Quantitative Lymphatic Vessel Trait Analysis Suggests Vcam1 As Candidate Modifier Gene of Inflammatory Bowel Disease
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Deregulated Gene Expression Pathways in Myelodysplastic Syndrome Hematopoietic Stem Cells
Leukemia (2010) 24, 756–764 & 2010 Macmillan Publishers Limited All rights reserved 0887-6924/10 $32.00 www.nature.com/leu ORIGINAL ARTICLE Deregulated gene expression pathways in myelodysplastic syndrome hematopoietic stem cells A Pellagatti1, M Cazzola2, A Giagounidis3, J Perry1, L Malcovati2, MG Della Porta2,MJa¨dersten4, S Killick5, A Verma6, CJ Norbury7, E Hellstro¨m-Lindberg4, JS Wainscoat1 and J Boultwood1 1LRF Molecular Haematology Unit, NDCLS, John Radcliffe Hospital, Oxford, UK; 2Department of Hematology Oncology, University of Pavia Medical School, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; 3Medizinische Klinik II, St Johannes Hospital, Duisburg, Germany; 4Division of Hematology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden; 5Department of Haematology, Royal Bournemouth Hospital, Bournemouth, UK; 6Albert Einstein College of Medicine, Bronx, NY, USA and 7Sir William Dunn School of Pathology, University of Oxford, Oxford, UK To gain insight into the molecular pathogenesis of the the World Health Organization.6,7 Patients with refractory myelodysplastic syndromes (MDS), we performed global gene anemia (RA) with or without ringed sideroblasts, according to expression profiling and pathway analysis on the hemato- poietic stem cells (HSC) of 183 MDS patients as compared with the the French–American–British classification, were subdivided HSC of 17 healthy controls. The most significantly deregulated based on the presence or absence of multilineage dysplasia. In pathways in MDS include interferon signaling, thrombopoietin addition, patients with RA with excess blasts (RAEB) were signaling and the Wnt pathways. Among the most signifi- subdivided into two categories, RAEB1 and RAEB2, based on the cantly deregulated gene pathways in early MDS are immuno- percentage of bone marrow blasts. -
Mea6 Controls VLDL Transport Through the Coordinated Regulation of COPII Assembly
Cell Research (2016) 26:787-804. npg © 2016 IBCB, SIBS, CAS All rights reserved 1001-0602/16 $ 32.00 ORIGINAL ARTICLE www.nature.com/cr Mea6 controls VLDL transport through the coordinated regulation of COPII assembly Yaqing Wang1, *, Liang Liu1, 2, *, Hongsheng Zhang1, 2, Junwan Fan1, 2, Feng Zhang1, 2, Mei Yu3, Lei Shi1, Lin Yang1, Sin Man Lam1, Huimin Wang4, Xiaowei Chen4, Yingchun Wang1, Fei Gao5, Guanghou Shui1, Zhiheng Xu1, 6 1State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; 2University of Chinese Academy of Sciences, Beijing 100101, China; 3School of Life Science, Shandong University, Jinan 250100, China; 4Institute of Molecular Medicine, Peking University, Beijing 100871, China; 5State Key Laboratory of Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; 6Translation- al Medical Center for Stem Cell Therapy, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China Lipid accumulation, which may be caused by the disturbance in very low density lipoprotein (VLDL) secretion in the liver, can lead to fatty liver disease. VLDL is synthesized in endoplasmic reticulum (ER) and transported to Golgi apparatus for secretion into plasma. However, the underlying molecular mechanism for VLDL transport is still poor- ly understood. Here we show that hepatocyte-specific deletion of meningioma-expressed antigen 6 (Mea6)/cutaneous T cell lymphoma-associated antigen 5C (cTAGE5C) leads to severe fatty liver and hypolipemia in mice. Quantitative lip- idomic and proteomic analyses indicate that Mea6/cTAGE5 deletion impairs the secretion of different types of lipids and proteins, including VLDL, from the liver. -
Small Cell Ovarian Carcinoma: Genomic Stability and Responsiveness to Therapeutics
Gamwell et al. Orphanet Journal of Rare Diseases 2013, 8:33 http://www.ojrd.com/content/8/1/33 RESEARCH Open Access Small cell ovarian carcinoma: genomic stability and responsiveness to therapeutics Lisa F Gamwell1,2, Karen Gambaro3, Maria Merziotis2, Colleen Crane2, Suzanna L Arcand4, Valerie Bourada1,2, Christopher Davis2, Jeremy A Squire6, David G Huntsman7,8, Patricia N Tonin3,4,5 and Barbara C Vanderhyden1,2* Abstract Background: The biology of small cell ovarian carcinoma of the hypercalcemic type (SCCOHT), which is a rare and aggressive form of ovarian cancer, is poorly understood. Tumourigenicity, in vitro growth characteristics, genetic and genomic anomalies, and sensitivity to standard and novel chemotherapeutic treatments were investigated in the unique SCCOHT cell line, BIN-67, to provide further insight in the biology of this rare type of ovarian cancer. Method: The tumourigenic potential of BIN-67 cells was determined and the tumours formed in a xenograft model was compared to human SCCOHT. DNA sequencing, spectral karyotyping and high density SNP array analysis was performed. The sensitivity of the BIN-67 cells to standard chemotherapeutic agents and to vesicular stomatitis virus (VSV) and the JX-594 vaccinia virus was tested. Results: BIN-67 cells were capable of forming spheroids in hanging drop cultures. When xenografted into immunodeficient mice, BIN-67 cells developed into tumours that reflected the hypercalcemia and histology of human SCCOHT, notably intense expression of WT-1 and vimentin, and lack of expression of inhibin. Somatic mutations in TP53 and the most common activating mutations in KRAS and BRAF were not found in BIN-67 cells by DNA sequencing. -
Analysis of Trans Esnps Infers Regulatory Network Architecture
Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Anat Kreimer All rights reserved ABSTRACT Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation. -
Mouse Lyve1 Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Lyve1 Knockout Project (CRISPR/Cas9) Objective: To create a Lyve1 knockout Mouse model (C57BL/6N) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Lyve1 gene (NCBI Reference Sequence: NM_053247 ; Ensembl: ENSMUSG00000030787 ) is located on Mouse chromosome 7. 6 exons are identified, with the ATG start codon in exon 1 and the TAG stop codon in exon 6 (Transcript: ENSMUST00000033050). Exon 2~5 will be selected as target site. Cas9 and gRNA will be co-injected into fertilized eggs for KO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Mice homozygous for one allele display enlarged lymphatic vessels and increased interstitial-lymphatic flow. However, mice homozygous for a second allele do not display any abnormalities in the lymphatic system. Exon 2 starts from about 9.01% of the coding region. Exon 2~5 covers 71.8% of the coding region. The size of effective KO region: ~6930 bp. The KO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 2 3 4 5 6 Legends Exon of mouse Lyve1 Knockout region Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot (up) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section upstream of Exon 2 is aligned with itself to determine if there are tandem repeats. No significant tandem repeat is found in the dot plot matrix. So this region is suitable for PCR screening or sequencing analysis. -
Transcriptomic Profiling of Equine and Viral Genes in Peripheral Blood
pathogens Article Transcriptomic Profiling of Equine and Viral Genes in Peripheral Blood Mononuclear Cells in Horses during Equine Herpesvirus 1 Infection Lila M. Zarski 1, Patty Sue D. Weber 2, Yao Lee 1 and Gisela Soboll Hussey 1,* 1 Department of Pathobiology and Diagnostic Investigation, Michigan State University, East Lansing, MI 48824, USA; [email protected] (L.M.Z.); [email protected] (Y.L.) 2 Department of Large Animal Clinical Sciences, Michigan State University, East Lansing, MI 48824, USA; [email protected] * Correspondence: [email protected] Abstract: Equine herpesvirus 1 (EHV-1) affects horses worldwide and causes respiratory dis- ease, abortions, and equine herpesvirus myeloencephalopathy (EHM). Following infection, a cell- associated viremia is established in the peripheral blood mononuclear cells (PBMCs). This viremia is essential for transport of EHV-1 to secondary infection sites where subsequent immunopathol- ogy results in diseases such as abortion or EHM. Because of the central role of PBMCs in EHV-1 pathogenesis, our goal was to establish a gene expression analysis of host and equine herpesvirus genes during EHV-1 viremia using RNA sequencing. When comparing transcriptomes of PBMCs during peak viremia to those prior to EHV-1 infection, we found 51 differentially expressed equine genes (48 upregulated and 3 downregulated). After gene ontology analysis, processes such as the interferon defense response, response to chemokines, the complement protein activation cascade, cell adhesion, and coagulation were overrepresented during viremia. Additionally, transcripts for EHV-1, EHV-2, and EHV-5 were identified in pre- and post-EHV-1-infection samples. Looking at Citation: Zarski, L.M.; Weber, P.S.D.; micro RNAs (miRNAs), 278 known equine miRNAs and 855 potentially novel equine miRNAs were Lee, Y.; Soboll Hussey, G. -
Primate Specific Retrotransposons, Svas, in the Evolution of Networks That Alter Brain Function
Title: Primate specific retrotransposons, SVAs, in the evolution of networks that alter brain function. Olga Vasieva1*, Sultan Cetiner1, Abigail Savage2, Gerald G. Schumann3, Vivien J Bubb2, John P Quinn2*, 1 Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, U.K 2 Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, The University of Liverpool, Liverpool L69 3BX, UK 3 Division of Medical Biotechnology, Paul-Ehrlich-Institut, Langen, D-63225 Germany *. Corresponding author Olga Vasieva: Institute of Integrative Biology, Department of Comparative genomics, University of Liverpool, Liverpool, L69 7ZB, [email protected] ; Tel: (+44) 151 795 4456; FAX:(+44) 151 795 4406 John Quinn: Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, The University of Liverpool, Liverpool L69 3BX, UK, [email protected]; Tel: (+44) 151 794 5498. Key words: SVA, trans-mobilisation, behaviour, brain, evolution, psychiatric disorders 1 Abstract The hominid-specific non-LTR retrotransposon termed SINE–VNTR–Alu (SVA) is the youngest of the transposable elements in the human genome. The propagation of the most ancient SVA type A took place about 13.5 Myrs ago, and the youngest SVA types appeared in the human genome after the chimpanzee divergence. Functional enrichment analysis of genes associated with SVA insertions demonstrated their strong link to multiple ontological categories attributed to brain function and the disorders. SVA types that expanded their presence in the human genome at different stages of hominoid life history were also associated with progressively evolving behavioural features that indicated a potential impact of SVA propagation on a cognitive ability of a modern human. -
Supplemental Information Proximity Interactions Among Centrosome
Current Biology, Volume 24 Supplemental Information Proximity Interactions among Centrosome Components Identify Regulators of Centriole Duplication Elif Nur Firat-Karalar, Navin Rauniyar, John R. Yates III, and Tim Stearns Figure S1 A Myc Streptavidin -tubulin Merge Myc Streptavidin -tubulin Merge BirA*-PLK4 BirA*-CEP63 BirA*- CEP192 BirA*- CEP152 - BirA*-CCDC67 BirA* CEP152 CPAP BirA*- B C Streptavidin PCM1 Merge Myc-BirA* -CEP63 PCM1 -tubulin Merge BirA*- CEP63 DMSO - BirA* CEP63 nocodazole BirA*- CCDC67 Figure S2 A GFP – + – + GFP-CEP152 + – + – Myc-CDK5RAP2 + + + + (225 kDa) Myc-CDK5RAP2 (216 kDa) GFP-CEP152 (27 kDa) GFP Input (5%) IP: GFP B GFP-CEP152 truncation proteins Inputs (5%) IP: GFP kDa 1-7481-10441-1290218-1654749-16541045-16541-7481-10441-1290218-1654749-16541045-1654 250- Myc-CDK5RAP2 150- 150- 100- 75- GFP-CEP152 Figure S3 A B CEP63 – – + – – + GFP CCDC14 KIAA0753 Centrosome + – – + – – GFP-CCDC14 CEP152 binding binding binding targeting – + – – + – GFP-KIAA0753 GFP-KIAA0753 (140 kDa) 1-496 N M C 150- 100- GFP-CCDC14 (115 kDa) 1-424 N M – 136-496 M C – 50- CEP63 (63 kDa) 1-135 N – 37- GFP (27 kDa) 136-424 M – kDa 425-496 C – – Inputs (2%) IP: GFP C GFP-CEP63 truncation proteins D GFP-CEP63 truncation proteins Inputs (5%) IP: GFP Inputs (5%) IP: GFP kDa kDa 1-135136-424425-4961-424136-496FL Ctl 1-135136-424425-4961-424136-496FL Ctl 1-135136-424425-4961-424136-496FL Ctl 1-135136-424425-4961-424136-496FL Ctl Myc- 150- Myc- 100- CCDC14 KIAA0753 100- 100- 75- 75- GFP- GFP- 50- CEP63 50- CEP63 37- 37- Figure S4 A siCtl -
Phenotypic Modulation of Smooth Muscle Cells in Atherosclerosis Is Associated with Downregulation of LMOD1, SYNPO2, PDLIM7, PLN and SYNM
Markers of smooth muscle cells Perisic et al. Phenotypic modulation of smooth muscle cells in atherosclerosis is associated with downregulation of LMOD1, SYNPO2, PDLIM7, PLN and SYNM Perisic L1, Rykaczewska U1, Razuvaev A1, Sabater-Lleal M2, Lengquist M1, Miller CL3, Ericsson I1, Röhl S1, Kronqvist M1, Aldi S1, Magné J2, Vesterlund M4, Li Y2, Yin H2, Gonzalez Diez M2, Roy J1, Baldassarre D5,6, Veglia F6, Humphries SE7, de Faire U8,9, Tremoli E5,6, on behalf of the IMPROVE study group, Odeberg J10, Vukojević V11, Lehtiö J4, Maegdefessel L2, Ehrenborg E2, Paulsson-Berne G2, Hansson GK2, Lindeman JHN12, Eriksson P2, Quertermous T3, Hamsten A2, Hedin U1 1Department of Molecular Medicine and Surgery, Karolinska Institutet, Sweden, 2Department of Medicine, Karolinska Institutet, Sweden, 3Division of Vascular Surgery, Stanford University, USA, 4Science for Life Laboratory, Sweden, 5Dipartimento di Scienze Farmacologiche e Biomolecolari, Università di Milano, Milan, Italy, 6Centro Cardiologico Monzino, IRCCS, Milan, Italy, 7British Heart Foundation Laboratories, University College of London, Department of Medicine, Rayne Building, London, United Kingdom, 8Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, 9Department of Cardiology, Karolinska University Hospital Solna, Karolinska Institutet, Stockholm, Sweden, 10Science for Life Laboratory, Department of Proteomics, Sweden, 11Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Sweden, 12Department of Vascular -
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). -
The Transcriptomic Landscape of Prostate Cancer Development and Progression: an Integrative Analysis
cancers Article The Transcriptomic Landscape of Prostate Cancer Development and Progression: An Integrative Analysis Jacek Marzec 1,† , Helen Ross-Adams 1,*,† , Stefano Pirrò 1 , Jun Wang 1 , Yanan Zhu 2, Xueying Mao 2, Emanuela Gadaleta 1 , Amar S. Ahmad 3, Bernard V. North 3, Solène-Florence Kammerer-Jacquet 2, Elzbieta Stankiewicz 2, Sakunthala C. Kudahetti 2, Luis Beltran 4, Guoping Ren 5, Daniel M. Berney 2,4, Yong-Jie Lu 2 and Claude Chelala 1,6,* 1 Bioinformatics Unit, Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK; [email protected] (J.M.); [email protected] (S.P.); [email protected] (J.W.); [email protected] (E.G.) 2 Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK; [email protected] (Y.Z.); [email protected] (X.M.); solenefl[email protected] (S.-F.K.-J.); [email protected] (E.S.); [email protected] (S.C.K.); [email protected] (D.M.B.); [email protected] (Y.-J.L.) 3 Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine, Queen Mary University of London, London EC1M 6BQ, UK; [email protected] (A.S.A.); [email protected] (B.V.N.) 4 Department of Pathology, Barts Health NHS, London E1 F1R, UK; [email protected] 5 Department of Pathology, The First Affiliated Hospital, Zhejiang University Medical College, Hangzhou 310058, China; [email protected] 6 Centre for Computational Biology, Life Sciences Initiative, Queen Mary University London, London EC1M 6BQ, UK * Correspondence: [email protected] (H.R.-A.); [email protected] (C.C.) † These authors contributed equally to this work. -
Variation in Protein Coding Genes Identifies Information
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.