis. 1 Center for Cell Clearance, Department of Microbiology, Immunology, and Cancer Biology and Carter Immunology Center, University of Virginia, Charlottesville, VA, USA. 2 Inova Schar Cancer Institute, Inova Center for Personalized Health, Fairfax, VA, USA. 3 Inflammation Research Centre, VIB, and the Department of Biomedical Molecular Biology, Ghent Univeristy, Ghent, Belgium.