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Supporting Information Redefining the breast cancer exosome proteome by tandem mass tag quantitative proteomics and multivariate cluster analysis. David J Clarkǂ*1,2, William E Fondrieǂ2,3, Zhongping Liao4, Phyllis I Hanson5, Amy Fulton2, Li Mao1,2, Austin J Yang2 1 Department of Oncology and Diagnostic Sciences, University of Maryland School of Dentistry, Baltimore, MD 2 Marlene and Stewart Greenebaum Cancer Center, University of Maryland, Baltimore, MD 3Center for Vascular and Inflammatory Diseases, University of Maryland School of Medicine, Baltimore, MD 4 Lily Research Laboratory, Eli Lily and Company, Indianapolis, IN 5Department of Cell Biology and Physiology, Washington University, St. Louis, MO ǂ These authors contributed equally to this work. *Corresponding author: [email protected] 410-706-6114 Table of Contents Figure S-1: TMT log2 protein ratios were plotted by 100K enrichment against Optiprep/100K enrichment. (pg. S-2) Figure S-2: Immunoblot of SEC fractions, and Venn diagram of SEC and TMT-SVM analysis. (pg. S-3) Table S-1 Protein identifications, TMT ratios, SVM Classification and SVM Probability of 10K, 100K, and Optiprep lysates derived from SKBR3B conditioned media. (pg. S-4) Table S-2. Plasma membrane markers and their respective SVM Classification (pg. S-75) Table S-3. Identified proteins from our SEC analysis with respective Protein Prophet probability (pg. S-76) S-1 Figure S-1. Known exosome markers localize to quadrant corresponding to 100K enrichment. TMT log2 protein ratios were plotted by 100K enrichment (x-axis) against Optiprep/100K enrichment (y-axis). Selected Exosome (red) and Non-exosome (blue) protein markers are annotated. S-2 Figure S-2. SEC fractions and LC-MS/MS analysis. (A) Immunoblot for known exosome markers ALIX and CD63 in the recovered 500 L SEC fractions. Fractions 9 and 10 were positive for both exosome markers. (B) Venn diagram showing overlap of the SVM exosome cluster and SVM non-exosome cluster with the SEC analysis. S-3 Table S-1. Protein identifications, TMT ratios, SVM Classification and SVM Probability of 10K, 100K, and Optiprep lysates derived from SKBR3B conditioned media. TMT ratios were calculated to the log base 2 prior to plotting and SVM cluster analysis Log 2 ratio Protein Uniprot TMT 130/129 TMT 131/129 TMT 131/130 SVM SVM Gene Symbol Protein Description Group Accession +/- SD +/- SD +/- SD 100K/1 Opti/10 Opti/100 Classification Probability 0K K K 1 A5D8V6 VPS37C Vacuolar protein sorting-associated protein 37C 0.886 +/- 0.094 0.974 +/- 0.396 1.064 +/- 0.334 -0.175 -0.038 0.09 Non-Exosome 0.57 2 B7ZAQ6-2 GPR89A Isoform 2 of Golgi pH regulator A 0.333 +/- 0.053 0.225 +/- 0.085 0.651 +/- 0.151 -1.585 -2.152 -0.62 Non-Exosome 0.90 3 O00186 STXBP3 Syntaxin-binding protein 3 1.188 +/- 0.172 0.507 +/- 0.033 0.44 +/- 0.092 0.248 -0.981 -1.184 Non-Exosome 0.73 4 O00220 TNFRSF10A Tumor necrosis factor receptor superfamily member 10A 0.835 +/- 0.205 0.755 +/- 0.145 1.008 +/- 0.421 -0.26 -0.405 0.011 Non-Exosome 0.73 5 O00232-2 PSMD12 Isoform 2 of 26S proteasome non-ATPase regulatory subunit 12 0.602 +/- 0.198 0.279 +/- 0.039 0.495 +/- 0.097 -0.731 -1.841 -1.014 Non-Exosome 0.92 6 O00442-2 RTCA Isoform 2 of RNA 3'-terminal phosphate cyclase 0.248 +/- 0.038 0.192 +/- 0.002 0.79 +/- 0.115 -2.01 -2.383 -0.341 Non-Exosome 0.85 7 O00487 PSMD14 26S proteasome non-ATPase regulatory subunit 14 0.483 +/- 0.067 0.219 +/- 0.014 0.466 +/- 0.094 -1.049 -2.19 -1.1 Non-Exosome 0.91 8 O00622 CYR61 Protein CYR61 0.944 +/- 0.021 0.323 +/- 0.057 0.343 +/- 0.068 -0.084 -1.632 -1.542 Non-Exosome 0.86 9 O14684 PTGES Prostaglandin E synthase 0.475 +/- 0.045 0.324 +/- 0.069 0.675 +/- 0.082 -1.074 -1.625 -0.568 Non-Exosome 0.93 10 O14763-2 TNFRSF10B Isoform Short of Tumor necrosis factor receptor superfamily member 10B 1.202 +/- 0.768 0.652 +/- 0.448 0.515 +/- 0.044 0.266 -0.616 -0.958 Non-Exosome 0.63 11 O14908-2 GIPC1 Isoform 2 of PDZ domain-containing protein GIPC1 0.640 +/- 0.180 0.590 +/- 0.110 1.054 +/- 0.468 -0.644 -0.761 0.075 Non-Exosome 0.87 12 O14949 UQCRQ Cytochrome b-c1 complex subunit 8 0.282 +/- 0.082 0.210 +/- 0.025 0.784 +/- 0.141 -1.824 -2.252 -0.35 Non-Exosome 0.88 13 O15347 HMGB3 High mobility group protein B3 0.265 +/- 0.015 0.140 +/- 0.060 0.543 +/- 0.257 -1.916 -2.837 -0.881 Non-Exosome 0.80 14 O43657 TSPAN6 Tetraspanin-6 2.473 +/- 0.908 2.482 +/- 1.226 0.95 +/- 0.147 1.306 1.312 -0.074 Exosome 0.88 15 O43677 NDUFC1 NADH dehydrogenase [ubiquinone] 1 subunit C1; mitochondrial 0.362 +/- 0.242 0.237 +/- 0.137 0.726 +/- 0.107 -1.467 -2.079 -0.462 Non-Exosome 0.91 16 O43854-2 EDIL3 Isoform 2 of EGF-like repeat and discoidin I-like domain-containing protein 3 3.947 +/- 0.580 3.450 +/- 0.616 0.87 +/- 0.028 1.981 1.787 -0.201 Exosome 0.87 17 O60524-3 NEMF Isoform 3 of Nuclear export mediator factor NEMF 0.968 +/- 0.032 0.585 +/- 0.505 0.588 +/- 0.502 -0.048 -0.773 -0.767 Non-Exosome 0.77 18 O60841 EIF5B Eukaryotic translation initiation factor 5B 0.656 +/- 0.044 0.285 +/- 0.065 0.43 +/- 0.07 -0.608 -1.811 -1.219 Non-Exosome 0.91 19 O60888-2 CUTA Isoform A of Protein CutA 0.830 +/- 0.440 0.260 +/- 0.110 0.338 +/- 0.047 -0.269 -1.943 -1.565 Non-Exosome 0.88 20 O75396 SEC22B Vesicle-trafficking protein SEC22b 0.576 +/- 0.041 0.509 +/- 0.226 0.916 +/- 0.458 -0.795 -0.975 -0.127 Non-Exosome 0.90 21 O75436-2 VPS26A Isoform 2 of Vacuolar protein sorting-associated protein 26A 0.668 +/- 0.372 0.289 +/- 0.041 0.578 +/- 0.261 -0.583 -1.792 -0.79 Non-Exosome 0.91 22 O75531 BANF1 Barrier-to-autointegration factor 0.421 +/- 0.051 0.169 +/- 0.009 0.405 +/- 0.027 -1.249 -2.563 -1.303 Non-Exosome 0.88 23 O75694-2 NUP155 Isoform 2 of Nuclear pore complex protein Nup155 0.766 +/- 0.297 0.359 +/- 0.147 0.464 +/- 0.012 -0.385 -1.48 -1.109 Non-Exosome 0.90 24 O75695 RP2 Protein XRP2 1.041 +/- 0.081 1.058 +/- 0.292 1.044 +/- 0.362 0.058 0.081 0.062 Exosome 0.56 25 O75718 CRTAP Cartilage-associated protein 0.481 +/- 0.161 0.150 +/- 0.095 0.277 +/- 0.106 -1.057 -2.734 -1.85 Non-Exosome 0.85 26 O75844 ZMPSTE24 CAAX prenyl protease 1 homolog 0.346 +/- 0.024 0.163 +/- 0.013 0.471 +/- 0.004 -1.532 -2.618 -1.087 Non-Exosome 0.86 27 O75915 ARL6IP5 PRA1 family protein 3 0.379 +/- 0.007 0.236 +/- 0.016 0.622 +/- 0.031 -1.399 -2.083 -0.686 Non-Exosome 0.91 S-4 28 O75935-2 DCTN3 Isoform 2 of Dynactin subunit 3 0.610 +/- 0.000 0.295 +/- 0.045 0.484 +/- 0.074 -0.713 -1.761 -1.048 Non-Exosome 0.92 29 O76003 GLRX3 Glutaredoxin-3 0.473 +/- 0.233 0.278 +/- 0.098 0.642 +/- 0.108 -1.079 -1.845 -0.64 Non-Exosome 0.93 30 O94826 TOMM70A Mitochondrial import receptor subunit TOM70 0.413 +/- 0.036 0.326 +/- 0.024 0.789 +/- 0.009 -1.277 -1.619 -0.341 Non-Exosome 0.93 31 O94874-2 UFL1 Isoform 2 of E3 UFM1-protein ligase 1 0.270 +/- 0.030 0.125 +/- 0.005 0.471 +/- 0.071 -1.889 -3 -1.087 Non-Exosome 0.78 32 O95084 PRSS23 Serine protease 23 2.591 +/- 0.140 1.330 +/- 0.053 0.514 +/- 0.007 1.374 0.411 -0.961 Exosome 0.86 33 O95810 SDPR Serum deprivation-response protein 0.520 +/- 0.140 0.368 +/- 0.108 0.822 +/- 0.428 -0.943 -1.444 -0.283 Non-Exosome 0.93 34 O95861-2 BPNT1 Isoform 2 of 3'(2');5'-bisphosphate nucleotidase 1 0.608 +/- 0.042 0.590 +/- 0.130 0.96 +/- 0.148 -0.717 -0.761 -0.059 Non-Exosome 0.87 35 O95881 TXNDC12 Thioredoxin domain-containing protein 12 0.235 +/- 0.041 0.107 +/- 0.004 0.466 +/- 0.065 -2.087 -3.224 -1.101 Non-Exosome 0.72 36 P00403 MT-CO2 Cytochrome c oxidase subunit 2 0.367 +/- 0.024 0.141 +/- 0.038 0.392 +/- 0.13 -1.446 -2.827 -1.35 Non-Exosome 0.83 37 P00734 F2 Prothrombin 1.992 +/- 0.130 0.637 +/- 0.100 0.318 +/- 0.03 0.994 -0.651 -1.654 Exosome 0.60 38 P00747 PLG Plasminogen 0.790 +/- 0.250 0.307 +/- 0.067 0.402 +/- 0.042 -0.34 -1.704 -1.315 Non-Exosome 0.90 39 P02792 FTL Ferritin light chain 1.022 +/- 0.178 0.461 +/- 0.066 0.453 +/- 0.014 0.032 -1.118 -1.142 Non-Exosome 0.81 40 P04040 CAT Catalase 0.597 +/- 0.310 0.340 +/- 0.175 0.571 +/- 0.003 -0.744 -1.556 -0.809 Non-Exosome 0.92 41 P04080 CSTB Cystatin-B 0.572 +/- 0.024 0.278 +/- 0.000 0.487 +/- 0.02 -0.807 -1.848 -1.039 Non-Exosome 0.92 42 P05121-2 SERPINE1 Isoform 2 of Plasminogen activator inhibitor 1 0.943 +/- 0.397 0.330 +/- 0.030 0.409 +/- 0.14 -0.084 -1.599 -1.291 Non-Exosome 0.86 43 P05161 ISG15 Ubiquitin-like protein ISG15 0.583 +/- 0.044 0.294 +/- 0.025 0.504 +/- 0.005 -0.778 -1.765 -0.988 Non-Exosome 0.92 44 P06400 RB1 Retinoblastoma-associated protein 0.295 +/- 0.035 0.165 +/- 0.005 0.565 +/- 0.05 -1.761 -2.599 -0.823 Non-Exosome 0.85 45 P07305-2 H1F0 Isoform 2 of Histone H1.0 0.320 +/- 0.050 0.095 +/- 0.035 0.322 +/- 0.16 -1.644 -3.396 -1.636 Non-Exosome 0.72 46 P07602-2 PSAP Isoform Sap-mu-6 of Prosaposin 0.416 +/- 0.024 0.191 +/- 0.021 0.465 +/- 0.076 -1.267 -2.385 -1.105 Non-Exosome 0.89 47 P07996 THBS1 Thrombospondin-1 1.992 +/- 0.862 0.605 +/- 0.102 0.346 +/- 0.098 0.994 -0.726 -1.53 Exosome 0.58 48 P08240-2 SRPR Isoform 2 of Signal recognition particle receptor subunit alpha 0.302 +/- 0.012 0.181 +/- 0.044 0.606 +/- 0.17 -1.729 -2.467 -0.723 Non-Exosome 0.87 49 P08572 COL4A2 Collagen alpha-2(IV) chain 0.480 +/- 0.000 0.415 +/- 0.085 0.865 +/- 0.177 -1.059 -1.269 -0.21 Non-Exosome 0.92 50 P08574 CYC1 Cytochrome c1; heme protein; mitochondrial 0.461 +/- 0.087 0.255 +/- 0.011 0.569 +/- 0.084 -1.117 -1.971 -0.813 Non-Exosome 0.92 51 P0C6C1 ANKRD34C Ankyrin repeat domain-containing protein 34C 1.275 +/- 0.415 0.990 +/- 0.490 0.729 +/- 0.147 0.35 -0.014 -0.457 Exosome 0.62 52 P0DME0 SETSIP Protein SETSIP 0.485 +/- 0.055 0.305 +/- 0.155 0.6 +/- 0.252 -1.044 -1.713 -0.736 Non-Exosome 0.93 53 P10606 COX5B Cytochrome c oxidase subunit
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  • Characterization of Acute Myeloid Leukemia with Del(9Q) – Impact of the Genes in the Minimally Deleted Region T ⁎ Isabel S

    Characterization of Acute Myeloid Leukemia with Del(9Q) – Impact of the Genes in the Minimally Deleted Region T ⁎ Isabel S

    Leukemia Research 76 (2019) 15–23 Contents lists available at ScienceDirect Leukemia Research journal homepage: www.elsevier.com/locate/leukres Research paper Characterization of acute myeloid leukemia with del(9q) – Impact of the genes in the minimally deleted region T ⁎ Isabel S. Naarmann-de Vriesa, , Yvonne Sackmanna,b, Felicitas Kleina,b, Antje Ostareck-Lederera, Dirk H. Ostarecka, Edgar Jostb, Gerhard Ehningerc, Tim H. Brümmendorfb, Gernot Marxa, ⁎⁎ Christoph Rölligc, Christian Thiedec, Martina Crysandtb, a Department of Intensive Care Medicine, University Hospital RWTH Aachen University, Aachen, Germany b Department of Hematology, Oncology, Hemostaseology and Stem Cell Transplantation, University Hospital RWTH Aachen University, Aachen, Germany c Medical Department I, University Hospital of the Technical University Dresden, Dresden, Germany ARTICLE INFO ABSTRACT Keywords: Acute myeloid leukemia is an aggressive disease that arises from clonal expansion of malignant hematopoietic AML precursor cells of the bone marrow. Deletions on the long arm of chromosome 9 (del(9q)) are observed in 2% of del(9q) acute myeloid leukemia patients. Our deletion analysis in a cohort of 31 del(9q) acute myeloid leukemia patients HNRNPK further supports the importance of a minimally deleted region composed of seven genes potentially involved in CEBPA leukemogenesis: GKAP1, KIF27, C9ORF64, HNRNPK, RMI1, SLC28A3 and NTRK2. Importantly, among them HNRNPK, encoding heterogeneous nuclear ribonucleoprotein K is proposed to function in leukemogenesis. We show that expression of HNRNPK and the other genes of the minimally deleted region is significantly reduced in patients with del(9q) compared with normal karyotype acute myeloid leukemia. Also, two mRNAs interacting with heterogeneous nuclear ribonucleoprotein K, namely CDKN1A and CEBPA are significantly downregulated.
  • ANALYSIS of GENE PATHWAYS INVOLVED in DCIS PROGRESSION in RESPONSE to ACIDIC EXTRACELLULAR Ph Neha Aggarwal1, Jennifer Rothberg2, Robert J

    ANALYSIS of GENE PATHWAYS INVOLVED in DCIS PROGRESSION in RESPONSE to ACIDIC EXTRACELLULAR Ph Neha Aggarwal1, Jennifer Rothberg2, Robert J

    ANALYSIS OF GENE PATHWAYS INVOLVED IN DCIS PROGRESSION IN RESPONSE TO ACIDIC EXTRACELLULAR pH Neha Aggarwal1, Jennifer Rothberg2, Robert J. Gillies3 and Bonnie F. Sloane4 & Douglas Yingst 1Department of Physiology, 2Cancer Biology Program, and 4Department of Pharmacology, Wayne State University School of Medicine, Detroit, MI, 48201; 3H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33602 Breast cancer is the most commonly diagnosed cancer in women in USA and has a high mortality rate, second only to lung cancer. About 85% of total 63300 new cases of breast cancer are predicted to be ductal carcinoma in situ (DCIS) in 2012. We are interested in identifying markers that are predictive of changes that occur in the breast microenvironment as a result of the presence of premalignant lesions such as DCIS that are poised to develop into breast cancer. A critical barrier to cancer progression is its ability to survive in the acidic microenvironment characteristic of breast cancer. As the breast is comprised of different cell types, we performed gene expression analysis using Affymetrix gene chip HG U133 plus 2.0 array of 3 DCIS cell lines grown in 3D at neutral and acidic pH. We then computed the significantly changed genes at acidic pH for three DCIS cell lines and found 6 common and 121 similar genes. IPA core analysis of these genes revealed the interferon-signaling (IFN) pathway to be significantly altered. STAT1 was one key transcription factor that was upregulated and that might be driving downstream signaling as a response to acidic microenvironment. We are validating some of the downstream targets of the IFN pathway using qPCR.
  • Variation in Protein Coding Genes Identifies Information Flow

    Variation in Protein Coding Genes Identifies Information Flow

    bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. 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. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. 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. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number.