Evolutionarily Conserved Intercalated Disc Protein Tmem65 Regulates Cardiac Conduction and Connexin 43 Function
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Evolution, Expression and Meiotic Behavior of Genes Involved in Chromosome Segregation of Monotremes
G C A T T A C G G C A T genes Article Evolution, Expression and Meiotic Behavior of Genes Involved in Chromosome Segregation of Monotremes Filip Pajpach , Linda Shearwin-Whyatt and Frank Grützner * School of Biological Sciences, The University of Adelaide, Adelaide, SA 5005, Australia; fi[email protected] (F.P.); [email protected] (L.S.-W.) * Correspondence: [email protected] Abstract: Chromosome segregation at mitosis and meiosis is a highly dynamic and tightly regulated process that involves a large number of components. Due to the fundamental nature of chromosome segregation, many genes involved in this process are evolutionarily highly conserved, but duplica- tions and functional diversification has occurred in various lineages. In order to better understand the evolution of genes involved in chromosome segregation in mammals, we analyzed some of the key components in the basal mammalian lineage of egg-laying mammals. The chromosome passenger complex is a multiprotein complex central to chromosome segregation during both mitosis and meio- sis. It consists of survivin, borealin, inner centromere protein, and Aurora kinase B or C. We confirm the absence of Aurora kinase C in marsupials and show its absence in both platypus and echidna, which supports the current model of the evolution of Aurora kinases. High expression of AURKBC, an ancestor of AURKB and AURKC present in monotremes, suggests that this gene is performing all necessary meiotic functions in monotremes. Other genes of the chromosome passenger complex complex are present and conserved in monotremes, suggesting that their function has been preserved Citation: Pajpach, F.; in mammals. -
Environmental Influences on Endothelial Gene Expression
ENDOTHELIAL CELL GENE EXPRESSION John Matthew Jeff Herbert Supervisors: Prof. Roy Bicknell and Dr. Victoria Heath PhD thesis University of Birmingham August 2012 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. ABSTRACT Tumour angiogenesis is a vital process in the pathology of tumour development and metastasis. Targeting markers of tumour endothelium provide a means of targeted destruction of a tumours oxygen and nutrient supply via destruction of tumour vasculature, which in turn ultimately leads to beneficial consequences to patients. Although current anti -angiogenic and vascular targeting strategies help patients, more potently in combination with chemo therapy, there is still a need for more tumour endothelial marker discoveries as current treatments have cardiovascular and other side effects. For the first time, the analyses of in-vivo biotinylation of an embryonic system is performed to obtain putative vascular targets. Also for the first time, deep sequencing is applied to freshly isolated tumour and normal endothelial cells from lung, colon and bladder tissues for the identification of pan-vascular-targets. Integration of the proteomic, deep sequencing, public cDNA libraries and microarrays, delivers 5,892 putative vascular targets to the science community. -
1 Supporting Information for a Microrna Network Regulates
Supporting Information for A microRNA Network Regulates Expression and Biosynthesis of CFTR and CFTR-ΔF508 Shyam Ramachandrana,b, Philip H. Karpc, Peng Jiangc, Lynda S. Ostedgaardc, Amy E. Walza, John T. Fishere, Shaf Keshavjeeh, Kim A. Lennoxi, Ashley M. Jacobii, Scott D. Rosei, Mark A. Behlkei, Michael J. Welshb,c,d,g, Yi Xingb,c,f, Paul B. McCray Jr.a,b,c Author Affiliations: Department of Pediatricsa, Interdisciplinary Program in Geneticsb, Departments of Internal Medicinec, Molecular Physiology and Biophysicsd, Anatomy and Cell Biologye, Biomedical Engineeringf, Howard Hughes Medical Instituteg, Carver College of Medicine, University of Iowa, Iowa City, IA-52242 Division of Thoracic Surgeryh, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada-M5G 2C4 Integrated DNA Technologiesi, Coralville, IA-52241 To whom correspondence should be addressed: Email: [email protected] (M.J.W.); yi- [email protected] (Y.X.); Email: [email protected] (P.B.M.) This PDF file includes: Materials and Methods References Fig. S1. miR-138 regulates SIN3A in a dose-dependent and site-specific manner. Fig. S2. miR-138 regulates endogenous SIN3A protein expression. Fig. S3. miR-138 regulates endogenous CFTR protein expression in Calu-3 cells. Fig. S4. miR-138 regulates endogenous CFTR protein expression in primary human airway epithelia. Fig. S5. miR-138 regulates CFTR expression in HeLa cells. Fig. S6. miR-138 regulates CFTR expression in HEK293T cells. Fig. S7. HeLa cells exhibit CFTR channel activity. Fig. S8. miR-138 improves CFTR processing. Fig. S9. miR-138 improves CFTR-ΔF508 processing. Fig. S10. SIN3A inhibition yields partial rescue of Cl- transport in CF epithelia. -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
Plakophilin-2 Haploinsufficiency Causes Calcium Handling
International Journal of Molecular Sciences Article Plakophilin-2 Haploinsufficiency Causes Calcium Handling Deficits and Modulates the Cardiac Response Towards Stress Chantal J.M. van Opbergen 1 , Maartje Noorman 1, Anna Pfenniger 2, Jaël S. Copier 1, Sarah H. Vermij 2,3 , Zhen Li 2, Roel van der Nagel 1, Mingliang Zhang 2, Jacques M.T. de Bakker 1,4, Aaron M. Glass 5, Peter J. Mohler 6,7, Steven M. Taffet 5, Marc A. Vos 1, Harold V.M. van Rijen 1, Mario Delmar 2 and Toon A.B. van Veen 1,* 1 Department of Medical Physiology, Division of Heart & Lungs, University Medical Center Utrecht, Yalelaan 50, 3584CM Utrecht, The Netherlands 2 Division of Cardiology, NYU School of Medicine, New York, NY 10016, USA 3 Institute of Biochemistry and Molecular Medicine, University of Bern, 3012 Bern, Switzerland 4 Department of Medical Biology, Academic Medical Center Amsterdam, 1105AZ Amsterdam, The Netherlands 5 Department of Microbiology and Immunology, SUNY Upstate Medical University, Syracuse, NY 13210, USA 6 Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University College of Medicine and Wexner Medical Center, Columbus, OH 43210, USA 7 Departments of Physiology & Cell Biology and Internal Medicine, Division of Cardiovascular Medicine, The Ohio State University College of Medicine Wexner Medical Center, Columbus, OH 43210, USA * Correspondence: [email protected] Received: 1 August 2019; Accepted: 19 August 2019; Published: 21 August 2019 Abstract: Human variants in plakophilin-2 (PKP2) associate with most cases of familial arrhythmogenic cardiomyopathy (ACM). Recent studies show that PKP2 not only maintains intercellular coupling, but also regulates transcription of genes involved in Ca2+ cycling and cardiac rhythm. -
Detection Rate. FC: Fold Change. GO: Gene Ontology. AUC: Area Under
Figure S1. Flow chart of the study. DR: detection rate. FC: fold change. GO: Gene Ontology. AUC: area under curve. KEGG: Kyoto Encyclopedia of Genes and Genomes. GEO: Gene Expression Omnibus. Figure S2. Independent validation of miRNAs and the classifier. A, ΔCt of 6 selected miRNAs in the independent cohort. *, P < 0.05. **, P < 0.01. ***, P < 0.001. B, Receiver operating curve of the classifier in the independent cohort. AUC: area under curve. Figure S3. Venn of predicted target genes from 3 platforms. Figure S4. GO enrichment analysis. GO: Gene Ontology. Table S1. miRNA expression profile in screening stage. Table S2. miRNA expression profile in validation stage. Table S3. Efficacy of the classifier. Table S4. miRNA expression profile in independent validation stage. Table S5. Predicted target genes. Table S6a. KEGG enrichment analysis. KEGG: Kyoto Encyclopedia of Genes and Genomes. Table S6b. GO_BP enrichment analysis. GO: Gene Ontology. BP: Biological Process. Table S6c. GO_CC enrichment analysis. CC: Cellular Component. Table S6d. GO_MF enrichment analysis. MF: Molecular Function. Table S7. GEO expression array datasets involved in this study. GEO: Gene Expression Omnibus. Table S8a. Predicted target genes covered by the selected GEO datasets. GEO: Gene Expression Omnibus. 1 Table S8b. Expression profiles of predicted target genes of hsa-miR-26b-5p in GEO datasets. Table S8c. Expression profiles of predicted target genes of hsa-miR-146b-5p in GEO datasets. Table S8d. Expression profiles of predicted target genes of hsa-miR-191-5p in GEO datasets. Table S8e. Expression profiles of predicted target genes of hsa-miR-484 in GEO datasets. -
Flotillin 1 Antibody (R31142)
Flotillin 1 Antibody (R31142) Catalog No. Formulation Size R31142 0.5mg/ml if reconstituted with 0.2ml sterile DI water 100 ug Bulk quote request Availability 1-3 business days Species Reactivity Human, Mouse, Rat Format Antigen affinity purified Clonality Polyclonal (rabbit origin) Isotype Rabbit IgG Purity Antigen affinity Buffer Lyophilized from 1X PBS with 2.5% BSA and 0.025% sodium azide/thimerosal UniProt O75955 Applications Western blot : 0.5-1ug/ml Limitations This Flotillin 1 antibody is available for research use only. Western blot testing of Flotillin 1 antbody; Lane 1: rat lung; 2: (r) brain; 3: (r) ovary; 4: human SMMC-7721; 5: (h) MFC-7 cell lysate. Expected/observed molecular weight ~49kDa. Description Flotillin 1 is a protein that in humans is encoded by the FLOT1 gene. The International Radiation Hybrid Mapping Consortium mapped the gene to chromosome 6. Bickel et al.(1997) found that mouse Flot1 behaves as a resident integral membrane protein of caveolae. It consistently copurified with Flot2 and with caveolin-1 in the purification of caveolin-rich membranes. Hazarika et al.(1999) found that stable transfection of Flot1, which they called ESA/flotillin-2, in COS-1 cells induced filopodia formation and changed the epithelial morphology to that of neuronal cells. Santamaria et al.(2005) found that prostate tumor overexpressed gene-1 interacted with Flotillin1 in detergent-insoluble membrane fractions. Flotillin1 colocalized with PTOV1 at the plasma membrane and in the nucleus, and it entered the nucleus concomitant with PTOV1 shortly before initiation of S phase. Application Notes The stated application concentrations are suggested starting amounts. -
1714 Gene Comprehensive Cancer Panel Enriched for Clinically Actionable Genes with Additional Biologically Relevant Genes 400-500X Average Coverage on Tumor
xO GENE PANEL 1714 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes 400-500x average coverage on tumor Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 IL6R KAT5 ACVR2A -
Snipa Snpcard
SNiPAcard Block annotations Block info genomic range chr6:29,970,960-30,792,117 block size 821,158 bp variant count 107 variants Basic features Conservation/deleteriousness Linked genes μ = -0.141 [-3.802 – ABCF1 , AL662797.1 , AL662800.2 , ATAT1 , C6orf136 , DHX16 , FLOT1 4.141] , GNL1 , HCG17 , HCG18 , HCG19P , HCG20 , HCG4P3 , HLA-J , HLA-L , HLA-N , IER3 , LINC00243 , MDC1 , MICC , MRPS18B , NRM , PAIP1P1 , PPP1R10 , PPP1R11 , PPP1R18 , PTMAP1 , RN7SL353P , phyloP gene(s) hit or close-by RNF39 , RPP21 , SUCLA2P1 , TRIM10 , TRIM15 , TRIM26 , TRIM26BP , TRIM31 , TRIM31-AS1 , TRIM39 , TRIM39-RPP21 , TRIM40 , TUBB , UBQLN1P1 , XXbac-BPG252P9.10 , XXbac-BPG252P9.9 , XXbac-BPG283O16.9 , Y_RNA , ZNRD1 , ZNRD1-AS1 , ZNRD1-AS1_3 μ = 0.130 [0 – 1] BTN3A2 , BTN3A3 , C2 , CCHCR1 , CFB , FLOT1 , GABBR1 , HCG17 , HCG20 , HCG22 , HCP5 , HIST1H3I , HLA-A , HLA-B , HLA-C , HLA-F- AS1 , HLA-G , HLA-H , HLA-J , HLA-K , HLA-L , HLA-U , HLA-V , IER3 phastCons eQTL gene(s) , LINC00243 , MICB , NDUFS1 , OR5V1 , PPP1R18 , PSORS1C1 , RNF39 , RPP21 , TMEM154 , TRIM26BP , TRIM31 , TRIM39-RPP21 , VARS2 , WASF5P , ZFP57 , ZNRD1 , ZNRD1-AS1_3 μ = -0.462 [-9.98 – 5.1] ATAT1 , ATAT1 , ATAT1 , ATAT1 , ATAT1 , ATAT1 , ATAT1 , DDR1 , DDR1 , DDR1 , DDR1 , DDR1 , DDR1 , DDR1 , FLOT1 , FLOT1 , FLOT1 , FLOT1 , FLOT1 , FLOT1 , FLOT1 , FLOT1 , HCG19P , HCG19P potentially regulated , HCG19P , HCG19P , HCG19P , HCG19P , HCG19P , NRM , NRM , GERP++ gene(s) NRM , NRM , NRM , NRM , NRM , RNF39 , RNF39 , RNF39 , RNF39 , RNF39 , RNF39 , RNF39 , RNF39 , TRIM26 , TRIM26 , TRIM26 -
Fra-2 Overexpression Upregulates Pro-Metastatic Cell-Adhesion
Fra-2 Overexpression Upregulates Pro-metastatic Cell-adhesion Molecules, Promotes Pulmonary Metastasis and Reduces Survival in a Spontaneous Xenograft Model of Human Breast Cancer Sabrina Arnold University Medical Center Hamburg-Eppendorf: Universitatsklinikum Hamburg-Eppendorf Jan Kortland University Medical Center Hamburg-Eppendorf: Universitatsklinikum Hamburg-Eppendorf Diana V. Maltseva National Faculty of Biology and Biotechnology, National Research University Higher School of Economics Timur R. Samatov Evotec International GmbH Susanne Lezius University Medical Center Hamburg-Eppendorf: Universitatsklinikum Hamburg-Eppendorf Alexander G. Tonevitsky Far Eastern Federal University Karin Milde-Langosch University Medical Center Hamburg-Eppendorf: Universitatsklinikum Hamburg-Eppendorf Daniel Wicklein University Medical Center Hamburg-Eppendorf: Universitatsklinikum Hamburg-Eppendorf Udo Schumacher University Medical Center Hamburg-Eppendorf: Universitatsklinikum Hamburg-Eppendorf Christine Stürken ( [email protected] ) endorf: Universitatsklinikum Hamburg-Eppendorf https://orcid.org/0000-0003-3997-3304 Research Article Keywords: Breast Cancer, Transcription factor, AP-1, Fra-2, Metastasis Posted Date: June 15th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-592522/v1 Page 1/33 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 2/33 Abstract Purpose: The transcription factor Fra-2 affects the invasive potential of breast cancer cells by dysregulating adhesion molecules in vitro. Previous results suggested that it upregulates the expression of E- and P- selectin ligands. Such selectin ligands are important members of the leukocyte adhesion cascade, which govern the adhesion and transmigration of cancer cells into the stroma of the host organ of metastasis. As so far, no in vivo data are available, this study was designed to elucidate the role of Fra-2 expression in a spontaneous breast cancer metastasis xenograft model. -
Identification of Key Pathways and Genes in Dementia Via Integrated Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2021.04.18.440371; this version posted July 19, 2021. 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. Identification of Key Pathways and Genes in Dementia via Integrated Bioinformatics Analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karnataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2021.04.18.440371; this version posted July 19, 2021. 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. Abstract To provide a better understanding of dementia at the molecular level, this study aimed to identify the genes and key pathways associated with dementia by using integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing dataset GSE153960 derived from the Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) between patients with dementia and healthy controls were identified. With DEGs, we performed a series of functional enrichment analyses. Then, a protein–protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network was constructed, analyzed and visualized, with which the hub genes miRNAs and TFs nodes were screened out. Finally, validation of hub genes was performed by using receiver operating characteristic curve (ROC) analysis. -
Human Induced Pluripotent Stem Cell–Derived Podocytes Mature Into Vascularized Glomeruli Upon Experimental Transplantation
BASIC RESEARCH www.jasn.org Human Induced Pluripotent Stem Cell–Derived Podocytes Mature into Vascularized Glomeruli upon Experimental Transplantation † Sazia Sharmin,* Atsuhiro Taguchi,* Yusuke Kaku,* Yasuhiro Yoshimura,* Tomoko Ohmori,* ‡ † ‡ Tetsushi Sakuma, Masashi Mukoyama, Takashi Yamamoto, Hidetake Kurihara,§ and | Ryuichi Nishinakamura* *Department of Kidney Development, Institute of Molecular Embryology and Genetics, and †Department of Nephrology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan; ‡Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, Hiroshima, Japan; §Division of Anatomy, Juntendo University School of Medicine, Tokyo, Japan; and |Japan Science and Technology Agency, CREST, Kumamoto, Japan ABSTRACT Glomerular podocytes express proteins, such as nephrin, that constitute the slit diaphragm, thereby contributing to the filtration process in the kidney. Glomerular development has been analyzed mainly in mice, whereas analysis of human kidney development has been minimal because of limited access to embryonic kidneys. We previously reported the induction of three-dimensional primordial glomeruli from human induced pluripotent stem (iPS) cells. Here, using transcription activator–like effector nuclease-mediated homologous recombination, we generated human iPS cell lines that express green fluorescent protein (GFP) in the NPHS1 locus, which encodes nephrin, and we show that GFP expression facilitated accurate visualization of nephrin-positive podocyte formation in