Epigenetic Modifications in Muscle Regeneration and Progression Of
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Hypoxia-Driven Effects in Cancer: Characterization, Mechanisms, and Therapeutic Implications
cells Review Hypoxia-Driven Effects in Cancer: Characterization, Mechanisms, and Therapeutic Implications Rachel Shi, Chengheng Liao and Qing Zhang * Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; [email protected] (R.S.); [email protected] (C.L.) * Correspondence: [email protected]; Tel.: +1-214-645-4671 Abstract: Hypoxia, a common feature of solid tumors, greatly hinders the efficacy of conventional cancer treatments such as chemo-, radio-, and immunotherapy. The depletion of oxygen in proliferat- ing and advanced tumors causes an array of genetic, transcriptional, and metabolic adaptations that promote survival, metastasis, and a clinically malignant phenotype. At the nexus of these intercon- nected pathways are hypoxia-inducible factors (HIFs) which orchestrate transcriptional responses under hypoxia. The following review summarizes current literature regarding effects of hypoxia on DNA repair, metastasis, epithelial-to-mesenchymal transition, the cancer stem cell phenotype, and therapy resistance. We also discuss mechanisms and pathways, such as HIF signaling, mitochon- drial dynamics, exosomes, and the unfolded protein response, that contribute to hypoxia-induced phenotypic changes. Finally, novel therapeutics that target the hypoxic tumor microenvironment or interfere with hypoxia-induced pathways are reviewed. Keywords: hypoxia; metastasis; hypoxia-inducible factors; chemoresistance Citation: Shi, R.; Liao, C.; Zhang, Q. Hypoxia-Driven Effects in Cancer: 1. Introduction Characterization, Mechanisms, and Understanding the mechanisms by which cells sense oxygen and maintain oxygen Therapeutic Implications. Cells 2021, homeostasis is of pivotal importance for science and medicine. Only in recent decades have 10, 678. https://doi.org/10.3390/ breakthrough discoveries of mechanisms for eukaryotic oxygen sensing been made. -
Replication of FTO Gene Associated with Lean Mass in a Meta
www.nature.com/scientificreports OPEN Replication of FTO Gene associated with lean mass in a Meta-Analysis of Genome-Wide Association Studies Shu Ran1, Zi-Xuan Jiang1, Xiao He1, Yu Liu1, Yu-Xue Zhang1, Lei Zhang2,3, Yu-Fang Pei3,4, Meng Zhang5, Rong Hai6, Gui-Shan Gu7, Bao-Lin Liu1, Qing Tian8, Yong-Hong Zhang3,4, Jing-Yu Wang7 & Hong-Wen Deng8* Sarcopenia is characterized by low skeletal muscle, a complex trait with high heritability. With the dramatically increasing prevalence of obesity, obesity and sarcopenia occur simultaneously, a condition known as sarcopenic obesity. Fat mass and obesity-associated (FTO) gene is a candidate gene of obesity. To identify associations between lean mass and FTO gene, we performed a genome-wide association study (GWAS) of lean mass index (LMI) in 2207 unrelated Caucasian subjects and replicated major fndings in two replication samples including 6,004 unrelated Caucasian and 38,292 unrelated Caucasian. We found 29 single nucleotide polymorphisms (SNPs) in FTO signifcantly associated with sarcopenia (combined p-values ranging from 5.92 × 10−12 to 1.69 × 10−9). Potential biological functions of SNPs were analyzed by HaploReg v4.1, RegulomeDB, GTEx, IMPC and STRING. Our results provide suggestive evidence that FTO gene is associated with lean mass. Sarcopenia is a complex disease described as the age-associated loss of skeletal muscle mass, strength and func- tion impairment1,2. Te low skeletal muscle mass will lead to many public health problems such as sarcopenia, osteoporosis and increased mortality3,4, especially in the elderly. Skeletal muscle is heritable with heritability esti- mates of 30–85% for muscle strength and 45–90% for muscle mass5. -
Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897 -
Table 2. Significant
Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S. -
Interplay Between Epigenetics and Metabolism in Oncogenesis: Mechanisms and Therapeutic Approaches
OPEN Oncogene (2017) 36, 3359–3374 www.nature.com/onc REVIEW Interplay between epigenetics and metabolism in oncogenesis: mechanisms and therapeutic approaches CC Wong1, Y Qian2,3 and J Yu1 Epigenetic and metabolic alterations in cancer cells are highly intertwined. Oncogene-driven metabolic rewiring modifies the epigenetic landscape via modulating the activities of DNA and histone modification enzymes at the metabolite level. Conversely, epigenetic mechanisms regulate the expression of metabolic genes, thereby altering the metabolome. Epigenetic-metabolomic interplay has a critical role in tumourigenesis by coordinately sustaining cell proliferation, metastasis and pluripotency. Understanding the link between epigenetics and metabolism could unravel novel molecular targets, whose intervention may lead to improvements in cancer treatment. In this review, we summarized the recent discoveries linking epigenetics and metabolism and their underlying roles in tumorigenesis; and highlighted the promising molecular targets, with an update on the development of small molecule or biologic inhibitors against these abnormalities in cancer. Oncogene (2017) 36, 3359–3374; doi:10.1038/onc.2016.485; published online 16 January 2017 INTRODUCTION metabolic genes have also been identified as driver genes It has been appreciated since the early days of cancer research mutated in some cancers, such as isocitrate dehydrogenase 1 16 17 that the metabolic profiles of tumor cells differ significantly from and 2 (IDH1/2) in gliomas and acute myeloid leukemia (AML), 18 normal cells. Cancer cells have high metabolic demands and they succinate dehydrogenase (SDH) in paragangliomas and fuma- utilize nutrients with an altered metabolic program to support rate hydratase (FH) in hereditary leiomyomatosis and renal cell 19 their high proliferative rates and adapt to the hostile tumor cancer (HLRCC). -
Catalysis by the Jmjc Histone Demethylase KDM4A Integrates Substrate Dynamics, Correlated Motions Cite This: Chem
Chemical Science View Article Online EDGE ARTICLE View Journal | View Issue Catalysis by the JmjC histone demethylase KDM4A integrates substrate dynamics, correlated motions Cite this: Chem. Sci., 2020, 11, 9950 † All publication charges for this article and molecular orbital control have been paid for by the Royal Society a a b of Chemistry Rajeev Ramanan, Shobhit S. Chaturvedi, Nicolai Lehnert, Christopher J. Schofield, c Tatyana G. Karabencheva-Christova *a and Christo Z. Christov *a 3 The N -methyl lysine status of histones is important in the regulation of eukaryotic transcription. The Fe(II) and 2-oxoglutarate (2OG) -dependent JmjC domain enzymes are the largest family of histone N3-methyl lysine demethylases (KDMs). The human KDM4 subfamily of JmjC KDMs is linked with multiple cancers and some of its members are medicinal chemistry targets. We describe the use of combined molecular dynamics (MD) and Quantum Mechanical/Molecular Mechanical (QM/MM) methods to study the mechanism of KDM4A, which catalyzes demethylation of both tri- and di-methylated forms of histone Creative Commons Attribution-NonCommercial 3.0 Unported Licence. H3 at K9 and K36. The results show that the oxygen activation at the active site of KDM4A is optimized towards the generation of the reactive Fe(IV)-oxo intermediate. Factors including the substrate binding mode, correlated motions of the protein and histone substrates, and molecular orbital control synergistically contribute to the reactivity of the Fe(IV)-oxo intermediate. In silico substitutions were performed to investigate the roles of residues (Lys241, Tyr177, and Asn290) in substrate orientation. The Lys241Ala substitution abolishes activity due to altered substrate orientation consistent with reported experimental studies. -
Mutant IDH, (R)-2-Hydroxyglutarate, and Cancer
Downloaded from genesdev.cshlp.org on October 1, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW What a difference a hydroxyl makes: mutant IDH, (R)-2-hydroxyglutarate, and cancer Julie-Aurore Losman1 and William G. Kaelin Jr.1,2,3 1Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02215, USA; 2Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA Mutations in metabolic enzymes, including isocitrate whether altered cellular metabolism is a cause of cancer dehydrogenase 1 (IDH1) and IDH2, in cancer strongly or merely an adaptive response of cancer cells in the face implicate altered metabolism in tumorigenesis. IDH1 of accelerated cell proliferation is still a topic of some and IDH2 catalyze the interconversion of isocitrate and debate. 2-oxoglutarate (2OG). 2OG is a TCA cycle intermediate The recent identification of cancer-associated muta- and an essential cofactor for many enzymes, including tions in three metabolic enzymes suggests that altered JmjC domain-containing histone demethylases, TET cellular metabolism can indeed be a cause of some 5-methylcytosine hydroxylases, and EglN prolyl-4-hydrox- cancers (Pollard et al. 2003; King et al. 2006; Raimundo ylases. Cancer-associated IDH mutations alter the enzymes et al. 2011). Two of these enzymes, fumarate hydratase such that they reduce 2OG to the structurally similar (FH) and succinate dehydrogenase (SDH), are bone fide metabolite (R)-2-hydroxyglutarate [(R)-2HG]. Here we tumor suppressors, and loss-of-function mutations in FH review what is known about the molecular mechanisms and SDH have been identified in various cancers, in- of transformation by mutant IDH and discuss their im- cluding renal cell carcinomas and paragangliomas. -
An Animal Model with a Cardiomyocyte-Specific Deletion of Estrogen Receptor Alpha: Functional, Metabolic, and Differential Netwo
Washington University School of Medicine Digital Commons@Becker Open Access Publications 2014 An animal model with a cardiomyocyte-specific deletion of estrogen receptor alpha: Functional, metabolic, and differential network analysis Sriram Devanathan Washington University School of Medicine in St. Louis Timothy Whitehead Washington University School of Medicine in St. Louis George G. Schweitzer Washington University School of Medicine in St. Louis Nicole Fettig Washington University School of Medicine in St. Louis Attila Kovacs Washington University School of Medicine in St. Louis See next page for additional authors Follow this and additional works at: https://digitalcommons.wustl.edu/open_access_pubs Recommended Citation Devanathan, Sriram; Whitehead, Timothy; Schweitzer, George G.; Fettig, Nicole; Kovacs, Attila; Korach, Kenneth S.; Finck, Brian N.; and Shoghi, Kooresh I., ,"An animal model with a cardiomyocyte-specific deletion of estrogen receptor alpha: Functional, metabolic, and differential network analysis." PLoS One.9,7. e101900. (2014). https://digitalcommons.wustl.edu/open_access_pubs/3326 This Open Access Publication is brought to you for free and open access by Digital Commons@Becker. It has been accepted for inclusion in Open Access Publications by an authorized administrator of Digital Commons@Becker. For more information, please contact [email protected]. Authors Sriram Devanathan, Timothy Whitehead, George G. Schweitzer, Nicole Fettig, Attila Kovacs, Kenneth S. Korach, Brian N. Finck, and Kooresh I. Shoghi This open access publication is available at Digital Commons@Becker: https://digitalcommons.wustl.edu/open_access_pubs/3326 An Animal Model with a Cardiomyocyte-Specific Deletion of Estrogen Receptor Alpha: Functional, Metabolic, and Differential Network Analysis Sriram Devanathan1, Timothy Whitehead1, George G. Schweitzer2, Nicole Fettig1, Attila Kovacs3, Kenneth S. -
ARTICLE Doi:10.1038/Nature10523
ARTICLE doi:10.1038/nature10523 Spatio-temporal transcriptome of the human brain Hyo Jung Kang1*, Yuka Imamura Kawasawa1*, Feng Cheng1*, Ying Zhu1*, Xuming Xu1*, Mingfeng Li1*, Andre´ M. M. Sousa1,2, Mihovil Pletikos1,3, Kyle A. Meyer1, Goran Sedmak1,3, Tobias Guennel4, Yurae Shin1, Matthew B. Johnson1,Zˇeljka Krsnik1, Simone Mayer1,5, Sofia Fertuzinhos1, Sheila Umlauf6, Steven N. Lisgo7, Alexander Vortmeyer8, Daniel R. Weinberger9, Shrikant Mane6, Thomas M. Hyde9,10, Anita Huttner8, Mark Reimers4, Joel E. Kleinman9 & Nenad Sˇestan1 Brain development and function depend on the precise regulation of gene expression. However, our understanding of the complexity and dynamics of the transcriptome of the human brain is incomplete. Here we report the generation and analysis of exon-level transcriptome and associated genotyping data, representing males and females of different ethnicities, from multiple brain regions and neocortical areas of developing and adult post-mortem human brains. We found that 86 per cent of the genes analysed were expressed, and that 90 per cent of these were differentially regulated at the whole-transcript or exon level across brain regions and/or time. The majority of these spatio-temporal differences were detected before birth, with subsequent increases in the similarity among regional transcriptomes. The transcriptome is organized into distinct co-expression networks, and shows sex-biased gene expression and exon usage. We also profiled trajectories of genes associated with neurobiological categories and diseases, and identified associations between single nucleotide polymorphisms and gene expression. This study provides a comprehensive data set on the human brain transcriptome and insights into the transcriptional foundations of human neurodevelopment. -
An Enhancer Directs Differential Expression of the Linked Mrf4 and Myf5 Myogenic Regulatory Genes in the Mouse
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Developmental Biology 269 (2004) 595–608 www.elsevier.com/locate/ydbio An enhancer directs differential expression of the linked Mrf4 and Myf5 myogenic regulatory genes in the mouse Ted Hung-Tse Chang, Michael Primig,1 Juliette Hadchouel,2 Shahragim Tajbakhsh,3 Didier Rocancourt, Anne Fernandez,4 Roland Kappler,5 Harry Scherthan,6 and Margaret Buckingham* De´partement de Biologie du De´veloppement, CNRS URA 2578, Institut Pasteur, 75724 Paris Cedex 15, France Received for publication 25 September 2003, revised 3 February 2004, accepted 6 February 2004 Abstract The myogenic regulatory factors, Mrf4 and Myf5, play a key role in skeletal muscle formation. An enhancer trap approach, devised to isolate positive-acting elements from a 200-kb YAC covering the mouse Mrf4–Myf5 locus in a C2 myoblast assay, yielded an enhancer, A17, which mapped at À8kb5V of Mrf4 and À17 kb 5V of Myf5. An E-box bound by complexes containing the USF transcription factor is critical for enhancer activity. In transgenic mice, A17 gave two distinct and mutually exclusive expression profiles before birth, which correspond to two phases of Mrf4 transcription. Linked to the Tk or Mrf4 minimal promoters, the nlacZ reporter was expressed either in embryonic myotomes, or later in fetal muscle, with the majority of Mrf4 lines showing embryonic expression. When linked to the Myf5 minimal promoter, only fetal muscle expression was detected. These observations identify A17 as a sequence that targets sites of myogenesis in vivo and raise questions about the mutually exclusive modes of expression and possible promoter/enhancer interactions at the Mrf4–Myf5 locus. -
Ebf2 Is a Selective Marker of Brown and Beige Adipogenic Precursor Cells
Ebf2 is a selective marker of brown and beige adipogenic precursor cells Wenshan Wanga,b, Megan Kissiga,b, Sona Rajakumaria,b, Li Huanga,b, Hee-woong Lima,c, Kyoung-Jae Wona,c, and Patrick Sealea,b,1 aInstitute for Diabetes, Obesity, Metabolism, bDepartment of Cell and Developmental Biology, and cDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 Edited by C. Ronald Kahn, Joslin Diabetes Center, Harvard Medical School, Boston, MA, and approved August 8, 2014 (received for review July 8, 2014) Brown adipocytes and muscle and dorsal dermis descend from transcriptomic analyses identified a brown-preadipose–specific precursor cells in the dermomyotome, but the factors that regulate gene signature, which included early B-cell factor 2 (Ebf2), a crit- commitment to the brown adipose lineage are unknown. Here, we ical transcriptional regulator in mature brown adipocytes (18). prospectively isolated and determined the molecular profile of We found that brown/beige preadipose activity in embryos and embryonic brown preadipose cells. Brown adipogenic precursor adult fat depots was restricted to Ebf2-expressing cells. Further- activity in embryos was confined to platelet-derived growth factor more, Ebf2 deficiency in brown preadipose cells reduced the ex- + Cre α , myogenic factor 5 -lineage–marked cells. RNA-sequence anal- pression of nearly all brown preadipose signature genes, whereas ysis identified early B-cell factor 2 (Ebf2) as one of the most selec- ectopic expression of Ebf2 in myoblasts activated brown pre- tively expressed genes in this cell fraction. Importantly, Ebf2- GFP adipose-selective genes. Altogether, our study reveals that expressing cells purified from Ebf2 embryos or brown fat tissue Ebf2 is a specific marker of brown/beige preadipose cells and did not express myoblast or dermal cell markers and uniformly dif- that Ebf2 functions at this stage to control precursor identity. -
1714 Gene Comprehensive Cancer Panel Enriched for Clinically Actionable Genes with Additional Biologically Relevant Genes 400-500X Average Coverage on Tumor
xO GENE PANEL 1714 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes 400-500x average coverage on tumor Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 IL6R KAT5 ACVR2A