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SUPPLEMENTARY TABLES AND FIGURE LEGENDS Supplementary Figure 1. Quantitation of MYC levels in vivo and in vitro. a) MYC levels in cell lines 6814, 6816, 5720, 966, and 6780 (corresponding to first half of Figure 1a in main text). MYC is normalized to tubulin. b) MYC quantitations (normalized to tubulin) for cell lines Daudi, Raji, Jujoye, KRA, KRB, GM, and 6780 corresponding to second half of Figure 1a. c) In vivo MYC quantitations, for mice treated with 0-0.5 ug/ml doxycycline in their drinking water. MYC is normalized to tubulin. d) Quantitation of changing MYC levels during in vitro titration, normalized to tubulin. e) Levels of Odc (normalized to tubulin) follow MYC levels in titration series. Supplementary Figure 2. Evaluation of doxycycline concentration in the plasma of mice treated with doxycycline in their drinking water. Luciferase expressing CHO cells (Tet- off) (Clonethech Inc) that is responsive to doxycycline by turning off luciferase expression was treated with different concentrations of doxycycline in culture. A standard curve (blue line) correlating luciferase activity (y-axis) with treatment of doxycycline (x- axis) was generated for the CHO cell in culture. Plasma from mice treated with different concentrations of doxycycline in their drinking water was separated and added to the media of the CHO cells. Luciferase activity was measured and plotted on the standard curve (see legend box). The actual concentration of doxycycline in the plasma was extrapolated for the luciferase activity measured. The doxycycline concentration 0.2 ng/ml measured in the plasma of mice correlates with 0.05 μg/ml doxycycline treatment in the drinking water of mice, the in vivo threshold for tumor regression. Supplementary Figure 3. Comparison between microarray gene expression analysis and quantitative PCR analysis. Candidate gene expression profiles were extracted from the microarray analysis and compared to the profile determined by quantitative PCR. Supplementary Figure 4. Overview of MYC interactions significant in the titration experiment, generated by IPA. Red and green colors indicate gene probes that were identified in the SAM analysis to be differentially expressed above and below threshold, respectively. Red nodes represent array probes that were up-regulated when MYC was high, while green nodes were down-regulated as MYC levels declined. The strength of coloration indicates the mean fold change between the above- and below-threshold samples. Supplementary Figure 5. MYC inactivation and differentiation. a) Changes in the expression of surface proteins of the double positive CD4+/CD8+ T-cell were analyzed using a custom printed antibody array. MYC expressing tumor cells were labeled with a green Cy3 dye; and cells where MYC had been inactivated for 24 hrs were labeled with a red Cy5 dye. We customized an antibody array to capture live cells based on the expression of surface proteins. Over 90 different antibodies against lymphoid surface proteins were spotted on glass slides (Supplementary Table 3). Antibody spots that bound cells were grouped according to the ratio of red (MYC ON) or green (MYC OFF) cells. Top 2 rows MYC ON>MYC OFF; third row MYC ON=MYC OFF; bottom row MYC ON<MYC OFF. b) MYC expressing tumor cells were analyzed for CD5 and CD29 expression by FACS. Cells were treated with doxycycline to regulate different levels of MYC expression for 24 hrs. Supplementary Figure 6. MYC inactivation and changes in phosphorylation. Analysis of changes in phospho-protein expression by phospho-flow FACS analysis. MYC- induced lymphoma cell lines were treated for 24 and 36 hours, as indicated. The response to MYC inactivation was determined and compared with the basal state, shown in the profile in black. Cells were analyzed for changes in phosphorylation of 56 different phospho-protein epitopes. Significant changes are indicated by the yellow color. Supplementary Table 1. Changes in known Myc target genes, detected by StepMiner. First column indicates position in titration series, second column shows the number of probes that are up/down-regulated at that step. Column 3 lists the symbols for genes at that step. Step N Gene symbols Up-regulated 1 5 2210404E10Rik Hmox2 Rpl21 Tgfbr2 Tk1 Akap12 Btg1 Ccnd3 Cd3e Creb1 Daxx Ddx17 Ddx5 Gadd45g Irf3 Itm2b Lims1 Msn Phb Ppp2r5e 3 29 Prpsap1 Psmd5 Rac2 Rb1 Rock1 Sdcbp Tgfbr2 Thy1 Tle3 Tmsb4x Trip12 Tyk2 Usp11 Vasp 4 12 Bbc3 Btg1 Casp8 Clcn6 Klf7 Kns2 Mapk7 Mcl1 Mst1 Nr1d1 Pdgfra Ptprn 5 16 Bcl2 Btg1 Cast Clcn2 Cmas Dusp6 Evpl Fads2 Gak Gamt Gnpat Mdm2 Pdk2 Prkab1 Rps12 Traf2 6 9 Arpc4 Ccng2 Ddx5 Epc1 Itm2b Pex6 Rfc2 Sgpl1 Ubl3 7 4 Col6a3 Itpkb Spna2 Vamp1 8 3 Gdap1 Myo1b Vamp1 9 4 Atf6 Il1rap Naga Rara 10 6 Clcn6 Crebl2 Dgcr6 Galc Hoxd3 Ifnar1 Down-regulated 1 3 Atf4 Blzf1 Lamb2 2 10 Ak3 Akap12 Aldh2 Asns Ppp2r4 Rpl13a Slc25a4 Sord Srpr Ubqln1 Alg5 B3galt1 Blk Cad Cxcl12 Eef2 Ell Ephb2 Fkbp4 Gtpbp4 Hip2 Hk2 Hmbs Iars Klf4 Klf4 Nmt1 3 20 Ppp3ca Slc12a2 Zfp95 Ak3 Asns Atf4 Bmp4 Cacybp Cct5 Cdkn1a Csda Ctnna2 Ctps Cycs Ddx18 Dnaja2 Dscr2 Eprs Ero1l Ero1l Fpgs Fzd5 Gcn1l1 Hadhb Hdgf Hmga1 Hnrpa1 Hs3st2 Hspd1 Hspd1 Ifrd2 Insr Klf4 Mat2a Mcc Mcm6 Mettl1 Mgst3 Mir16 Mitf Mki67ip Mpp6 Mrpl1 Nap1l1 Nap1l1 Ncbp2 Ncl Nol1 4 114 Nup155 Nup54 Nup88 Odc1 Pa2g4 Pa2g4 Paics Pcmt1 Pgk1 Pgk1 Pkm2 Pmm2 Pole3 Ppid Prep Psma5 Psmc5 Psmd7 Psmd7 Psph Ptp4a1 Rad54l Ran Rbm8a Rnf4 Rqcd1 Sco1 Sec61b Sfrs2 Slc16a1 Slc25a3 Slc2a1 Slc7a5 Snx3 Snx5 Srp68 Srpk1 Suclg1 Syngr2 Tcta Thop1 Timm9 Txnl4 Ubqln1 Usp1 Vapa Vdac1 Vdac2 Wdr4 Wnt10b Yme1l1 Adk Ak2 Akap1 Akap1 Ap4s1 Apex1 Arl1 Ash2l Atp1b3 Atp5b Atp5g2 Bax Bcat1 Bcat1 Cct3 Cct6a Cherp Clcn3 Clns1a Coro1c Ctps Cyp51 Ddb1 Dffa Dnajb1 Eif2b1 Eif3s10 Eif3s2 Eif3s8 Eif3s9 Eif4b Eif4ebp2 Eif5a Etf1 Fasn Fbl Gabpa Hmbs Hnrpdl Hnrpdl Hoxd13 Hspa4 Hspa8 Hspe1 Imp4 Impdh1 Impdh2 Jtv1 Khsrp Klf1 Ldha Lmna Magoh Mcm7 Metap2 Mthfd1 Mthfd2 5 112 Mybbp1a Mybbp1a Naca Nme2 Nol5a Nola1 Nolc1 Nras Nudc Pcm1 Pex14 Pfkm Phgdh Pmm2 Pold2 Polr2h Ppat Prdx3 Psma1 Psmb5 Qdpr Rnps1 Rpl13a Rpl13a Rpl13a Rpl27a Rpl27 Rpl37 Rplp1 Rpp30 Rps7 Rps9 Rps9 Sap30 Scarb1 Sfrs1 Shmt1 Smn1 Snrpd3 Snx3 Snx5 Srm Suclg1 Syngr2 Tbl3 Tfam Tfdp1 Timm23 Timm9 Timm9 Trap1 Txnl4 Uqcrc2 Wbscr1 Wdr3 6 7 Clcn3 Eef2 Mrpl1 Mybl2 Nol1 Psme3 Snrpa Cdc2l1 Fzd5 Mybbp1a Ppp2r4 Rpl13a Rpl13a Rpl13a Rpl13a Rpl13a Rpl30 Rps27l Rps5 Slc12a2 7 14 Tgif2 8 2 Ak2 Rpl13a 9 7 Cbs Esd Mdh1 Mdh1 Ppp2ca Psmb7 Surf6 Arfrp1 Atp5c1 Calm2 Casp3 Cks2 Dpagt1 H2afz Hnrph1 Lta4h Mcm3 Ngfrap1 Prdx4 Rad50 Rbm3 10 25 Rpl13 Rpl23 Rpl38 Rpl7 Rps16 Rps17 Rps25 Rps26 Tk1 Top1 Ube2c Supplementary Table 2. Comparison of levels of phosphorylated proteins measured by FACs, to the gene expression of the protein or its upstream activators, as MYC is inactivated. ‘-‘ indicates no significant change. Phospho- Change Gene Gene Phosphorylated by Gene protein upon MYC expression expression of and inactivation change upon upstream phospho- MYC activator site inactivation Raf1 Raf1 - S43 phosphorylated by PKA Prkacb up S43 Up (Prkaca, Prkacg, Prkacb) weakly S259 Up S259 is phosphorylated by Ppp2ca down Akt/PKA/PKB and dephosphorylated by PP2A (Ppp2ca, Ppp2cb) Stat3 Up Stat3 - P38mapk (mapk14) Mapk14 S727 MEK/ERK (mek1) down OSM (oncostatin M) OSM up irak1 Irak1 up Mapk14 up Mek Up Map2k1 - PAK1 (Cdkn1a) PAK1 up S298 Vav Up vav1 Up Lck Lck up Y160 P38 Up mapk14 Down CD26 (Dpp4/Dppiv) is Dpp4 (CD26) upstream effector of up phosphorylation Supplementary Table 3. Antibodies spotted for the Cell Array. Annexin V CD29 CD62L I-A/I-E CD1d CD30 CD62P I-Ab CD2 CD31 CD69 I-AB CD3ε CD34 CD71 I-Ad CD3e CD41 CD90 I-Ek CD3ξ CD44 CD90 IgG1 CD4 CD44 CD95 IgM CD5 CD45 CD95 Integrin β7 chain CD8A CD45R (B220) CD102 Jagged CD11a CD45R/CT1 CD103 LPAM-1 CD11b CD45RA CD106 MAC-3 CD11c CD47 CD117 Mad-CAM-1 CD123 CD49a CD132 M-Cadherin CD13 CD49b CD135 Pan endothelial cell Ag CD16/32 CD49c CD140a PD-L1 (B7-H1) CD18 CD49d CD144 PD-L2 (B7-dc) CD19 CD49e Contactin R-Cadherin CD24 CD49f FLK-1 Sca-1 CD25 CD51 Forssman antigen Syndecan-1 CD28 CD61 GR-1 (LY-6D) TCRγδ CD29 CD62E H-2Kq ThB (Ly-6D) Supplementary Table 4. Comparison of behavior of surface protein levels, measured by antibody array and confirmed by FACS, to expression level of corresponding gene as MYC is inactivated. Surface protein FACS Gene expression CD44 down up/down (multiple probes) CD3e up up CD8a/b down up CD5 up up CD28 down up .
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    International Journal of Environmental Research and Public Health Article Functional Gene Clusters in Global Pathogenesis of Clear Cell Carcinoma of the Ovary Discovered by Integrated Analysis of Transcriptomes Yueh-Han Hsu 1,2, Peng-Hui Wang 1,2,3,4,5 and Chia-Ming Chang 1,2,* 1 Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei 112, Taiwan; [email protected] (Y.-H.H.); [email protected] (P.-H.W.) 2 School of Medicine, National Yang-Ming University, Taipei 112, Taiwan 3 Institute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan 4 Department of Medical Research, China Medical University Hospital, Taichung 440, Taiwan 5 Female Cancer Foundation, Taipei 104, Taiwan * Correspondence: [email protected]; Tel.: +886-2-2875-7826; Fax: +886-2-5570-2788 Received: 27 April 2020; Accepted: 31 May 2020; Published: 2 June 2020 Abstract: Clear cell carcinoma of the ovary (ovarian clear cell carcinoma (OCCC)) is one epithelial ovarian carcinoma that is known to have a poor prognosis and a tendency for being refractory to treatment due to unclear pathogenesis. Published investigations of OCCC have mainly focused only on individual genes and lack of systematic integrated research to analyze the pathogenesis of OCCC in a genome-wide perspective. Thus, we conducted an integrated analysis using transcriptome datasets from a public domain database to determine genes that may be implicated in the pathogenesis involved in OCCC carcinogenesis. We used the data obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) DataSets. We found six interactive functional gene clusters in the pathogenesis network of OCCC, including ribosomal protein, eukaryotic translation initiation factors, lactate, prostaglandin, proteasome, and insulin-like growth factor.
  • Characterization and Proteomic-Transcriptomic Investigation of Monocarboxylate Transporter 6 Knockout Mice

    Characterization and Proteomic-Transcriptomic Investigation of Monocarboxylate Transporter 6 Knockout Mice

    Supplemental material to this article can be found at: http://molpharm.aspetjournals.org/content/suppl/2019/07/10/mol.119.116731.DC1 1521-0111/96/3/364–376$35.00 https://doi.org/10.1124/mol.119.116731 MOLECULAR PHARMACOLOGY Mol Pharmacol 96:364–376, September 2019 Copyright ª 2019 by The American Society for Pharmacology and Experimental Therapeutics Characterization and Proteomic-Transcriptomic Investigation of Monocarboxylate Transporter 6 Knockout Mice: Evidence of a Potential Role in Glucose and Lipid Metabolism s Robert S. Jones, Chengjian Tu, Ming Zhang, Jun Qu, and Marilyn E. Morris Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, New York (R.S.J., C.T., J.Q., M.E.M.); and New York State Center of Excellence in Bioinformatics and Life Sciences, Buffalo, New York (C.T., M.Z., J.Q.) Received March 21, 2019; accepted June 27, 2019 Downloaded from ABSTRACT Monocarboxylate transporter 6 [(MCT6), SLC16A5] is an or- Mct62/2 mice demonstrated significant changes in 199 genes phan transporter with no known endogenous substrates or in the liver compared with Mct61/1 mice. In silico biological physiological role. Previous in vitro and in vivo experiments pathway analyses revealed significant changes in proteins and molpharm.aspetjournals.org investigated MCT6 substrate/inhibitor specificity in Xenopus genes involved in glucose and lipid metabolism–associated laevis oocytes; however, these data remain limited. Transcrip- pathways. This study is the first to provide evidence for an tomic changes in the livers of mice undergoing different dieting association of Mct6 in the regulation of glucose and lipid schemes have suggested that Mct6 plays a role in glucose metabolism.