DOCSLIB.ORG

PDF Output of CLIC (Clustering by Inferred Co-Expression)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

PDF Output of CLIC (clustering by inferred co-expression) Dataset: Num of genes in input gene set: 47 Total number of genes: 16493 CLIC PDF output has three sections: 1) Overview of Co-Expression Modules (CEMs) Heatmap shows pairwise correlations between all genes in the input query gene set. Red lines shows the partition of input genes into CEMs, ordered by CEM strength. Each row shows one gene, and the brightness of squares indicates its correlations with other genes. Gene symbols are shown at left side and on the top of the heatmap. 2) Details of each CEM and its expansion CEM+ Top panel shows the posterior selection probability (dataset weights) for top GEO series datasets. Bottom panel shows the CEM genes (blue rows) as well as expanded CEM+ genes (green rows). Each column is one GEO series dataset, sorted by their posterior probability of being selected. The brightness of squares indicates the gene's correlations with CEM genes in the corresponding dataset. CEM+ includes genes that co-express with CEM genes in high-weight datasets, measured by LLR score. 3) Details of each GEO series dataset and its expression profile: Top panel shows the detailed information (e.g. title, summary) for the GEO series dataset. Bottom panel shows the background distribution and the expression profile for CEM genes in this dataset. Overview of Co-Expression Modules (CEMs) with Dataset Weighting Scale of average Pearson correlations Num of Genes in Query Geneset: 47. Num of CEMs: 1. 0.0 0.2 0.4 0.6 0.8 1.0 Col6a2 Col6a1 Col6a3 Bgn Sspn Crispld2 Aimp1 Cav2 Bloc1s6 Stx2 Nrsn1 Ap1g2 Yipf2 Pcsk1 Gnas Nptx1 Ap3s1 Nts Prg2 1500015O10Rik Tmem168 Plekhf2 Yipf1 Ovgp1 Bloc1s3 Shh Pcsk2 Galnt15 Syt13 Rab11a Nrsn2 Aph1b Vgf Exoc3l Cnst Astl Cdk16 B230216G23Rik Yipf3 Ap1ar Cpa3 Bloc1s5 Rab3d Hck Pigr 0610007P14Rik Myrip Col6a2 Col6a1 Col6a3 CEM 1 (611 datasets) Bgn Sspn Crispld2 Aimp1 Cav2 Bloc1s6 Stx2 Nrsn1 Ap1g2 Yipf2 Pcsk1 Gnas Nptx1 Ap3s1 Nts Prg2 1500015O10Rik Tmem168 Plekhf2 Yipf1 Ovgp1 Bloc1s3 Shh Pcsk2 Singletons Galnt15 Syt13 Rab11a Nrsn2 Aph1b Vgf Exoc3l Cnst Astl Cdk16 B230216G23Rik Yipf3 Ap1ar Cpa3 Bloc1s5 Rab3d Hck Pigr 0610007P14Rik Myrip Symbol Num ofCEMGenes:6.Predicted183.SelectedDatasets:611.Strength:11.5 CEM 1,Geneset"[G]transportvesicle",Page1 Tmem119 Serpinh1 Adamts2 Serpinf1 Crispld2 Col12a1 Efemp2 Leprel2 Ccdc80 Fkbp10 Col3a1 Col5a1 Col1a1 Col5a2 Col1a2 Col6a3 Col6a1 Col6a2 Col4a1 Col4a2 Pcolce Pdgfrb Lama2 Lama4 Olfml3 Cdh11 Aebp1 Cd248 Igfbp7 Fkbp7 Thbs2 Mxra8 Mmp2 Sparc Postn Bicc1 Fbln5 Loxl2 Rcn3 Sspn Fstl1 Nid1 Lhfp Ppic Lum Mgp Bgn Lox Dpt Islr 0.0 1.0 GSE16925 [15] GSE35785 [10] GSE13106 [10] Only showingfirst200datasets-Seetxtoutputforfulldetails. GSE18907 [12] GSE44339 [14] GSE6383 [6] GSE14395 [24] GSE39391 [21] GSE51608 [6] GSE14354 [6] GSE40368 [10] GSE56542 [8] GSE19979 [6] GSE4193 [8] GSE53299 [6] GSE5038 [9] GSE12956 [10] GSE8044 [6] GSE12618 [6] GSE27848 [16] GSE41759 [14] GSE54349 [6] GSE40156 [42] GSE44101 [6] GSE41342 [26] GSE42601 [6] GSE26299 [108] GSE5671 [18] GSE7694 [12] GSE45820 [6] GSE23845 [15] GSE15267 [8] GSE7810 [9] GSE9013 [12] GSE13302 [30] GSE10556 [6] GSE13874 [14] GSE34618 [7] GSE11898 [9] GSE18281 [33] GSE11759 [6] GSE37191 [12] GSE48790 [8] GSE54207 [9] GSE25029 [56] GSE28277 [10] GSE18534 [15] GSE27987 [31] GSE34552 [10] GSE6210 [12] GSE16691 [12] GSE42008 [6] GSE6589 [11] GSE5037 [18] GSE35091 [11] GSE47772 [24] GSE36229 [14] GSE35396 [24] GSE9400 [8] GSE8025 [21] GSE31244 [6] GSE10813 [12] GSE35961 [12] GSE49346 [6] GSE42753 [6] GSE24625 [12] GSE26290 [12] GSE47777 [8] GSE28664 [17] GSE13044 [59] GSE13526 [6] GSE32334 [19] GSE53403 [16] GSE13408 [14] GSE39984 [18] GSE6881 [10] GSE18660 [10] GSE23408 [39] GSE27630 [8] GSE29485 [12] GSE27932 [14] GSE7069 [8] GSE32277 [33] GSE24793 [8] GSE20577 [12] GSE52550 [12] GSE22925 [14] GSE40856 [8] GSE27261 [8] GSE7342 [12] GSE27546 [51] GSE15580 [14] GSE50824 [19] GSE29813 [18] GSE15794 [6] GSE15772 [8] GSE28389 [20] GSE29766 [40] GSE17462 [8] GSE59672 [12] GSE20177 [14] GSE7020 [8] GSE13563 [6] GSE9098 [12] GSE52101 [17] GSE24295 [7] GSE8024 [8] GSE32966 [24] GSE11148 [6] GSE33134 [31] GSE30855 [6] GSE18135 [18] GSE27568 [16] GSE58307 [20] GSE11443 [6] GSE38257 [14] GSE32095 [24] GSE32937 [8] GSE29975 [6] GSE5011 [10] GSE22371 [6] GSE34091 [8] GSE31431 [34] GSE27713 [7] GSE23598 [8] GSE30852 [6] GSE43779 [6] GSE18586 [9] GSE53951 [10] GSE5245 [16] GSE10965 [8] GSE18587 [9] GSE7196 [6] GSE33471 [12] GSE12498 [12] GSE44175 [18] GSE8788 [6] GSE5333 [16] GSE4695 [6] GSE15401 [18] GSE51686 [9] GSE10113 [12] GSE16496 [102] GSE9123 [8] GSE33726 [48] GSE38224 [12] GSE45968 [6] GSE14481 [12] GSE9044 [6] GSE55162 [8] GSE24276 [6] GSE22989 [10] GSE9711 [6] GSE19793 [32] CEM+ CEM GSE15155 [12] GSE51213 [16] GSE51483 [45] GSE21576 [10] GSE52474 [154] GSE35322 [20] 0.0 GSE14088 [9] GSE22841 [12] GSE4230 [8] Scale ofaveragePearsoncorrelations GSE19194 [14] GSE18742 [13] GSE6540 [12] GSE31106 [18] GSE42049 [8] GSE51628 [15] 0.2 GSE6867 [6] GSE36415 [14] GSE51432 [15] GSE17373 [24] GSE50439 [15] GSE30561 [6] GSE51108 [6] GSE31013 [12] GSE27302 [16] 0.4 GSE9735 [9] GSE22251 [9] GSE56777 [8] GSE59437 [30] GSE17797 [19] GSE34279 [30] GSE9892 [12] GSE30863 [20] GSE20523 [17] 0.6 GSE31598 [12] GSE53986 [16] GSE22307 [23] GSE46211 [18] GSE13963 [15] GSE13148 [10] GSE16790 [18] GSE15729 [15] GSE17817 [6] 0.8 GSE18395 [8] GSE33860 [28] GSE33308 [10] GSE32598 [11] Score 59.90 59.93 60.05 60.47 60.73 62.20 63.21 63.80 66.16 67.96 69.43 69.75 71.99 73.39 74.07 76.41 77.08 77.93 78.02 78.03 79.22 79.75 81.29 82.82 87.62 88.06 89.67 90.12 91.69 99.88 100.50 101.60 102.94 104.83 110.87 116.01 118.66 123.72 125.23 147.40 158.25 166.57 168.73 187.35 1.0 Notes Symbol Num ofCEMGenes:6.Predicted183.SelectedDatasets:611.Strength:11.5 CEM 1,Geneset"[G]transportvesicle",Page2 Fam114a1 Adamts12 Tmem45a Prkcdbp Col15a1 Col16a1 Olfml2b Emilin1 Tgfb1i1 Fkbp14 Gpr153 Gpr124 Mmp23 B3gnt9 Scara3 Smoc2 Pmp22 Kdelr3 Lrrc17 Scarf2 Copz2 Svep1 Fkbp9 Pdgfrl Mfap4 Mfap5 Matn2 Ednra Srpx2 Ltbp3 Tgfb3 Tagln Loxl3 Crtap Htra3 Gpx7 Aspn Cd34 Pxdn Cav1 Reck Srpx Myl9 Rhoj Ctsk Slit3 Ogn Gsn Tnc Fn1 0.0 1.0 GSE16925 [15] GSE35785 [10] GSE13106 [10] Only showingfirst200datasets-Seetxtoutputforfulldetails. GSE18907 [12] GSE44339 [14] GSE6383 [6] GSE14395 [24] GSE39391 [21] GSE51608 [6] GSE14354 [6] GSE40368 [10] GSE56542 [8] GSE19979 [6] GSE4193 [8] GSE53299 [6] GSE5038 [9] GSE12956 [10] GSE8044 [6] GSE12618 [6] GSE27848 [16] GSE41759 [14] GSE54349 [6] GSE40156 [42] GSE44101 [6] GSE41342 [26] GSE42601 [6] GSE26299 [108] GSE5671 [18] GSE7694 [12] GSE45820 [6] GSE23845 [15] GSE15267 [8] GSE7810 [9] GSE9013 [12] GSE13302 [30] GSE10556 [6] GSE13874 [14] GSE34618 [7] GSE11898 [9] GSE18281 [33] GSE11759 [6] GSE37191 [12] GSE48790 [8] GSE54207 [9] GSE25029 [56] GSE28277 [10] GSE18534 [15] GSE27987 [31] GSE34552 [10] GSE6210 [12] GSE16691 [12] GSE42008 [6] GSE6589 [11] GSE5037 [18] GSE35091 [11] GSE47772 [24] GSE36229 [14] GSE35396 [24] GSE9400 [8] GSE8025 [21] GSE31244 [6] GSE10813 [12] GSE35961 [12] GSE49346 [6] GSE42753 [6] GSE24625 [12] GSE26290 [12] GSE47777 [8] GSE28664 [17] GSE13044 [59] GSE13526 [6] GSE32334 [19] GSE53403 [16] GSE13408 [14] GSE39984 [18] GSE6881 [10] GSE18660 [10] GSE23408 [39] GSE27630 [8] GSE29485 [12] GSE27932 [14] GSE7069 [8] GSE32277 [33] GSE24793 [8] GSE20577 [12] GSE52550 [12] GSE22925 [14] GSE40856 [8] GSE27261 [8] GSE7342 [12] GSE27546 [51] GSE15580 [14] GSE50824 [19] GSE29813 [18] GSE15794 [6] GSE15772 [8] GSE28389 [20] GSE29766 [40] GSE17462 [8] GSE59672 [12] GSE20177 [14] GSE7020 [8] GSE13563 [6] GSE9098 [12] GSE52101 [17] GSE24295 [7] GSE8024 [8] GSE32966 [24] GSE11148 [6] GSE33134 [31] GSE30855 [6] GSE18135 [18] GSE27568 [16] GSE58307 [20] GSE11443 [6] GSE38257 [14] GSE32095 [24] GSE32937 [8] GSE29975 [6] GSE5011 [10] GSE22371 [6] GSE34091 [8] GSE31431 [34] GSE27713 [7] GSE23598 [8] GSE30852 [6] GSE43779 [6] GSE18586 [9] GSE53951 [10] GSE5245 [16] GSE10965 [8] GSE18587 [9] GSE7196 [6] GSE33471 [12] GSE12498 [12] GSE44175 [18] GSE8788 [6] GSE5333 [16] GSE4695 [6] GSE15401 [18] GSE51686 [9] GSE10113 [12] GSE16496 [102] GSE9123 [8] GSE33726 [48] GSE38224 [12] GSE45968 [6] GSE14481 [12] GSE9044 [6] GSE55162 [8] GSE24276 [6] GSE22989 [10] GSE9711 [6] GSE19793 [32] CEM+ CEM GSE15155 [12] GSE51213 [16] GSE51483 [45] GSE21576 [10] GSE52474 [154] GSE35322 [20] 0.0 GSE14088 [9] GSE22841 [12] GSE4230 [8] Scale ofaveragePearsoncorrelations GSE19194 [14] GSE18742 [13] GSE6540 [12] GSE31106 [18] GSE42049 [8] GSE51628 [15] 0.2 GSE6867 [6] GSE36415 [14] GSE51432 [15] GSE17373 [24] GSE50439 [15] GSE30561 [6] GSE51108 [6] GSE31013 [12] GSE27302 [16] 0.4 GSE9735 [9] GSE22251 [9] GSE56777 [8] GSE59437 [30] GSE17797 [19] GSE34279 [30] GSE9892 [12] GSE30863 [20] GSE20523 [17] 0.6 GSE31598 [12] GSE53986 [16] GSE22307 [23] GSE46211 [18] GSE13963 [15] GSE13148 [10] GSE16790 [18] GSE15729 [15] GSE17817 [6] 0.8 GSE18395 [8] GSE33860 [28] GSE33308 [10] GSE32598 [11] Score 29.05 29.57 30.00 30.03 30.47 30.73 30.81 30.98 31.52 32.47 32.66 35.86 36.63 36.63 37.04 37.10 38.04 38.71 38.86 39.24 39.40 39.80 41.23 41.90 42.01 43.15 43.80 44.04 44.68 44.78 45.38 45.55 45.82 45.91 46.33 47.35 50.55 50.77 52.13 52.81 52.87 53.46 53.57 53.62 55.73 56.29 56.64 57.19 58.04 58.95 1.0 Notes Symbol Num ofCEMGenes:6.Predicted183.SelectedDatasets:611.Strength:11.5 CEM 1,Geneset"[G]transportvesicle",Page3 Fam198b Serping1 Adamts5 Colec12 Col14a1 C1qtnf2 Creb3l1 Sparcl1 Cyp1b1 Efemp1 Casp12 Fndc3b Myadm Cmtm3 Col5a3 Kdelc2 Lepre1 Lamb1 Lgals1 Lamc1 Cspg4 Abca9 Anxa5 Vstm4 Wwtr1 Igfbp6 Eva1b Wisp1 Mfap2 Actg2 Emp3 Ltbp2 Fmod Itm2a Loxl1 Htra1 Gpx8 Gas1 Ehd2 Ptgis Dkk3 Pkd2 Tns1 Nbl1 Calu Dsel Ctgf Vim Fap Axl 0.0 1.0 GSE16925 [15] GSE35785 [10] GSE13106 [10] Only showingfirst200datasets-Seetxtoutputforfulldetails.
Recommended publications
  • Small Cell Ovarian Carcinoma: Genomic Stability and Responsiveness to Therapeutics

    Small Cell Ovarian Carcinoma: Genomic Stability and Responsiveness to Therapeutics

    Gamwell et al. Orphanet Journal of Rare Diseases 2013, 8:33 http://www.ojrd.com/content/8/1/33 RESEARCH Open Access Small cell ovarian carcinoma: genomic stability and responsiveness to therapeutics Lisa F Gamwell1,2, Karen Gambaro3, Maria Merziotis2, Colleen Crane2, Suzanna L Arcand4, Valerie Bourada1,2, Christopher Davis2, Jeremy A Squire6, David G Huntsman7,8, Patricia N Tonin3,4,5 and Barbara C Vanderhyden1,2* Abstract Background: The biology of small cell ovarian carcinoma of the hypercalcemic type (SCCOHT), which is a rare and aggressive form of ovarian cancer, is poorly understood. Tumourigenicity, in vitro growth characteristics, genetic and genomic anomalies, and sensitivity to standard and novel chemotherapeutic treatments were investigated in the unique SCCOHT cell line, BIN-67, to provide further insight in the biology of this rare type of ovarian cancer. Method: The tumourigenic potential of BIN-67 cells was determined and the tumours formed in a xenograft model was compared to human SCCOHT. DNA sequencing, spectral karyotyping and high density SNP array analysis was performed. The sensitivity of the BIN-67 cells to standard chemotherapeutic agents and to vesicular stomatitis virus (VSV) and the JX-594 vaccinia virus was tested. Results: BIN-67 cells were capable of forming spheroids in hanging drop cultures. When xenografted into immunodeficient mice, BIN-67 cells developed into tumours that reflected the hypercalcemia and histology of human SCCOHT, notably intense expression of WT-1 and vimentin, and lack of expression of inhibin. Somatic mutations in TP53 and the most common activating mutations in KRAS and BRAF were not found in BIN-67 cells by DNA sequencing.
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    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.
  • WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT

    WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT

    (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization I International Bureau (10) International Publication Number (43) International Publication Date WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT (51) International Patent Classification: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, C12Q 1/68 (2018.01) A61P 31/18 (2006.01) DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, C12Q 1/70 (2006.01) HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (21) International Application Number: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, PCT/US2018/056167 OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (22) International Filing Date: SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 16 October 2018 (16. 10.2018) TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (26) Publication Language: English GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (30) Priority Data: UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, 62/573,025 16 October 2017 (16. 10.2017) US TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, ΓΕ , IS, IT, LT, LU, LV, (71) Applicant: MASSACHUSETTS INSTITUTE OF MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TECHNOLOGY [US/US]; 77 Massachusetts Avenue, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, Cambridge, Massachusetts 02139 (US).
  • 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
  • Supplementary Table 1

    Supplementary Table 1

    Supplementary Table 1. 492 genes are unique to 0 h post-heat timepoint. The name, p-value, fold change, location and family of each gene are indicated. Genes were filtered for an absolute value log2 ration 1.5 and a significance value of p ≤ 0.05. Symbol p-value Log Gene Name Location Family Ratio ABCA13 1.87E-02 3.292 ATP-binding cassette, sub-family unknown transporter A (ABC1), member 13 ABCB1 1.93E-02 −1.819 ATP-binding cassette, sub-family Plasma transporter B (MDR/TAP), member 1 Membrane ABCC3 2.83E-02 2.016 ATP-binding cassette, sub-family Plasma transporter C (CFTR/MRP), member 3 Membrane ABHD6 7.79E-03 −2.717 abhydrolase domain containing 6 Cytoplasm enzyme ACAT1 4.10E-02 3.009 acetyl-CoA acetyltransferase 1 Cytoplasm enzyme ACBD4 2.66E-03 1.722 acyl-CoA binding domain unknown other containing 4 ACSL5 1.86E-02 −2.876 acyl-CoA synthetase long-chain Cytoplasm enzyme family member 5 ADAM23 3.33E-02 −3.008 ADAM metallopeptidase domain Plasma peptidase 23 Membrane ADAM29 5.58E-03 3.463 ADAM metallopeptidase domain Plasma peptidase 29 Membrane ADAMTS17 2.67E-04 3.051 ADAM metallopeptidase with Extracellular other thrombospondin type 1 motif, 17 Space ADCYAP1R1 1.20E-02 1.848 adenylate cyclase activating Plasma G-protein polypeptide 1 (pituitary) receptor Membrane coupled type I receptor ADH6 (includes 4.02E-02 −1.845 alcohol dehydrogenase 6 (class Cytoplasm enzyme EG:130) V) AHSA2 1.54E-04 −1.6 AHA1, activator of heat shock unknown other 90kDa protein ATPase homolog 2 (yeast) AK5 3.32E-02 1.658 adenylate kinase 5 Cytoplasm kinase AK7
  • Identification of Genomic Targets of Krüppel-Like Factor 9 in Mouse Hippocampal

    Identification of Genomic Targets of Krüppel-Like Factor 9 in Mouse Hippocampal

    Identification of Genomic Targets of Krüppel-like Factor 9 in Mouse Hippocampal Neurons: Evidence for a role in modulating peripheral circadian clocks by Joseph R. Knoedler A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Neuroscience) in the University of Michigan 2016 Doctoral Committee: Professor Robert J. Denver, Chair Professor Daniel Goldman Professor Diane Robins Professor Audrey Seasholtz Associate Professor Bing Ye ©Joseph R. Knoedler All Rights Reserved 2016 To my parents, who never once questioned my decision to become the other kind of doctor, And to Lucy, who has pushed me to be a better person from day one. ii Acknowledgements I have a huge number of people to thank for having made it to this point, so in no particular order: -I would like to thank my adviser, Dr. Robert J. Denver, for his guidance, encouragement, and patience over the last seven years; his mentorship has been indispensable for my growth as a scientist -I would also like to thank my committee members, Drs. Audrey Seasholtz, Dan Goldman, Diane Robins and Bing Ye, for their constructive feedback and their willingness to meet in a frequently cold, windowless room across campus from where they work -I am hugely indebted to Pia Bagamasbad and Yasuhiro Kyono for teaching me almost everything I know about molecular biology and bioinformatics, and to Arasakumar Subramani for his tireless work during the home stretch to my dissertation -I am grateful for the Neuroscience Program leadership and staff, in particular
  • 1 Phosphoproteomics Reveals That the Hvps34 Regulated SGK3 Kinase

    1 Phosphoproteomics Reveals That the Hvps34 Regulated SGK3 Kinase

    bioRxiv preprint doi: https://doi.org/10.1101/741652; this version posted August 20, 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 4.0 International license. Phosphoproteomics reveals that the hVPS34 regulated SGK3 kinase specifically phosphorylates endosomal proteins including Syntaxin-7, Syntaxin-12, RFIP4 and WDR44 Nazma Malik1, 2, Raja S Nirujogi1, Julien Peltier1, 3, Thomas Macartney1, Melanie Wightman1, Alan R Prescott4 , RoBert Gourlay1, Matthias Trost1, 5, Dario R. Alessi1, * Athanasios Karapetsas1, * 1 Medical Research Council (MRC) Protein Phosphorylation and UBiquitylation Unit, School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK 2 Current address: Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA 92037 3 Current address: Department of Bioanalysis, Immunogenicity & Biomarkers, GlaxoSmithKline R&D, Park Road, Ware, SG12 0DP, UK 4 Dundee Imaging Facility, School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK 5 Current address: Faculty of Medical Sciences, Institute for Cell and Molecular Biosciences, Newcastle upon Tyne NE2 4HH, UK *Correspondence to Athanasios Karapetsas ([email protected]) and Dario R Alessi ([email protected]) Abstract The serum- and glucocorticoid-regulated kinase (SGK) isoforms contriBute resistance to cancer therapies targeting the PI3K pathway. SGKs are homologous to Akt and these kinases display overlapping specificity and phosphorylate several suBstrates at the same residues, such as TSC2 to promote tumor growth By switching on the mTORC1 pathway.
  • Continuously Active Transcriptional Programs Are Required to Build Expansive Serotonergic Axon Architectures

    Continuously Active Transcriptional Programs Are Required to Build Expansive Serotonergic Axon Architectures

    CONTINUOUSLY ACTIVE TRANSCRIPTIONAL PROGRAMS ARE REQUIRED TO BUILD EXPANSIVE SEROTONERGIC AXON ARCHITECTURES By LAUREN JANINE DONOVAN Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Dissertation Advisor: Evan S. Deneris Department of Neurosciences CASE WESTERN RESERVE UNIVERSITY January 2020 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of Lauren Janine Donovan candidate for the degree of Doctor of Philosophy*. Committee Chair Jerry Silver, Ph.D. Committee Member Evan Deneris, Ph.D. Committee Member Heather Broihier, Ph.D. Committee Member Ron Conlon, Ph.D. Committee Member Pola Philippidou, Ph.D. Date of Defense August 29th, 2019 *We also certify that written approval has been obtained for any proprietary material contained therein. ii TABLE OF CONTENTS List of Figures……………………………………………………………………….….vii Abstract………………………………………….………………………………..….…1 CHAPTER 1. INTRODUCTION………………………………………………...……..3 GENERAL INTRODUCTION TO SEROTONIN………………………………….….4 Serotonin: Discovery and function………………………….……………...4 Serotonin Biosynthesis…………………………..…………………………..6 Manipulation of the serotonin system in humans……………………….6 Human mutations in 5-HT related genes………………………………….9 SEROTONIN NEURON NEUROGENESIS……………..………………………….11 5-HT neuron specification……………..………………………………...…11 Development of 5-HT neurons……………..………………………………13 NEUROANATOMY……………..……………………………………………………..13 Cytoarchitecture ……………..………………………………………………13 Adult Ascending 5-HT axon projection system ………………………..14
  • A Sleeping Beauty Transposon-Mediated Screen Identifies Murine Susceptibility Genes for Adenomatous Polyposis Coli (Apc)-Dependent Intestinal Tumorigenesis

    A Sleeping Beauty Transposon-Mediated Screen Identifies Murine Susceptibility Genes for Adenomatous Polyposis Coli (Apc)-Dependent Intestinal Tumorigenesis

    A Sleeping Beauty transposon-mediated screen identifies murine susceptibility genes for adenomatous polyposis coli (Apc)-dependent intestinal tumorigenesis Timothy K. Starra,1, Patricia M. Scottb, Benjamin M. Marshb, Lei Zhaob, Bich L. N. Thanb, M. Gerard O’Sullivana,c, Aaron L. Sarverd, Adam J. Dupuye, David A. Largaespadaa, and Robert T. Cormierb,1 aDepartment of Genetics, Cell Biology and Development, Center for Genome Engineering, Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455; bDepartment of Biochemistry and Molecular Biology, University of Minnesota Medical School, Duluth, MN 55812; cDepartment of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN 55108; dDepartment of Biostatistics and Informatics, Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455; and eDepartment of Anatomy and Cell Biology, University of Iowa, Iowa City, IA 52242 Edited* by William F. Dove, University of Wisconsin, Madison, WI, and approved March 2, 2011 (received for review December 1, 2010) Min Min It is proposed that a progressive series of mutations and epigenetic conducted the screen in mice carrying the Apc allele. Apc events leads to human colorectal cancer (CRC) and metastasis. mice harbor a T→A nonsense mutation in the Apc gene (4, 5) Furthermore, data from resequencing of the coding regions of that results in a truncated protein product that is unable to bind human CRC suggests that a relatively large number of mutations β-catenin and promote its degradation, thus leading to abnormal occur in individual human CRC, most at low frequency. The levels of β-catenin protein and up-regulation of β-catenin target functional role of these low-frequency mutations in CRC, and genes such as cyclin D1 (Ccnd1) and myelocytomatosis oncogene specifically how they may cooperate with high-frequency muta- (C-Myc).
  • Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection

    Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection

    Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ......................................................
  • SUPPORTING INFORMATION for Regulation of Gene Expression By

    SUPPORTING INFORMATION for Regulation of Gene Expression By

    SUPPORTING INFORMATION for Regulation of gene expression by the BLM helicase correlates with the presence of G4 motifs Giang Huong Nguyen1,2, Weiliang Tang3, Ana I. Robles1, Richard P. Beyer4, Lucas T. Gray5, Judith A. Welsh1, Aaron J. Schetter1, Kensuke Kumamoto1,6, Xin Wei Wang1, Ian D. Hickson2,7, Nancy Maizels5, 3,8 1 Raymond J. Monnat, Jr. and Curtis C. Harris 1Laboratory of Human Carcinogenesis, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, U.S.A; 2Department of Medical Oncology, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, U.K.; 3Department of Pathology, University of Washington, Seattle, WA U.S.A.; 4 Center for Ecogenetics and Environmental Health, University of Washington, Seattle, WA U.S.A.; 5Department of Immunology and Department of Biochemistry, University of Washington, Seattle, WA U.S.A.; 6Department of Organ Regulatory Surgery, Fukushima Medical University, Fukushima, Japan; 7Cellular and Molecular Medicine, Nordea Center for Healthy Aging, University of Copenhagen, Denmark; 8Department of Genome Sciences, University of WA, Seattle, WA U.S.A. SI Index: Supporting Information for this manuscript includes the following 19 items. A more detailed Materials and Methods section is followed by 18 Tables and Figures in order of their appearance in the manuscript text: 1) SI Materials and Methods 2) Figure S1. Study design and experimental workflow. 3) Figure S2. Immunoblot verification of BLM depletion from human fibroblasts. 4) Figure S3. PCA of mRNA and miRNA expression in BLM-depleted human fibroblasts. 5) Figure S4. qPCR confirmation of mRNA array data. 6) Table S1. BS patient and control detail.
  • Supplemental Solier

    Supplemental Solier

    Supplementary Figure 1. Importance of Exon numbers for transcript downregulation by CPT Numbers of down-regulated genes for four groups of comparable size genes, differing only by the number of exons. Supplementary Figure 2. CPT up-regulates the p53 signaling pathway genes A, List of the GO categories for the up-regulated genes in CPT-treated HCT116 cells (p<0.05). In bold: GO category also present for the genes that are up-regulated in CPT- treated MCF7 cells. B, List of the up-regulated genes in both CPT-treated HCT116 cells and CPT-treated MCF7 cells (CPT 4 h). C, RT-PCR showing the effect of CPT on JUN and H2AFJ transcripts. Control cells were exposed to DMSO. β2 microglobulin (β2) mRNA was used as control. Supplementary Figure 3. Down-regulation of RNA degradation-related genes after CPT treatment A, “RNA degradation” pathway from KEGG. The genes with “red stars” were down- regulated genes after CPT treatment. B, Affy Exon array data for the “CNOT” genes. The log2 difference for the “CNOT” genes expression depending on CPT treatment was normalized to the untreated controls. C, RT-PCR showing the effect of CPT on “CNOT” genes down-regulation. HCT116 cells were treated with CPT (10 µM, 20 h) and CNOT6L, CNOT2, CNOT4 and CNOT6 mRNA were analysed by RT-PCR. Control cells were exposed to DMSO. β2 microglobulin (β2) mRNA was used as control. D, CNOT6L down-regulation after CPT treatment. CNOT6L transcript was analysed by Q- PCR. Supplementary Figure 4. Down-regulation of ubiquitin-related genes after CPT treatment A, “Ubiquitin-mediated proteolysis” pathway from KEGG.