CRISPR/Cas9 Screen for Functional MYC Binding Sites Reveals MYC-Dependent Vulnerabilities in K562 Cells
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PARSANA-DISSERTATION-2020.Pdf
DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks. -
Sequence Analysis and Comparative Study of the Protein Subunits of Archaeal Rnase P
biomolecules Review Sequence Analysis and Comparative Study of the Protein Subunits of Archaeal RNase P Manoj P. Samanta 1,*,†, Stella M. Lai 2,3,†, Charles J. Daniels 3,4 and Venkat Gopalan 2,3,* 1 Systemix Institute, Redmond, WA 98053, USA 2 Department of Chemistry & Biochemistry, The Ohio State University, Columbus, OH 43210, USA; [email protected] 3 Center for RNA Biology, The Ohio State University, Columbus, OH 43210, USA; [email protected] 4 Department of Microbiology, The Ohio State University, Columbus, OH 43210, USA * Correspondence: [email protected] (M.P.S.); [email protected] (V.G.); Tel.: +1-425-898-8187 (M.P.S.); +1-614-292-1332 (V.G.) † These authors contributed equally to this work. Academic Editor: Denis Drainas Received: 1 March 2016; Accepted: 8 April 2016; Published: 20 April 2016 Abstract: RNase P, a ribozyme-based ribonucleoprotein (RNP) complex that catalyzes tRNA 51-maturation, is ubiquitous in all domains of life, but the evolution of its protein components (RNase P proteins, RPPs) is not well understood. Archaeal RPPs may provide clues on how the complex evolved from an ancient ribozyme to an RNP with multiple archaeal and eukaryotic (homologous) RPPs, which are unrelated to the single bacterial RPP. Here, we analyzed the sequence and structure of archaeal RPPs from over 600 available genomes. All five RPPs are found in eight archaeal phyla, suggesting that these RPPs arose early in archaeal evolutionary history. The putative ancestral genomic loci of archaeal RPPs include genes encoding several members of ribosome, exosome, and proteasome complexes, which may indicate coevolution/coordinate regulation of RNase P with other core cellular machineries. -
Sequence Overlap Between Autosomal and Sex-Linked Probes on the Illumina Humanmethylation27 Microarray
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Genomics 97 (2011) 214–222 Contents lists available at ScienceDirect Genomics journal homepage: www.elsevier.com/locate/ygeno Sequence overlap between autosomal and sex-linked probes on the Illumina HumanMethylation27 microarray Yi-an Chen a,b, Sanaa Choufani a, Jose Carlos Ferreira a,b, Daria Grafodatskaya a, Darci T. Butcher a, Rosanna Weksberg a,b,⁎ a Program in Genetics and Genome Biology, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada b Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada article info abstract Article history: The Illumina Infinium HumanMethylation27 BeadChip (Illumina 27k) microarray is a high-throughput Received 12 August 2010 platform capable of interrogating the human DNA methylome. In a search for autosomal sex-specific DNA Accepted 18 December 2010 methylation using this microarray, we discovered autosomal CpG loci showing significant methylation Available online 4 January 2011 differences between the sexes. However, we found that the majority of these probes cross-reacted with sequences from sex chromosomes. Moreover, we determined that 6–10% of the microarray probes are non- Keywords: specific and map to highly homologous genomic sequences. Using probes targeting different CpGs that are Illumina Infinium HumanMethylation 27 BeadChip exact duplicates of each other, we investigated the precision of these repeat measurements and concluded Non-specific cross-reactive probe that the overall precision of this microarray is excellent. In addition, we identified a small number of probes Sex-specific DNA methylation targeting CpGs that include single-nucleotide polymorphisms. -
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. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
Alternative Haplotypes of Antigen Processing Genes in Zebrafish Diverged Early in Vertebrate Evolution
Alternative haplotypes of antigen processing genes in PNAS PLUS zebrafish diverged early in vertebrate evolution Sean C. McConnella,1, Kyle M. Hernandezb, Dustin J. Wciselc,d, Ross N. Kettleboroughe, Derek L. Stemplee, Jeffrey A. Yoderc,d,f, Jorge Andradeb, and Jill L. O. de Jonga,1 aSection of Hematology-Oncology and Stem Cell Transplant, Department of Pediatrics, The University of Chicago, Chicago, IL 60637; bCenter for Research Informatics, The University of Chicago, Chicago, IL 60637; cDepartment of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, NC 27607; dGenomic Sciences Graduate Program, North Carolina State University, Raleigh, NC 27607; eVertebrate Development and Genetics, Wellcome Trust Sanger Institute, Cambridge CB10 1SA, United Kingdom; and fComparative Medicine Institute, North Carolina State University, Raleigh, NC 27607 Edited by Peter Parham, Stanford University School of Medicine, Stanford, CA, and accepted by Editorial Board Member Peter Cresswell June 23, 2016 (received for review May 16, 2016) Antigen processing and presentation genes found within the Recent genomic studies have offered considerable insights into MHC are among the most highly polymorphic genes of vertebrate the evolution of the vertebrate adaptive immune system by com- genomes, providing populations with diverse immune responses to a paring phylogenetically divergent species (11–14). Throughout wide array of pathogens. Here, we describe transcriptome, exome, vertebrates, gene linkage within the MHC region is highly con- and whole-genome sequencing of clonal zebrafish, uncovering the served. For example, MHCI and antigen processing genes remain most extensive diversity within the antigen processing and presen- tightly linked in sharks, members of the oldest vertebrate lineage tation genes of any species yet examined. -
Genomic Landscape of Somatic Alterations in Esophageal Squamous Cell Carcinoma and Gastric Cancer Nan Hu1, Mitsutaka Kadota2, Huaitian Liu2, Christian C
Published OnlineFirst February 8, 2016; DOI: 10.1158/0008-5472.CAN-15-0338 Cancer Integrated Systems and Technologies Research Genomic Landscape of Somatic Alterations in Esophageal Squamous Cell Carcinoma and Gastric Cancer Nan Hu1, Mitsutaka Kadota2, Huaitian Liu2, Christian C. Abnet1, Hua Su1, Hailong Wu2, Neal D. Freedman1, Howard H. Yang2, Chaoyu Wang1, Chunhua Yan2, Lemin Wang1, Sheryl Gere2, Amy Hutchinson1,3, Guohong Song2, Yuan Wang4, Ti Ding4, You-Lin Qiao5, Jill Koshiol1, Sanford M. Dawsey1, Carol Giffen6, Alisa M. Goldstein1, Philip R. Taylor1, and Maxwell P. Lee2 Abstract Gastric cancer and esophageal cancer are the second and that oxidation of guanine may be a potential mechanism sixth leading causes of cancer-related death worldwide. Multi- underlying cancer mutagenesis. Furthermore, we identified ple genomic alterations underlying gastric cancer and esoph- genes with mutations in gastric cancer and ESCC, including ageal squamous cell carcinoma (ESCC) have been identified, well-known cancer genes, TP53, JAK3, BRCA2, FGF2, FBXW7, but the full spectrum of genomic structural variations and MSH3, PTCH, NF1, ERBB2, and CHEK2, and potentially novel mutations have yet to be uncovered. Here, we report the results cancer-associated genes, KISS1R, AMH, MNX1, WNK2,and of whole-genome sequencing of 30 samples comprising tumor PRKRIR. Finally, we identified recurrent chromosome altera- and blood from 15 patients, four of whom presented with tions in at least 30% of tumors in genes, including MACROD2, ESCC, seven with gastric cardia adenocarcinoma (GCA), and FHIT,andPARK2 that were often intragenic deletions. These four with gastric noncardia adenocarcinoma. Analyses revealed structural alterations were validated using the The Cancer that an A>CmutationwascommoninGCA,andinadditionto Genome Atlas dataset. -
HSD17B8 (NM 014234) Human Tagged ORF Clone – RG203806
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for RG203806 HSD17B8 (NM_014234) Human Tagged ORF Clone Product data: Product Type: Expression Plasmids Product Name: HSD17B8 (NM_014234) Human Tagged ORF Clone Tag: TurboGFP Symbol: HSD17B8 Synonyms: D6S2245E; dJ1033B10.9; FABG; FABGL; H2-KE6; HKE6; KE6; RING2; SDR30C1 Vector: pCMV6-AC-GFP (PS100010) E. coli Selection: Ampicillin (100 ug/mL) Cell Selection: Neomycin ORF Nucleotide >RG203806 representing NM_014234 Sequence: Red=Cloning site Blue=ORF Green=Tags(s) TTTTGTAATACGACTCACTATAGGGCGGCCGGGAATTCGTCGACTGGATCCGGTACCGAGGAGATCTGCC GCCGCGATCGCC ATGGCGTCTCAGCTCCAGAACCGACTCCGCTCCGCACTGGCCTTGGTCACAGGTGCGGGGAGCGGCATCG GCCGAGCGGTCAGTGTACGCCTGGCCGGAGAGGGGGCCACCGTAGCTGCCTGCGACCTGGACCGGGCAGC GGCACAGGAGACGGTGCGGCTGCTGGGCGGGCCAGGGAGCAAGGAGGGGCCGCCCCGAGGGAACCATGCT GCCTTCCAGGCTGACGTGTCTGAGGCCAGGGCCGCCAGGTGCCTGCTGGAACAAGTGCAGGCCTGCTTTT CTCGCCCACCATCTGTCGTTGTGTCCTGTGCGGGCATCACCCAGGATGAGTTTCTGCTGCACATGTCTGA GGATGACTGGGACAAAGTCATAGCTGTCAACCTCAAGGGCACCTTCCTAGTCACTCAGGCTGCAGCACAA GCCCTGGTGTCCAATGGTTGTCGTGGTTCCATCATCAACATCAGTAGCATCGTAGGAAAGGTGGGGAACG TGGGGCAGACAAACTATGCAGCATCCAAGGCTGGAGTGATTGGGCTGACCCAGACCGCAGCCCGGGAGCT TGGACGACATGGGATCCGCTGTAACTCTGTCCTCCCAGGGTTCATTGCAACACCCATGACACAGAAAGTG CCACAGAAAGTGGTGGACAAGATTACTGAAATGATCCCGATGGGACACTTGGGGGACCCTGAGGATGTGG CAGATGTGGTCGCATTCTTGGCATCTGAAGATAGTGGATACATCACAGGGACCTCAGTGGAAGTCACTGG AGGTCTTTTCATG ACGCGTACGCGGCCGCTCGAG -
Association of Gene Ontology Categories with Decay Rate for Hepg2 Experiments These Tables Show Details for All Gene Ontology Categories
Supplementary Table 1: Association of Gene Ontology Categories with Decay Rate for HepG2 Experiments These tables show details for all Gene Ontology categories. Inferences for manual classification scheme shown at the bottom. Those categories used in Figure 1A are highlighted in bold. Standard Deviations are shown in parentheses. P-values less than 1E-20 are indicated with a "0". Rate r (hour^-1) Half-life < 2hr. Decay % GO Number Category Name Probe Sets Group Non-Group Distribution p-value In-Group Non-Group Representation p-value GO:0006350 transcription 1523 0.221 (0.009) 0.127 (0.002) FASTER 0 13.1 (0.4) 4.5 (0.1) OVER 0 GO:0006351 transcription, DNA-dependent 1498 0.220 (0.009) 0.127 (0.002) FASTER 0 13.0 (0.4) 4.5 (0.1) OVER 0 GO:0006355 regulation of transcription, DNA-dependent 1163 0.230 (0.011) 0.128 (0.002) FASTER 5.00E-21 14.2 (0.5) 4.6 (0.1) OVER 0 GO:0006366 transcription from Pol II promoter 845 0.225 (0.012) 0.130 (0.002) FASTER 1.88E-14 13.0 (0.5) 4.8 (0.1) OVER 0 GO:0006139 nucleobase, nucleoside, nucleotide and nucleic acid metabolism3004 0.173 (0.006) 0.127 (0.002) FASTER 1.28E-12 8.4 (0.2) 4.5 (0.1) OVER 0 GO:0006357 regulation of transcription from Pol II promoter 487 0.231 (0.016) 0.132 (0.002) FASTER 6.05E-10 13.5 (0.6) 4.9 (0.1) OVER 0 GO:0008283 cell proliferation 625 0.189 (0.014) 0.132 (0.002) FASTER 1.95E-05 10.1 (0.6) 5.0 (0.1) OVER 1.50E-20 GO:0006513 monoubiquitination 36 0.305 (0.049) 0.134 (0.002) FASTER 2.69E-04 25.4 (4.4) 5.1 (0.1) OVER 2.04E-06 GO:0007050 cell cycle arrest 57 0.311 (0.054) 0.133 (0.002) -
Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement. -
Epigenetic Reprogramming Underlies Efficacy of DNA Demethylation
www.nature.com/scientificreports OPEN Epigenetic reprogramming underlies efcacy of DNA demethylation therapy in osteosarcomas Naofumi Asano 1,2, Hideyuki Takeshima3, Satoshi Yamashita3, Hironori Takamatsu2,3, Naoko Hattori3, Takashi Kubo4, Akihiko Yoshida5, Eisuke Kobayashi6, Robert Nakayama2, Morio Matsumoto2, Masaya Nakamura2, Hitoshi Ichikawa 4, Akira Kawai6, Tadashi Kondo1 & Toshikazu Ushijima 3* Osteosarcoma (OS) patients with metastasis or recurrent tumors still sufer from poor prognosis. Studies have indicated the efcacy of DNA demethylation therapy for OS, but the underlying mechanism is still unclear. Here, we aimed to clarify the mechanism of how epigenetic therapy has therapeutic efcacy in OS. Treatment of four OS cell lines with a DNA demethylating agent, 5-aza-2′- deoxycytidine (5-aza-dC) treatment, markedly suppressed their growth, and in vivo efcacy was further confrmed using two OS xenografts. Genome-wide DNA methylation analysis showed that 10 of 28 primary OS had large numbers of methylated CpG islands while the remaining 18 OS did not, clustering together with normal tissue samples and Ewing sarcoma samples. Among the genes aberrantly methylated in primary OS, genes involved in skeletal system morphogenesis were present. Searching for methylation-silenced genes by expression microarray screening of two OS cell lines after 5-aza-dC treatment revealed that multiple tumor-suppressor and osteo/chondrogenesis-related genes were re-activated by 5-aza-dC treatment of OS cells. Simultaneous activation of multiple genes related to osteogenesis and cell proliferation, namely epigenetic reprogramming, was considered to underlie the efcacy of DNA demethylation therapy in OS. Osteosarcoma (OS) is the most common malignant tumor of the bone in children and adolescents1. -
(P -Value<0.05, Fold Change≥1.4), 4 Vs. 0 Gy Irradiation
Table S1: Significant differentially expressed genes (P -Value<0.05, Fold Change≥1.4), 4 vs. 0 Gy irradiation Genbank Fold Change P -Value Gene Symbol Description Accession Q9F8M7_CARHY (Q9F8M7) DTDP-glucose 4,6-dehydratase (Fragment), partial (9%) 6.70 0.017399678 THC2699065 [THC2719287] 5.53 0.003379195 BC013657 BC013657 Homo sapiens cDNA clone IMAGE:4152983, partial cds. [BC013657] 5.10 0.024641735 THC2750781 Ciliary dynein heavy chain 5 (Axonemal beta dynein heavy chain 5) (HL1). 4.07 0.04353262 DNAH5 [Source:Uniprot/SWISSPROT;Acc:Q8TE73] [ENST00000382416] 3.81 0.002855909 NM_145263 SPATA18 Homo sapiens spermatogenesis associated 18 homolog (rat) (SPATA18), mRNA [NM_145263] AA418814 zw01a02.s1 Soares_NhHMPu_S1 Homo sapiens cDNA clone IMAGE:767978 3', 3.69 0.03203913 AA418814 AA418814 mRNA sequence [AA418814] AL356953 leucine-rich repeat-containing G protein-coupled receptor 6 {Homo sapiens} (exp=0; 3.63 0.0277936 THC2705989 wgp=1; cg=0), partial (4%) [THC2752981] AA484677 ne64a07.s1 NCI_CGAP_Alv1 Homo sapiens cDNA clone IMAGE:909012, mRNA 3.63 0.027098073 AA484677 AA484677 sequence [AA484677] oe06h09.s1 NCI_CGAP_Ov2 Homo sapiens cDNA clone IMAGE:1385153, mRNA sequence 3.48 0.04468495 AA837799 AA837799 [AA837799] Homo sapiens hypothetical protein LOC340109, mRNA (cDNA clone IMAGE:5578073), partial 3.27 0.031178378 BC039509 LOC643401 cds. [BC039509] Homo sapiens Fas (TNF receptor superfamily, member 6) (FAS), transcript variant 1, mRNA 3.24 0.022156298 NM_000043 FAS [NM_000043] 3.20 0.021043295 A_32_P125056 BF803942 CM2-CI0135-021100-477-g08 CI0135 Homo sapiens cDNA, mRNA sequence 3.04 0.043389246 BF803942 BF803942 [BF803942] 3.03 0.002430239 NM_015920 RPS27L Homo sapiens ribosomal protein S27-like (RPS27L), mRNA [NM_015920] Homo sapiens tumor necrosis factor receptor superfamily, member 10c, decoy without an 2.98 0.021202829 NM_003841 TNFRSF10C intracellular domain (TNFRSF10C), mRNA [NM_003841] 2.97 0.03243901 AB002384 C6orf32 Homo sapiens mRNA for KIAA0386 gene, partial cds.