Supporting Information
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. -
Further Delineation of Chromosomal Consensus Regions in Primary
Leukemia (2007) 21, 2463–2469 & 2007 Nature Publishing Group All rights reserved 0887-6924/07 $30.00 www.nature.com/leu ORIGINAL ARTICLE Further delineation of chromosomal consensus regions in primary mediastinal B-cell lymphomas: an analysis of 37 tumor samples using high-resolution genomic profiling (array-CGH) S Wessendorf1,6, TFE Barth2,6, A Viardot1, A Mueller3, HA Kestler3, H Kohlhammer1, P Lichter4, M Bentz5,HDo¨hner1,PMo¨ller2 and C Schwaenen1 1Klinik fu¨r Innere Medizin III, Zentrum fu¨r Innere Medizin der Universita¨t Ulm, Ulm, Germany; 2Institut fu¨r Pathologie, Universita¨t Ulm, Ulm, Germany; 3Forschungsdozentur Bioinformatik, Universita¨t Ulm, Ulm, Germany; 4Abt. Molekulare Genetik, Deutsches Krebsforschungszentrum, Heidelberg, Germany and 5Sta¨dtisches Klinikum Karlsruhe, Karlsruhe, Germany Primary mediastinal B-cell lymphoma (PMBL) is an aggressive the expression of BSAP, BOB1, OCT2, PAX5 and PU1 was extranodal B-cell non-Hodgkin’s lymphoma with specific clin- added to the spectrum typical of PMBL features.9 ical, histopathological and genomic features. To characterize Genetically, a pattern of highly recurrent karyotype alterations further the genotype of PMBL, we analyzed 37 tumor samples and PMBL cell lines Med-B1 and Karpas1106P using array- with the hallmark of chromosomal gains of the subtelomeric based comparative genomic hybridization (matrix- or array- region of chromosome 9 supported the concept of a unique CGH) to a 2.8k genomic microarray. Due to a higher genomic disease entity that distinguishes PMBL from other B-cell non- resolution, we identified altered chromosomal regions in much Hodgkin’s lymphomas.10,11 Together with less specific gains on higher frequencies compared with standard CGH: for example, 2p15 and frequent mutations of the SOCS1 gene, a notable þ 9p24 (68%), þ 2p15 (51%), þ 7q22 (32%), þ 9q34 (32%), genomic similarity to classical Hodgkin’s lymphoma was þ 11q23 (18%), þ 12q (30%) and þ 18q21 (24%). -
Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897 -
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. -
Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7 -
Interconversion Between Active and Inactive TATA-Binding Protein
1446–1459 Nucleic Acids Research, 2012, Vol. 40, No. 4 Published online 19 October 2011 doi:10.1093/nar/gkr802 Interconversion between active and inactive TATA-binding protein transcription complexes in the mouse genome Mohamed-Amin Choukrallah, Dominique Kobi1, Igor Martianov1, W. W. M. Pim Pijnappel2,4, Nikolai Mischerikow2,3, Tao Ye1, Albert J. R. Heck3,4,5, H. Th. Marc Timmers2,4 and Irwin Davidson1,* 1Institut de Ge´ ne´ tique et de Biologie Mole´ culaire et Cellulaire, CNRS/INSERM/ULP, 1 Rue Laurent Fries, 67404 Illkirch Ce´ dex, France, 2Molecular Cancer Research, University Medical Center Utrecht, Universiteitsweg 100, 3Biomolecular Mass Spectrometry and Proteomics Group, Bijvoet Centre for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 4Netherlands Proteomics Centre and 5Centre for Biomedical Genetics, Padualaan 8, 3584 CH Utrecht, The Netherlands Received June 10, 2011; Revised September 12, 2011; Accepted September 13, 2011 ABSTRACT preinitiation complex comprising B-TFIID that primes the promoter for productive preinitiation The TATA binding protein (TBP) plays a pivotal role complex formation in mammalian cells. in RNA polymerase II (Pol II) transcription through incorporation into the TFIID and B-TFIID complexes. The role of mammalian B-TFIID composed of TBP INTRODUCTION and B-TAF1 is poorly understood. Using a com- Accurate initiation of transcription by RNA polymerase plementation system in genetically modified mouse II (Pol II) requires the assembly of the multiprotein cells where endogenous TBP can be condi- preinitiation complex (PIC) on the core promoter around tionally inactivated and replaced by exogenous the mRNA start site (1–3). Amongst the basal transcrip- mutant TBP coupled to tandem affinity purification tion factors in this process is the TFIID complex com- and mass spectrometry, we identify two TBP prising the TATA binding protein (TBP) and a set of 13–14 TBP-associated factors (TAFs) (4–7). -
Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights Into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle
animals Article Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle Masoumeh Naserkheil 1 , Abolfazl Bahrami 1 , Deukhwan Lee 2,* and Hossein Mehrban 3 1 Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; [email protected] (M.N.); [email protected] (A.B.) 2 Department of Animal Life and Environment Sciences, Hankyong National University, Jungang-ro 327, Anseong-si, Gyeonggi-do 17579, Korea 3 Department of Animal Science, Shahrekord University, Shahrekord 88186-34141, Iran; [email protected] * Correspondence: [email protected]; Tel.: +82-31-670-5091 Received: 25 August 2020; Accepted: 6 October 2020; Published: 9 October 2020 Simple Summary: Hanwoo is an indigenous cattle breed in Korea and popular for meat production owing to its rapid growth and high-quality meat. Its yearling weight and carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score) are economically important for the selection of young and proven bulls. In recent decades, the advent of high throughput genotyping technologies has made it possible to perform genome-wide association studies (GWAS) for the detection of genomic regions associated with traits of economic interest in different species. In this study, we conducted a weighted single-step genome-wide association study which combines all genotypes, phenotypes and pedigree data in one step (ssGBLUP). It allows for the use of all SNPs simultaneously along with all phenotypes from genotyped and ungenotyped animals. Our results revealed 33 relevant genomic regions related to the traits of interest. -
Original Article Diagnostic and Prognostic Values of Forkhead Box D4 Gene in Colonic Adenocarcinoma
Int J Clin Exp Pathol 2020;13(10):2615-2627 www.ijcep.com /ISSN:1936-2625/IJCEP0117403 Original Article Diagnostic and prognostic values of forkhead box D4 gene in colonic adenocarcinoma Qiu-Xia Li1, Ning-Qin Li2, Jin-Yuan Liao2 1Department of Health Management and Division of Physical Examination, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, People’s Republic of China; 2Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, People’s Republic of China Received July 2, 2020; Accepted August 31, 2020; Epub October 1, 2020; Published October 15, 2020 Abstract: Previous studies found that Forkhead box D4 (FOXD4) overexpressed in human colorectal cancer had the worst prognosis. However, the diagnostic value and further mechanism have not been fully researched. Statistical examinations for FOXD4 expression colon adenocarcinoma (COAD) patients were obtained from The Cancer Genome Atlas (TCGA). Survival analysis was used to assess its prognostic value. Nomogram model was used for visual pre- diction of patient survival rate. The online functional enrichment analysis tool was used to evaluate the biological functions and pathways of FOXD4 and its co-expressed genes. Receiver operating characteristic curve analysis suggested that FOXD4 might be a diagnostic biomarker for COAD (P<0.001, area under the curve [AUC]=0.728, 95% confidence interval [CI]=0.669-0.787). Low expression ofFOXD4 was associated with a good clinical outcome (P=0.001, HR=0.517, 95% CI=0.341-0.782). A total of 797 genes were correlated with FOXD4 and associated with cell proliferation, cell differentiation, nuclear matrix, Rap1 signaling pathway, RNA transport, and VEGF signaling pathway. -
Supplemental Materials Supplemental Table 1
Electronic Supplementary Material (ESI) for RSC Advances. This journal is © The Royal Society of Chemistry 2016 Supplemental Materials Supplemental Table 1. The differentially expressed proteins from rat pancreas identified by proteomics (SAP vs. SO) No. Protein name Gene name ratio P value 1 Metallothionein Mt1m 3.35 6.34E-07 2 Neutrophil antibiotic peptide NP-2 Defa 3.3 8.39E-07 3 Ilf2 protein Ilf2 3.18 1.75E-06 4 Numb isoform o/o rCG 3.12 2.73E-06 5 Lysozyme Lyz2 3.01 5.63E-06 6 Glucagon Gcg 2.89 1.17E-05 7 Serine protease HTRA1 Htra1 2.75 2.97E-05 8 Alpha 2 macroglobulin cardiac isoform (Fragment) 2.75 2.97E-05 9 Myosin IF (Predicted) Myo1f 2.65 5.53E-05 10 Neuroendocrine secretory protein 55 Gnas 2.61 7.60E-05 11 Matrix metallopeptidase 8 Mmp8 2.57 9.47E-05 12 Protein Tnks1bp1 Tnks1bp1 2.53 1.22E-04 13 Alpha-parvin Parva 2.47 1.78E-04 14 C4b-binding protein alpha chain C4bpa 2.42 2.53E-04 15 Protein KTI12 homolog Kti12 2.41 2.74E-04 16 Protein Rab11fip5 Rab11fip5 2.41 2.84E-04 17 Protein Mcpt1l3 Mcpt1l3 2.33 4.43E-04 18 Phospholipase B-like 1 Plbd1 2.33 4.76E-04 Aldehyde dehydrogenase (NAD), cytosolic 19 2.32 4.93E-04 (Fragments) 20 Protein Dpy19l2 Dpy19l2 2.3 5.68E-04 21 Regenerating islet-derived 3 alpha, isoform CRA_a Reg3a 2.27 6.74E-04 22 60S acidic ribosomal protein P1 Rplp1 2.26 7.22E-04 23 Serum albumin Alb 2.25 7.98E-04 24 Ribonuclease 4 Rnase4 2.24 8.25E-04 25 Cct-5 protein (Fragment) Cct5 2.24 8.52E-04 26 Protein S100-A9 S100a9 2.22 9.71E-04 27 Creatine kinase M-type Ckm 2.21 1.00E-03 28 Protein Larp4b Larp4b 2.18 1.25E-03 -
INO80C Remodeler Maintains Genomic Stability by Preventing Promiscuous Transcription at Replication Origins
HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Cell Rep Manuscript Author . Author manuscript; Manuscript Author available in PMC 2020 October 07. Published in final edited form as: Cell Rep. 2020 September 08; 32(10): 108106. doi:10.1016/j.celrep.2020.108106. INO80C Remodeler Maintains Genomic Stability by Preventing Promiscuous Transcription at Replication Origins Salih Topal1,4, Christopher Van1,4, Yong Xue2,3,4, Michael F. Carey2,*, Craig L. Peterson1,5,* 1Program in Molecular Medicine, University of Massachusetts Medical School, 373 Plantation Street, Worcester, MA 01605, USA 2Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA 3Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, Jiangsu Ocean University, Lianyungang 222005, China 4These authors contributed equally 5Lead Contact SUMMARY The proper coordination of transcription with DNA replication and repair is central for genomic stability. We investigate how the INO80C chromatin remodeling enzyme might coordinate these genomic processes. We find that INO80C co-localizes with the origin recognition complex (ORC) at yeast replication origins and is bound to replication initiation sites in mouse embryonic stem cells (mESCs). In yeast· INO80C recruitment requires origin sequences but does not require ORC· suggesting that recruitment is independent of pre-replication complex assembly. In both yeast and ESCs· INO80C co-localizes at origins with Mot1 and NC2 transcription factors· and genetic studies suggest that they function together to promote genome stability. Interestingly· nascent transcript sequencing demonstrates that INO80C and Mot1 prevent pervasive transcription through origin sequences· and absence of these factors leads to formation of new DNA double-strand breaks. -
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 -
Open Huisinga Thesis Revisions Full All
The Pennsylvania State University The Graduate School Department of Biochemistry and Molecular Biology GLOBAL REGULATION OF GENE EXPRESSION IN SACCHAROMYCES CEREVISIAE VIA TATA BINDING PROTEIN REGULATORY FACTORS A Thesis in Biochemistry, Microbiology, and Molecular Biology by Kathryn L. Huisinga Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2005 The thesis of Kathryn L. Huisinga was reviewed and approved* by the following: B. Franklin Pugh Professor of Biochemistry and Molecular Biology Thesis Advisor Chair of Committee Joseph C. Reese Associate Professor of Biochemistry and Molecular Biology Ross C. Hardison T. Ming Chu Professor of Biochemistry and Molecular Biology Naomi S. Altman Associate Professor of Statistics Robert A. Schlegal Professor of Biochemistry and Molecular Biology Head of the Department of Biochemistry and Molecular Biology *Signatures are on file in the Graduate School ABSTRACT The TATA Binding Protein (TBP) is a key component of gene regulation. It binds to the promoter region of eukaryotic genes and facilitates assembly of the transcription initiation machinery, including RNA Polymerase II. Many proteins interact with TBP to both positively and negatively regulate gene expression. My thesis utilized genome-wide expression profiling in Saccharomyces cerevisiae to define the target genes of, and relationships between, the factors that regulate transcription via TBP. I found the SAGA and TFIID co-activator complexes, both of which can deliver TBP to promoters, make overlapping contributions to the expression of nearly all yeast genes. The SAGA complex functions predominantly at ~10% of the genome, targeting genes that contain TATA boxes and are up regulated upon an environmental stress response.