Renoprotective Effect of Combined Inhibition of Angiotensin-Converting Enzyme and Histone Deacetylase
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
ACTR2 Antibody / Arp2 (RQ5865)
ACTR2 Antibody / Arp2 (RQ5865) Catalog No. Formulation Size RQ5865 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, Monkey Format Antigen affinity purified Clonality Polyclonal (rabbit origin) Isotype Rabbit IgG Purity Affinity purified Buffer Lyophilized from 1X PBS with 2% Trehalose and 0.025% sodium azide UniProt P61160 Applications Western blot : 0.5-1ug/ml Immunohistochemistry : 1-2ug/ml Immunofluorescence : 2-4ug/ml Flow cytometry : 1-3ug/million cells Direct ELISA : 0.1-0.5ug/ml Limitations This ACTR2 antibody is available for research use only. IHC staining of FFPE human breast cancer with ACTR2 antibody. HIER: boil tissue sections in pH8 EDTA for 20 min and allow to cool before testing. Immunofluorescent staining of FFPE human A549 cells with ACTR2 antibody (green) and DAPI nuclear stain (blue). HIER: steam section in pH6 citrate buffer for 20 min. Western blot testing of 1) rat kidney, 2) rat spleen, 3) mouse HEPA1-6, 4) mouse SP2/0, 5) monkey COS-7 and human 6) U-87 MG, 7) Jurkat, 8) PC-3 and 9) U-2 OS lysate with ACTR2 antibody. Predicted molecular weight ~45 kDa. Flow cytometry testing of human A431 cells with ACTR2 antibody at 1ug/million cells (blocked with goat sera); Red=cells alone, Green=isotype control, Blue= ACTR2 antibody. Flow cytometry testing of mouse ANA-1 cells with ACTR2 antibody at 1ug/million cells (blocked with goat sera); Red=cells alone, Green=isotype control, Blue= ACTR2 antibody. Description The specific function of this gene has not yet been determined; however, the protein it encodes is known to be a major constituent of the ARP2/3 complex. -
A Network-Informed Analysis of SARS-Cov-2 and Hemophagocytic Lymphohistiocytosis Genes' Interactions Points to Neutrophil Extr
medRxiv preprint doi: https://doi.org/10.1101/2020.07.01.20144121; this version posted July 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . 1 A network-informed analysis of SARS-CoV-2 and hemophagocytic 2 lymphohistiocytosis genes’ interactions points to Neutrophil Extracellular Traps as 3 mediators of thromBosis in COVID-19 4 5 Jun Ding1, David Earl Hostallero2, Mohamed Reda El Khili2, Gregory Fonseca3, Simon 6 Millette4, Nuzha Noorah3, Myriam Guay-Belzile3, Jonathan Spicer5, Noriko Daneshtalab6, 7 Martin Sirois7, Karine Tremblay8, Amin Emad2,* and Simon Rousseau3,* 8 9 10 1Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, 15204 11 12 2Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada. 13 14 3The Meakins-Christie Laboratories at the Research Institute of the McGill University Heath 15 Centre Research Institute, 1001 Boul. Décarie, Montréal, H4A 3J1, Canada. 16 17 4Goodman Cancer Research Centre, McGill University 18 19 5Division of Thoracic and Upper Gastrointestinal Surgery, McGill University Health Centre 20 Research Institute, 1001 Boul. Décarie, Montréal, H4A 3J1, Canada. 21 22 6School of Pharmacy, Memorial University of Newfoundland, 300 Prince Philip Drive, Health 23 Sciences Center, St. John’s, Newfoundland, Canada, A1B 3V6 24 25 7Montreal Heart Institute and Department of pharmacology and physiology, Faculty of medicine, 26 Université de Montréal. 27 28 8Pharmacology-physiology Department, Faculty of Medicine and Health Sciences, Université de 29 Sherbrooke, Centre intégré universitaire de santé et de services sociaux du Saguenay–Lac-Saint- 30 Jean (Chicoutimi University Hospital) Research Center, Saguenay, QC, Canada. -
ATAP00021-Recombinant Human ALDH1A1 Protein
ATAGENIX LABORATORIES Catalog Number:ATAP00021 Recombinant Human ALDH1A1 protein Product Details Summary English name Recombinant Human ALDH1A1 protein Purity >90% as determined by SDS-PAGE Endotoxin level Please contact with the lab for this information. Construction A DNA sequence encoding the human ALDH1A1 (Met1-Ser501) was fused with His tag Accession # P00352 Host E.coli Species Homo sapiens (Human) Predicted Molecular Mass 52.58 kDa Formulation Supplied as solution form in PBS pH 7.5 or lyophilized from PBS pH 7.5. Shipping In general, proteins are provided as lyophilized powder/frozen liquid. They are shipped out with dry ice/blue ice unless customers require otherwise. Stability &Storage Use a manual defrost freezer and avoid repeated freeze thaw cycles. Store at 2 to 8 °C for one week . Store at -20 to -80 °C for twelve months from the date of receipt. Reconstitution Reconstitute in sterile water for a stock solution.A copy of datasheet will be provided with the products, please refer to it for details. Background Background Aldehyde dehydrogenase 1 family, member A1 (ALDH1A1), also known as Aldehyde dehydrogenase 1 (ALDH1), or Retinaldehyde Dehydrogenase 1 (RALDH1), is an enzyme that is expressed at high levels in stem cells and that has been suggested to regulate stem cell function. The retinaldehyde dehydrogenase (RALDH) subfamily of ALDHs, composed of ALDH1A1, ALDH1A2, ALDH1A3, and ALDH8A1, regulate development by catalyzing retinoic acid biosynthesis. The ALDH1A1 protein belongs to the aldehyde dehydrogenases family of proteins. Aldehyde dehydrogenase is the second enzyme of the major oxidative pathway of alcohol metabolism. ALDH1A1 also belongs to the group of corneal crystallins that Web:www.atagenix.com E-mail: [email protected] Tel: 027-87433958 ATAGENIX LABORATORIES Catalog Number:ATAP00021 Recombinant Human ALDH1A1 protein help maintain the transparency of the cornea. -
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. -
PGENETICS-D-11-00413 Title: Integrating
Editorial Manager(tm) for PLoS Genetics Manuscript Draft Manuscript Number: PGENETICS-D-11-00413 Title: Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets Short Title: Integrative association analysis of gene sets Article Type: Research Article Section/Category: Natural Variation Keywords: gene set analysis; eQTL; integrative genomics Corresponding Author: Sayan Mukherjee Corresponding Author's Institution: Duke University First Author: Qing Xiong Order of Authors: Qing Xiong;Nicola Ancona;Elizabeth Hauser;Sayan Mukherjee;Terrence Furey Abstract: Background: Single variant or single gene analyses generally account for only a small proportion of the phenotypic variation in complex traits. Alternatively, gene set or pathway association analyses are playing an increasingly important role in uncovering genetic architectures of complex traits through the identification of systematic genetic interactions. Two dominant paradigms for gene set analyses are association analyses based on SNP genotypes and those based on gene expression profiles. However, gene-disease association can manifest in many ways such as alterations of gene expression, genotype and copy number, thus an integrative approach combining multiple forms of evidence can more accurately and comprehensively capture pathway associations. Methodology: We have developed a single statistical framework, Gene Set Association Analysis (GSAA), that simultaneously measures genome-wide patterns of genetic variation and gene expression variation -
New Approaches to Functional Process Discovery in HPV 16-Associated Cervical Cancer Cells by Gene Ontology
Cancer Research and Treatment 2003;35(4):304-313 New Approaches to Functional Process Discovery in HPV 16-Associated Cervical Cancer Cells by Gene Ontology Yong-Wan Kim, Ph.D.1, Min-Je Suh, M.S.1, Jin-Sik Bae, M.S.1, Su Mi Bae, M.S.1, Joo Hee Yoon, M.D.2, Soo Young Hur, M.D.2, Jae Hoon Kim, M.D.2, Duck Young Ro, M.D.2, Joon Mo Lee, M.D.2, Sung Eun Namkoong, M.D.2, Chong Kook Kim, Ph.D.3 and Woong Shick Ahn, M.D.2 1Catholic Research Institutes of Medical Science, 2Department of Obstetrics and Gynecology, College of Medicine, The Catholic University of Korea, Seoul; 3College of Pharmacy, Seoul National University, Seoul, Korea Purpose: This study utilized both mRNA differential significant genes of unknown function affected by the display and the Gene Ontology (GO) analysis to char- HPV-16-derived pathway. The GO analysis suggested that acterize the multiple interactions of a number of genes the cervical cancer cells underwent repression of the with gene expression profiles involved in the HPV-16- cancer-specific cell adhesive properties. Also, genes induced cervical carcinogenesis. belonging to DNA metabolism, such as DNA repair and Materials and Methods: mRNA differential displays, replication, were strongly down-regulated, whereas sig- with HPV-16 positive cervical cancer cell line (SiHa), and nificant increases were shown in the protein degradation normal human keratinocyte cell line (HaCaT) as a con- and synthesis. trol, were used. Each human gene has several biological Conclusion: The GO analysis can overcome the com- functions in the Gene Ontology; therefore, several func- plexity of the gene expression profile of the HPV-16- tions of each gene were chosen to establish a powerful associated pathway, identify several cancer-specific cel- cervical carcinogenesis pathway. -
Transcriptomic Characterization of Fibrolamellar Hepatocellular
Transcriptomic characterization of fibrolamellar PNAS PLUS hepatocellular carcinoma Elana P. Simona, Catherine A. Freijeb, Benjamin A. Farbera,c, Gadi Lalazara, David G. Darcya,c, Joshua N. Honeymana,c, Rachel Chiaroni-Clarkea, Brian D. Dilld, Henrik Molinad, Umesh K. Bhanote, Michael P. La Quagliac, Brad R. Rosenbergb,f, and Sanford M. Simona,1 aLaboratory of Cellular Biophysics, The Rockefeller University, New York, NY 10065; bPresidential Fellows Laboratory, The Rockefeller University, New York, NY 10065; cDivision of Pediatric Surgery, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10065; dProteomics Resource Center, The Rockefeller University, New York, NY 10065; ePathology Core Facility, Memorial Sloan-Kettering Cancer Center, New York, NY 10065; and fJohn C. Whitehead Presidential Fellows Program, The Rockefeller University, New York, NY 10065 Edited by Susan S. Taylor, University of California, San Diego, La Jolla, CA, and approved September 22, 2015 (received for review December 29, 2014) Fibrolamellar hepatocellular carcinoma (FLHCC) tumors all carry a exon of DNAJB1 and all but the first exon of PRKACA. This deletion of ∼400 kb in chromosome 19, resulting in a fusion of the produced a chimeric RNA transcript and a translated chimeric genes for the heat shock protein, DNAJ (Hsp40) homolog, subfam- protein that retains the full catalytic activity of wild-type PKA. ily B, member 1, DNAJB1, and the catalytic subunit of protein ki- This chimeric protein was found in 15 of 15 FLHCC patients nase A, PRKACA. The resulting chimeric transcript produces a (21) in the absence of any other recurrent mutations in the DNA fusion protein that retains kinase activity. -
Supplementary Table S1. Upregulated Genes Differentially
Supplementary Table S1. Upregulated genes differentially expressed in athletes (p < 0.05 and 1.3-fold change) Gene Symbol p Value Fold Change 221051_s_at NMRK2 0.01 2.38 236518_at CCDC183 0.00 2.05 218804_at ANO1 0.00 2.05 234675_x_at 0.01 2.02 207076_s_at ASS1 0.00 1.85 209135_at ASPH 0.02 1.81 228434_at BTNL9 0.03 1.81 229985_at BTNL9 0.01 1.79 215795_at MYH7B 0.01 1.78 217979_at TSPAN13 0.01 1.77 230992_at BTNL9 0.01 1.75 226884_at LRRN1 0.03 1.74 220039_s_at CDKAL1 0.01 1.73 236520_at 0.02 1.72 219895_at TMEM255A 0.04 1.72 201030_x_at LDHB 0.00 1.69 233824_at 0.00 1.69 232257_s_at 0.05 1.67 236359_at SCN4B 0.04 1.64 242868_at 0.00 1.63 1557286_at 0.01 1.63 202780_at OXCT1 0.01 1.63 1556542_a_at 0.04 1.63 209992_at PFKFB2 0.04 1.63 205247_at NOTCH4 0.01 1.62 1554182_at TRIM73///TRIM74 0.00 1.61 232892_at MIR1-1HG 0.02 1.61 204726_at CDH13 0.01 1.6 1561167_at 0.01 1.6 1565821_at 0.01 1.6 210169_at SEC14L5 0.01 1.6 236963_at 0.02 1.6 1552880_at SEC16B 0.02 1.6 235228_at CCDC85A 0.02 1.6 1568623_a_at SLC35E4 0.00 1.59 204844_at ENPEP 0.00 1.59 1552256_a_at SCARB1 0.02 1.59 1557283_a_at ZNF519 0.02 1.59 1557293_at LINC00969 0.03 1.59 231644_at 0.01 1.58 228115_at GAREM1 0.01 1.58 223687_s_at LY6K 0.02 1.58 231779_at IRAK2 0.03 1.58 243332_at LOC105379610 0.04 1.58 232118_at 0.01 1.57 203423_at RBP1 0.02 1.57 AMY1A///AMY1B///AMY1C///AMY2A///AMY2B// 208498_s_at 0.03 1.57 /AMYP1 237154_at LOC101930114 0.00 1.56 1559691_at 0.01 1.56 243481_at RHOJ 0.03 1.56 238834_at MYLK3 0.01 1.55 213438_at NFASC 0.02 1.55 242290_at TACC1 0.04 1.55 ANKRD20A1///ANKRD20A12P///ANKRD20A2/// -
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. -
Product Name: NFKB1 (Ser893) Polyclonal Antibody, ALEXA FLUOR® 594 Conjugated Catalog No
Product Name: NFKB1 (Ser893) Polyclonal Antibody, ALEXA FLUOR® 594 Conjugated Catalog No. : TAP01-94487R-A594 Intended Use: For Research Use Only. Not for used in diagnostic procedures. Size 100ul Concentration 1ug/ul Gene ID 4790 ISO Type Rabbit IgG Clone N/A Immunogen Range 880-900/968 Conjugation ALEXA FLUOR® 594 Subcellular Locations Cytoplasm, Nucleus Applications IF(IHC-P) Cross Reactive Species Human Source KLH conjugated synthetic phosphopeptide derived from human NF KappaB p105 around the phosphorylation site of Ser893 Applications with IF(IHC-P)(1:50-200) Dilutions Purification Purified by Protein A. Background NF-kappa-B is a pleiotropic transcription factor present in almost all cell types and is the endpoint of a series of signal transduction events that are initiated by a vast array of stimuli related to many biological processes such as inflammation, immunity, differentiation, cell growth, tumorigenesis and apoptosis. NF-kappa-B is a homo- or heterodimeric complex formed by the Rel-like domain-containing proteins RELA/p65, RELB, NFKB1/p105, NFKB1/p50, REL and NFKB2/p52 and the heterodimeric p65-p50 complexappears to be most abundant one. The dimers bind at kappa-B sites in the DNA of their target genes and the individual dimers have distinct preferences for different kappa-B sites that they can bind with distinguishable affinity and specificity. Differentdimer combinations act as transcriptional activators or repressors, respectively. NF-kappa-B is controlled by various mechanisms of post-translational modification and subcellular compartmentalization as well as by interactions with other cofactors or corepressors. NF-kappa-B complexes are held in the cytoplasm in an inactive state complexed with members of the NF-kappa-B inhibitor (I-kappa-B) family. -
A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules Natasha L
www.nature.com/scientificreports OPEN A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules Natasha L. Patel-Murray1, Miriam Adam2, Nhan Huynh2, Brook T. Wassie2, Pamela Milani2 & Ernest Fraenkel 2,3* High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncovering MoAs through an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomics. Examining compounds with benefcial efects in models of Huntington’s Disease, we found common MoAs for compounds with unrelated structures, connectivity scores, and binding targets. The approach also predicted highly divergent MoAs for two FDA-approved antihistamines. We experimentally validated these efects, demonstrating that one antihistamine activates autophagy, while the other targets bioenergetics. The use of multiple omics was essential, as some MoAs were virtually undetectable in specifc assays. Our approach does not require reference compounds or large databases of experimental data in related systems and thus can be applied to the study of agents with uncharacterized MoAs and to rare or understudied diseases. Unknown modes of action of drug candidates can lead to unpredicted consequences on efectiveness and safety. Computational methods, such as the analysis of gene signatures, and high-throughput experimental methods have accelerated the discovery of lead compounds that afect a specifc target or phenotype1–3. However, these advances have not dramatically changed the rate of drug approvals. Between 2000 and 2015, 86% of drug can- didates failed to earn FDA approval, with toxicity or a lack of efcacy being common reasons for their clinical trial termination4,5. -
High-Grade Glioneuronal Tumor with an ARHGEF2–NTRK1 Fusion Gene
Brain Tumor Pathology (2019) 36:121–128 https://doi.org/10.1007/s10014-019-00345-y CASE REPORT High‑grade glioneuronal tumor with an ARHGEF2–NTRK1 fusion gene Kazuhiko Kurozumi1 · Yoshiko Nakano2 · Joji Ishida1 · Takehiro Tanaka3 · Masatomo Doi4 · Junko Hirato5 · Akihiko Yoshida6 · Kana Washio7 · Akira Shimada7 · Takashi Kohno8 · Koichi Ichimura2 · Hiroyuki Yanai9 · Isao Date1 Received: 1 March 2019 / Accepted: 20 March 2019 / Published online: 22 April 2019 © The Japan Society of Brain Tumor Pathology 2019 Abstract Here, we report a highly unusual case of high-grade glioneuronal tumor with a neurotrophic tropomyosin receptor kinase (NTRK) fusion gene. A 13-year-old girl presented with headache and vomiting and MRI detected two cystic lesions bilaterally in the frontal areas with surrounding edema. The left larger tumor was removed by left frontal craniotomy. The tumor was diagnosed as a high-grade glioneuronal tumor, unclassifed. Methylation profling classifed it as a difuse leptomeningeal glioneuronal tumor (DLGNT) with low confdence. This tumor showed genotypes frequently found in DLGNT such as 1p/19q codeletion without IDH mutation and, however, did not have the typical DLGNT clinical and histological features. RNA sequencing identifed an ARHGEF2 (encoding Rho/Rac guanine nucleotide exchange factor 2)–NTRK1 fusion gene. The presence of recurrent NTRK fusion in glioneuronal tumors has an important implication in the clinical decision making and opens up a possibility of novel targeted therapy. Keywords Pediatric brain tumor · 1p19qLOH · RNA sequencing · NTRK1 Introduction diagnosis of glioneuronal tumors has been sometimes chal- lenging. However, unique molecular signatures have recently Mixed glioneuronal tumors are rare group of brain tumors been identifed, enabling creation of classifcation schemes that consist of glial and neuronal components.