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Supplemental Table S4: Highest Expressed Transcripts in the Axons of Hesc-Neurons

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Supplemental Table S4: Highest expressed transcripts in the axons of hESC‐neurons AACS AC090044.2 AGAP8 AP3B2 ATP6 BRSK2 ABC7 AC092155.1 AGAP9 APLN ATP6AP1 BSG ABCC6 AC092635.1 AGTRAP APOA1BP ATP6V0E2 BST1 ABCF1 AC093627.7 AIPL1 APOA2 ATP6V1F BSX ABHD17A AC093627.8 AK1 APOA5 ATP9A BTG2 ABI3 AC093724.2 AK2 APOBEC3B ATXN7L1 BX571672.4 ABLIM1 AC093822.1 AK8 APOC2 AURKAIP1 C10ORF12 ABLIM3 AC103965.1 AKAP8 APOC4 AURKB C10ORF43 ABT1 AC104024.2 AKNA AQP1 AZI1 C10ORF54 AC002310.11 AC108066.1 AKR1B1 AQP12A B3GALNT1 C10ORF71 AC002398.9 AC108938.4 AKR1B1P6 ARAP1 B3GNT1 C11ORF39 AC003084.2 AC126281.3 AKT1 ARF3 B3GNT6 C11ORF87 AC004985.12 AC126281.8 AL133247.2 ARHGAP22‐IT1 B3GNT8 C11ORF9 AC005062.3 AC127391.1 AL928768.3 ARHGAP35 B3GNTL1 C11ORF93 AC006003.3 AC128677.4 ALAS1 ARHGAP4 B4GALT2 C12ORF57 AC006070.12 AC129778.7 ALDH1L1 ARHGEF15 BAIAP3 C12ORF68 AC006482.1 AC138969.4 ALDH3B1 ARHGEF16 BANP C12ORF75 AC006548.20 ACBD3 ALDOA ARHGEF38‐IT1 BARHL1 C14ORF144 AC007036.6 ACTA1 ALG12 ARID1B BASP1 C14ORF79 AC007040.5 ACTB ALPI ARID3A BASP1P1 C16ORF11 AC007193.6 ACTG1 AMOTL1 ARL17 BAT2 C16ORF54 AC007381.3 ACTR2 ANKDD1A ARL17A BATF2 C16ORF74 AC008993.5 ACTR3BP5 ANKRD20A19P ARL17B BBC3 C16ORF91 AC009505.4 ADAMTSL2 ANKRD23 ARL4C BCDIN3D C17ORF103 AC010084.1 ADAP1 ANKRD27 ARL6IP4 BCL2L2 C17ORF62 AC010891.2 ADAP2 ANKRD36B ARL8A BDKRB2 C17ORF63 AC011330.5 ADH1C ANKRD39 ARMCX4 BEX1 C19ORF35 AC011718.3 ADIG ANKRD65 ARMS2 BEX4 C19ORF48 AC011737.2 ADM ANO1 ARPC1A BGN C19ORF55 AC011995.1 ADM5 ANP32AP1 ARPC2 BHLHA15 C19ORF60 AC012318.3 ADORA2A ANXA11 ARRB2 BLOC1S3 C19ORF71 AC013271.3 ADPRHL1 ANXA8 ASB16 BMP8A C19ORF80 AC016710.1 ADRM1 ANXA8L1 ASGR2 BMS1 C1ORF123 AC022210.2 ADSL ANXA8L2 ASIP BMS1P5 C1ORF138 AC023490.1 AE000658.31 AP000350.7 ASPG BNIP3 C1ORF159 AC023490.2 AEBP1 AP000462.3 ATAD3B BNIP3L C1ORF170 AC068580.5 AEN AP000704.5 ATF1 BNIP3P1 C1ORF95 AC069368.3 AF015262.2 AP001055.6 ATF5 BOK C1QL4 AC073046.25 AFF2 AP001271.3 ATOH1 BOLA2 C1QTNF6 AC074183.3 AGAP10 AP001476.4 ATP13A5 BOLA2B C20ORF100 AC074183.4 AGAP11 AP001631.10 ATP1B2 BOP1 C20ORF141 AC074389.7 AGAP4 AP002414.1 ATP2B4 BRD1 C20ORF197 AC083864.3 AGAP5 AP002856.6 ATP5D BRF1 C20ORF57 AC084121.16 AGAP6 AP1M1 ATP5I BRI3 C21ORF128 AC087491.2 AGAP7 AP2S1 ATP5SL BRI3BP C21ORF15 AC090044.2 AGAP8 AP3B2 ATP6 BRSK2 C21ORF67 C21ORF67 CCDC107 CEACAM20 CLDND2 CRNN CTD‐2369P2.10 C22ORF32 CCDC12 CEBPB CLEC11A CRTC1 CTD‐2545M3.6 C2ORF48 CCDC130 CEBPD CLK2P CRYBB2 CTD‐2547E10.2 C2ORF57 CCDC153 CECR6 CLPB CRYBB3 CTD‐2555C10.3 C2ORF62 CCDC157 CECR9 CLPP CRYGD CTD‐3051D23.3 C2ORF71 CCDC69 CELA2A CLUHP3 CSAG1 CTD‐3148I10.9 C2ORF91 CCDC71L CELA2B CLYBL CSAG2 CTD‐3193O13.9 C4A CCDC74A CELF5 CMKLR1 CSAG3 CTD‐3194G12.2 C4B CCDC74B CELSR2 CNBP CSH2 CTD‐3232M19.2 C4B_2 CCDC86 CEMP1 CNFN CSNK1E CTDNEP1 C4ORF6 CCDC90B CENPT CNN1 CSNK2B CTF1 C5ORF60 CCDC94 CEP164 CNN2 CSPG4P8 CTGLF10P C6ORF15 CCL13 CEP170B CNOT3 CST2 CTGLF11P C6ORF205 CCL19 CERCAM CNPY3 CST3 CTGLF12P C6ORF48 CCL21 CERS1 CNTFR CST4 CTGLF8P C7ORF41 CCL3L1 CES1 CNTNAP3 CST6 CTGLF9P C7ORF65 CCL3L3 CES1P2 CNTNAP3B CST7 CTRB1 C8ORF46 CCL7 CFTR COA4 CSTB CTRL C9ORF114 CD151 CGB COL11A2 CT45A1 CTSD C9ORF141 CD2 CGB1 COL20A1 CT45A2 CTSL1P2 C9ORF142 CD300C CGB2 COL23A1 CT45A3 CTXN1 C9ORF50 CD300LG CGB5 COL25A1 CT45A4 CX3CL1 CA5BP1 CD33 CGB8 COL26A1 CT45A5 CXCL2 CABLES2 CD5 CHAMP1 COL5A3 CT45A6 CXCR4 CACNA1G CD5L CHCHD10 COL6A2 CT47B1 CXORF40B CACTIN CD63 CHGB COL6A3 CTA CXORF49 CADM3 CD7 CHMP1A COMMD7 CTAG2 CXORF49B CALHM3 CD79A CHMP4B COPG1 CTAGE15 CXORF51A CALM1 CD81 CHMP6 COPS6 CTAGE4 CXORF51B CALM2 CD8A CHPF2 COX1 CTAGE6 CXORF61 CALM3 CD93 CHRFAM7A COX19 CTAGE8 CYAT1 CALML3 CD99 CHRM4 COX2 CTAGE9 CYGB CALML6 CDC37 CHRNA4 COX3 CTB‐31O20.4 CYP11B1 CAMK2N1 CDH24 CHRNB2 COX6A2 CTB‐96E2.2 CYP21A1P CAMKV CDK18 CHRNE COX7A2 CTC‐228N24.3 CYP21A2 CAMTA2 CDK2AP2P2 CHTF18 CPLX1 CTC‐260F20.3 CYP2A6 CAPN10 CDK5R2 CIC CPLX2 CTC‐276P9.1 CYP2A7 CAPN12 CDKN2D CKAP2 CPLX3 CTC‐276P9.3 CYP2A7P1 CARM1 CDO1 CKMT1A CPNE2 CTC‐497E21.5 CYP2D6 CARS CDR1 CKMT1B CPNE7 CTC‐518B2.8 CYSTM1 CASKIN2 CDRT15 CKS1B CPSF6 CTD‐2034I21.2 CYTB CASP14L CDRT15P2 CLCF1 CPT1C CTD‐2193G5.1 CYTL1 CASP16 CDX1 CLDN19 CRABP1 CTD‐2207P18.1 DADB CBX1 CDX2 CLDN25 CRB2 CTD‐2331C18.5 DAMA CCDC104 CDYL CLDN3 CRLF1 CTD‐2331C18.9 DAND5 CCDC107 CEACAM20 CLDND2 CRNN CTD‐2369P2.10 DAPK3 DAPK3 DNM1P41 EEF1B2 FAIM2 FAM83E FLJ44790 DARC DNM1P46 EEF1D FAM101B FAM83H FLJ45340 DASS DNMT1 EEF1G FAM106A FAM86B2 FLJ45445 DAZL DOC2B EFCC1 FAM106B FAM86C2P FLJ45513 DCAF15 DOK2 EFHD2 FAM106CP FAM86DP FLNC DCDC1 DOK4 EFS FAM109A FAM89B FLT3LG DCDC5 DOK7 EGFL7 FAM110A FAM90A10P FLYWCH1 DCTN3 DOT1L EIF3C FAM120B FAM90A24P FLYWCH2 DCTN6 DPCD EIF3CL FAM132A FAM90A25P FNDC4 DDIT4 DPCR1 EIF3D FAM138E FAM90A27P FOLR4 DDR1 DPM3 EIF3G FAM150B FAM90A7P FOXA2 DDT DPP7 EIF4A1 FAM159B FAM95A FOXD4L6 DDX11 DPY19L2 EIF4H FAM170B FAM99B FOXH1 DDX11L10 DRD5 ELAVL3 FAM184B FANK1 FOXN4 DDX54 DSTN ELOVL6 FAM201A FAT3 FOXP3 DEAF1 DTX2P1 ELP3 FAM203A FBRS FOXP4 DEDD2 DTX4 EMC9 FAM203B FBXL14 FOXS1 DEFB124 DUSP15 EME2 FAM207A FBXO9 FRMD8P1 DEFB128 DUSP2 EMID1 FAM213A FCGR1B FRMPD3 DEFT1P DUSP26 EMID2 FAM21A FCGR1C FSTL3 DEFT1P2 DUX2 EMP3 FAM21B FCN2 FTH1P3 DEXI DUX4 EMX1 FAM21C FCN3 FTL DGCR11 DUX4L10 ENGASE FAM21D FDX1L FTSJD2 DGKZ DUX4L11 ENHO FAM21EP FEM1B FUT5 DGKZP1 DUX4L12 ENTPD6 FAM21FP FERD3L FUT6 DHRS4L1 DUX4L13 EPB42 FAM222A FGD1 FXYD2 DHRS4L2 DUX4L14 EPC1 FAM222B FGF13 FXYD6 DHRS7 DUX4L15 EPN1 FAM226B FGF14 FZD4 DIRAS2 DUX4L2 EPN2 FAM22E FIBP GABARAPL2 DISC1‐IT1 DUX4L3 EPN2‐IT1 FAM25A FJX1 GABBR1 DKFZP434F142 DUX4L4 EPN3 FAM25B FKBP1B GABRR2 DKFZP434L187 DUX4L5 EPOR FAM25C FKBP3 GADD45GIP1 DKFZP779M0652 DUX4L6 EPS15L1 FAM25D FKBP8 GAGE1 DKK3 DUX4L7 ERCC1 FAM25E FKSG49 GAGE12B DLK2 DUX4L8 ERVK3‐1 FAM25G FKSG52 GAGE12C DLX1 DUX4L9 ETFB FAM25HP FLI1 GAGE12D DMRTB1 DVL1 ETS2 FAM27A FLJ22184 GAGE12E DMRTC1 DYNLL1 EVPL FAM27B FLJ33630 GAGE12F DMRTC1B DYNLRB1 EXOC7 FAM27C FLJ35934 GAGE12G DMRTC2 EAF1 F11R FAM27E2 FLJ39739 GAGE12H DNAJC5 ECSCR F2R FAM32A FLJ41423 GAGE12I DNAJC6 EDC4 F8A1 FAM46B FLJ41733 GAGE12J DNAJC9 EDF1 F8A2 FAM58A FLJ42102 GAGE13 DNM1 EEF1A1 F8A3 FAM74A3 FLJ42351 GAGE2A DNM1P35 EEF1A2 FADS1 FAM78B FLJ42875 GAGE2B DNM1P41 EEF1B2 FAIM2 FAM83E FLJ44790 GAGE2C GAGE2C GOLGA2P1 GPR35 HCG27 HNRNPCP5 IGHV1‐24 IGKV5‐2 GAGE2D GOLGA2P2Y GPSM1 HCG9 HNRNPK IGHV2‐70 IGLC1 GAGE2E GOLGA2P3Y GPSM3 HCN4 HOXA IGHV3‐13 IGLC2 GAGE4 GOLGA6A GPT HCST HOXB IGHV3‐30 IGLC3 GAGE5 GOLGA6B GRAPL HDAC10 HOXC IGHV3‐33 IGLJ2 GAGE6 GOLGA6C GREM1 HDAC7 HOXD10 IGHV3‐53 IGLJ3 GAGE7 GOLGA6D GRIFIN HDDC2 HPCA IGHV3OR16‐7 IGLJ7 GAGE8 GOLGA6L1 GRIN3B HDGFRP3 HR IGHV3OR16‐8 IGLL1 GAL3ST1 GOLGA6L10 GRM4 HEATR7A HRAS IGHV4‐31 IGLV GAL3ST2 GOLGA6L3 GS1 HERC2P9 HRC IGHV4‐34 IGLV10‐54 GALK1 GOLGA6L4 GSDMD HEXDC HSP90AB1 IGHV4‐59 IGLV1‐36 GALNS GOLGA6L5 GSTA1 HHIPL2 HSPA12B IGHV4‐61 IGLV1‐44 GALR2 GOLGA6L6 GSTA5 HIF3A HSPA1A IGHV5‐51 IGLV2‐11 GAP43 GOLGA6L9 GSTP1 HINT1 HSPA1B IGHV6‐1 IGLV2‐14 GAS6 GOLGA8A GSTTP1 HIPK2 HSPB1 IGK IGLV2‐8 GAS8 GOLGA8B GSX2 HIST1H1E HSPBP1 IGKC IGLV3‐10 GATA1 GOLGA8CP GTF2I HIST1H2BB HTR3A IGKJ5 IGLV3‐12 GDF1 GOLGA8DP GUCA2A HIST1H2BH ICAM3 IGKV1‐12 IGLV3‐16 GDF15 GOLGA8EP GUCA2B HIST1H2BK IDH2 IGKV1‐16 IGLV3‐25 GDI1 GOLGA8F GUSB HIST1H2BL IER3 IGKV1‐17 IGLV3‐9 GGT1 GOLGA8G GZMM HIST1H3A IFI6 IGKV1‐27 IGLV4‐3 GGT2 GOLGA8I H1F0 HIST1H3B IFITM2 IGKV1‐33 IGSF23 GGT5 GOLGA8J H1FOO HIST1H3C IFITM3 IGKV1‐37 IGSF3 GGTLC2 GOLGA8M H2AFJ HIST1H3D IFNA14 IGKV1‐39 IL17RA GGTLC3 GOLGA8N H3F3A HIST1H3E IFNA2 IGKV1‐9 IL23A GH2 GOLGA8O H3F3AP4 HIST1H3F IFNA5 IGKV1D‐12 IL2RB GHRHR GOLGA8Q H3F3AP6 HIST1H3G IFNL3 IGKV1D‐16 IL4R GIPC1 GOLGA8R H3F3B HIST1H3H IGDCC3 IGKV1D‐33 IL9R GIPC3 GOLGA8T H3F3BP1 HIST1H3I IGFALS IGKV1D‐37 ILF3 GJA4 GOLM1 H3F3C HIST1H3J IGFBP4 IGKV1D‐39 IMP4 GK‐IT1 GP1BB HAGH HIST2H2BA IGFBP6 IGKV1OR10‐1 INSM1 GLI2 GPC1 HAGHL HIST3H2BB IGH IGKV1OR‐2 INTS4L2 GLTSCR2 GPD1L HAMP HLA‐A IGHA1 IGKV1OR2‐118 IPO9 GLYCTK GPER HAS3 HLA‐DOA IGHA2 IGKV2‐28 IQSEC1 GLYR1 GPHA2 HAUS5 HLA‐DPB2 IGHD2‐15 IGKV2‐30 IQSEC3 GMFG GPIHBP1 HBA1 HLA‐DQB1 IGHD2‐21 IGKV2D‐24 IRF9 GNAO1 GPKOW HBA2 HLA‐F IGHE IGKV2D‐28 IRGQ GNAS GPR101 HBCBP HLA‐L IGHEP1 IGKV2D‐30 IRX3 GNAZ GPR123 HBE1 HMGA1 IGHG1 IGKV3‐11 ISG15 GNB2 GPR137 HBM HMGA1P7 IGHG3 IGKV3‐15 ISM2 GNB3 GPR142 HCAR3 HMGN2P15 IGHG4 IGKV3‐20 ISOC2 GNG3 GPR144 HCCAT4 HMOX1 IGHJ1 IGKV3D‐11 ITGB2 GNG7 GPR148 HCFC1 HMOX2 IGHJ2 IGKV3D‐15 ITGB5 GNRH2 GPR153 HCFC1R1 HNRNPA0 IGHM IGKV3D‐20 ITPA GOLGA2 GPR3 HCG23 HNRNPA1P10 IGHV1‐12 IGKV3D‐7 ITPK1 GOLGA2P1 GPR35 HCG27 HNRNPCP5 IGHV1‐24 IGKV5‐2 ITPRIPL2 ITPRIPL2 KIF4A KRTAP5 LIMS3 LOC100128593 IWS1 KIF5C KRTAP5‐1 LIMS3L LOC100128644 IZUMO4 KIFC1 KRTAP5‐4 LIN37 LOC100128818 JDP2 KIR2DL1 KRTAP5‐7 LINC00083 LOC100128946 JMJD4 KIR3DL1 KRTAP5‐8 LINC00087 LOC100129027 JPH4 KIR3DL2 KRTAP9‐2 LINC00163 LOC100129033 JSRP1 KIR3DL3 KRTAP9‐3 LINC00173 LOC100129060 JUND KIRREL‐IT1 KRTAP9‐4 LINC00202‐1 LOC100129129 JUP KISS1 KRTAP9‐6 LINC00226 LOC100129138 KANK2 KISS1R KRTAP9‐7 LINC00273 LOC100129427 KANK3 KLC1 KRTAP9‐8 LINC00280 LOC100129434 KATNB1 KLF2 KRTAP9‐9 LINC00349 LOC100129473 KBTBD6 KLF4 KRTDAP LINC00350 LOC100129476 KCNC3 KLK11 KSR1 LINC00391 LOC100129540 KCNF1 KLK2 LAD1 LINC00415 LOC100129596 KCNG1 KLK6 LAMTOR1 LINC00482 LOC100129860 KCNG2 KLK8 LAMTOR4 LINC00484 LOC100130015 KCNIP3 KLK9 LARP1 LINC00485 LOC100130071 KCNJ4 KNOP1 LAT2 LINC00489 LOC100130093 KCNK16 KRAS LCE2A LINC00516 LOC100130111 KCNK4 KRBA2 LCE3A LINC00528 LOC100130285 KCNK7 KREMEN2 LCE3B LINC00566 LOC100130370 KCNMB2 KRT16P2 LCN12 LINC00577 LOC100130480 KCNMB2‐IT1 KRT18P15 LDHB LINC00582 LOC100130713 KCNQ1DN KRT18P49 LDHD LINC00599 LOC100131608 KCNS1 KRT18P54 LDLRAD2 LINC00637 LOC100131825 KCNT1 KRT19P1 LDLRAD4 LINC00665 LOC100132055 KCP KRT23 LDOC1 LINC00684 LOC100132062 KCTD13 KRT6B LEPROTL1 LINC00686 LOC100132174 KDM6B KRT8 LEUTX LINC00693 LOC100132202 KHDC1 KRT81 LGALS1 LINC00701 LOC100132249 KHDC1L KRT85 LGALS4 LINC00839 LOC100132813 KHDC3L KRTAP10‐10 LGALS7 LINC00864 LOC100132999 KHSRP KRTAP10‐11 LGALS7B LINC00875 LOC100133091 KIAA1161 KRTAP10‐12 LGALS8 LINC00888 LOC100133161 KIAA1598 KRTAP10‐2 LGALS9B LINC00957 LOC100133251 KIAA1609 KRTAP10‐3 LGALS9C LINC00961 LOC100134361 KIAA2018 KRTAP10‐6 LGALSL LL22NC03‐121E8.
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    Chr CNV Start CNV Stop Gene Gene Feature 1 37261312 37269719

    chr CNV start CNV stop Gene Gene feature 1 37261312 37269719 Tmem131 closest upstream gene 1 37261312 37269719 Cnga3 closest downstream gene 1 41160869 41180390 Tmem182 closest upstream gene 1 41160869 41180390 2610017I09Rik closest downstream gene 1 66835123 66839616 1110028C15Rik in region 2 88714200 88719211 Olfr1206 closest upstream gene 2 88714200 88719211 Olfr1208 closest downstream gene 2 154840037 154846228 a in region 3 30065831 30417157 Mecom closest upstream gene 3 30065831 30417157 Arpm1 closest downstream gene 3 35476875 35495913 Sox2ot closest upstream gene 3 35476875 35495913 Atp11b closest downstream gene 3 39563408 39598697 Fat4 closest upstream gene 3 39563408 39598697 Intu closest downstream gene 3 94246481 94410611 Celf3 in region 3 94246481 94410611 Mrpl9 in region 3 94246481 94410611 Riiad1 in region 3 94246481 94410611 Snx27 in region 3 104311901 104319916 Lrig2 in region 3 144613709 144619149 Clca6 in region 3 144613709 144619149 Clca6 in region 4 108673 137301 Vmn1r2 closest downstream gene 4 3353037 5882883 6330407A03Rik in region 4 3353037 5882883 Chchd7 in region 4 3353037 5882883 Fam110b in region 4 3353037 5882883 Impad1 in region 4 3353037 5882883 Lyn in region 4 3353037 5882883 Mos in region 4 3353037 5882883 Penk in region 4 3353037 5882883 Plag1 in region 4 3353037 5882883 Rps20 in region 4 3353037 5882883 Sdr16c5 in region 4 3353037 5882883 Sdr16c6 in region 4 3353037 5882883 Tgs1 in region 4 3353037 5882883 Tmem68 in region 4 5919294 6304249 Cyp7a1 in region 4 5919294 6304249 Sdcbp in region 4 5919294
  • Clinical, Molecular, and Immune Analysis of Dabrafenib-Trametinib

    Clinical, Molecular, and Immune Analysis of Dabrafenib-Trametinib

    Supplementary Online Content Chen G, McQuade JL, Panka DJ, et al. Clinical, molecular and immune analysis of dabrafenib-trametinib combination treatment for metastatic melanoma that progressed during BRAF inhibitor monotherapy: a phase 2 clinical trial. JAMA Oncology. Published online April 28, 2016. doi:10.1001/jamaoncol.2016.0509. eMethods. eReferences. eTable 1. Clinical efficacy eTable 2. Adverse events eTable 3. Correlation of baseline patient characteristics with treatment outcomes eTable 4. Patient responses and baseline IHC results eFigure 1. Kaplan-Meier analysis of overall survival eFigure 2. Correlation between IHC and RNAseq results eFigure 3. pPRAS40 expression and PFS eFigure 4. Baseline and treatment-induced changes in immune infiltrates eFigure 5. PD-L1 expression eTable 5. Nonsynonymous mutations detected by WES in baseline tumors This supplementary material has been provided by the authors to give readers additional information about their work. © 2016 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/30/2021 eMethods Whole exome sequencing Whole exome capture libraries for both tumor and normal samples were constructed using 100ng genomic DNA input and following the protocol as described by Fisher et al.,3 with the following adapter modification: Illumina paired end adapters were replaced with palindromic forked adapters with unique 8 base index sequences embedded within the adapter. In-solution hybrid selection was performed using the Illumina Rapid Capture Exome enrichment kit with 38Mb target territory (29Mb baited). The targeted region includes 98.3% of the intervals in the Refseq exome database. Dual-indexed libraries were pooled into groups of up to 96 samples prior to hybridization.
  • The Hypothalamus As a Hub for SARS-Cov-2 Brain Infection and Pathogenesis

    The Hypothalamus As a Hub for SARS-Cov-2 Brain Infection and Pathogenesis

    bioRxiv preprint doi: https://doi.org/10.1101/2020.06.08.139329; this version posted June 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. The hypothalamus as a hub for SARS-CoV-2 brain infection and pathogenesis Sreekala Nampoothiri1,2#, Florent Sauve1,2#, Gaëtan Ternier1,2ƒ, Daniela Fernandois1,2 ƒ, Caio Coelho1,2, Monica ImBernon1,2, Eleonora Deligia1,2, Romain PerBet1, Vincent Florent1,2,3, Marc Baroncini1,2, Florence Pasquier1,4, François Trottein5, Claude-Alain Maurage1,2, Virginie Mattot1,2‡, Paolo GiacoBini1,2‡, S. Rasika1,2‡*, Vincent Prevot1,2‡* 1 Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, DistAlz, UMR-S 1172, Lille, France 2 LaBoratorY of Development and PlasticitY of the Neuroendocrine Brain, FHU 1000 daYs for health, EGID, School of Medicine, Lille, France 3 Nutrition, Arras General Hospital, Arras, France 4 Centre mémoire ressources et recherche, CHU Lille, LiCEND, Lille, France 5 Univ. Lille, CNRS, INSERM, CHU Lille, Institut Pasteur de Lille, U1019 - UMR 8204 - CIIL - Center for Infection and ImmunitY of Lille (CIIL), Lille, France. # and ƒ These authors contriButed equallY to this work. ‡ These authors directed this work *Correspondence to: [email protected] and [email protected] Short title: Covid-19: the hypothalamic hypothesis 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.08.139329; this version posted June 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • Supplementary Data

    Supplementary Data

    A Suppl. Figure 1 A 1.0 p = 0.0192 0.8 0.6 0.4 Proportion Surviving 0.2 Gain Normal 0.0 0122436486072 Months After Esophagectomy B p = 0.034 Below Median Proportion Surviving Above Median Months After Esophagectomy Suppl. Figure 2 Suppl. Figure 3 Suppl. Figure 4 Suppl. Figure 5 A Score 0 Score 1 Score 2 Score 3 N=25, (22.5% of patients) N=53, (47.7% of patients) N=23, (20.7% of patients) N=10, (9.0% of patients) B 60 50 40 30 % of Patients 20 10 0 1 23 Score SUPPLEMENTARY MATERIAL Supplementary methods: Real-time PCR analysis. Real-time PCR analysis for CDK4 expression was performed in 108 tumors using Assays-on-Demand (AOD, Life technologies Corp, Carlsbad, CA). 88ng of cDNA (RNA equivalents) template was used in each qPCR reaction and assays were performed in triplicate. Quantification of CDK4 expression was measured relative to the geometric mean of five endogenous control genes (HMBS, POLRA, TBP, PGK, UCBH). Network-based analysis. Genes that were differently expressed between CDK6-positive and CDK6- negative samples with a p-value < 0.05 and fold-change >0.5 were identified using limma package from bioconductor (1). Network-based analysis was performed using the functional interaction (FI) network (2). Briefly, this consists of 10956 proteins and more than 200,000 curated functional interactions. We calculated pairwise shortest paths among genes of interest in the FI network, hierarchically clustered them based on the average linkage method, and then selected a cluster containing more than 80% altered genes.
  • Copy Number Profiling of Brazilian Astrocytomas

    Copy Number Profiling of Brazilian Astrocytomas

    INVESTIGATION Copy Number Profiling of Brazilian Astrocytomas Lucas Tadeu Bidinotto,*,† Raul Torrieri,* Alan Mackay,‡,§ Gisele Caravina Almeida,** Marta Viana-Pereira,††,‡‡ Adriana Cruvinel-Carloni,* Maria Luisa Spina,*,† Nathalia Cristina Campanella,* Weder Pereira de Menezes,* Carlos Afonso Clara,§§ Aline Paixão Becker,* Chris Jones,‡,§ and Rui Manuel Reis*,††,‡‡,1 §§ *Molecular Oncology Research Center, **Department of Pathology, and Department of Neurosurgery, Barretos Cancer Hospital, São Paulo, 14784 400, Brazil, †Barretos School of Health Sciences, Dr. Paulo Prata – FACISB, São Paulo, 14785 § 002, Brazil, ‡Division of Molecular Pathology and Division of Cancer Therapeutics, The Institute of Cancer Research, London, SM2 5NG, UK, ††Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, 4704 553, Portugal, and ‡‡3B’s – PT Government Associate Laboratory, Braga/Guimarães, 4704 553, Portugal ABSTRACT Copy number alterations (CNA) are one of the driving mechanisms of glioma tumorigenesis, KEYWORDS and are currently used as important biomarkers in the routine setting. Therefore, we performed CNA genomics profiling of 65 astrocytomas of distinct malignant grades (WHO grade I–IV) of Brazilian origin, using array- glioblastomas CGH and microsatellite instability analysis (MSI), and investigated their correlation with TERT and IDH1 gliomas mutational status and clinico-pathological features. Furthermore, in silico analysis using the Oncomine TERT database was performed to validate our findings and extend the findings to gene expression level. We IDH1 found that the number of genomic alterations increases in accordance with glioma grade. In glioblastomas (GBM), the most common alterations were gene amplifications (PDGFRA, KIT, KDR, EGFR, and MET) and deletions (CDKN2A and PTEN). Log-rank analysis correlated EGFR amplification and/or chr7 gain with better survival of the patients.
  • Transcriptomic Analysis Identifies Genes and Pathways Related to Myrmecophagy in the Malayan Pangolin (Manis Javanica)

    Transcriptomic Analysis Identifies Genes and Pathways Related to Myrmecophagy in the Malayan Pangolin (Manis Javanica)

    Transcriptomic analysis identifies genes and pathways related to myrmecophagy in the Malayan pangolin (Manis javanica) Jing-E Ma1, Lin-Miao Li1, Hai-Ying Jiang1, Xiu-Juan Zhang1, Juan Li1, Guan-Yu Li1, Li-Hong Yuan1, Jun Wu2 and Jin-Ping Chen1 1 Guangdong Key Laboratory of Animal Conservation and Resource, Guangdong Public Laboratory of Wild Animal Conservation and Utilization, Guangdong Institute of Applied Biological Resources, Guangzhou, Guangdong, China 2 Wildlife Disease Surveillance and Molecular Ecology Research Center, Nanjing Institute of Environmental Sciences under Ministry of Environmental Protection, Nanjing, Jiangsu, China ABSTRACT The Malayan pangolin (Manis javanica) is an unusual, scale-covered, toothless mammal that specializes in myrmecophagy. Due to their threatened status and continuing decline in the wild, concerted efforts have been made to conserve and rescue this species in captivity in China. Maintaining this species in captivity is a significant challenge, partly because little is known of the molecular mechanisms of its digestive system. Here, the first large-scale sequencing analyses of the salivary gland, liver and small intestine transcriptomes of an adult M. javanica genome were performed, and the results were compared with published liver transcriptome profiles for a pregnant M. javanica female. A total of 24,452 transcripts were obtained, among which 22,538 were annotated on the basis of seven databases. In addition, 3,373 new genes were predicted, of which 1,459 were annotated. Several pathways were found to be involved in myrmecophagy, including olfactory transduction, amino sugar and nucleotide sugar metabolism, lipid metabolism, and terpenoid and polyketide metabolism pathways. Many of the annotated transcripts were involved in digestive functions: 997 transcripts were related Submitted 31 July 2017 Accepted 15 November 2017 to sensory perception, 129 were related to digestive enzyme gene families, and 199 were Published 22 December 2017 related to molecular transporters.
  • STAT3 Targets Suggest Mechanisms of Aggressive Tumorigenesis in Diffuse Large B Cell Lymphoma

    STAT3 Targets Suggest Mechanisms of Aggressive Tumorigenesis in Diffuse Large B Cell Lymphoma

    STAT3 Targets Suggest Mechanisms of Aggressive Tumorigenesis in Diffuse Large B Cell Lymphoma Jennifer Hardee*,§, Zhengqing Ouyang*,1,2,3, Yuping Zhang*,4 , Anshul Kundaje*,†, Philippe Lacroute*, Michael Snyder*,5 *Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305; §Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520; and †Department of Computer Science, Stanford University School of Engineering, Stanford, CA 94305 1The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030 2Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269 3Department of Genetics and Developmental Biology, University of Connecticut Health Center, Farmington, CT 06030 4Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT 06520 5Corresponding author: Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305. Email: [email protected] DOI: 10.1534/g3.113.007674 Figure S1 STAT3 immunoblotting and immunoprecipitation with sc-482. Western blot and IPs show a band consistent with expected size (88 kDa) of STAT3. (A) Western blot using antibody sc-482 versus nuclear lysates. Lanes contain (from left to right) lysate from K562 cells, GM12878 cells, HeLa S3 cells, and HepG2 cells. (B) IP of STAT3 using sc-482 in HeLa S3 cells. Lane 1: input nuclear lysate; lane 2: unbound material from IP with sc-482; lane 3: material IP’d with sc-482; lane 4: material IP’d using control rabbit IgG. Arrow indicates the band of interest. (C) IP of STAT3 using sc-482 in K562 cells. Lane 1: input nuclear lysate; lane 2: material IP’d using control rabbit IgG; lane 3: material IP’d with sc-482.
  • Supplementary Data

    Supplementary Data

    SUPPLEMENTARY METHODS 1) Characterisation of OCCC cell line gene expression profiles using Prediction Analysis for Microarrays (PAM) The ovarian cancer dataset from Hendrix et al (25) was used to predict the phenotypes of the cell lines used in this study. Hendrix et al (25) analysed a series of 103 ovarian samples using the Affymetrix U133A array platform (GEO: GSE6008). This dataset comprises clear cell (n=8), endometrioid (n=37), mucinous (n=13) and serous epithelial (n=41) primary ovarian carcinomas and samples from 4 normal ovaries. To build the predictor, the Prediction Analysis of Microarrays (PAM) package in R environment was employed (http://rss.acs.unt.edu/Rdoc/library/pamr/html/00Index.html). When more than one probe described the expression of a given gene, we used the probe with the highest median absolute deviation across the samples. The dataset from Hendrix et al. (25) and the dataset of OCCC cell lines described in this manuscript were then overlaid on the basis of 11536 common unique HGNC gene symbols. Only the 99 primary ovarian cancers samples and the four normal ovary samples were used to build the predictor. Following leave one out cross-validation, a predictor based upon 126 genes was able to identify correctly the four distinct phenotypes of primary ovarian tumour samples with a misclassification rate of 18.3%. This predictor was subsequently applied to the expression data from the 12 OCCC cell lines to determine the likeliest phenotype of the OCCC cell lines compared to primary ovarian cancers. Posterior probabilities were estimated for each cell line in comparison to the following phenotypes: clear cell, endometrioid, mucinous and serous epithelial.