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Novel Gene Fusions in Glioblastoma Tumor Tissue and Matched Patient Plasma
cancers Article Novel Gene Fusions in Glioblastoma Tumor Tissue and Matched Patient Plasma 1, 1, 1 1 1 Lan Wang y, Anudeep Yekula y, Koushik Muralidharan , Julia L. Small , Zachary S. Rosh , Keiko M. Kang 1,2, Bob S. Carter 1,* and Leonora Balaj 1,* 1 Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA; [email protected] (L.W.); [email protected] (A.Y.); [email protected] (K.M.); [email protected] (J.L.S.); [email protected] (Z.S.R.); [email protected] (K.M.K.) 2 School of Medicine, University of California San Diego, San Diego, CA 92092, USA * Correspondence: [email protected] (B.S.C.); [email protected] (L.B.) These authors contributed equally. y Received: 11 March 2020; Accepted: 7 May 2020; Published: 13 May 2020 Abstract: Sequencing studies have provided novel insights into the heterogeneous molecular landscape of glioblastoma (GBM), unveiling a subset of patients with gene fusions. Tissue biopsy is highly invasive, limited by sampling frequency and incompletely representative of intra-tumor heterogeneity. Extracellular vesicle-based liquid biopsy provides a minimally invasive alternative to diagnose and monitor tumor-specific molecular aberrations in patient biofluids. Here, we used targeted RNA sequencing to screen GBM tissue and the matched plasma of patients (n = 9) for RNA fusion transcripts. We identified two novel fusion transcripts in GBM tissue and five novel fusions in the matched plasma of GBM patients. The fusion transcripts FGFR3-TACC3 and VTI1A-TCF7L2 were detected in both tissue and matched plasma. -
Supplementary Table S1. Upregulated Genes Differentially
Supplementary Table S1. Upregulated genes differentially expressed in athletes (p < 0.05 and 1.3-fold change) Gene Symbol p Value Fold Change 221051_s_at NMRK2 0.01 2.38 236518_at CCDC183 0.00 2.05 218804_at ANO1 0.00 2.05 234675_x_at 0.01 2.02 207076_s_at ASS1 0.00 1.85 209135_at ASPH 0.02 1.81 228434_at BTNL9 0.03 1.81 229985_at BTNL9 0.01 1.79 215795_at MYH7B 0.01 1.78 217979_at TSPAN13 0.01 1.77 230992_at BTNL9 0.01 1.75 226884_at LRRN1 0.03 1.74 220039_s_at CDKAL1 0.01 1.73 236520_at 0.02 1.72 219895_at TMEM255A 0.04 1.72 201030_x_at LDHB 0.00 1.69 233824_at 0.00 1.69 232257_s_at 0.05 1.67 236359_at SCN4B 0.04 1.64 242868_at 0.00 1.63 1557286_at 0.01 1.63 202780_at OXCT1 0.01 1.63 1556542_a_at 0.04 1.63 209992_at PFKFB2 0.04 1.63 205247_at NOTCH4 0.01 1.62 1554182_at TRIM73///TRIM74 0.00 1.61 232892_at MIR1-1HG 0.02 1.61 204726_at CDH13 0.01 1.6 1561167_at 0.01 1.6 1565821_at 0.01 1.6 210169_at SEC14L5 0.01 1.6 236963_at 0.02 1.6 1552880_at SEC16B 0.02 1.6 235228_at CCDC85A 0.02 1.6 1568623_a_at SLC35E4 0.00 1.59 204844_at ENPEP 0.00 1.59 1552256_a_at SCARB1 0.02 1.59 1557283_a_at ZNF519 0.02 1.59 1557293_at LINC00969 0.03 1.59 231644_at 0.01 1.58 228115_at GAREM1 0.01 1.58 223687_s_at LY6K 0.02 1.58 231779_at IRAK2 0.03 1.58 243332_at LOC105379610 0.04 1.58 232118_at 0.01 1.57 203423_at RBP1 0.02 1.57 AMY1A///AMY1B///AMY1C///AMY2A///AMY2B// 208498_s_at 0.03 1.57 /AMYP1 237154_at LOC101930114 0.00 1.56 1559691_at 0.01 1.56 243481_at RHOJ 0.03 1.56 238834_at MYLK3 0.01 1.55 213438_at NFASC 0.02 1.55 242290_at TACC1 0.04 1.55 ANKRD20A1///ANKRD20A12P///ANKRD20A2/// -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
High-Throughput Discovery of Novel Developmental Phenotypes
High-throughput discovery of novel developmental phenotypes The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Dickinson, M. E., A. M. Flenniken, X. Ji, L. Teboul, M. D. Wong, J. K. White, T. F. Meehan, et al. 2016. “High-throughput discovery of novel developmental phenotypes.” Nature 537 (7621): 508-514. doi:10.1038/nature19356. http://dx.doi.org/10.1038/nature19356. Published Version doi:10.1038/nature19356 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:32071918 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Nature. Manuscript Author Author manuscript; Manuscript Author available in PMC 2017 March 14. Published in final edited form as: Nature. 2016 September 22; 537(7621): 508–514. doi:10.1038/nature19356. High-throughput discovery of novel developmental phenotypes A full list of authors and affiliations appears at the end of the article. Abstract Approximately one third of all mammalian genes are essential for life. Phenotypes resulting from mouse knockouts of these genes have provided tremendous insight into gene function and congenital disorders. As part of the International Mouse Phenotyping Consortium effort to generate and phenotypically characterize 5000 knockout mouse lines, we have identified 410 Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms #Corresponding author: [email protected]. -
1 Isolation and Characterization of Cul1-7, a Recessive Allele Of
Genetics: Published Articles Ahead of Print, published on February 25, 2009 as 10.1534/genetics.108.097675 Isolation and Characterization of cul1-7, a Recessive Allele of CULLIN1 that Disrupts SCF Function at the C-terminus of CUL1 in Arabidopsis thaliana Jonathan Gilkerson*,†, Jianhong Hu‡, Jessica Brown*, Alexander Jones* 1, Tai-ping Sun‡ and Judy Callis*,†,2 *Department of Molecular and Cellular Biology and †Plant Biology Graduate Group, University of California-Davis, Davis, CA 95616, ‡Department of Biology, Duke University, Durham, North Carolina 27708-1000 1 Current Address: Department of Plant and Microbial Biology, UC-Berkeley, Berkeley, CA 94720-3102 1 Running head: Characterization of cul1-7 Key words: Protein Degradation, Aux/IAA, RGA, SCF Ubiquitin Ligase, RBX1 2Corresponding Author: Judy Callis Department of Molecular and Cellular Biology University of California-Davis One Shields Avenue Davis, CA 95616 Phone: 530-752-1015 Fax: 530-752-3085 E-mail: [email protected] 2 ABSTRACT Many aspects of plant biology depend on the ubiquitin proteasome system for degradation of regulatory proteins. Ubiquitin E3 ligases confer substrate specificity in this pathway, and SCF-type ligases comprise a major class of E3s. SCF ligases have four subunits: SKP1, CUL1, RBX1, and an F-box protein for substrate recognition. The Aux/IAAs are a well- characterized family of SCF substrates in plants. Here, we report characterization of a mutant isolated from a genetic screen in Arabidopsis thaliana designed to identify plants defective in degradation of an Aux/IAA fusion protein, Aux/IAA1-luciferase (IAA1-LUC). This mutant exhibited four-fold slower IAA1-LUC degradation compared to the progenitor line, and seedlings displayed altered auxin responses. -
Vascular Inflammatory Response Deneddylase-1/SENP8 in Fine
Central Role for Endothelial Human Deneddylase-1/SENP8 in Fine-Tuning the Vascular Inflammatory Response This information is current as Stefan F. Ehrentraut, Douglas J. Kominsky, Louise E. of September 30, 2021. Glover, Eric L. Campbell, Caleb J. Kelly, Brittelle E. Bowers, Amanda J. Bayless and Sean P. Colgan J Immunol 2013; 190:392-400; Prepublished online 3 December 2012; doi: 10.4049/jimmunol.1202041 Downloaded from http://www.jimmunol.org/content/190/1/392 Supplementary http://www.jimmunol.org/content/suppl/2012/12/03/jimmunol.120204 Material 1.DC1 http://www.jimmunol.org/ References This article cites 53 articles, 15 of which you can access for free at: http://www.jimmunol.org/content/190/1/392.full#ref-list-1 Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision by guest on September 30, 2021 • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2012 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Central Role for Endothelial Human Deneddylase-1/SENP8 in Fine-Tuning the Vascular Inflammatory Response Stefan F. -
Neddylation: a Novel Modulator of the Tumor Microenvironment Lisha Zhou1,2*†, Yanyu Jiang3†, Qin Luo1, Lihui Li1 and Lijun Jia1*
Zhou et al. Molecular Cancer (2019) 18:77 https://doi.org/10.1186/s12943-019-0979-1 REVIEW Open Access Neddylation: a novel modulator of the tumor microenvironment Lisha Zhou1,2*†, Yanyu Jiang3†, Qin Luo1, Lihui Li1 and Lijun Jia1* Abstract Neddylation, a post-translational modification that adds an ubiquitin-like protein NEDD8 to substrate proteins, modulates many important biological processes, including tumorigenesis. The process of protein neddylation is overactivated in multiple human cancers, providing a sound rationale for its targeting as an attractive anticancer therapeutic strategy, as evidence by the development of NEDD8-activating enzyme (NAE) inhibitor MLN4924 (also known as pevonedistat). Neddylation inhibition by MLN4924 exerts significantly anticancer effects mainly by triggering cell apoptosis, senescence and autophagy. Recently, intensive evidences reveal that inhibition of neddylation pathway, in addition to acting on tumor cells, also influences the functions of multiple important components of the tumor microenvironment (TME), including immune cells, cancer-associated fibroblasts (CAFs), cancer-associated endothelial cells (CAEs) and some factors, all of which are crucial for tumorigenesis. Here, we briefly summarize the latest progresses in this field to clarify the roles of neddylation in the TME, thus highlighting the overall anticancer efficacy of neddylaton inhibition. Keywords: Neddylation, Tumor microenvironment, Tumor-derived factors, Cancer-associated fibroblasts, Cancer- associated endothelial cells, Immune cells Introduction Overall, binding of NEDD8 molecules to target proteins Neddylation is a reversible covalent conjugation of an can affect their stability, subcellular localization, conform- ubiquitin-like molecule NEDD8 (neuronal precursor ation and function [4]. The best-characterized substrates cell-expressed developmentally down-regulated protein of neddylation are the cullin subunits of Cullin-RING li- 8) to a lysine residue of the substrate protein [1, 2]. -
BRCC3 Mutations in Myeloid Neoplasms
Myeloid Malignancies SUPPLEMENTARY APPENDIX BRCC3 mutations in myeloid neoplasms Dayong Huang, 1,2 * Yasunobu Nagata, 3* Vera Grossmann, 4 Tomas Radivoyevitch, 5 Yusuke Okuno, 3 Genta Nagae, 6 Naoko Hosono, 2 Susanne Schnittger, 4 Masashi Sanada, 3 Bartlomiej Przychodzen, 2 Ayana Kon, 3 Chantana Polprasert, 2 Wenyi Shen, 2 Michael J. Clemente, 2 James G. Phillips, 2 Tamara Alpermann, 4 Kenichi Yoshida, 3 Niroshan Nadarajah, 4 7 Mikkael A. Sekeres, Kevin Oakley, 8 Nhu Nguyen, 8 Yuichi Shiraishi, 9 Yusuke Shiozawa, 3 Kenichi Chiba, 9 Hiroko Tanaka, 10 H. Phillip Koeffler, 11,12 Hans-Ulrich Klein, 13 Martin Dugas, 13 Hiroyuki Aburatani, 6 Satoru Miyano, 9,10 Claudia Haferlach, 4 Wolfgang Kern, 4 Torsten Haferlach, 4 Yang Du, 8 Seishi Ogawa, 3 and Hideki Makishima 2,3 1Department of Hematology, Beijing Friendship Hospital, Capital Medical University, Beijing, China; 2Department of Translational Hema - tology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA; 3Department of Pathology and Tumor Biol - ogy, Kyoto University, Japan; 4Munich Leukemia Laboratory (MLL), Germany; 5Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, OH, USA; 6Genome Science Division, Research Center for Advanced Science and Technology, The University of Tokyo, Japan; 7Leukemia Program, Taussig Cancer Institute, Cleveland Clinic, OH, USA; 8Department of Pediatrics, Uni - formed Services University of the Health Sciences, Bethesda, MD, USA; 9Laboratory of DNA Information Analysis, Human Genome Cen - ter, Institute of Medical Science, The University of Tokyo, Japan; 10 Laboratory of Sequence Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, Japan; 11 Department of Hematology/Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA; 12 Cancer Science Institute of Singapore, National University of Singapore; and 13 Institute of Medical Informatics, University of Mün - ster, Germany *DH and YN contributed equally to this work. -
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 -
Fusion Transcripts and Transcribed Retrotransposed Loci Discovered Through Comprehensive Transcriptome Analysis Using Paired-End Ditags (Pets)
Downloaded from genome.cshlp.org on September 24, 2021 - Published by Cold Spring Harbor Laboratory Press Letter Fusion transcripts and transcribed retrotransposed loci discovered through comprehensive transcriptome analysis using Paired-End diTags (PETs) Yijun Ruan,1,6 Hong Sain Ooi,2 Siew Woh Choo,2 Kuo Ping Chiu,2 Xiao Dong Zhao,1 K.G. Srinivasan,1 Fei Yao,1 Chiou Yu Choo,1 Jun Liu,1 Pramila Ariyaratne,2 Wilson G.W. Bin,2 Vladimir A. Kuznetsov,2 Atif Shahab,3 Wing-Kin Sung,2,4 Guillaume Bourque,2 Nallasivam Palanisamy,5 and Chia-Lin Wei1,6 1Genome Technology and Biology Group, Genome Institute of Singapore, Singapore 138672, Singapore; 2Information and Mathematical Science Group, Genome Institute of Singapore, Singapore 138672, Singapore; 3Bioinformatics Institute, Singapore 138671, Singapore; 4School of Computing, National University of Singapore, Singapore 117543, Singapore; 5Cancer Biology Group, Genome Institute of Singapore, Singapore 138672, Singapore Identification of unconventional functional features such as fusion transcripts is a challenging task in the effort to annotate all functional DNA elements in the human genome. Paired-End diTag (PET) analysis possesses a unique capability to accurately and efficiently characterize the two ends of DNA fragments, which may have either normal or unusual compositions. This unique nature of PET analysis makes it an ideal tool for uncovering unconventional features residing in the human genome. Using the PET approach for comprehensive transcriptome analysis, we were able to identify fusion transcripts derived from genome rearrangements and actively expressed retrotransposed pseudogenes, which would be difficult to capture by other means. Here, we demonstrate this unique capability through the analysis of 865,000 individual transcripts in two types of cancer cells. -
Inhibition of SCF Ubiquitin Ligases by Engineered Ubiquitin Variants That Target the Cul1 Binding Site on the Skp1–F-Box Interface
Inhibition of SCF ubiquitin ligases by engineered ubiquitin variants that target the Cul1 binding site on the Skp1–F-box interface Maryna Gorelika,b, Stephen Orlickyc, Maria A. Sartoria,b, Xiaojing Tangc, Edyta Marcona,b, Igor Kurinovd, Jack F. Greenblatta,b, Mike Tyersc,e, Jason Moffata,b, Frank Sicheric,f, and Sachdev S. Sidhua,b,1 aBanting and Best Department of Medical Research, Terrence Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada M5S 3E1; bDepartment of Molecular Genetics, Terrence Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada M5S 3E1; cLunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada M5G 1X5; dDepartment of Chemistry and Chemical Biology, Cornell University, Argonne, IL 60439; eInstitut de Recherche en Immunologie et Cancérologie, Université de Montréal, Montreal, QC, Canada H3C 3J7; and fDepartment of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 3E1 Edited by Mark Estelle, University of California, San Diego, La Jolla, CA, and approved February 17, 2016 (received for review October 14, 2015) Skp1–Cul1–F-box (SCF) E3 ligases play key roles in multiple cellular are subdivided into three subfamilies based on the structure of processes through ubiquitination and subsequent degradation of their substrate binding domains, including WD40, LRR, and substrate proteins. Although Skp1 and Cul1 are invariant compo- other domains, referred to as the Fbw, Fbl, and Fbo subfamilies, nents of all SCF complexes, the 69 different human F-box proteins respectively (3). are variable substrate binding modules that determine specificity. Numerous F-box proteins are involved in processes relevant to SCF E3 ligases are activated in many cancers and inhibitors could tumorigenesis, including cell proliferation, cell cycle progression, have therapeutic potential. -
Improved Detection of Gene Fusions by Applying Statistical Methods Reveals New Oncogenic RNA Cancer Drivers
bioRxiv preprint doi: https://doi.org/10.1101/659078; this version posted June 3, 2019. 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. Improved detection of gene fusions by applying statistical methods reveals new oncogenic RNA cancer drivers Roozbeh Dehghannasiri1, Donald Eric Freeman1,2, Milos Jordanski3, Gillian L. Hsieh1, Ana Damljanovic4, Erik Lehnert4, Julia Salzman1,2,5* Author affiliation 1Department of Biochemistry, Stanford University, Stanford, CA 94305 2Department of Biomedical Data Science, Stanford University, Stanford, CA 94305 3Department of Computer Science, University of Belgrade, Belgrade, Serbia 4Seven Bridges Genomics, Cambridge, MA 02142 5Stanford Cancer Institute, Stanford, CA 94305 *Corresponding author [email protected] Short Abstract: The extent to which gene fusions function as drivers of cancer remains a critical open question. Current algorithms do not sufficiently identify false-positive fusions arising during library preparation, sequencing, and alignment. Here, we introduce a new algorithm, DEEPEST, that uses statistical modeling to minimize false-positives while increasing the sensitivity of fusion detection. In 9,946 tumor RNA-sequencing datasets from The Cancer Genome Atlas (TCGA) across 33 tumor types, DEEPEST identifies 31,007 fusions, 30% more than identified by other methods, while calling ten-fold fewer false-positive fusions in non-transformed human tissues. We leverage the increased precision of DEEPEST to discover new cancer biology. For example, 888 new candidate oncogenes are identified based on over-representation in DEEPEST-Fusion calls, and 1,078 previously unreported fusions involving long intergenic noncoding RNAs partners, demonstrating a previously unappreciated prevalence and potential for function.