The Dynamic Interplay Between Homeodomain Transcription Factors and Chromatin Environment Regulates Proneural Factor Outcomes
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Watsonjn2018.Pdf (1.780Mb)
UNIVERSITY OF CENTRAL OKLAHOMA Edmond, Oklahoma Department of Biology Investigating Differential Gene Expression in vivo of Cardiac Birth Defects in an Avian Model of Maternal Phenylketonuria A THESIS SUBMITTED TO THE GRADUATE FACULTY In partial fulfillment of the requirements For the degree of MASTER OF SCIENCE IN BIOLOGY By Jamie N. Watson Edmond, OK June 5, 2018 J. Watson/Dr. Nikki Seagraves ii J. Watson/Dr. Nikki Seagraves Acknowledgements It is difficult to articulate the amount of gratitude I have for the support and encouragement I have received throughout my master’s thesis. Many people have added value and support to my life during this time. I am thankful for the education, experience, and friendships I have gained at the University of Central Oklahoma. First, I would like to thank Dr. Nikki Seagraves for her mentorship and friendship. I lucked out when I met her. I have enjoyed working on this project and I am very thankful for her support. I would like thank Thomas Crane for his support and patience throughout my master’s degree. I would like to thank Dr. Shannon Conley for her continued mentorship and support. I would like to thank Liz Bullen and Dr. Eric Howard for their training and help on this project. I would like to thank Kristy Meyer for her friendship and help throughout graduate school. I would like to thank my committee members Dr. Robert Brennan and Dr. Lilian Chooback for their advisement on this project. Also, I would like to thank the biology faculty and staff. I would like to thank the Seagraves lab members: Jailene Canales, Kayley Pate, Mckayla Muse, Grace Thetford, Kody Harvey, Jordan Guffey, and Kayle Patatanian for their hard work and support. -
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). -
Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7 -
Homeobox Transcription Factor Prox1 in Sympathetic Ganglia of Vertebrate Embryos: Correlation with Human Stage 4S Neuroblastoma
0031-3998/10/6802-0112 Vol. 68, No. 2, 2010 PEDIATRIC RESEARCH Printed in U.S.A. Copyright © 2010 International Pediatric Research Foundation, Inc. Homeobox Transcription Factor Prox1 in Sympathetic Ganglia of Vertebrate Embryos: Correlation With Human Stage 4s Neuroblastoma JU¨ RGEN BECKER, BAIGANG WANG, HELENA PAVLAKOVIC, KERSTIN BUTTLER, AND JO¨ RG WILTING Department of Anatomy and Cell Biology, University Medicine Goettingen, 37075 Goettingen, Germany ABSTRACT: Previously, we observed expression of the homeobox progression of tumors derived from these tissues has not been transcription factor Prox1 in neuroectodermal embryonic tissues. investigated. We have studied Prox1 expression in the sym- Besides essential functions during embryonic development, Prox1 pathetic nervous system of avian and murine embryos, and in has been implicated in both progression and suppression of malig- childhood tumors derived from this tissue: neuroblastoma nancies. Here, we show that Prox1 is expressed in embryonic sym- (NB). We show that Prox1 is expressed in sympathetic neu- pathetic trunk ganglia of avian and murine embryos. Prox1 protein is rons during early stages of development at similar levels as in localized in the nucleus of neurofilament-positive sympathetic neu- rons. Sympathetic progenitors represent the cell of origin of neuro- lymphatic endothelial cells (LECs), but at greatly reduced blastoma (NB), the most frequent solid extracranial malignancy of levels in human NB cell lines. Studies of primary NB of all children. NB may progress to life-threatening stage 4, or regress stages (stages 1–4s) show significantly higher amounts of spontaneously in the special stage 4s. By qRT-PCR, we show that Prox1 mRNA in stage 4s. -
Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1. -
SUPPLEMENTARY MATERIAL Bone Morphogenetic Protein 4 Promotes
www.intjdevbiol.com doi: 10.1387/ijdb.160040mk SUPPLEMENTARY MATERIAL corresponding to: Bone morphogenetic protein 4 promotes craniofacial neural crest induction from human pluripotent stem cells SUMIYO MIMURA, MIKA SUGA, KAORI OKADA, MASAKI KINEHARA, HIROKI NIKAWA and MIHO K. FURUE* *Address correspondence to: Miho Kusuda Furue. Laboratory of Stem Cell Cultures, National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8, Saito-Asagi, Ibaraki, Osaka 567-0085, Japan. Tel: 81-72-641-9819. Fax: 81-72-641-9812. E-mail: [email protected] Full text for this paper is available at: http://dx.doi.org/10.1387/ijdb.160040mk TABLE S1 PRIMER LIST FOR QRT-PCR Gene forward reverse AP2α AATTTCTCAACCGACAACATT ATCTGTTTTGTAGCCAGGAGC CDX2 CTGGAGCTGGAGAAGGAGTTTC ATTTTAACCTGCCTCTCAGAGAGC DLX1 AGTTTGCAGTTGCAGGCTTT CCCTGCTTCATCAGCTTCTT FOXD3 CAGCGGTTCGGCGGGAGG TGAGTGAGAGGTTGTGGCGGATG GAPDH CAAAGTTGTCATGGATGACC CCATGGAGAAGGCTGGGG MSX1 GGATCAGACTTCGGAGAGTGAACT GCCTTCCCTTTAACCCTCACA NANOG TGAACCTCAGCTACAAACAG TGGTGGTAGGAAGAGTAAAG OCT4 GACAGGGGGAGGGGAGGAGCTAGG CTTCCCTCCAACCAGTTGCCCCAAA PAX3 TTGCAATGGCCTCTCAC AGGGGAGAGCGCGTAATC PAX6 GTCCATCTTTGCTTGGGAAA TAGCCAGGTTGCGAAGAACT p75 TCATCCCTGTCTATTGCTCCA TGTTCTGCTTGCAGCTGTTC SOX9 AATGGAGCAGCGAAATCAAC CAGAGAGATTTAGCACACTGATC SOX10 GACCAGTACCCGCACCTG CGCTTGTCACTTTCGTTCAG Suppl. Fig. S1. Comparison of the gene expression profiles of the ES cells and the cells induced by NC and NC-B condition. Scatter plots compares the normalized expression of every gene on the array (refer to Table S3). The central line -
A Simple Differentiation Protocol for Generation of Induced
1 Article, Special Issue "Induced Pluripotent Stem Cells in Neurodegenerative Diseases: Application for Therapy and 2 Disease Modeling" 3 A simple differentiation protocol for generation of 4 induced pluripotent stem cell-derived basal forebrain 5 cholinergic neurons for Alzheimer’s disease and 6 frontotemporal dementia disease modeling 7 Supplemental information 8 Method 1. Reprogramming and characterisation of MBE2960 healthy control iPSC line 9 The iPSCs were generated using skin fibroblasts obtained from subjects over the age of 18 years 10 by episomal method as described [40]. Briefly, reprogramming was performed on passage 8-10 11 fibroblasts by nucleofection (Lonza Amaxa Nucleofector) with episomal vectors expressing 12 OCT4, SOX2, KLF4, L-MYC, LIN28 and shRNA against p53 [41] in feeder- and serum- free 13 conditions using TeSR-E7 medium (Stemcell Technologies). Subsequently, reprogrammed 14 colonies were manually dissected to establish clonal cell lines [42]. Three clones were assessed 15 for pluripotency markers via immunocytochemistry (Figure S1A). The iPSC line was expanded 16 and characterised. Embryoid bodies were obtained as described [43] and using tri-lineage 17 differentiation kit (Stemcell Technologies). Germ layer differentiation was assessed by 18 immunochemistry (Figure S1B). Copy number variation (CNV) analysis of original fibroblasts 19 and iPSCs from MBE2960 (Figure S1C) was performed using Illumina HumanCore Beadchip 20 arrays as we described [40]. CNV analyses were performed using PennCNV and QuantiSNP with 21 default parameter settings [44,45]. Chromosomal aberrations were deemed to involve at least 10 22 contiguous single nucleotide polymorphisms (SNPs) or a genomic region spanning at least 1MB 23 [44,45]. The B allele frequency (BAF) and the log R ratio (LRR) were extracted from 24 GenomeStudio (Illumina) for representation (Figure S1D). -
Diagnosis of FOXG1 Syndrome Caused by Recurrent Balanced Chromosomal Rearrangements: Case Study and Literature Review Connor P
Craig et al. Mol Cytogenet (2020) 13:40 https://doi.org/10.1186/s13039-020-00506-1 CASE REPORT Open Access Diagnosis of FOXG1 syndrome caused by recurrent balanced chromosomal rearrangements: case study and literature review Connor P. Craig1,2, Emily Calamaro3, Chin‑To Fong3, Anwar M. Iqbal1, Alexander R. Paciorkowski3,4,5,6 and Bin Zhang1,3* Abstract Background: The FOXG1 gene plays a vital role in mammalian brain diferentiation and development. Intra‑ and intergenic mutations resulting in loss of function or altered expression of the FOXG1 gene cause FOXG1 syndrome. The hallmarks of this syndrome are severe developmental delay with absent verbal language, post‑natal growth restric‑ tion, post‑natal microcephaly, and a recognizable movement disorder characterized by chorea and dystonia. Case presentation: Here we describe a case of a 7‑year‑old male patient found to have a de novo balanced translo‑ cation between chromosome 3 at band 3q14.1 and chromosome 14 at band 14q12 via G‑banding chromosome and Fluorescence In Situ Hybridization (FISH) analyses. This rearrangement disrupts the proximity of FOXG1 to a previously described smallest region of deletion overlap (SRO), likely resulting in haploinsufciency. Conclusions: This case adds to the growing body of literature implicating chromosomal structural variants in the manifestation of this disorder and highlights the vital role of cis‑acting regulatory elements in the normal expression of this gene. Finally, we propose a protocol for refex FISH analysis to improve diagnostic efciency for patients with suspected FOXG1 syndrome. Keywords: FOXG1, Haploinsufciency, Postnatal microcephaly, FISH, Enhancer, Chromosomal rearrangement, Diagnosis Introduction brain development, with high levels of expression in the Te Forkhead Box G1 (FOXG1) gene [OMIM: 164874], developing fetal telencephalon [1–4]. -
Notch Signal Controls Several Steps of Inner Ear Development Norio Yamamoto and Matthew W
NIDCD Notch signal controls several steps of inner ear development Norio Yamamoto and Matthew W. Kelley Section on Developmental Neuroscience Section on Developmental Neuroscience National Institute on Deafness and Other Communication Disorders National Institutes of Health Abstract Problem addressed RBP-J mutant cochleae did not have any supporting cells Notch signaling has been reported to contribute to inner ear development, however, its specific functions remain unclear, partly because of discrepancies between the phenotypes of mutant mice with single deletion of specific Notch related genes. These discrepancies are probably due to functional compensation by other Notch Figure 4 receptors or ligands. Foxg1 Cre;RBP-J floxed/+ Foxg1 Cre;RBP-J floxed/floxed A B C D To determine the effects of complete elimination of Notch signaling, we used a conditional knockout of the Rbpsuh gene. RBP-J protein is a critical transcriptional p27 Phalloidin p27 Phalloidin To test if mutant cochleae contained supporting cells we co-activator for all Notch molecules and thus deletion of this protein inhibits all Notch signaling. examined expression of supporting cell markers such as p27 and Prox1. On E17.5 p27 was expressed in supporting cells Methods and Measures ***** ***** under or between hair cells such as inner pharyngeal cells, Floxed Rbpsuh mice were crossed with Foxg1-Cre knock-in mice to delete the Rbpsuh gene in the inner ear. Inner ear phenotypes in Rbpsuh conditional knockout Deiter's cells and pillar cells (asterisks in figure 4 A and B). mice were determined at various developmental stages using immunohistochemistry. But no p27 expression was detected in those regions of RBP-J E Prox1 F Phalloidin G Prox1 H Phalloidin conditional knockout mice (Figure 4 C and D). -
Methylome and Transcriptome Maps of Human Visceral and Subcutaneous
www.nature.com/scientificreports OPEN Methylome and transcriptome maps of human visceral and subcutaneous adipocytes reveal Received: 9 April 2019 Accepted: 11 June 2019 key epigenetic diferences at Published: xx xx xxxx developmental genes Stephen T. Bradford1,2,3, Shalima S. Nair1,3, Aaron L. Statham1, Susan J. van Dijk2, Timothy J. Peters 1,3,4, Firoz Anwar 2, Hugh J. French 1, Julius Z. H. von Martels1, Brodie Sutclife2, Madhavi P. Maddugoda1, Michelle Peranec1, Hilal Varinli1,2,5, Rosanna Arnoldy1, Michael Buckley1,4, Jason P. Ross2, Elena Zotenko1,3, Jenny Z. Song1, Clare Stirzaker1,3, Denis C. Bauer2, Wenjia Qu1, Michael M. Swarbrick6, Helen L. Lutgers1,7, Reginald V. Lord8, Katherine Samaras9,10, Peter L. Molloy 2 & Susan J. Clark 1,3 Adipocytes support key metabolic and endocrine functions of adipose tissue. Lipid is stored in two major classes of depots, namely visceral adipose (VA) and subcutaneous adipose (SA) depots. Increased visceral adiposity is associated with adverse health outcomes, whereas the impact of SA tissue is relatively metabolically benign. The precise molecular features associated with the functional diferences between the adipose depots are still not well understood. Here, we characterised transcriptomes and methylomes of isolated adipocytes from matched SA and VA tissues of individuals with normal BMI to identify epigenetic diferences and their contribution to cell type and depot-specifc function. We found that DNA methylomes were notably distinct between diferent adipocyte depots and were associated with diferential gene expression within pathways fundamental to adipocyte function. Most striking diferential methylation was found at transcription factor and developmental genes. Our fndings highlight the importance of developmental origins in the function of diferent fat depots. -
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