2019 NHGRI Symposium
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Supplemental Figure 1. Vimentin
Double mutant specific genes Transcript gene_assignment Gene Symbol RefSeq FDR Fold- FDR Fold- FDR Fold- ID (single vs. Change (double Change (double Change wt) (single vs. wt) (double vs. single) (double vs. wt) vs. wt) vs. single) 10485013 BC085239 // 1110051M20Rik // RIKEN cDNA 1110051M20 gene // 2 E1 // 228356 /// NM 1110051M20Ri BC085239 0.164013 -1.38517 0.0345128 -2.24228 0.154535 -1.61877 k 10358717 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 /// BC 1700025G04Rik NM_197990 0.142593 -1.37878 0.0212926 -3.13385 0.093068 -2.27291 10358713 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 1700025G04Rik NM_197990 0.0655213 -1.71563 0.0222468 -2.32498 0.166843 -1.35517 10481312 NM_027283 // 1700026L06Rik // RIKEN cDNA 1700026L06 gene // 2 A3 // 69987 /// EN 1700026L06Rik NM_027283 0.0503754 -1.46385 0.0140999 -2.19537 0.0825609 -1.49972 10351465 BC150846 // 1700084C01Rik // RIKEN cDNA 1700084C01 gene // 1 H3 // 78465 /// NM_ 1700084C01Rik BC150846 0.107391 -1.5916 0.0385418 -2.05801 0.295457 -1.29305 10569654 AK007416 // 1810010D01Rik // RIKEN cDNA 1810010D01 gene // 7 F5 // 381935 /// XR 1810010D01Rik AK007416 0.145576 1.69432 0.0476957 2.51662 0.288571 1.48533 10508883 NM_001083916 // 1810019J16Rik // RIKEN cDNA 1810019J16 gene // 4 D2.3 // 69073 / 1810019J16Rik NM_001083916 0.0533206 1.57139 0.0145433 2.56417 0.0836674 1.63179 10585282 ENSMUST00000050829 // 2010007H06Rik // RIKEN cDNA 2010007H06 gene // --- // 6984 2010007H06Rik ENSMUST00000050829 0.129914 -1.71998 0.0434862 -2.51672 -
Upregulation of Peroxisome Proliferator-Activated Receptor-Α And
Upregulation of peroxisome proliferator-activated receptor-α and the lipid metabolism pathway promotes carcinogenesis of ampullary cancer Chih-Yang Wang, Ying-Jui Chao, Yi-Ling Chen, Tzu-Wen Wang, Nam Nhut Phan, Hui-Ping Hsu, Yan-Shen Shan, Ming-Derg Lai 1 Supplementary Table 1. Demographics and clinical outcomes of five patients with ampullary cancer Time of Tumor Time to Age Differentia survival/ Sex Staging size Morphology Recurrence recurrence Condition (years) tion expired (cm) (months) (months) T2N0, 51 F 211 Polypoid Unknown No -- Survived 193 stage Ib T2N0, 2.41.5 58 F Mixed Good Yes 14 Expired 17 stage Ib 0.6 T3N0, 4.53.5 68 M Polypoid Good No -- Survived 162 stage IIA 1.2 T3N0, 66 M 110.8 Ulcerative Good Yes 64 Expired 227 stage IIA T3N0, 60 M 21.81 Mixed Moderate Yes 5.6 Expired 16.7 stage IIA 2 Supplementary Table 2. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of an ampullary cancer microarray using the Database for Annotation, Visualization and Integrated Discovery (DAVID). This table contains only pathways with p values that ranged 0.0001~0.05. KEGG Pathway p value Genes Pentose and 1.50E-04 UGT1A6, CRYL1, UGT1A8, AKR1B1, UGT2B11, UGT2A3, glucuronate UGT2B10, UGT2B7, XYLB interconversions Drug metabolism 1.63E-04 CYP3A4, XDH, UGT1A6, CYP3A5, CES2, CYP3A7, UGT1A8, NAT2, UGT2B11, DPYD, UGT2A3, UGT2B10, UGT2B7 Maturity-onset 2.43E-04 HNF1A, HNF4A, SLC2A2, PKLR, NEUROD1, HNF4G, diabetes of the PDX1, NR5A2, NKX2-2 young Starch and sucrose 6.03E-04 GBA3, UGT1A6, G6PC, UGT1A8, ENPP3, MGAM, SI, metabolism -
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. -
Figure S1. DMD Module Network. the Network Is Formed by 260 Genes from Disgenet and 1101 Interactions from STRING. Red Nodes Are the Five Seed Candidate Genes
Figure S1. DMD module network. The network is formed by 260 genes from DisGeNET and 1101 interactions from STRING. Red nodes are the five seed candidate genes. Figure S2. DMD module network is more connected than a random module of the same size. It is shown the distribution of the largest connected component of 10.000 random modules of the same size of the DMD module network. The green line (x=260) represents the DMD largest connected component, obtaining a z-score=8.9. Figure S3. Shared genes between BMD and DMD signature. A) A meta-analysis of three microarray datasets (GSE3307, GSE13608 and GSE109178) was performed for the identification of differentially expressed genes (DEGs) in BMD muscle biopsies as compared to normal muscle biopsies. Briefly, the GSE13608 dataset included 6 samples of skeletal muscle biopsy from healthy people and 5 samples from BMD patients. Biopsies were taken from either biceps brachii, triceps brachii or deltoid. The GSE3307 dataset included 17 samples of skeletal muscle biopsy from healthy people and 10 samples from BMD patients. The GSE109178 dataset included 14 samples of controls and 11 samples from BMD patients. For both GSE3307 and GSE10917 datasets, biopsies were taken at the time of diagnosis and from the vastus lateralis. For the meta-analysis of GSE13608, GSE3307 and GSE109178, a random effects model of effect size measure was used to integrate gene expression patterns from the two datasets. Genes with an adjusted p value (FDR) < 0.05 and an │effect size│>2 were identified as DEGs and selected for further analysis. A significant number of DEGs (p<0.001) were in common with the DMD signature genes (blue nodes), as determined by a hypergeometric test assessing the significance of the overlap between the BMD DEGs and the number of DMD signature genes B) MCODE analysis of the overlapping genes between BMD DEGs and DMD signature genes. -
The Concise Guide to Pharmacology 2019/20
Edinburgh Research Explorer THE CONCISE GUIDE TO PHARMACOLOGY 2019/20 Citation for published version: Cgtp Collaborators 2019, 'THE CONCISE GUIDE TO PHARMACOLOGY 2019/20: Transporters', British Journal of Pharmacology, vol. 176 Suppl 1, pp. S397-S493. https://doi.org/10.1111/bph.14753 Digital Object Identifier (DOI): 10.1111/bph.14753 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: British Journal of Pharmacology General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 28. Sep. 2021 S.P.H. Alexander et al. The Concise Guide to PHARMACOLOGY 2019/20: Transporters. British Journal of Pharmacology (2019) 176, S397–S493 THE CONCISE GUIDE TO PHARMACOLOGY 2019/20: Transporters Stephen PH Alexander1 , Eamonn Kelly2, Alistair Mathie3 ,JohnAPeters4 , Emma L Veale3 , Jane F Armstrong5 , Elena Faccenda5 ,SimonDHarding5 ,AdamJPawson5 , Joanna L -
Towards the Elucidation of Orphan Lysosomal Transporters Quentin Verdon
Towards the Elucidation of Orphan Lysosomal Transporters Quentin Verdon To cite this version: Quentin Verdon. Towards the Elucidation of Orphan Lysosomal Transporters. Cancer. Université Paris Saclay (COmUE), 2016. English. NNT : 2016SACLS144. tel-01827233 HAL Id: tel-01827233 https://tel.archives-ouvertes.fr/tel-01827233 Submitted on 2 Jul 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. NNT : 2016SACLS144 THESE DE DOCTORAT DE L’UNIVERSITE PARIS-SACLAY PREPAREE A L’UNIVERSITE PARIS-SUD ECOLE DOCTORALE N°568 BIOSIGNE | Signalisations et réseaux intégratifs en biologie Spécialité de doctorat : aspects moléculaires et cellulaires de la biologie Par Mr Quentin Verdon Towards the elucidation of orphan lysosomal transporters: several shots on target and one goal Thèse présentée et soutenue à Paris le 29/06/2016 » : Composition du Jury : Mr Le Maire Marc Professeur, Université Paris-Sud Président Mr Birman Serge Directeur de recherche, CNRS Rapporteur Mr Murray James Assistant professor, Trinity college Dublin Rapporteur Mr Goud Bruno Directeur de recherche, CNRS Examinateur Mr Gasnier Bruno Directeur de recherche, CNRS Directeur de thèse Mme Sagné Corinne Chargée de recherche, INSERM Co-directeur de thèse Table of contents Remerciements (acknowledgements) 6 Abbreviations 7 Abstracts 10 Introduction 12 1 Physiology of lysosomes 12 1.1 Discovery and generalities 12 1.2 Degradative function 13 1.3. -
Type of the Paper (Article
Supplementary Material A Proteomics Study on the Mechanism of Nutmeg-induced Hepatotoxicity Wei Xia 1, †, Zhipeng Cao 1, †, Xiaoyu Zhang 1 and Lina Gao 1,* 1 School of Forensic Medicine, China Medical University, Shenyang 110122, P. R. China; lessen- [email protected] (W.X.); [email protected] (Z.C.); [email protected] (X.Z.) † The authors contributed equally to this work. * Correspondence: [email protected] Figure S1. Table S1. Peptide fraction separation liquid chromatography elution gradient table. Time (min) Flow rate (mL/min) Mobile phase A (%) Mobile phase B (%) 0 1 97 3 10 1 95 5 30 1 80 20 48 1 60 40 50 1 50 50 53 1 30 70 54 1 0 100 1 Table 2. Liquid chromatography elution gradient table. Time (min) Flow rate (nL/min) Mobile phase A (%) Mobile phase B (%) 0 600 94 6 2 600 83 17 82 600 60 40 84 600 50 50 85 600 45 55 90 600 0 100 Table S3. The analysis parameter of Proteome Discoverer 2.2. Item Value Type of Quantification Reporter Quantification (TMT) Enzyme Trypsin Max.Missed Cleavage Sites 2 Precursor Mass Tolerance 10 ppm Fragment Mass Tolerance 0.02 Da Dynamic Modification Oxidation/+15.995 Da (M) and TMT /+229.163 Da (K,Y) N-Terminal Modification Acetyl/+42.011 Da (N-Terminal) and TMT /+229.163 Da (N-Terminal) Static Modification Carbamidomethyl/+57.021 Da (C) 2 Table S4. The DEPs between the low-dose group and the control group. Protein Gene Fold Change P value Trend mRNA H2-K1 0.380 0.010 down Glutamine synthetase 0.426 0.022 down Annexin Anxa6 0.447 0.032 down mRNA H2-D1 0.467 0.002 down Ribokinase Rbks 0.487 0.000 -
Toxicological Profile for Nitrate and Nitrite
NITRATE AND NITRITE 29 3. HEALTH EFFECTS 3.1 INTRODUCTION The primary purpose of this chapter is to provide public health officials, physicians, toxicologists, and other interested individuals and groups with an overall perspective on the toxicology of nitrate and nitrite. It contains descriptions and evaluations of toxicological studies and epidemiological investigations and provides conclusions, where possible, on the relevance of toxicity and toxicokinetic data to public health. A glossary and list of acronyms, abbreviations, and symbols can be found at the end of this profile. 3.2 DISCUSSION OF HEALTH EFFECTS BY ROUTE OF EXPOSURE To help public health professionals and others address the needs of persons living or working near hazardous waste sites, the information in this section is organized first by route of exposure (inhalation, oral, and dermal) and then by health effect (e.g., death, systemic, immunological, neurological, reproductive, developmental, and carcinogenic effects). These data are discussed in terms of three exposure periods: acute (14 days or less), intermediate (15–364 days), and chronic (365 days or more). Levels of significant exposure for each route and duration are presented in tables and illustrated in figures. The points in the figures showing no-observed-adverse-effect levels (NOAELs) or lowest- observed-adverse-effect levels (LOAELs) reflect the actual doses (levels of exposure) used in the studies. LOAELs have been classified into "less serious" or "serious" effects. "Serious" effects are those that evoke failure in a biological system and can lead to morbidity or mortality (e.g., acute respiratory distress or death). "Less serious" effects are those that are not expected to cause significant dysfunction or death, or those whose significance to the organism is not entirely clear. -
Sialic Acid Storage Disease
Sialic acid storage disease Description Sialic acid storage disease is an inherited disorder that primarily affects the nervous system. People with sialic acid storage disease have signs and symptoms that may vary widely in severity. This disorder is generally classified into one of three forms: infantile free sialic acid storage disease, Salla disease, and intermediate severe Salla disease. Infantile free sialic acid storage disease (ISSD) is the most severe form of this disorder. Babies with this condition have severe developmental delay, weak muscle tone ( hypotonia), and failure to gain weight and grow at the expected rate (failure to thrive). They may have unusual facial features that are often described as "coarse," seizures, bone malformations, an enlarged liver and spleen (hepatosplenomegaly), and an enlarged heart (cardiomegaly). The abdomen may be swollen due to the enlarged organs and an abnormal buildup of fluid in the abdominal cavity (ascites). Affected infants may have a condition called hydrops fetalis in which excess fluid accumulates in the body before birth. Children with this severe form of the condition usually live only into early childhood. Salla disease is a less severe form of sialic acid storage disease. Babies with Salla disease usually begin exhibiting hypotonia during the first year of life and go on to experience progressive neurological problems. Signs and symptoms of Salla disease include intellectual disability and developmental delay, seizures, problems with movement and balance (ataxia), abnormal tensing of the muscles (spasticity), and involuntary slow, sinuous movements of the limbs (athetosis). Individuals with Salla disease usually survive into adulthood. People with intermediate severe Salla disease have signs and symptoms that fall between those of ISSD and Salla disease in severity. -
Folate Cycle Enzyme MTHFD1L Confers Metabolic Advantages in Hepatocellular Carcinoma
RESEARCH ARTICLE The Journal of Clinical Investigation Folate cycle enzyme MTHFD1L confers metabolic advantages in hepatocellular carcinoma Derek Lee,1 Iris Ming-Jing Xu,1 David Kung-Chun Chiu,1 Robin Kit-Ho Lai,1 Aki Pui-Wah Tse,1 Lynna Lan Li,1 Cheuk-Ting Law,1 Felice Ho-Ching Tsang,1 Larry Lai Wei,1 Cerise Yuen-Ki Chan,1 Chun-Ming Wong,1,2 Irene Oi-Lin Ng,1,2 and Carmen Chak-Lui Wong1,2 1Department of Pathology and 2State Key Laboratory for Liver Research, The University of Hong Kong, Hong Kong, China. Cancer cells preferentially utilize glucose and glutamine, which provide macromolecules and antioxidants that sustain rapid cell division. Metabolic reprogramming in cancer drives an increased glycolytic rate that supports maximal production of these nutrients. The folate cycle, through transfer of a carbon unit between tetrahydrofolate and its derivatives in the cytoplasmic and mitochondrial compartments, produces other metabolites that are essential for cell growth, including nucleotides, methionine, and the antioxidant NADPH. Here, using hepatocellular carcinoma (HCC) as a cancer model, we have observed a reduction in growth rate upon withdrawal of folate. We found that an enzyme in the folate cycle, methylenetetrahydrofolate dehydrogenase 1–like (MTHFD1L), plays an essential role in support of cancer growth. We determined that MTHFD1L is transcriptionally activated by NRF2, a master regulator of redox homeostasis. Our observations further suggest that MTHFD1L contributes to the production and accumulation of NADPH to levels that are sufficient to combat oxidative stress in cancer cells. The elevation of oxidative stress through MTHFD1L knockdown or the use of methotrexate, an antifolate drug, sensitizes cancer cells to sorafenib, a targeted therapy for HCC. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Upregulation of E3 Ubiquitin Ligase CBLC Enhances EGFR
Published OnlineFirst June 26, 2018; DOI: 10.1158/0008-5472.CAN-17-3858 Cancer Tumor Biology and Immunology Research Upregulation of E3 Ubiquitin Ligase CBLC Enhances EGFR Dysregulation and Signaling in Lung Adenocarcinoma Shiao-Ya Hong1, Yu-Rung Kao1, Te-Chang Lee1, and Cheng-Wen Wu1,2,3,4 Abstract CBLC (CBL proto-oncogene c) belongs to the CBL protein ubiquitinated and positively regulated aEGFR stability family, which has E3 ubiquitin ligase activity toward activated through the conjugation of polyubiquitin by K6 and K11 receptor tyrosine kinases. CBLC is frequently upregulated in linkages. This CBLC-mediated polyubiquitination promoted non–small cell lung cancer (NSCLC), yet very little is known either preferential recycling of aEGFR back to the plasma about the functions of CBLC in tumorigenesis. Here we show membrane or trafficking to the cell nucleus. IHC analyses that CBLC is an epigenetically demethylated target and its revealed a positive correlation between phospho-EGFR and expression can be upregulated in NSCLC after treatment with CBLC in lung adenocarcinoma. In summary, we demonstrate a the DNA methylation inhibitor 50-azacytidine. Depletion novel mechanism by which aEGFR escapes lysosomal degra- of CBLC significantly inhibited cell viability and clonogenicity dation in a CBLC/ubiquitin-dependent manner to sustain its in vitro and reduced tumor growth in a xenograft model. CBLC activation. Our work identifies CBLC as a potential diagnostic silencing further sensitized EGFR-mutated NSCLC cells to biomarker and also points to its utilization as a novel thera- treatment with tyrosine kinase inhibitors. Conversely, ectopic peutic target for NSCLC therapy. expression of CBLC enhanced the activation of EGFR and downstream ERK1/2 signaling after ligand stimulation by Significance: This work demonstrates the role of CBLC expres- competing with CBL for EGFR binding.