DOCSLIB.ORG

Name Description Type Chromosome Normal Methylation Cancer

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Name Description Type Chromosome Normal Methylation Cancer Supplementary Table S7. List of genes that are heavily methylated in normal liver and become demethylated in HCC normal_ cancer_ direction name description type chromosome methylation methylation _meth Alpha-2-macroglobulin-like protein 1 Precursor (C3 and PZP-like alpha-2- A2ML1 macroglobulin domain-containing protein_coding protein 9) [Source:UniProtKB/Swiss- Prot;Acc:A8K2U0] 12 0.99 0.96 -1 Retinal-specific ATP-binding cassette transporter (ATP-binding cassette sub- family A member 4)(RIM ABC ABCA4 transporter)(RIM protein_coding protein)(RmP)(Stargardt disease protein) [Source:UniProtKB/Swiss- Prot;Acc:P78363] 1 0.96 0.96 -1 Hypothetical gene supported by BX648692 AC005692.1 protein_coding [Source:UniProtKB/TrEMBL;Acc:A4D1T 2] 7 0.99 0.97 -1 Putative uncharacterized protein AC005863.1 FLJ45831 [Source:UniProtKB/Swiss- protein_coding Prot;Acc:Q6ZS49] 17 0.97 1 -1 cDNA FLJ45743 fis, clone KIDNE2016464 (HCG2045177) AC009271.7 protein_coding [Source:UniProtKB/TrEMBL;Acc:Q6ZS 83] 18 1 0.91 -1 Putative uncharacterized protein AC009365.9-1 FLJ40288 [Source:UniProtKB/Swiss- protein_coding Prot;Acc:A4D1N5] 7 0.91 0.84 -1 Putative uncharacterized protein AC013469.8-2 LOC643905 [Source:UniProtKB/Swiss- protein_coding Prot;Acc:Q8WXC7] 2 0.95 0.95 -1 Uncharacterized protein FLJ46481 AC105915.4 [Source:UniProtKB/Swiss- protein_coding Prot;Acc:Q6ZRC1] 4 0.97 0.94 -1 Supplementary Table S7. List of genes that are heavily methylated in normal liver and become demethylated in HCC normal_ cancer_ direction name description type chromosome methylation methylation _meth Protein GREB1 (Gene regulated in breast cancer 1 protein) AC110754.4 protein_coding [Source:UniProtKB/Swiss- Prot;Acc:Q4ZG55] 2 0.96 0.86 -1 Protein GREB1 (Gene regulated in breast cancer 1 protein) AC110754.4 protein_coding [Source:UniProtKB/Swiss- Prot;Acc:Q4ZG55] 2 0.88 0.87 0 UPF0640 protein AC112215.3-1 [Source:UniProtKB/Swiss- protein_coding Prot;Acc:Q8WVI0] 3 0.96 0.9 -1 AC116655.7-2 NA pseudogene 4 1 1 -1 cDNA FLJ40448 fis, clone TESTI2040815 AC138028.1-3 protein_coding [Source:UniProtKB/TrEMBL;Acc:Q8N7 R2] 16 0.91 0.89 -1 Acrosin Precursor (EC 3.4.21.10) [Contains Acrosin light chain;Acrosin ACR protein_coding heavy chain] [Source:UniProtKB/Swiss- Prot;Acc:P10323] 22 0.98 0.91 -1 Long-chain-fatty-acid--CoA ligase ACSBG1 (EC 6.2.1.3)(Acyl-CoA synthetase bubblegum family member ACSBG1 protein_coding 1)(hsBGM)(hsBG)(hBG1)(Lipodisin) [Source:UniProtKB/Swiss- Prot;Acc:Q96GR2] 15 0.99 0.97 -1 Long-chain-fatty-acid--CoA ligase 5 (EC 6.2.1.3)(Long-chain acyl-CoA ACSL5 synthetase 5)(LACS 5) protein_coding [Source:UniProtKB/Swiss- Prot;Acc:Q9ULC5] 10 0.96 0.94 -1 Supplementary Table S7. List of genes that are heavily methylated in normal liver and become demethylated in HCC normal_ cancer_ direction name description type chromosome methylation methylation _meth Beta-actin-like protein 2 (Kappa-actin) ACTBL2 [Source:UniProtKB/Swiss- protein_coding Prot;Acc:Q562R1] 5 0.91 0.88 -1 Actin, gamma-enteric smooth muscle (Smooth muscle gamma-actin)(Gamma- ACTG2 2-actin)(Alpha-actin-3) protein_coding [Source:UniProtKB/Swiss- Prot;Acc:P63267] 2 1 0.99 -1 Adenosine deaminase domain- containing protein 1 (Testis nuclear ADAD1 RNA-binding protein) protein_coding [Source:UniProtKB/Swiss- Prot;Acc:Q96M93] 4 0.91 0.89 -1 ADAM 18 Precursor (A disintegrin and metalloproteinase domain 18)(Transmembrane metalloproteinase- ADAM18 like, disintegrin-like, and cysteine-rich protein_coding protein III)(tMDC III) [Source:UniProtKB/Swiss- Prot;Acc:Q9Y3Q7] 8 0.96 1 -1 Disintegrin and metalloproteinase domain-containing protein 2 Precursor (ADAM 2)(Fertilin subunit beta)(PH- ADAM2 30)(PH30)(PH30-beta)(Cancer/testis protein_coding antigen 15)(CT15) [Source:UniProtKB/Swiss- Prot;Acc:Q99965] 8 1 0.98 -1 Supplementary Table S7. List of genes that are heavily methylated in normal liver and become demethylated in HCC normal_ cancer_ direction name description type chromosome methylation methylation _meth ADAM DEC1 Precursor (EC 3.4.24.-)(A disintegrin and metalloproteinase domain-like protein decysin 1)(ADAM- ADAMDEC1 protein_coding like protein decysin 1) [Source:UniProtKB/Swiss- Prot;Acc:O15204] 8 0.71 0.67 0 Adenosine A3 receptor ADORA3 [Source:UniProtKB/Swiss- protein_coding Prot;Acc:P33765] 1 0.99 0.98 -1 Cytosolic carboxypeptidase 4 (EC 3.4.17.-)(ATP/GTP-binding protein-like AGBL1 protein_coding 1) [Source:UniProtKB/Swiss- Prot;Acc:Q96MI9] 15 0.94 0.92 -1 Anterior gradient protein 2 homolog Precursor (hAG-2)(AG-2)(Secreted cement gland protein XAG-2 AGR2 protein_coding homolog)(HPC8) [Source:UniProtKB/Swiss- Prot;Acc:O95994] 7 0.99 0.92 -1 AL121755.23 NA retrotransposed 20 0.96 0.91 -1 AL132709.5-3 NA miRNA 14 0.93 0.9 -1 AL365217.10-1 NA pseudogene 6 0.97 0.93 -1 HCG2044975Putative uncharacterized protein ENSP00000357662 ; AL589787.16 protein_coding [Source:UniProtKB/TrEMBL;Acc:B7WP 59] 10 0.9 0.89 -1 Putative uncharacterized protein ENSP00000350482 AL773578.1 protein_coding [Source:UniProtKB/TrEMBL;Acc:B7WN N0] 21 0.92 0.77 -1 Supplementary Table S7. List of genes that are heavily methylated in normal liver and become demethylated in HCC normal_ cancer_ direction name description type chromosome methylation methylation _meth Arachidonate 12-lipoxygenase, 12R type (12R-lipoxygenase)(12R-LOX)(EC ALOX12B 1.13.11.-)(Epidermis-type lipoxygenase protein_coding 12) [Source:UniProtKB/Swiss- Prot;Acc:O75342] 17 0.99 1 -1 Arachidonate 5-lipoxygenase-activating protein (FLAP)(MK-886-binding protein) ALOX5AP protein_coding [Source:UniProtKB/Swiss- Prot;Acc:P20292] 13 0.91 0.92 -1 Ameloblastin Precursor AMBN [Source:UniProtKB/Swiss- protein_coding Prot;Acc:Q9NP70] 4 0.97 0.92 -1 Protein AMBP Precursor [Contains Alpha-1-microglobulin(Protein HC)(Complex-forming glycoprotein heterogeneous in charge)(Alpha-1 AMBP protein_coding microglycoprotein);Inter-alpha-trypsin inhibitor light chain(ITI-LC)(Bikunin)(HI- 30)(Uronic-acid-rich protein)(EDC1);Trypstati 9 0.97 0.85 -1 Ankyrin repeat domain-containing protein 23 (Diabetes-related ankyrin ANKRD23 repeat protein)(Muscle ankyrin repeat protein_coding protein 3) [Source:UniProtKB/Swiss- Prot;Acc:Q86SG2] 2 0.98 0.97 -1 Ankyrin repeat domain-containing ANKRD55 protein 55 [Source:UniProtKB/Swiss- protein_coding Prot;Acc:Q3KP44] 5 1 0.99 -1 Supplementary Table S7. List of genes that are heavily methylated in normal liver and become demethylated in HCC normal_ cancer_ direction name description type chromosome methylation methylation _meth Ankyrin repeat domain-containing protein 7 (Testis-specific protein ANKRD7 protein_coding TSA806) [Source:UniProtKB/Swiss- Prot;Acc:Q92527] 7 1 1 -1 Anoctamin-2 (Transmembrane protein ANO2 16B) [Source:UniProtKB/Swiss- protein_coding Prot;Acc:Q9NQ90] 12 0.99 0.94 -1 Acidic leucine-rich nuclear phosphoprotein 32 family member C (Phosphoprotein 32-related protein ANP32C protein_coding 1)(Tumorigenic protein pp32r1) [Source:UniProtKB/Swiss- Prot;Acc:O43423] 4 0.91 0.88 -1 AP001525.6-2 NA miRNA 18 0.97 0.94 -1 AP003778.3-1 NA pseudogene 11 0.91 0.82 -1 Probable DNA dC->dU-editing enzyme APOBEC-3B (EC 3.5.4.-)(Phorbolin-1- APOBEC3B related protein)(Phorbolin-2/3) protein_coding [Source:UniProtKB/Swiss- Prot;Acc:Q9UH17] 22 0.88 0.88 0 Aquaporin-10 (AQP-10)(Small intestine AQP10 aquaporin) [Source:UniProtKB/Swiss- protein_coding Prot;Acc:Q96PS8] 1 0.98 0.9 -1 Aquaporin-2 (AQP-2)(Aquaporin- CD)(AQP-CD)(Water channel protein for renal collecting duct)(ADH water AQP2 channel)(Collecting duct water channel protein_coding protein)(WCH-CD) [Source:UniProtKB/Swiss- Prot;Acc:P41181] 12 0.95 0.91 -1 Supplementary Table S7. List of genes that are heavily methylated in normal liver and become demethylated in HCC normal_ cancer_ direction name description type chromosome methylation methylation _meth Aquaporin-6 (AQP-6)(Aquaporin-2- like)(Kidney-specific aquaporin)(hKID) AQP6 protein_coding [Source:UniProtKB/Swiss- Prot;Acc:Q13520] 12 0.99 0.98 -1 Rho guanine nucleotide exchange factor 18 (114 kDa Rho-specific guanine nucleotide exchange factor)(p114-Rho- ARHGEF18 protein_coding GEF)(p114RhoGEF)(Septin-associated RhoGEF)(SA-RhoGEF) [Source:UniProtKB/Swiss- Prot;Acc:Q6ZSZ5] 19 0.93 0.93 -1 Ankyrin repeat and SOCS box protein 4 ASB4 (ASB-4) [Source:UniProtKB/Swiss- protein_coding Prot;Acc:Q9Y574] 7 0.95 0.81 -1 B1 bradykinin receptor (BK-1 receptor)(B1R) BDKRB1 protein_coding [Source:UniProtKB/Swiss- Prot;Acc:P46663] 14 0.96 0.96 -1 Bestrophin-2 (Vitelliform macular dystrophy 2-like protein 1) BEST2 protein_coding [Source:UniProtKB/Swiss- Prot;Acc:Q8NFU1] 19 0.98 0.94 -1 Bombesin receptor subtype-3 (BRS-3) BRS3 [Source:UniProtKB/Swiss- protein_coding Prot;Acc:P32247] X 0.91 0.87 -1 Butyrophilin-like protein 9 Precursor BTNL9 [Source:UniProtKB/Swiss- protein_coding Prot;Acc:Q6UXG8] 5 0.93 0.8 -1 Putative uncharacterized protein C10orf113 C10orf113 [Source:UniProtKB/Swiss- protein_coding Prot;Acc:Q5VZT2] 10 0.95 0.94 -1 Supplementary Table S7. List of genes that are heavily methylated in normal liver and become demethylated in HCC normal_ cancer_ direction name description type chromosome methylation methylation _meth Protein SPATIAL (Stromal protein associated with thymii and lymph node C10orf27 protein_coding homolog) [Source:UniProtKB/Swiss- Prot;Acc:Q96M53] 10 0.9 0.93 -1 Uncharacterized protein C11orf44 C11orf44 Precursor [Source:UniProtKB/Swiss- protein_coding Prot;Acc:Q8N8P7] 11 0.97 1 -1 Uncharacterized protein C11orf66 C11orf66 [Source:UniProtKB/Swiss- protein_coding Prot;Acc:Q7Z5V6] 11 1 0.97 -1 Putative uncharacterized protein C14orf177 C14orf177 [Source:UniProtKB/Swiss-
Load more

Recommended publications
  • 1 the Genetic Architecture of the Human Cerebral Cortex. Katrina L. Grasby1*, Neda Jahanshad2*, Jodie N. Painter1, Lucía Colodr
    bioRxiv preprint doi: https://doi.org/10.1101/399402; this version posted September 9, 2018. 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. The genetic architecture of the human cerebral cortex. Katrina L. Grasby1*, Neda Jahanshad2*, Jodie N. Painter1, Lucía Colodro-Conde1, Janita Bralten3,4, Derrek P. Hibar2,5, Penelope A. Lind1, Fabrizio Pizzagalli2, Christopher R.K. Ching2,6, Mary Agnes B. McMahon2, Natalia Shatokhina2, Leo C.P. Zsembik7, Ingrid Agartz8,9,10,11, Saud Alhusaini12,13, Marcio A.A. Almeida14, Dag Alnæs8,9, Inge K. Amlien15, Micael Andersson16,17, Tyler Ard18, Nicola J. Armstrong19, Allison Ashley-Koch20, Manon Bernard21, Rachel M. Brouwer22, Elizabeth E.L. Buimer22, Robin Bülow23, Christian Bürger24, Dara M. Cannon25, Mallar Chakravarty26,27, Qiang Chen28, Joshua W. Cheung2, Baptiste Couvy-Duchesne29,30,31, Anders M. Dale32,33, Shareefa Dalvie34, Tânia K. de Araujo35, Greig I. de Zubicaray36, Sonja M.C. de Zwarte22, Anouk den Braber37,38, Nhat Trung Doan8,9, Katharina Dohm24, Stefan Ehrlich39, Hannah-Ruth Engelbrecht40, Susanne Erk41, Chun Chieh Fan42, Iryna O. Fedko37, Sonya F. Foley43, Judith M. Ford44, Masaki Fukunaga45, Melanie E. Garrett20, Tian Ge46,47, Sudheer Giddaluru48, Aaron L. Goldman28, Nynke A. Groenewold34, Dominik Grotegerd24, Tiril P. Gurholt8,9,10, Boris A. Gutman2,49, Narelle K. Hansell31, Mathew A. Harris50,51, Marc B. Harrison2, Courtney C. Haswell52,53, Michael Hauser20, Stefan Herms54,55,56, Dirk J. Heslenfeld57, New Fei Ho58, David Hoehn59, Per Hoffmann54,55,60, Laurena Holleran25, Martine Hoogman3,4, Jouke-Jan Hottenga37, Masashi Ikeda61, Deborah Janowitz62, Iris E.
    [Show full text]
  • 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.
    [Show full text]
  • Genetic Variation Across the Human Olfactory Receptor Repertoire Alters Odor Perception
    bioRxiv preprint doi: https://doi.org/10.1101/212431; this version posted November 1, 2017. 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 4.0 International license. Genetic variation across the human olfactory receptor repertoire alters odor perception Casey Trimmer1,*, Andreas Keller2, Nicolle R. Murphy1, Lindsey L. Snyder1, Jason R. Willer3, Maira Nagai4,5, Nicholas Katsanis3, Leslie B. Vosshall2,6,7, Hiroaki Matsunami4,8, and Joel D. Mainland1,9 1Monell Chemical Senses Center, Philadelphia, Pennsylvania, USA 2Laboratory of Neurogenetics and Behavior, The Rockefeller University, New York, New York, USA 3Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, USA 4Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, USA 5Department of Biochemistry, University of Sao Paulo, Sao Paulo, Brazil 6Howard Hughes Medical Institute, New York, New York, USA 7Kavli Neural Systems Institute, New York, New York, USA 8Department of Neurobiology and Duke Institute for Brain Sciences, Duke University Medical Center, Durham, North Carolina, USA 9Department of Neuroscience, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA *[email protected] ABSTRACT The human olfactory receptor repertoire is characterized by an abundance of genetic variation that affects receptor response, but the perceptual effects of this variation are unclear. To address this issue, we sequenced the OR repertoire in 332 individuals and examined the relationship between genetic variation and 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity.
    [Show full text]
  • SNP Genotypes of Olfactory Receptor Genes Associated with Olfactory Ability in German Shepherd Dogs
    SHORT COMMUNICATION doi: 10.1111/age.12389 SNP genotypes of olfactory receptor genes associated with olfactory ability in German Shepherd dogs † ‡ – M. Yang*, G.-J. Geng , W. Zhang , L. Cui§, H.-X. Zhang and J.-L. Zheng‡ † *Police-dog Technology Department, National Police University of China, Shenyang, Liaoning 110034, China. Technology Department, ‡ Shenyang Traffic Police Detachment, Shenyang, Liaoning 110001, China. Forensic Medicine Department, National Police University of China, Shenyang, Liaoning 110854, China. §Document Inspection Department, National Police University of China, Shenyang, Liaoning – 110854, China. Mark Inspection Department, National Police University of China, Shenyang, Liaoning 110854, China. Summary To find out the relationship between SNP genotypes of canine olfactory receptor genes and olfactory ability, 28 males and 20 females from German Shepherd dogs in police service were scored by odor detection tests and analyzed using the Beckman GenomeLab SNPstream. The representative 22 SNP loci from the exonic regions of 12 olfactory receptor genes were investigated, and three kinds of odor (human, ice drug and trinitrotoluene) were detected. The results showed that the SNP genotypes at the OR10H1-like:c.632C>T, OR10H1-like:c.770A>T, OR2K2-like:c.518G>A, OR4C11-like: c.511T>G and OR4C11-like:c.692G>A loci had a statistically significant effect on the scenting abilities (P < 0.001). The kind of odor influenced the performances of the dogs (P < 0.001). In addition, there were interactions between genotype and the kind of odor at the following loci: OR10H1-like:c.632C>T, OR10H1-like:c.770A>T, OR4C11-like:c.511T>G and OR4C11-like:c.692G>A(P< 0.001).
    [Show full text]
  • 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.
    [Show full text]
  • Evaluation of Chromatin Accessibility in Prefrontal Cortex of Individuals with Schizophrenia
    ARTICLE DOI: 10.1038/s41467-018-05379-y OPEN Evaluation of chromatin accessibility in prefrontal cortex of individuals with schizophrenia Julien Bryois 1, Melanie E. Garrett2, Lingyun Song3, Alexias Safi3, Paola Giusti-Rodriguez 4, Graham D. Johnson 3, Annie W. Shieh13, Alfonso Buil5, John F. Fullard6, Panos Roussos 6,7,8, Pamela Sklar6, Schahram Akbarian 6, Vahram Haroutunian 6,9, Craig A. Stockmeier 10, Gregory A. Wray3,11, Kevin P. White12, Chunyu Liu13, Timothy E. Reddy 3,14, Allison Ashley-Koch2,15, Patrick F. Sullivan 1,4,16 & Gregory E. Crawford 3,17 1234567890():,; Schizophrenia genome-wide association studies have identified >150 regions of the genome associated with disease risk, yet there is little evidence that coding mutations contribute to this disorder. To explore the mechanism of non-coding regulatory elements in schizophrenia, we performed ATAC-seq on adult prefrontal cortex brain samples from 135 individuals with schizophrenia and 137 controls, and identified 118,152 ATAC-seq peaks. These accessible chromatin regions in the brain are highly enriched for schizophrenia SNP heritability. Accessible chromatin regions that overlap evolutionarily conserved regions exhibit an even higher heritability enrichment, indicating that sequence conservation can further refine functional risk variants. We identify few differences in chromatin accessibility between cases and controls, in contrast to thousands of age-related differential accessible chromatin regions. Altogether, we characterize chromatin accessibility in the human prefrontal cortex, the effect of schizophrenia and age on chromatin accessibility, and provide evidence that our dataset will allow for fine mapping of risk variants. 1 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17177 Stockholm, Sweden.
    [Show full text]
  • Identification of Potential Markers for Type 2 Diabetes Mellitus Via Bioinformatics Analysis
    1868 MOLECULAR MEDICINE REPORTS 22: 1868-1882, 2020 Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis YANA LU1, YIHANG LI1, GUANG LI1* and HAITAO LU2* 1Key Laboratory of Dai and Southern Medicine of Xishuangbanna Dai Autonomous Prefecture, Yunnan Branch, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Jinghong, Yunnan 666100; 2Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, P.R. China Received March 20, 2019; Accepted January 20, 2020 DOI: 10.3892/mmr.2020.11281 Abstract. Type 2 diabetes mellitus (T2DM) is a multifactorial and cell proliferation’. These candidate genes were also involved in multigenetic disease, and its pathogenesis is complex and largely different signaling pathways associated with ‘PI3K/Akt signaling unknown. In the present study, microarray data (GSE201966) of pathway’, ‘Rap1 signaling pathway’, ‘Ras signaling pathway’ β-cell enriched tissue obtained by laser capture microdissection and ‘MAPK signaling pathway’, which are highly associated were downloaded, including 10 control and 10 type 2 diabetic with the development of T2DM. Furthermore, a microRNA subjects. A comprehensive bioinformatics analysis of microarray (miR)-target gene regulatory network and a transcription data in the context of protein-protein interaction (PPI) networks factor-target gene regulatory network were constructed based was employed, combined with subcellular location information on miRNet and NetworkAnalyst databases, respectively. to mine the potential candidate genes for T2DM and provide Notably, hsa-miR‑192-5p, hsa-miR‑124-5p and hsa-miR‑335-5p further insight on the possible mechanisms involved. First, appeared to be involved in T2DM by potentially regulating the differential analysis screened 108 differentially expressed expression of various candidate genes, including procollagen genes.
    [Show full text]
  • UNIVERSITY of CALIFORNIA Los Angeles
    UNIVERSITY OF CALIFORNIA Los Angeles Integrating molecular phenotypes and gene expression to characterize DNA variants for cardiometabolic traits A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Human Genetics by Alejandra Rodriguez 2018 ABSTRACT OF THE DISSERTATION Integrating molecular phenotypes and gene expression to characterize DNA variants for cardiometabolic traits by Alejandra Rodriguez Doctor of Philosophy in Human Genetics University of California, Los Angeles, 2018 Professor Päivi Elisabeth Pajukanta, Chair In-depth understanding of cardiovascular disease etiology requires characterization of its genetic, environmental, and molecular architecture. Genetic architecture can be defined as the characteristics of genetic variation responsible for broad-sense phenotypic heritability. Massively parallel sequencing has generated thousands of genomic datasets in diverse human tissues. Integration of such datasets using data mining methods has been used to extract biological meaning and has significantly advanced our understanding of the genome-wide nucleotide sequence, its regulatory elements, and overall chromatin architecture. This dissertation presents integration of “omics” data sets to understand the genetic architecture and molecular mechanisms of cardiovascular lipid disorders (further reviewed in Chapter 1). In 2013, Daphna Weissglas-Volkov and coworkers1 published an association between the chromosome 18q11.2 genomic region and hypertriglyceridemia in a genome-wide
    [Show full text]
  • Cellular and Molecular Signatures in the Disease Tissue of Early
    Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of
    [Show full text]
  • Microarray‑Based Identification of Nerve Growth‑Promoting Genes in Neurofibromatosis Type I
    192 MOLECULAR MEDICINE REPORTS 9: 192-196, 2014 Microarray‑based identification of nerve growth‑promoting genes in neurofibromatosis type I YUFEI LIU1*, DONG KANG2*, CHUNYAN LI3, GUANG XU4, YAN TAN5 and JINHONG WANG6 1Department of Pediatric Intensive Care Unit, The First Affiliated Hospital of Jilin University, Changchun, Jilin 130000; 2Huiqiao Department, South Hospital of Southern Medical University, Guangdong 510630; Departments of 3Pediatric Respiratory and 4Infectious Diseases, The First Affiliated Hospital of Jilin University; 5Cancer Biotherapy Center, People's Hospital of Jilin Province, Changchun, Jilin 130000; 6Department of Respiratory, The Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong 510630, P.R. China Received May 16, 2013; Accepted October 29, 2013 DOI: 10.3892/mmr.2013.1785 Abstract. As a genetic disease, neurofibromatosis patients, target molecules contributing to nerve growth during type 1 (NF1) is characterized by abnormalities in multiple NF1 development were investigated, which aided in improving tissues derived from the neural crest, including neoplasms in our understanding of this disease, and may provide a novel the ends of the limbs. The exact mechanism of nerve growth direction for nerve repair and regeneration. in NF1 is unclear. In the present study, the gene expression profile of nerves in healthy controls and NF1 patients with Introduction macrodactylia of the fingers or toes were analyzed in order to identify possible genes associated with nerve growth. The Neurofibromatosis is characterized by café-au-lait spots, Whole Human Genome Microarray was selected to screen for iris Lisch nodules and cutaneous or subcutaneous skin different gene expression profiles, and the result was analyzed tumors termedneurofibromas (1).
    [Show full text]
  • Noelia Díaz Blanco
    Effects of environmental factors on the gonadal transcriptome of European sea bass (Dicentrarchus labrax), juvenile growth and sex ratios Noelia Díaz Blanco Ph.D. thesis 2014 Submitted in partial fulfillment of the requirements for the Ph.D. degree from the Universitat Pompeu Fabra (UPF). This work has been carried out at the Group of Biology of Reproduction (GBR), at the Department of Renewable Marine Resources of the Institute of Marine Sciences (ICM-CSIC). Thesis supervisor: Dr. Francesc Piferrer Professor d’Investigació Institut de Ciències del Mar (ICM-CSIC) i ii A mis padres A Xavi iii iv Acknowledgements This thesis has been made possible by the support of many people who in one way or another, many times unknowingly, gave me the strength to overcome this "long and winding road". First of all, I would like to thank my supervisor, Dr. Francesc Piferrer, for his patience, guidance and wise advice throughout all this Ph.D. experience. But above all, for the trust he placed on me almost seven years ago when he offered me the opportunity to be part of his team. Thanks also for teaching me how to question always everything, for sharing with me your enthusiasm for science and for giving me the opportunity of learning from you by participating in many projects, collaborations and scientific meetings. I am also thankful to my colleagues (former and present Group of Biology of Reproduction members) for your support and encouragement throughout this journey. To the “exGBRs”, thanks for helping me with my first steps into this world. Working as an undergrad with you Dr.
    [Show full text]
  • RSC Advances
    RSC Advances This is an Accepted Manuscript, which has been through the Royal Society of Chemistry peer review process and has been accepted for publication. Accepted Manuscripts are published online shortly after acceptance, before technical editing, formatting and proof reading. Using this free service, authors can make their results available to the community, in citable form, before we publish the edited article. This Accepted Manuscript will be replaced by the edited, formatted and paginated article as soon as this is available. You can find more information about Accepted Manuscripts in the Information for Authors. Please note that technical editing may introduce minor changes to the text and/or graphics, which may alter content. The journal’s standard Terms & Conditions and the Ethical guidelines still apply. In no event shall the Royal Society of Chemistry be held responsible for any errors or omissions in this Accepted Manuscript or any consequences arising from the use of any information it contains. www.rsc.org/advances Page 1 of 8 RSC Advances The Effects of Solvent Composition on the Affinity of a Peptide towards Hair Keratin: Experimental and Molecular Dynamics Data. Egipto Antunes a, Célia F. Cruz a, Nuno G. Azoia a, Artur Cavaco-Paulo a* a CEB – Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal Table of Contents Manuscript Accepted Molecular dynamics simulations with a developed hair protofibril model demonstrated capability to improve a peptide uptake by hair shafts. Advances RSC RSC Advances Page 2 of 8 RSC Advances RSCPublishing ARTICLE The Effects of Solvent Composition on the Affinity of a Peptide towards Hair Keratin: Experimental and Cite this: DOI: 10.1039/x0xx00000x Molecular Dynamics Data.
    [Show full text]