DOCSLIB.ORG

Whole Genome Promoter-‐Proximal Regions

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Whole Genome Promoter-‐Proximal Regions TAF1 YY1 TBP E2F4 E2F6 ELF1 MAX POLR2A HMGN3 ZBTB7A CCNT2 EGR1 ETS1 SIN3A HDAC2 GABPA MXI1 MYC CHD2 IRF1 GTF2F1 THAP1 SP2 REST NRF1 USF1 FOS SP1 SRF SPI1 SIX5 CTCF RAD21 SMC3 CTCFL ZNF263 BCLAF1 TAF7 RDBP ZBTB33 BCL3 ATF3 USF2 NFE2 SETDB1 TRIM28 ZNF274 NR2C2 GATA1 GATA2 TAL1 EP300 SMARCA4 SMARCB1 SIRT6 JUNB JUND JUN FOSL1 MAFK CEBPB HDAC8 STAT1 STAT2 BDP1 POLR3A BRF1 GTF3C2 BRF2 TAF1 YY1 TBP E2F4 E2F6 ELF1 MAX POLR2A HMGN3 ZBTB7A CCNT2 EGR1 ETS1 SIN3A HDAC2 GABPA MXI1 MYC CHD2 IRF1 GTF2F1 THAP1 SP2 REST NRF1 USF1 FOS SP1 SRF SPI1 SIX5 CTCF RAD21 SMC3 CTCFL ZNF263 BCLAF1 TAF7 RDBP ZBTB33 BCL3 ATF3 USF2 NFE2 SETDB1 TRIM28 ZNF274 NR2C2 GATA1 GATA2 TAL1 EP300 SMARCA4 SMARCB1 SIRT6 JUNB JUND JUN FOSL1 MAFK CEBPB HDAC8 STAT1 STAT2 BDP1 POLR3A BRF1 GTF3C2 BRF2 TAF1 TAF1 TBP TBP YY1 YY1 ELF1 ELF1 MAX MAX E2F4 E2F4 E2F6 E2F6 IRF1 IRF1 EGR1 EGR1 ZBTB7A ZBTB7A ETS1 ETS1 SIN3A SIN3A CCNT2 CCNT2 HMGN3 HMGN3 HDAC2 HDAC2 GABPA GABPA CHD2 CHD2 POLR2A POLR2A GTF2F1 GTF2F1 MXI1 MXI1 MYC MYC THAP1 THAP1 SP1 SP1 SP2 SP2 NRF1 NRF1 REST REST SIX5 SIX5 B SRF SRF SPI1 SPI1 RAD21 RAD21 SMC3 SMC3 CTCF CTCF CTCFL CTCFL ZNF263 ZNF263 BCLAF1 BCLAF1 TAF7 TAF7 RDBP RDBP ZBTB33 ZBTB33 BCL3 BCL3 ATF3 ATF3 USF2 USF2 USF1 USF1 NFE2 NFE2 GATA1 GATA1 GATA2 GATA2 TAL1 TAL1 EP300 EP300 SMARCA4 SMARCA4 SMARCB1 SMARCB1 SIRT6 SIRT6 JUNB JUNB JUND JUND JUN JUN FOSL1 FOSL1 FOS FOS MAFK MAFK CEBPB CEBPB HDAC8 HDAC8 SETDB1 SETDB1 TRIM28 TRIM28 NR2C2 NR2C2 A ZNF274 ZNF274 STAT1 STAT1 STAT2 STAT2 BDP1 BDP1 POLR3A POLR3A BRF1 BRF1 GTF3C2 GTF3C2 BRF2 BRF2 Whole genome Promoter-proximal regions TAF1 YY1 TBP E2F4 E2F6 ELF1 MAX POLR2A HMGN3 ZBTB7A CCNT2 EGR1 ETS1 SIN3A HDAC2 GABPA MXI1 MYC CHD2 IRF1 GTF2F1 THAP1 SP2 REST NRF1 USF1 FOS SP1 SRF SPI1 SIX5 CTCF RAD21 SMC3 CTCFL ZNF263 BCLAF1 TAF7 RDBP ZBTB33 BCL3 ATF3 USF2 NFE2 SETDB1 TRIM28 ZNF274 NR2C2 GATA1 GATA2 TAL1 EP300 SMARCA4 SMARCB1 SIRT6 JUNB JUND JUN FOSL1 MAFK CEBPB HDAC8 STAT1 STAT2 BDP1 POLR3A BRF1 GTF3C2 BRF2 TAF1 TBP YY1 ELF1 MAX E2F4 E2F6 IRF1 Confidence EGR1 ZBTB7A ETS1 SIN3A High CCNT2 HMGN3 HDAC2 GABPA CHD2 POLR2A GTF2F1 MXI1 MYC THAP1 SP1 SP2 NRF1 REST SIX5 B SRF SPI1 RAD21 Medium SMC3 CTCF CTCFL ZNF263 BCLAF1 TAF7 RDBP ZBTB33 BCL3 ATF3 USF2 USF1 NFE2 GATA1 GATA2 TAL1 EP300 SMARCA4 Low SMARCB1 SIRT6 JUNB JUND 0 10 20 30 40 50 60 70 80 90 t100 JUN FOSL1 FOS MAFK Degree of co-association (z-score) CEBPB HDAC8 SETDB1 TRIM28 NR2C2 A ZNF274 STAT1 STAT2 BDP1 POLR3A BRF1 GTF3C2 BRF2 1 Intergenic regions HDAC2 GABPA MXI1 MYC CHD2 HDAC2 GABPA JUNB JUND JUN FOSL1 CHD2 EP300 SP1 HDAC2 NANOG Promoter- JUNB POLR2A HDAC2 proximal JUND GTF2F1 NANOG regions JUN MXI1 EP300 FOSL1 MYC SP1 HDAC2 GABPA MXI1 MYC CHD2 HDAC2 GABPA JUNB JUND JUN FOSL1 CHD2 EP300 SP1 HDAC2 NANOG JUNB POLR2A HDAC2 Intergenic JUND GTF2F1 NANOG regions JUN MXI1 EP300 FOSL1 MYC SP1 A (K562) B (K562) C (H1-hESC) 2 H1-hESC Promoter-proximal regions TAF1 TBP POLR2A TAF7 JUND YY1 SIN3A EGR1 NRF1 USF1 USF2 MAX ATF3 SRF SIX5 GABPA EP300 SP1 HDAC2 NANOG BCL11A POU5F1 RXRA CTBP2 SUZ12 TCF12 JUN RFX5 BCL3 CTCF RAD21 REST POLR2A TAF7 JUND TAF1 TBP YY1 SIN3A NRF1 EGR1 USF1 USF2 MAX GABPA ATF3 SRF SIX5 HDAC2 NANOG EP300 SP1 BCL11A POU5F1 RXRA JUN BCL3 CTCF RAD21 REST RFX5 CTBP2 SUZ12 TCF12 7 H1-hESC Intergenic regions TAF1 TBP POLR2A TAF7 JUND YY1 SIN3A EGR1 NRF1 USF1 USF2 MAX ATF3 SRF SIX5 GABPA EP300 SP1 HDAC2 NANOG BCL11A POU5F1 RXRA CTBP2 SUZ12 TCF12 JUN RFX5 BCL3 CTCF RAD21 REST POLR2A TAF7 JUND TAF1 TBP YY1 SIN3A NRF1 EGR1 USF1 USF2 MAX GABPA ATF3 SRF SIX5 HDAC2 NANOG EP300 SP1 BCL11A POU5F1 RXRA JUN BCL3 CTCF RAD21 REST RFX5 CTBP2 SUZ12 TCF12 8 K562 Whole-genome TAF1 YY1 TBP E2F4 E2F6 ELF1 MAX POLR2A HMGN3 ZBTB7A CCNT2 EGR1 ETS1 SIN3A HDAC2 GABPA MXI1 MYC CHD2 IRF1 GTF2F1 THAP1 SP2 REST NRF1 USF1 FOS SP1 SRF SPI1 SIX5 CTCF RAD21 SMC3 CTCFL ZNF263 BCLAF1 TAF7 RDBP ZBTB33 BCL3 ATF3 USF2 NFE2 SETDB1 TRIM28 ZNF274 NR2C2 GATA1 GATA2 TAL1 EP300 SMARCA4 SMARCB1 SIRT6 JUNB JUND JUN FOSL1 MAFK CEBPB HDAC8 STAT1 STAT2 BDP1 POLR3A BRF1 GTF3C2 BRF2 TAF1 TBP YY1 ELF1 MAX E2F4 E2F6 IRF1 EGR1 ZBTB7A ETS1 SIN3A CCNT2 HMGN3 HDAC2 GABPA CHD2 POLR2A GTF2F1 MXI1 MYC THAP1 SP1 SP2 NRF1 REST SIX5 SRF SPI1 RAD21 SMC3 CTCF CTCFL ZNF263 BCLAF1 TAF7 RDBP ZBTB33 BCL3 ATF3 USF2 USF1 NFE2 GATA1 GATA2 TAL1 EP300 SMARCA4 SMARCB1 SIRT6 JUNB JUND JUN FOSL1 FOS MAFK CEBPB HDAC8 SETDB1 TRIM28 NR2C2 ZNF274 STAT1 STAT2 BDP1 POLR3A BRF1 GTF3C2 BRF2 18 K562 Promoter-proximal regions TAF1 YY1 TBP E2F4 E2F6 ELF1 MAX POLR2A HMGN3 ZBTB7A CCNT2 EGR1 ETS1 SIN3A HDAC2 GABPA MXI1 MYC CHD2 IRF1 GTF2F1 THAP1 SP2 REST NRF1 USF1 FOS SP1 SRF SPI1 SIX5 CTCF RAD21 SMC3 CTCFL ZNF263 BCLAF1 TAF7 RDBP ZBTB33 BCL3 ATF3 USF2 NFE2 SETDB1 TRIM28 ZNF274 NR2C2 GATA1 GATA2 TAL1 EP300 SMARCA4 SMARCB1 SIRT6 JUNB JUND JUN FOSL1 MAFK CEBPB HDAC8 STAT1 STAT2 BDP1 POLR3A BRF1 GTF3C2 BRF2 TAF1 TBP YY1 ELF1 MAX E2F4 E2F6 IRF1 EGR1 ZBTB7A ETS1 SIN3A CCNT2 HMGN3 HDAC2 GABPA CHD2 POLR2A GTF2F1 MXI1 MYC THAP1 SP1 SP2 NRF1 REST SIX5 SRF SPI1 RAD21 SMC3 CTCF CTCFL ZNF263 BCLAF1 TAF7 RDBP ZBTB33 BCL3 ATF3 USF2 USF1 NFE2 GATA1 GATA2 TAL1 EP300 SMARCA4 SMARCB1 SIRT6 JUNB JUND JUN FOSL1 FOS MAFK CEBPB HDAC8 SETDB1 TRIM28 NR2C2 ZNF274 STAT1 STAT2 BDP1 POLR3A BRF1 GTF3C2 BRF2 19 K562 Intergenic regions TAF1 YY1 TBP E2F4 E2F6 ELF1 MAX POLR2A HMGN3 ZBTB7A CCNT2 EGR1 ETS1 SIN3A HDAC2 GABPA MXI1 MYC CHD2 IRF1 GTF2F1 THAP1 SP2 REST NRF1 USF1 FOS SP1 SRF SPI1 SIX5 CTCF RAD21 SMC3 CTCFL ZNF263 BCLAF1 TAF7 RDBP ZBTB33 BCL3 ATF3 USF2 NFE2 SETDB1 TRIM28 ZNF274 NR2C2 GATA1 GATA2 TAL1 EP300 SMARCA4 SMARCB1 SIRT6 JUNB JUND JUN FOSL1 MAFK CEBPB HDAC8 STAT1 STAT2 BDP1 POLR3A BRF1 GTF3C2 BRF2 TAF1 TBP YY1 ELF1 MAX E2F4 E2F6 IRF1 EGR1 ZBTB7A ETS1 SIN3A CCNT2 HMGN3 HDAC2 GABPA CHD2 POLR2A GTF2F1 MXI1 MYC THAP1 SP1 SP2 NRF1 REST SIX5 SRF SPI1 RAD21 SMC3 CTCF CTCFL ZNF263 BCLAF1 TAF7 RDBP ZBTB33 BCL3 ATF3 USF2 USF1 NFE2 GATA1 GATA2 TAL1 EP300 SMARCA4 SMARCB1 SIRT6 JUNB JUND JUN FOSL1 FOS MAFK CEBPB HDAC8 SETDB1 TRIM28 NR2C2 ZNF274 STAT1 STAT2 BDP1 POLR3A BRF1 GTF3C2 BRF2 20 .
Load more

Recommended publications
  • Table S1. List of Proteins in the BAHD1 Interactome
    Table S1. List of proteins in the BAHD1 interactome BAHD1 nuclear partners found in this work yeast two-hybrid screen Name Description Function Reference (a) Chromatin adapters HP1α (CBX5) chromobox homolog 5 (HP1 alpha) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins (20-23) HP1β (CBX1) chromobox homolog 1 (HP1 beta) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins HP1γ (CBX3) chromobox homolog 3 (HP1 gamma) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins MBD1 methyl-CpG binding domain protein 1 Binds methylated CpG dinucleotide and chromatin-associated proteins (22, 24-26) Chromatin modification enzymes CHD1 chromodomain helicase DNA binding protein 1 ATP-dependent chromatin remodeling activity (27-28) HDAC5 histone deacetylase 5 Histone deacetylase activity (23,29,30) SETDB1 (ESET;KMT1E) SET domain, bifurcated 1 Histone-lysine N-methyltransferase activity (31-34) Transcription factors GTF3C2 general transcription factor IIIC, polypeptide 2, beta 110kDa Required for RNA polymerase III-mediated transcription HEYL (Hey3) hairy/enhancer-of-split related with YRPW motif-like DNA-binding transcription factor with basic helix-loop-helix domain (35) KLF10 (TIEG1) Kruppel-like factor 10 DNA-binding transcription factor with C2H2 zinc finger domain (36) NR2F1 (COUP-TFI) nuclear receptor subfamily 2, group F, member 1 DNA-binding transcription factor with C4 type zinc finger domain (ligand-regulated) (36) PEG3 paternally expressed 3 DNA-binding transcription factor with
    [Show full text]
  • Genome-Wide Inference of Natural Selection on Human Transcription Factor Binding Sites
    ANALYSIS Genome-wide inference of natural selection on human transcription factor binding sites Leonardo Arbiza1, Ilan Gronau1, Bulent A Aksoy2, Melissa J Hubisz1, Brad Gulko3, Alon Keinan1–3 & Adam Siepel1–3 For decades, it has been hypothesized that gene regulation persistence in humans7,8. In addition, some genome-wide analyses has had a central role in human evolution, yet much remains have found bulk statistical evidence of natural selection in noncoding unknown about the genome-wide impact of regulatory regions near genes, presumably due to cis-regulatory elements9–12. mutations. Here we use whole-genome sequences and genome- Nevertheless, evidence in support of the overall prominence of wide chromatin immunoprecipitation and sequencing data to cis-regulatory mutations in evolutionary adaptation remains largely demonstrate that natural selection has profoundly influenced anecdotal and indirect, and there is continuing controversy about the human transcription factor binding sites since the divergence relative roles of regulatory and protein-coding sequences in evolu- of humans from chimpanzees 4–6 million years ago. Our tion8. Large-scale genomic studies of the evolution of transcription analysis uses a new probabilistic method, called INSIGHT, for factor binding sites have the potential to advance this debate, but a measuring the influence of selection on collections of short, major limitation of such studies so far has been a lack of precisely interspersed noncoding elements. We find that, on average, annotated binding sites across the genome. The analysis of con- transcription factor binding sites have experienced somewhat served noncoding sequences and/or promoter regions rather than weaker selection than protein-coding genes.
    [Show full text]
  • SOX4-Mediated Repression of Specific Trnas Inhibits Proliferation of Human Glioblastoma Cells
    SOX4-mediated repression of specific tRNAs inhibits proliferation of human glioblastoma cells Jianjing Yanga,b,c, Derek K. Smithc,d, Haoqi Nia,b,c,KeWua,b, Dongdong Huanga,b, Sishi Pana,b,c, Adwait A. Sathee, Yu Tangc,d, Meng-Lu Liuc,d, Chao Xinge,f,g, Chun-Li Zhangc,d,1, and Qichuan Zhugea,b,1 aDepartment of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China 325000; bZhejiang Provincial Key Laboratory of Aging and Neurological Disorder Research, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China 325000; cDepartment of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390; dHamon Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390; eMcDermott Center of Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390; fDepartment of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390; and gDepartment of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390 Edited by S. Altman, Yale University, New Haven, CT, and approved February 5, 2020 (received for review November 15, 2019) Transfer RNAs (tRNAs) are products of RNA polymerase III (Pol III) indicates that tRNA expression may also be under cell state- and essential for mRNA translation and ultimately cell growth and dependent regulations (12–16). proliferation. Whether and how individual tRNA genes are specif- In this study, we performed a systematic analysis on how ically regulated is not clear. Here, we report that SOX4, a well- NGN2/SOX4-mediated cell-fate reprogramming leads to cell known Pol II-dependent transcription factor that is critical for neuro- cycle exit of human glioblastoma cells.
    [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]
  • Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
    Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database.
    [Show full text]
  • An Amino-Terminal Domain of Mxil Mediates Anti-Myc Oncogenic Activity and Interacts with a Homolog of the Yeast Transcriptional Repressor SIN3
    CORE Metadata, citation and similar papers at core.ac.uk Provided by Elsevier - Publisher Connector Cell, Vol, 80, 777-786, March 10, 1995, Copyright © 1995 by Cell Press An Amino-Terminal Domain of Mxil Mediates Anti-Myc Oncogenic Activity and Interacts with a Homolog of the Yeast Transcriptional Repressor SIN3 Nicole Schreiber-Agus,*t Lynda Chin,*tt Ken Chen,t et al., 1990), and a carboxy-terminal a-helical domain re- Richard Torres, t Govinda Rao,§ Peter Guida,t quired for dimerization with another basic region-helix- Arthur h Skoultchi,§ and Ronald A. DePinhot Ioop-helix-leucine zipper (bHLH-LZ) protein, Max (Black- "rDepartments of Microbiology and Immunology wood and Eisenman, 1991; Prendergast et al., 1991). and of Medicine Many of the biochemical and biological activities of Myc §Department of Cell Biology appear to be highly dependent upon its association with ~Division of Dermatology Max (Blackwood and Eisenman, 1991 ; Prendergast et al., Albert Einstein College of Medicine 1991; Kretzner et al., 1992; Amati et al., 1993a, 1993b). Bronx, New York 10461 In addition to its key role as an obligate partner in transacti- vation-competent Myc-Max complexes, Max may also re- press Myc-responsive genes through the formation of Summary transactivation-inert complexes that are capable of bind- ing the Myc-Max recognition sequence (Blackwood et al., Documented interactions among members of the Myc 1992; Kato et al., 1992; Kretzner et al., 1992; Makela et superfamily support a yin-yang model for the regula- al., 1992; Mukherjee et al., 1992; Prendergast et al., 1992; tion of Myc-responsive genes in which t ransactivation- Ayer et al., 1993; Zervos et al., 1993).
    [Show full text]
  • Transcriptional Control of Tissue-Resident Memory T Cell Generation
    Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2019 © 2019 Filip Cvetkovski All rights reserved ABSTRACT Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Tissue-resident memory T cells (TRM) are a non-circulating subset of memory that are maintained at sites of pathogen entry and mediate optimal protection against reinfection. Lung TRM can be generated in response to respiratory infection or vaccination, however, the molecular pathways involved in CD4+TRM establishment have not been defined. Here, we performed transcriptional profiling of influenza-specific lung CD4+TRM following influenza infection to identify pathways implicated in CD4+TRM generation and homeostasis. Lung CD4+TRM displayed a unique transcriptional profile distinct from spleen memory, including up-regulation of a gene network induced by the transcription factor IRF4, a known regulator of effector T cell differentiation. In addition, the gene expression profile of lung CD4+TRM was enriched in gene sets previously described in tissue-resident regulatory T cells. Up-regulation of immunomodulatory molecules such as CTLA-4, PD-1, and ICOS, suggested a potential regulatory role for CD4+TRM in tissues. Using loss-of-function genetic experiments in mice, we demonstrate that IRF4 is required for the generation of lung-localized pathogen-specific effector CD4+T cells during acute influenza infection. Influenza-specific IRF4−/− T cells failed to fully express CD44, and maintained high levels of CD62L compared to wild type, suggesting a defect in complete differentiation into lung-tropic effector T cells.
    [Show full text]
  • Supplementary Data
    SUPPLEMENTARY DATA A cyclin D1-dependent transcriptional program predicts clinical outcome in mantle cell lymphoma Santiago Demajo et al. 1 SUPPLEMENTARY DATA INDEX Supplementary Methods p. 3 Supplementary References p. 8 Supplementary Tables (S1 to S5) p. 9 Supplementary Figures (S1 to S15) p. 17 2 SUPPLEMENTARY METHODS Western blot, immunoprecipitation, and qRT-PCR Western blot (WB) analysis was performed as previously described (1), using cyclin D1 (Santa Cruz Biotechnology, sc-753, RRID:AB_2070433) and tubulin (Sigma-Aldrich, T5168, RRID:AB_477579) antibodies. Co-immunoprecipitation assays were performed as described before (2), using cyclin D1 antibody (Santa Cruz Biotechnology, sc-8396, RRID:AB_627344) or control IgG (Santa Cruz Biotechnology, sc-2025, RRID:AB_737182) followed by protein G- magnetic beads (Invitrogen) incubation and elution with Glycine 100mM pH=2.5. Co-IP experiments were performed within five weeks after cell thawing. Cyclin D1 (Santa Cruz Biotechnology, sc-753), E2F4 (Bethyl, A302-134A, RRID:AB_1720353), FOXM1 (Santa Cruz Biotechnology, sc-502, RRID:AB_631523), and CBP (Santa Cruz Biotechnology, sc-7300, RRID:AB_626817) antibodies were used for WB detection. In figure 1A and supplementary figure S2A, the same blot was probed with cyclin D1 and tubulin antibodies by cutting the membrane. In figure 2H, cyclin D1 and CBP blots correspond to the same membrane while E2F4 and FOXM1 blots correspond to an independent membrane. Image acquisition was performed with ImageQuant LAS 4000 mini (GE Healthcare). Image processing and quantification were performed with Multi Gauge software (Fujifilm). For qRT-PCR analysis, cDNA was generated from 1 µg RNA with qScript cDNA Synthesis kit (Quantabio). qRT–PCR reaction was performed using SYBR green (Roche).
    [Show full text]
  • 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
    [Show full text]
  • The Id-Protein Family in Developmental and Cancer-Associated Pathways Cornelia Roschger and Chiara Cabrele*
    Roschger and Cabrele Cell Communication and Signaling (2017) 15:7 DOI 10.1186/s12964-016-0161-y REVIEW Open Access The Id-protein family in developmental and cancer-associated pathways Cornelia Roschger and Chiara Cabrele* Abstract Inhibitors of DNA binding and cell differentiation (Id) proteins are members of the large family of the helix-loop- helix (HLH) transcription factors, but they lack any DNA-binding motif. During development, the Id proteins play a key role in the regulation of cell-cycle progression and cell differentiation by modulating different cell-cycle regulators both by direct and indirect mechanisms. Several Id-protein interacting partners have been identified thus far, which belong to structurally and functionally unrelated families, including, among others, the class I and II bHLH transcription factors, the retinoblastoma protein and related pocket proteins, the paired-box transcription factors, and the S5a subunit of the 26 S proteasome. Although the HLH domain of the Id proteins is involved in most of their protein-protein interaction events, additional motifs located in their N-terminal and C-terminal regions are required for the recognition of diverse protein partners. The ability of the Id proteins to interact with structurally different proteins is likely to arise from their conformational flexibility: indeed, these proteins contain intrinsically disordered regions that, in the case of the HLH region, undergo folding upon self- or heteroassociation. Besides their crucial role for cell-fate determination and cell-cycle progression during development, other important cellular events have been related to the Id-protein expression in a number of pathologies. Dysregulated Id-protein expression has been associated with tumor growth, vascularization, invasiveness, metastasis, chemoresistance and stemness, as well as with various developmental defects and diseases.
    [Show full text]
  • Integration of 198 Chip-Seq Datasets Reveals Human Cis-Regulatory Regions
    Integration of 198 ChIP-seq datasets reveals human cis-regulatory regions Hamid Bolouri & Larry Ruzzo Thanks to Stephen Tapscott, Steven Henikoff & Zizhen Yao Slides will be available from: http://labs.fhcrc.org/bolouri/ Email [email protected] for manuscript (Bolouri & Ruzzo, submitted) Kleinjan & van Heyningen, Am. J. Hum. Genet., 2005, (76)8–32 Epstein, Briefings in Func. Genom. & Protemoics, 2009, 8(4)310-16 Regulation of SPi1 (Sfpi1, PU.1 protein) expression – part 1 miR155*, miR569# ~750nt promoter ~250nt promoter The antisense RNA • causes translational stalling • has its own promoter • requires distal SPI1 enhancer • is transcribed with/without SPI1. # Hikami et al, Arthritis & Rheumatism, 2011, 63(3):755–763 * Vigorito et al, 2007, Immunity 27, 847–859 Ebralidze et al, Genes & Development, 2008, 22: 2085-2092. Regulation of SPi1 expression – part 2 (mouse coordinates) Bidirectional ncRNA transcription proportional to PU.1 expression PU.1/ELF1/FLI1/GLI1 GATA1 GATA1 Sox4/TCF/LEF PU.1 RUNX1 SP1 RUNX1 RUNX1 SP1 ELF1 NF-kB SATB1 IKAROS PU.1 cJun/CEBP OCT1 cJun/CEBP 500b 500b 500b 500b 500b 750b 500b -18Kb -14Kb -12Kb -10Kb -9Kb Chou et al, Blood, 2009, 114: 983-994 Hoogenkamp et al, Molecular & Cellular Biology, 2007, 27(21):7425-7438 Zarnegar & Rothenberg, 2010, Mol. & cell Biol. 4922-4939 An NF-kB binding-site variant in the SPI1 URE reduces PU.1 expression & is GGGCCTCCCC correlated with AML GGGTCTTCCC Bonadies et al, Oncogene, 2009, 29(7):1062-72. SATB1 binding site A distal single nucleotide polymorphism alters long- range regulation of the PU.1 gene in acute myeloid leukemia Steidl et al, J Clin Invest.
    [Show full text]
  • Transcriptional and Post-Transcriptional Regulation of ATP-Binding Cassette Transporter Expression
    Transcriptional and Post-transcriptional Regulation of ATP-binding Cassette Transporter Expression by Aparna Chhibber DISSERTATION Submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in Pharmaceutical Sciences and Pbarmacogenomies in the Copyright 2014 by Aparna Chhibber ii Acknowledgements First and foremost, I would like to thank my advisor, Dr. Deanna Kroetz. More than just a research advisor, Deanna has clearly made it a priority to guide her students to become better scientists, and I am grateful for the countless hours she has spent editing papers, developing presentations, discussing research, and so much more. I would not have made it this far without her support and guidance. My thesis committee has provided valuable advice through the years. Dr. Nadav Ahituv in particular has been a source of support from my first year in the graduate program as my academic advisor, qualifying exam committee chair, and finally thesis committee member. Dr. Kathy Giacomini graciously stepped in as a member of my thesis committee in my 3rd year, and Dr. Steven Brenner provided valuable input as thesis committee member in my 2nd year. My labmates over the past five years have been incredible colleagues and friends. Dr. Svetlana Markova first welcomed me into the lab and taught me numerous laboratory techniques, and has always been willing to act as a sounding board. Michael Martin has been my partner-in-crime in the lab from the beginning, and has made my days in lab fly by. Dr. Yingmei Lui has made the lab run smoothly, and has always been willing to jump in to help me at a moment’s notice.
    [Show full text]