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Sean Raspet – Molecules
1. Commercial name: Fructaplex© IUPAC Name: 2-(3,3-dimethylcyclohexyl)-2,5,5-trimethyl-1,3-dioxane SMILES: CC1(C)CCCC(C1)C2(C)OCC(C)(C)CO2 Molecular weight: 240.39 g/mol Volume (cubic Angstroems): 258.88 Atoms number (non-hydrogen): 17 miLogP: 4.43 Structure: Biological Properties: Predicted Druglikenessi: GPCR ligand -0.23 Ion channel modulator -0.03 Kinase inhibitor -0.6 Nuclear receptor ligand 0.15 Protease inhibitor -0.28 Enzyme inhibitor 0.15 Commercial name: Fructaplex© IUPAC Name: 2-(3,3-dimethylcyclohexyl)-2,5,5-trimethyl-1,3-dioxane SMILES: CC1(C)CCCC(C1)C2(C)OCC(C)(C)CO2 Predicted Olfactory Receptor Activityii: OR2L13 83.715% OR1G1 82.761% OR10J5 80.569% OR2W1 78.180% OR7A2 77.696% 2. Commercial name: Sylvoxime© IUPAC Name: N-[4-(1-ethoxyethenyl)-3,3,5,5tetramethylcyclohexylidene]hydroxylamine SMILES: CCOC(=C)C1C(C)(C)CC(CC1(C)C)=NO Molecular weight: 239.36 Volume (cubic Angstroems): 252.83 Atoms number (non-hydrogen): 17 miLogP: 4.33 Structure: Biological Properties: Predicted Druglikeness: GPCR ligand -0.6 Ion channel modulator -0.41 Kinase inhibitor -0.93 Nuclear receptor ligand -0.17 Protease inhibitor -0.39 Enzyme inhibitor 0.01 Commercial name: Sylvoxime© IUPAC Name: N-[4-(1-ethoxyethenyl)-3,3,5,5tetramethylcyclohexylidene]hydroxylamine SMILES: CCOC(=C)C1C(C)(C)CC(CC1(C)C)=NO Predicted Olfactory Receptor Activity: OR52D1 71.900% OR1G1 70.394% 0R52I2 70.392% OR52I1 70.390% OR2Y1 70.378% 3. Commercial name: Hyperflor© IUPAC Name: 2-benzyl-1,3-dioxan-5-one SMILES: O=C1COC(CC2=CC=CC=C2)OC1 Molecular weight: 192.21 g/mol Volume -
A Multi-Cancer Gene Signature Associated with Stromal Activation
A multi-cancer gene signature associated with stromal activation Sandra Orsulic, PhD Professor of OB/GYN Women’s Cancer Program Samuel Oschin Comprehensive Cancer Institute I Cedars-Sinai Medical Center 1100011010 1001000101 0101001011 Stroma/ECM Prevents Tumor Invasion Stroma/ECM Promotes Tumor Invasion Tumor Growth and Stroma Remodeling Ki67-positive dividing cells cancer cells stroma Tumor Growth and Stroma Remodeling Ki67-positive dividing cells cancer • proliferative cells • targeted by chemotherapy • genetically unstable stroma Tumor Growth and Stroma Remodeling Ki67-positive dividing cells cancer • proliferating cells • targeted by chemotherapy • genetically unstable • slow proliferating • not targeted by chemotherapy stroma • genetically stable • enhanced remodeling predicts poor outcome Expression of COL11A1 in Intratumoral Stroma COL11A1 + Fat 0% Peritumoral ~1% stroma Tumor ~50% Intratumoral 100µm stroma COL11A1 Expression in Cancers and Corresponding Normal Tissues Smooth Muscle Actin Expression in Cancers and Corresponding Normal Tissues COL11A1 Expression in Cancers and Corresponding Normal Tissues BREAST COLORECTAL 1 2 1 2 3 1. Breast (61) 1. Colon (19) 2. Invasive ductal breast 2. Rectum (3) carcinoma (389) 3. Colon adenocarcinoma (101) Overexpression gene rank: 1 Overexpression gene rank: 3 (in top 1%) (in top 1%) p-value: 1.15E-73 p-value: 2.19E-44 t-test: 33.769 t-test: 27.871 fold change: 40.542 fold change: 32.796 Increase in COL11A1 Levels During Cancer Progression Breast Cancer Ductal Invasive Carcinoma Ductal In -
The Hypothalamus As a Hub for SARS-Cov-2 Brain Infection and Pathogenesis
bioRxiv preprint doi: https://doi.org/10.1101/2020.06.08.139329; this version posted June 19, 2020. 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. The hypothalamus as a hub for SARS-CoV-2 brain infection and pathogenesis Sreekala Nampoothiri1,2#, Florent Sauve1,2#, Gaëtan Ternier1,2ƒ, Daniela Fernandois1,2 ƒ, Caio Coelho1,2, Monica ImBernon1,2, Eleonora Deligia1,2, Romain PerBet1, Vincent Florent1,2,3, Marc Baroncini1,2, Florence Pasquier1,4, François Trottein5, Claude-Alain Maurage1,2, Virginie Mattot1,2‡, Paolo GiacoBini1,2‡, S. Rasika1,2‡*, Vincent Prevot1,2‡* 1 Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, DistAlz, UMR-S 1172, Lille, France 2 LaBoratorY of Development and PlasticitY of the Neuroendocrine Brain, FHU 1000 daYs for health, EGID, School of Medicine, Lille, France 3 Nutrition, Arras General Hospital, Arras, France 4 Centre mémoire ressources et recherche, CHU Lille, LiCEND, Lille, France 5 Univ. Lille, CNRS, INSERM, CHU Lille, Institut Pasteur de Lille, U1019 - UMR 8204 - CIIL - Center for Infection and ImmunitY of Lille (CIIL), Lille, France. # and ƒ These authors contriButed equallY to this work. ‡ These authors directed this work *Correspondence to: [email protected] and [email protected] Short title: Covid-19: the hypothalamic hypothesis 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.08.139329; this version posted June 19, 2020. 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. -
Genetic Variants Affecting Equivalent Protein Family Positions Reflect
www.nature.com/scientificreports OPEN Genetic variants afecting equivalent protein family positions refect human diversity Received: 13 January 2017 Francesco Raimondi1,2, Matthew J. Betts1,2, Qianhao Lu1,2, Asuka Inoue3,4, J. Silvio Gutkind5 & Accepted: 13 September 2017 Robert B. Russell 1,2 Published: xx xx xxxx Members of diverse protein families often perform overlapping or redundant functions meaning that diferent variations within them could refect diferences between individual organisms. We investigated likely functional positions within aligned protein families that contained a signifcant enrichment of nonsynonymous variants in genomes of healthy individuals. We identifed more than a thousand enriched positions across hundreds of family alignments with roles indicative of mammalian individuality, including sensory perception and the immune system. The most signifcant position is the Arginine from the Olfactory receptor “DRY” motif, which has more variants in healthy individuals than all other positions in the proteome. Odorant binding data suggests that these variants lead to receptor inactivity, and they are mostly mutually exclusive with other loss-of-function (stop/frameshift) variants. Some DRY Arginine variants correlate with smell preferences in sub-populations and all 2,504 humans studied contain a unique spectrum of active and inactive receptors. The many other variant enriched positions, across hundreds of other families might also provide insights into individual diferences. Genomes from healthy individuals1 illuminate both diseases2 and attributes of individuals or human popula- tions3,4. Perhaps the most common current use of these genomes is to provide background mutation rates to assess candidate disease mutations or to identify likely deleterious genetic alterations5,6. For example, ExAC6 is frequently used by human geneticists to determine variant frequency during searches for disease causing mutations. -
Supplementary Data
SUPPLEMENTARY METHODS 1) Characterisation of OCCC cell line gene expression profiles using Prediction Analysis for Microarrays (PAM) The ovarian cancer dataset from Hendrix et al (25) was used to predict the phenotypes of the cell lines used in this study. Hendrix et al (25) analysed a series of 103 ovarian samples using the Affymetrix U133A array platform (GEO: GSE6008). This dataset comprises clear cell (n=8), endometrioid (n=37), mucinous (n=13) and serous epithelial (n=41) primary ovarian carcinomas and samples from 4 normal ovaries. To build the predictor, the Prediction Analysis of Microarrays (PAM) package in R environment was employed (http://rss.acs.unt.edu/Rdoc/library/pamr/html/00Index.html). When more than one probe described the expression of a given gene, we used the probe with the highest median absolute deviation across the samples. The dataset from Hendrix et al. (25) and the dataset of OCCC cell lines described in this manuscript were then overlaid on the basis of 11536 common unique HGNC gene symbols. Only the 99 primary ovarian cancers samples and the four normal ovary samples were used to build the predictor. Following leave one out cross-validation, a predictor based upon 126 genes was able to identify correctly the four distinct phenotypes of primary ovarian tumour samples with a misclassification rate of 18.3%. This predictor was subsequently applied to the expression data from the 12 OCCC cell lines to determine the likeliest phenotype of the OCCC cell lines compared to primary ovarian cancers. Posterior probabilities were estimated for each cell line in comparison to the following phenotypes: clear cell, endometrioid, mucinous and serous epithelial. -
Us 2018 / 0305689 A1
US 20180305689A1 ( 19 ) United States (12 ) Patent Application Publication ( 10) Pub . No. : US 2018 /0305689 A1 Sætrom et al. ( 43 ) Pub . Date: Oct. 25 , 2018 ( 54 ) SARNA COMPOSITIONS AND METHODS OF plication No . 62 /150 , 895 , filed on Apr. 22 , 2015 , USE provisional application No . 62/ 150 ,904 , filed on Apr. 22 , 2015 , provisional application No. 62 / 150 , 908 , (71 ) Applicant: MINA THERAPEUTICS LIMITED , filed on Apr. 22 , 2015 , provisional application No. LONDON (GB ) 62 / 150 , 900 , filed on Apr. 22 , 2015 . (72 ) Inventors : Pål Sætrom , Trondheim (NO ) ; Endre Publication Classification Bakken Stovner , Trondheim (NO ) (51 ) Int . CI. C12N 15 / 113 (2006 .01 ) (21 ) Appl. No. : 15 /568 , 046 (52 ) U . S . CI. (22 ) PCT Filed : Apr. 21 , 2016 CPC .. .. .. C12N 15 / 113 ( 2013 .01 ) ; C12N 2310 / 34 ( 2013. 01 ) ; C12N 2310 /14 (2013 . 01 ) ; C12N ( 86 ) PCT No .: PCT/ GB2016 /051116 2310 / 11 (2013 .01 ) $ 371 ( c ) ( 1 ) , ( 2 ) Date : Oct . 20 , 2017 (57 ) ABSTRACT The invention relates to oligonucleotides , e . g . , saRNAS Related U . S . Application Data useful in upregulating the expression of a target gene and (60 ) Provisional application No . 62 / 150 ,892 , filed on Apr. therapeutic compositions comprising such oligonucleotides . 22 , 2015 , provisional application No . 62 / 150 ,893 , Methods of using the oligonucleotides and the therapeutic filed on Apr. 22 , 2015 , provisional application No . compositions are also provided . 62 / 150 ,897 , filed on Apr. 22 , 2015 , provisional ap Specification includes a Sequence Listing . SARNA sense strand (Fessenger 3 ' SARNA antisense strand (Guide ) Mathew, Si Target antisense RNA transcript, e . g . NAT Target Coding strand Gene Transcription start site ( T55 ) TY{ { ? ? Targeted Target transcript , e . -
Multi-Tissue Probabilistic Fine-Mapping of Transcriptome-Wide Association Study Identifies Cis-Regulated Genes for Miserableness
bioRxiv preprint doi: https://doi.org/10.1101/682633; this version posted June 26, 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-ND 4.0 International license. Multi-tissue probabilistic fine-mapping of transcriptome-wide association study identifies cis-regulated genes for miserableness Calwing Liao1,2 BSc, Veikko Vuokila2, Alexandre D Laporte2 BSc, Dan Spiegelman2 MSc, Patrick A. Dion2,3 PhD, Guy A. Rouleau1,2,3 * MD, PhD 1Department oF Human Genetics, McGill University, Montréal, Quebec, Canada 2Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada 3Department oF Neurology and Neurosurgery, McGill University, Montréal, Quebec, Canada Short summary: The First transcriptome-wide association study oF miserableness identiFies many genes including c7orf50 implicated in the trait. Word count: 1,522 excluding abstract and reFerences Tables: 3 Keywords: Miserableness, transcriptome-wide association study, TWAS *Correspondence: Dr. Guy A. Rouleau Montreal Neurological Institute and Hospital Department oF Neurology and Neurosurgery 3801 University Street, Montreal, QC Canada H3A 2B4. Tel: +1 514 398 2690 Fax: +1 514 398 8248 E-mail: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/682633; this version posted June 26, 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-ND 4.0 International license. Abstract (141 words) Miserableness is a behavioural trait that is characterized by strong negative Feelings in an individual. -
Explorations in Olfactory Receptor Structure and Function by Jianghai
Explorations in Olfactory Receptor Structure and Function by Jianghai Ho Department of Neurobiology Duke University Date:_______________________ Approved: ___________________________ Hiroaki Matsunami, Supervisor ___________________________ Jorg Grandl, Chair ___________________________ Marc Caron ___________________________ Sid Simon ___________________________ [Committee Member Name] Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Neurobiology in the Graduate School of Duke University 2014 ABSTRACT Explorations in Olfactory Receptor Structure and Function by Jianghai Ho Department of Neurobiology Duke University Date:_______________________ Approved: ___________________________ Hiroaki Matsunami, Supervisor ___________________________ Jorg Grandl, Chair ___________________________ Marc Caron ___________________________ Sid Simon ___________________________ [Committee Member Name] An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Neurobiology in the Graduate School of Duke University 2014 Copyright by Jianghai Ho 2014 Abstract Olfaction is one of the most primitive of our senses, and the olfactory receptors that mediate this very important chemical sense comprise the largest family of genes in the mammalian genome. It is therefore surprising that we understand so little of how olfactory receptors work. In particular we have a poor idea of what chemicals are detected by most of the olfactory receptors in the genome, and for those receptors which we have paired with ligands, we know relatively little about how the structure of these ligands can either activate or inhibit the activation of these receptors. Furthermore the large repertoire of olfactory receptors, which belong to the G protein coupled receptor (GPCR) superfamily, can serve as a model to contribute to our broader understanding of GPCR-ligand binding, especially since GPCRs are important pharmaceutical targets. -
Exome Array Analysis Identifies New Loci and Low-Frequency Variants Influencing Insulin Processing and Secretion
LETTERS Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion Jeroen R Huyghe1, Anne U Jackson1, Marie P Fogarty2, Martin L Buchkovich2, Alena Stančáková3, Heather M Stringham1, Xueling Sim1, Lingyao Yang1, Christian Fuchsberger1, Henna Cederberg3, Peter S Chines4, Tanya M Teslovich1, Jane M Romm5, Hua Ling5, Ivy McMullen5, Roxann Ingersoll5, Elizabeth W Pugh5, Kimberly F Doheny5, Benjamin M Neale6–8, Mark J Daly6–8, Johanna Kuusisto3, Laura J Scott1, Hyun Min Kang1, Francis S Collins4, Gonçalo R Abecasis1, Richard M Watanabe9,10, Michael Boehnke1,11, Markku Laakso3,11 & Karen L Mohlke2,11 Insulin secretion has a crucial role in glucose homeostasis, HumanExome Beadchip. Clinical characteristics of 8,229 analyzed and failure to secrete sufficient insulin is a hallmark of nondiabetic study participants are summarized in Supplementary type 2 diabetes. Genome-wide association studies (GWAS) Table 1. Among 242,071 variants passing quality control, 89,864 have identified loci contributing to insulin processing and (38.1%) were variable in the studied individuals; of these, 71,077 were secretion1,2; however, a substantial fraction of the genetic nonsynonymous, nonsense or located in splice sites (Supplementary contribution remains undefined. To examine low-frequency Table 2). We tested 59,029 variants with MAF > 0.05% for associa- (minor allele frequency (MAF) 0.5–5%) and rare (MAF < tion with insulin processing, secretion and glycemic traits, assuming 0.5%) nonsynonymous variants, we analyzed exome array additive allelic effects and using a linear mixed model to account for data in 8,229 nondiabetic Finnish males using the Illumina relatedness among study participants5. -
Online Supporting Information S2: Proteins in Each Negative Pathway
Online Supporting Information S2: Proteins in each negative pathway Index Proteins ADO,ACTA1,DEGS2,EPHA3,EPHB4,EPHX2,EPOR,EREG,FTH1,GAD1,HTR6, IGF1R,KIR2DL4,NCR3,NME7,NOTCH1,OR10S1,OR2T33,OR56B4,OR7A10, Negative_1 OR8G1,PDGFC,PLCZ1,PROC,PRPS2,PTAFR,SGPP2,STMN1,VDAC3,ATP6V0 A1,MAPKAPK2 DCC,IDS,VTN,ACTN2,AKR1B10,CACNA1A,CHIA,DAAM2,FUT5,GCLM,GNAZ Negative_2 ,ITPA,NEU4,NTF3,OR10A3,PAPSS1,PARD3,PLOD1,RGS3,SCLY,SHC1,TN FRSF4,TP53 Negative_3 DAO,CACNA1D,HMGCS2,LAMB4,OR56A3,PRKCQ,SLC25A5 IL5,LHB,PGD,ADCY3,ALDH1A3,ATP13A2,BUB3,CD244,CYFIP2,EPHX2,F CER1G,FGD1,FGF4,FZD9,HSD17B7,IL6R,ITGAV,LEFTY1,LIPG,MAN1C1, Negative_4 MPDZ,PGM1,PGM3,PIGM,PLD1,PPP3CC,TBXAS1,TKTL2,TPH2,YWHAQ,PPP 1R12A HK2,MOS,TKT,TNN,B3GALT4,B3GAT3,CASP7,CDH1,CYFIP1,EFNA5,EXTL 1,FCGR3B,FGF20,GSTA5,GUK1,HSD3B7,ITGB4,MCM6,MYH3,NOD1,OR10H Negative_5 1,OR1C1,OR1E1,OR4C11,OR56A3,PPA1,PRKAA1,PRKAB2,RDH5,SLC27A1 ,SLC2A4,SMPD2,STK36,THBS1,SERPINC1 TNR,ATP5A1,CNGB1,CX3CL1,DEGS1,DNMT3B,EFNB2,FMO2,GUCY1B3,JAG Negative_6 2,LARS2,NUMB,PCCB,PGAM1,PLA2G1B,PLOD2,PRDX6,PRPS1,RFXANK FER,MVD,PAH,ACTC1,ADCY4,ADCY8,CBR3,CLDN16,CPT1A,DDOST,DDX56 ,DKK1,EFNB1,EPHA8,FCGR3A,GLS2,GSTM1,GZMB,HADHA,IL13RA2,KIR2 Negative_7 DS4,KLRK1,LAMB4,LGMN,MAGI1,NUDT2,OR13A1,OR1I1,OR4D11,OR4X2, OR6K2,OR8B4,OXCT1,PIK3R4,PPM1A,PRKAG3,SELP,SPHK2,SUCLG1,TAS 1R2,TAS1R3,THY1,TUBA1C,ZIC2,AASDHPPT,SERPIND1 MTR,ACAT2,ADCY2,ATP5D,BMPR1A,CACNA1E,CD38,CYP2A7,DDIT4,EXTL Negative_8 1,FCER1G,FGD3,FZD5,ITGAM,MAPK8,NR4A1,OR10V1,OR4F17,OR52D1,O R8J3,PLD1,PPA1,PSEN2,SKP1,TACR3,VNN1,CTNNBIP1 APAF1,APOA1,CARD11,CCDC6,CSF3R,CYP4F2,DAPK1,FLOT1,GSTM1,IL2 -
Supplemental Table S18. Cellular Process Enrichment Analysis Output
Supplemental Table S18. -