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SUPPLEMENTARY FIGURE LEGENDS Figure S1. Comparative analysis of the sheep and human DC subset signatures by GSEA. The human GeneList consists in all the human genes for which at least one ProbeSet is present on the Affymetrix genome U133 PLUS 2.0 gene chip. The human ‘CD26+ over CD26-‘ GeneSet consists in the orthologous human genes to the sheep genes that were expressed at least two-fold higher in CD26+ L-DCs as compared to their CD26- counterparts. The human ‘CD26- over CD26+’ GeneSet is defined in a reciprocal manner. These GeneSets were used to query normalized microarray data from human blood BDCA3+, BDCA1+ and pDC subsets, using the GSEA tool from MIT (www.broad.mit.edu/gsea). The GeneSet permutation was selected as the method for calculation of statistical values to evaluate the significance of GeneSet enrichment between DC subsets. For each GeneSet, enrichment plots are shown for the comparison between the two DC subsets studied. The horizontal bar in graded color from red (left) to blue (right) represents the GeneList ranked from high expression in the DC subset indicated on the left to high expression in the DC subset indicated on the right. Equivalent expression between DC subsets is reached at the red to blue border. The vertical black lines (‘bar code’) represent the projection onto the ranked GeneList of the individual genes of the GeneSet. The curve in green corresponds to the calculation of the enrichment score (ES). The bold horizontal line indicates the 0 value for the ES. The more the green ES curve is shifted to the upper left of the graph, the more the GeneSet is enriched in the red DC subset. Conversely, the more the green ES curve is shifted to the lower right of the graph, the more the GeneSet is enriched in the blue DC subset. False discovery rate (FDR) q-values are indicated on the upper right corner of each plot, in red (resp. blue) when the GeneSet is enriched in the red (resp. blue) DC subset. A FDR q-value=0.01 means that the risk that a gene found differentially expressed between the two population is a false positive is 1%. The numbers of genes which are at least two-fold more highly expressed in one population as compared to the other are indicated below each enrichment plot under the label for the corresponding DC subset. The DC subset in which the GeneSet is enriched is indicated by a closed arrow and a bold legend. Figure S2. Comparative analysis of sheep DC and mouse DC subset signatures by GSEA. The GSEA analysis was performed with the same methodology as in Fig. S1, by querying normalized microarray data from mouse splenic CD8α+, CD11b+ and pDC subsets performed with the mouse Affymetrix genome 430 2.0 gene chip, using the mouse ‘CD26+ over CD26-‘ versus the ‘CD26- over CD26+‘ GeneSet defined in a similar manner as described for the human GeneSets in Fig. S1. Figure S3. FACS profile illustration of the naive allogeneic CD4+ T and CD8+ T cell proliferation induced by total L-DC, CD26+ L-DC and CD26- L-DC. Immunomagnetically selected L-DC, CD26+ and CD26- L-DC (5 X 104) were co- cultured with naive allogeneic CD4+ or CD8+ T cells (5 X 105) for 5 days. The percent of divided CD8+ T cells (A) and CD4+ T cells (B) was obtained from loss of the CFSE signal in gated 7-AAD- cells. A representative experiment is shown (the FL-4 channel corresponds to an unstained channel). Sheep L-DC #55 in A, and sheep L-DC #66 in B. T cells were from sheep #31. Figure S4. BTV-infected CD26+ L-DC and CD26- L-DC are equally efficient at stimulating BTV-specific autologous CD8+ T cells. Immunomagnetically selected L-DC, CD26+ and CD26- L-DC (> 85% purity, 5 X 104) were incubated with control BHK and BTV2-infected BHK cells. The next day, BTV2-immune efferent lymph- derived autologous enriched CD8+ T cells (5 X 105) were added to the pulsed DC. IFNγ was measured in the supernatants after 3 days and OD at 405 nm are reported. The results shown are from one representative experiment out of two performed with sheep #81. Table S1. Gene ontology annotation of the CD26- GeneSet Term PValue Genes extracellular space 9.3E-06 CSF1R, FST, LCAT, MBTPS1, CLEC3B, DHRS3, FOLR1, FMOD, ITGB5, CD180, SLC35A1, NPC2, PCOLCE, FXYD6, FGL2, IGF1R, PDE7A, CD14, TGFBI, BACE2, PLA2G2A, NKG7, CFD, HTRA1, TM9SF1, GBAS, A, IL1B, LACTB, CPD, CYP2J6, FURIN, IL1R1, C3, FGFR1, RARRES1, SIRPA, CLDN1, carboxylic acid 1.1E-04 PSPH, 2010111I01RIK, PRKCE, THA1, FOLR1, ALDH6A1, ACO2, SHMT2, ECH1, BMP4, FCER1A, UCP3, TARS2, ARG2, CPT1A, metabolic process FARSA, HADH, ACADSB, hydrolase 2.2E-04 2010111I01RIK, MBTPS1, ADA, CASP4, PDE3B, PDE7A, USP8, BACE2, PLA2G2A, EXOSC9, ADAM19, CFD, PSPH, HTRA1, SAMHD1, ASAH1, FAHD1, CPD, LACTB, FURIN, CLPP, PTPN18, C3, PEPD, SIRT2, TDG, PPT2, CTSC, ARG2, ELAC1, PGA5, CTSK, ACP6, catalytic activity 2.8E-04 MALT1, PPIH, CSF1R, PIGF, LCAT, SHMT2, ZDHHC6, ADA, CASP4, HSD17B10, ECH1, TARS2, C530044N13RIK, XRCC6, BACE2, PLA2G2A, EXOSC9, HTRA1, GPX1, SAMHD1, ASAH1, TM7SF2, THA1, CPD, FAHD1, ACO2, PTPN18, FGFR1, HMBS, PEPD, TDG, ALDH7A1, CTSC, CPT1A, CYP26B1, ELAC signal 3.7E-04 CSF1R, LCAT, CD180, NPC2, PCOLCE, FCER1A, FGL2, CREG1, PLA2G2A, TM9SF1, HTRA1, ASAH1, A, AMICA1, CPD, IL1R1, CD3D, FGFR1, SIRPA, TMED10, BMP4, CTSC, IL10RA, LRRC33, ECM1, CXCL3, FST, FOLR1, CLEC3B, MBTPS1, FMOD, ADAMTSL4, IL1RN, FXYD6, IGF1R, CD14, CAR12, inflammatory response 3.9E-04 PTAFR, PPARG, FCER1A, IL1B, FN1, CD14, C3, CD180, EDG3, IL1RN, CFD, organelle membrane 4.6E-04 TOMM40L, PIGF, SEC24D, FAHD1, NUP88, FURIN, TRAPPC3, GOLGA3, SHMT2, SLC40A1, TMED10, SLC35A1, HSD17B10, UCP3, RP23-419G22.2, CPT1A, PEX12, ATF6, AFTPH, HADH, proteolysis 9.1E-04 HTRA1, MALT1, 2010111I01RIK, D11BWG0434E, CPD, CLPP, MBTPS1, FURIN, C3, PEPD, CASP4, PCOLCE, CTSC, AURKAIP1, USP8, PGA5, CTSK, BACE2, ADAM19, ZFP3, CFD, mitochondrion 1.2E-03 TOMM40L, SHMT2, SLC40A1, HSD17B10, ECH1, RP23-419G22.2, CKB, TARS2, MRPL34, BCL2L1, PLA2G2A, GBAS, GPX1, LACTB, FAHD1, CLPP, ACO2, ALDH6A1, ALDH7A1, ARG2, UCP3, CPT1A, ACP6, HADH, ACADSB, cellular lipid metabolic 1.3E-03 2010111I01RIK, PIGF, LCAT, TM7SF2, MBTPS1, ECH1, NR1H3, YWHAH, FCER1A, UCP3, CPT1A, CDIPT, PLA2G2A, HADH, process NSDHL, ACADSB, OSBPL1A, protein binding 2.1E-03 MALT1, BIN3, VPS18, CSF1R, PIGF, SMAD4, CLDN11, CD180, CASP4, NPC2, PCOLCE, FGL2, FCER1A, PEX12, CREG1, PLA2G2A, BCL2L1, TCEA2, HTRA1, ACTL6A, PPARG, STAM2, A, IL1B, XAB2, SNRPA1, SEC24D, AMICA1, IL1R1, PTPN18, CD3D, FGFR1, DYSF, SIRPA, TRAF3, FCER2A, CLD 3-hydroxyacyl-CoA 2.8E-03 HSD17B10, RP23-419G22.2, HADH, dehydrogenase activity glycoprotein 3.0E-03 CSF1R, LCAT, CD180, NPC2, PCOLCE, FCER1A, FGL2, CREG1, TM9SF1, ASAH1, A, AMICA1, CPD, IL1R1, CD3D, FGFR1, SIRPA, FCER2A, CD36, PTAFR, BMP4, CTSC, IL10RA, LRRC33, ECM1, EDG3, ASGR2, FST, FOLR1, MBTPS1, CLEC4E, SLC40A1, FMOD, ADAMTSL4, IL1RN, CD14, CAR12, I cytoplasmic part 3.4E-03 MALT1, VPS18, PIGF, TRAPPC3, PREB, SHMT2, HSPB1, NPC2, HSD17B10, ECH1, TARS2, PEX12, MRPL34, BCL2L1, PLA2G2A, GPX1, PPARG, GBAS, ASAH1, TM7SF2, FAHD1, SEC24D, ACO2, TMED10, ARFGAP3, CD36, SRPRB, ALDH7A1, CTSC, CPT1A, ALG8, PAOX, ACADSB, TOMM40L, MBTPS1, G amino acid metabolic 3.8E-03 PSPH, BMP4, FCER1A, ARG2, TARS2, THA1, PRKCE, SHMT2, ALDH6A1, FARSA, process endopeptidase activity 4.9E-03 HTRA1, MALT1, MBTPS1, FURIN, CLPP, ADAMTSL4, CASP4, CTSC, USP8, PGA5, CTSK, BACE2, ADAM19, CFD, hematopoietic cell 5.0E-03 CSF1R, IL1B, CD14, IL1R1, CD3D, FCER2A, CD36, lineage 1 response to stress 5.2E-03 GPX1, PPARG, IL1B, XAB2, C3, HSPB1, DYSF, CD180, IL1RN, SIRT2, PTAFR, TDG, FCER1A, UCP3, FN1, XRCC6, CD14, ATF6, EDG3, CFD, golgi apparatus part 5.7E-03 SEC24D, FURIN, GOLGA3, MBTPS1, TRAPPC3, TMED10, AFTPH, SLC35A1, lysosome 6.2E-03 NPC2, PPT2, VPS18, CTSC, ASAH1, IFI30, CTSK, CD36, immune response 6.4E-03 CXCL3, MALT1, SAMHD1, IL1B, IL1R1, CLEC4E, C3, CLEC4N, ADA, IL1RN, CD180, FCER1A, CD14, TNFRSF13C, CFD, 2 Table S2. Gene ontology annotation of the CD26+ GeneSet Term PValue Genes phosphoprotein 1.3E-07 MAP4K4, PTPRA, CDC42EP3, TGM2, FXR2, DDX27, TMPO, IL6ST, PPP1R1A, ANPEP, PTPRE, ADAM17, FAS, XDH, RALBP1, GIT2, F11R, GOLPH3, CCNL1, ARPP19, AP1GBP1, BNC1, PRKCZ, MAPK11, EXOC2, 1700025G04RIK, HCK, RPL29, MGEA5, CASP3, ZDHHC20, TNF, MAPKAPK3, XRCC5, MYBL2 Type I diabetes mellitus 5.0E-07 H2-EB1, H2-EA, TNF, H2-DMB2, CD86, IL12B, H2-AB1, FAS, IL1A, H2-AA, hemopoiesis 6.4E-07 DNASE2A, CARD11, JAG1, HDAC7A, CHUK, KLF10, IL12B, IRF8, CD1D2, TNF, CD1D1, KDR, FAS, ID2, H2-AA, antigen processing and presentation 1.1E-06 H2-EB1, H2-EA, UNC93B1, CD1D1, H2-DMB2, H2-AB1, H2-AA, of exogenous antigen immune system process 1.4E-06 CARD11, CD8B1, IL12B, KLF10, CXCL9, CXCL10, CD1D1, CADM1, FAS, DNASE2A, H2-EB1, JAG1, H2-EA, HDAC7A, CHUK, IRAK1, H2-AB1, IRF8, CD1D2, CASP3, UNC93B1, TNF, H2-DMB2, KDR, ID2, IL1A, RMCS2, H2-AA, leukocyte differentiation 7.2E-06 CARD11, CD1D2, HDAC7A, CHUK, TNF, CD1D1, IL12B, KLF10, FAS, ID2, H2-AA, primary metabolic process 1.7E-05 ACVRL1, CAML, SEPHS1, TGM2, GPBP1, DHCR7, PPP1R1A, PGCP, L3MBTL2, PTPRE, PUS3, BMP6, LOC574418, OSBP, BNC1, STCH, MAPK11, IRF8, MARCH3, SNX6, HCK, CASP3, XRCC5, PPP1R16B, KDR, MYBL2, GTF3C1, BMP7, TMOD1, BATF3, FUS, SPCS3, KLF10, ASL, YTHDC1, EDF1, ROD1, immune response 1.8E-05 CARD11, H2-EB1, H2-EA, CD8B1, IRAK1, IL12B, H2-AB1, CXCL9, IRF8, CXCL10, CD1D2, UNC93B1, TNF, CD1D1, H2- DMB2, CADM1, FAS, IL1A, RMCS2, H2-AA, intracellular membrane-bound 2.4E-05 CAML, HS3ST1, GPBP1, DHCR7, D2ERTD750E, ZFP408, CRABP2, L3MBTL2, PUS3, H2-EA, LOC574418, AP1GBP1, organelle BNC1, STCH, IRF8, MARCH3, UNC93B1, OBFC2A, XRCC5, MYBL2, GTF3C1, BATF3, STAG1, FUS, KLF10, SPCS3, YTHDC1, EDF1, ROD1, NET1, EZH1, AK3, CADM1, STK38, PRSS23, cytoplasm 3.5E-05 CDC42EP3, CAML, PIK3AP1, SENP1, ACOT7, TGM2, FXR2, DHCR7, HS3ST1, RARS, ANPEP, L3MBTL2, CRABP2, CALD1, ADAM17, XDH, CLN8, MAD2L1, H2-EA, NUDT17, GOLPH3, GRN, ARPP19, AP1GBP1, BNC1, MRPL16, STCH, PRKCZ, EXOC2, MARCH3, SNX6, HCK, RPL29, MGEA5, CASP3, UNC93B Toll-like receptor signaling pathway 4.4E-05 AKT2, MAP2K1, CHUK, TNF, CD86, IRAK1, MAPK11, IL12B, CXCL9, CXCL10, 1.
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    Molecular BioSystems View Article Online PAPER View Journal | View Issue Small-molecule binding sites to explore protein– protein interactions in the cancer proteome† Cite this: Mol. BioSyst., 2016, 12,3067 David Xu,ab Shadia I. Jalal,c George W. Sledge Jr.d and Samy O. Meroueh*aef The Cancer Genome Atlas (TCGA) offers an unprecedented opportunity to identify small-molecule binding sites on proteins with overexpressed mRNA levels that correlate with poor survival. Here, we analyze RNA-seq and clinical data for 10 tumor types to identify genes that are both overexpressed and correlate with patient survival. Protein products of these genes were scanned for binding sites that possess shape and physicochemical properties that can accommodate small-molecule probes or therapeutic agents (druggable). These binding sites were classified as enzyme active sites (ENZ), protein–protein interaction sites (PPI), or other sites whose function is unknown (OTH). Interestingly, the overwhelming majority of binding sites were classified as OTH. We find that ENZ, PPI, and OTH binding sites often occurred on the same structure suggesting that many of these OTH cavities can be used for allosteric modulation of Creative Commons Attribution 3.0 Unported Licence. enzyme activity or protein–protein interactions with small molecules. We discovered several ENZ (PYCR1, QPRT,andHSPA6)andPPI(CASC5, ZBTB32,andCSAD) binding sites on proteins that have been seldom explored in cancer. We also found proteins that have been extensively studied in cancer that have not been previously explored with small molecules that harbor ENZ (PKMYT1, STEAP3,andNNMT) and PPI (HNF4A, MEF2B,andCBX2) binding sites. All binding sites were classified by the signaling pathways to Received 29th March 2016, which the protein that harbors them belongs using KEGG.
  • (RP105) Activates B Cells to Rapidly Produce Polyclonal Ig Via a T Cell and Myd88-Independent Pathway

    (RP105) Activates B Cells to Rapidly Produce Polyclonal Ig Via a T Cell and Myd88-Independent Pathway

    Anti-CD180 (RP105) Activates B Cells To Rapidly Produce Polyclonal Ig via a T Cell and MyD88-Independent Pathway This information is current as Jay W. Chaplin, Shinji Kasahara, Edward A. Clark and of October 6, 2021. Jeffrey A. Ledbetter J Immunol 2011; 187:4199-4209; Prepublished online 14 September 2011; doi: 10.4049/jimmunol.1100198 http://www.jimmunol.org/content/187/8/4199 Downloaded from Supplementary http://www.jimmunol.org/content/suppl/2011/09/14/jimmunol.110019 Material 8.DC1 http://www.jimmunol.org/ References This article cites 33 articles, 16 of which you can access for free at: http://www.jimmunol.org/content/187/8/4199.full#ref-list-1 Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists by guest on October 6, 2021 • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2011 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Anti-CD180 (RP105) Activates B Cells To Rapidly Produce Polyclonal Ig via a T Cell and MyD88-Independent Pathway Jay W.
  • Identi Cation of Novel Hub Genes Associated with Gastric Cancer

    Identi Cation of Novel Hub Genes Associated with Gastric Cancer

    Identication of Novel Hub Genes Associated With Gastric Cancer Using Integrated Bioinformatics Analysis Xiao-Qing Lu Department of Breast Surgery, the second hospital of Shanxi Medical University Jia-qian Zhang Shanxi Medical University Second Aliated Hospital https://orcid.org/0000-0001-9042-0305 Jun Qiao Department of Rheumatology, the Second Hospital of Shanxi Medical University Sheng-Xiao Zhang Department of Rheumatology, the Second Hospital of Shanxi Medical University Meng-Ting Qiu Department of Rheumatology, the Second Hospital of Shanxi Medical University Xiang-Rong Liu Department of Breast Surgery, the Second Hospital of Shanxi Medical University Xiao-Xia Chen Department of Breast Surgery, the Second Hospital of Shanxi Medical University Chong Gao Department of Pathology, Brigham and Women's Hospital, Harvard Medical School Huan-Hu Zhang ( [email protected] ) Department of Gastroenterology Shanxi Cancer Hospital Taiyuan, Shanxi 030001 China Research article Keywords: Gastric cancer, Bioinformatics analysis, Differentially expressed genes Posted Date: September 10th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-58756/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/25 Abstract Background: Gastric cancer (GC) is one of the most common solid malignant tumors worldwide with a high- recurrence-rate. Identifying the molecular signatures and specic biomarkers of GC might provide novel clues for GC prognosis and targeted therapy. Methods: Gene expression proles were obtained from the ArrayExpress and Gene Expression Omnibus database. Differentially expressed genes (DEGs) were picked out by R software. The hub genes were screened by cytoHubba plugin. Their prognostic values were assessed by Kaplan–Meier survival analyses and the gene expression proling interactive analysis (GEPIA).
  • Supplemental Figures 032819.Pptx

    Supplemental Figures 032819.Pptx

    Summary of Supplemental Figures and Tables 1) Supplemental Figure 1. Frequency of phenotypically defined endothelial cells is unchanged in aged mice. 2) Supplemental Figure 2. Aged LSKs have increased myeloid/megakaryocytic bias 3) Supplemental Figure 3. RBCs are decreased and Platelets are increased in peripheral blood of aged mice 4) Supplemental Figure 4. Aged BMME cultures have increased MSC populations. 5) Supplemental Figure 5. Aged BMME cells increase young LSK cell engraftment following competitive transplantation 6) Supplemental Figure 6. Flow cytometry gating strategy for sorting of young and aged murine Mφs 7) Supplemental Figure 7. Flow cytometry gating strategy for sorting of human Mφs 8) Supplemental Figure 8. Axl-/- LT-HSCs have increased cell engraftment following competitive transplantation 9) Supplemental Figure 9. Schematic representation of mechanisms by which age-dependent defects in marrow Mφs induce megakaryocytic bias in HSC. 10) Table S1. List of antibodies used in the flow cytometric and cell sorting analyses described 11) Table S2. List of cell populations analyzed and their respective immunophenotypes 12) Table S3. List of top GO-BP categories enriched for significantly upregulated genes from murine aged vs young marrow macrophages 13) Table S4. List of significant KEGG Pathways enriched for significantly upregulated genes from murine aged vs young marrow macrophages 1 A Sinusoidal Arteriolar BV786 – CD31 Sca1 – PE-Cy7 B C Arteriolar EC Sinusoidal ECs (Lin-, CD45-, CD31+ Sca1+) (CD45-Lin-CD31+Sca1-) 0.08 0.15 0.06 0.10 0.04 0.05 0.02 PercentLiveof Cells PercentLiveof Cells 0.00 0.00 Young Aged Young Aged Supplemental Figure 1.
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD

    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