As a New Hypoxia-Inducible Gene of October 1, 2021
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Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. -
Activated Peripheral-Blood-Derived Mononuclear Cells
Transcription factor expression in lipopolysaccharide- activated peripheral-blood-derived mononuclear cells Jared C. Roach*†, Kelly D. Smith*‡, Katie L. Strobe*, Stephanie M. Nissen*, Christian D. Haudenschild§, Daixing Zhou§, Thomas J. Vasicek¶, G. A. Heldʈ, Gustavo A. Stolovitzkyʈ, Leroy E. Hood*†, and Alan Aderem* *Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103; ‡Department of Pathology, University of Washington, Seattle, WA 98195; §Illumina, 25861 Industrial Boulevard, Hayward, CA 94545; ¶Medtronic, 710 Medtronic Parkway, Minneapolis, MN 55432; and ʈIBM Computational Biology Center, P.O. Box 218, Yorktown Heights, NY 10598 Contributed by Leroy E. Hood, August 21, 2007 (sent for review January 7, 2007) Transcription factors play a key role in integrating and modulating system. In this model system, we activated peripheral-blood-derived biological information. In this study, we comprehensively measured mononuclear cells, which can be loosely termed ‘‘macrophages,’’ the changing abundances of mRNAs over a time course of activation with lipopolysaccharide (LPS). We focused on the precise mea- of human peripheral-blood-derived mononuclear cells (‘‘macro- surement of mRNA concentrations. There is currently no high- phages’’) with lipopolysaccharide. Global and dynamic analysis of throughput technology that can precisely and sensitively measure all transcription factors in response to a physiological stimulus has yet to mRNAs in a system, although such technologies are likely to be be achieved in a human system, and our efforts significantly available in the near future. To demonstrate the potential utility of advanced this goal. We used multiple global high-throughput tech- such technologies, and to motivate their development and encour- nologies for measuring mRNA levels, including massively parallel age their use, we produced data from a combination of two distinct signature sequencing and GeneChip microarrays. -
A Cytoskeleton Regulator AVIL Drives Tumorigenesis in Glioblastoma
ARTICLE https://doi.org/10.1038/s41467-020-17279-1 OPEN A cytoskeleton regulator AVIL drives tumorigenesis in glioblastoma Zhongqiu Xie 1, Pawel Ł. Janczyk2,8, Ying Zhang3,8, Aiqun Liu4, Xinrui Shi1, Sandeep Singh 1, Loryn Facemire1, ✉ Kristopher Kubow5,ZiLi6, Yuemeng Jia1, Dorothy Schafer7, James W. Mandell1, Roger Abounader3 & Hui Li1,2 Glioblastoma is a deadly cancer, with no effective therapies. Better understanding and identification of selective targets are urgently needed. We found that advillin (AVIL) is 1234567890():,; overexpressed in all the glioblastomas we tested including glioblastoma stem/initiating cells, but hardly detectable in non-neoplastic astrocytes, neural stem cells or normal brain. Glioma patients with increased AVIL expression have a worse prognosis. Silencing AVIL nearly eradicated glioblastoma cells in culture, and dramatically inhibited in vivo xenografts in mice, but had no effect on normal control cells. Conversely, overexpressing AVIL promoted cell proliferation and migration, enabled fibroblasts to escape contact inhibition, and transformed immortalized astrocytes, supporting AVIL being a bona fide oncogene. We provide evidence that the tumorigenic effect of AVIL is partly mediated by FOXM1, which regulates LIN28B, whose expression also correlates with clinical prognosis. AVIL regulates the cytoskeleton through modulating F-actin, while mutants disrupting F-actin binding are defective in its tumorigenic capabilities. 1 Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA. 2 Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA. 3 Department of Microbiology, Immunology, and Cancer Biology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA. 4 Tumor Hospital, Guangxi Medical University, Nanning 530021, China. -
Supplemental Table S1
Entrez Gene Symbol Gene Name Affymetrix EST Glomchip SAGE Stanford Literature HPA confirmed Gene ID Profiling profiling Profiling Profiling array profiling confirmed 1 2 A2M alpha-2-macroglobulin 0 0 0 1 0 2 10347 ABCA7 ATP-binding cassette, sub-family A (ABC1), member 7 1 0 0 0 0 3 10350 ABCA9 ATP-binding cassette, sub-family A (ABC1), member 9 1 0 0 0 0 4 10057 ABCC5 ATP-binding cassette, sub-family C (CFTR/MRP), member 5 1 0 0 0 0 5 10060 ABCC9 ATP-binding cassette, sub-family C (CFTR/MRP), member 9 1 0 0 0 0 6 79575 ABHD8 abhydrolase domain containing 8 1 0 0 0 0 7 51225 ABI3 ABI gene family, member 3 1 0 1 0 0 8 29 ABR active BCR-related gene 1 0 0 0 0 9 25841 ABTB2 ankyrin repeat and BTB (POZ) domain containing 2 1 0 1 0 0 10 30 ACAA1 acetyl-Coenzyme A acyltransferase 1 (peroxisomal 3-oxoacyl-Coenzyme A thiol 0 1 0 0 0 11 43 ACHE acetylcholinesterase (Yt blood group) 1 0 0 0 0 12 58 ACTA1 actin, alpha 1, skeletal muscle 0 1 0 0 0 13 60 ACTB actin, beta 01000 1 14 71 ACTG1 actin, gamma 1 0 1 0 0 0 15 81 ACTN4 actinin, alpha 4 0 0 1 1 1 10700177 16 10096 ACTR3 ARP3 actin-related protein 3 homolog (yeast) 0 1 0 0 0 17 94 ACVRL1 activin A receptor type II-like 1 1 0 1 0 0 18 8038 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 1 0 0 0 0 19 8751 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 1 0 0 0 0 20 8728 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 1 0 0 0 0 21 81792 ADAMTS12 ADAM metallopeptidase with thrombospondin type 1 motif, 12 1 0 0 0 0 22 9507 ADAMTS4 ADAM metallopeptidase with thrombospondin type 1 -
Amplitude Modulation of Androgen Signaling by C-MYC
Downloaded from genesdev.cshlp.org on September 28, 2021 - Published by Cold Spring Harbor Laboratory Press Amplitude modulation of androgen signaling by c-MYC Min Ni,1,2 Yiwen Chen,3,4 Teng Fei,1,2 Dan Li,1,2 Elgene Lim,1,2 X. Shirley Liu,3,4,5 and Myles Brown1,2,5 1Division of Molecular and Cellular Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA; 2Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02215, USA; 3Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA; 4Harvard School of Public Health, Boston, Massachusetts 02215, USA Androgen-stimulated growth of the molecular apocrine breast cancer subtype is mediated by an androgen receptor (AR)-regulated transcriptional program. However, the molecular details of this AR-centered regulatory network and the roles of other transcription factors that cooperate with AR in the network remain elusive. Here we report a positive feed-forward loop that enhances breast cancer growth involving AR, AR coregulators, and downstream target genes. In the absence of an androgen signal, TCF7L2 interacts with FOXA1 at AR-binding sites and represses the basal expression of AR target genes, including MYC. Direct AR regulation of MYC cooperates with AR-mediated activation of HER2/HER3 signaling. HER2/HER3 signaling increases the transcriptional activity of MYC through phosphorylation of MAD1, leading to increased levels of MYC/MAX heterodimers. MYC in turn reinforces the transcriptional activation of androgen-responsive genes. These results reveal a novel regulatory network in molecular apocrine breast cancers regulated by androgen and AR in which MYC plays a central role as both a key target and a cooperating transcription factor to drive oncogenic growth. -
An Animal Model with a Cardiomyocyte-Specific Deletion of Estrogen Receptor Alpha: Functional, Metabolic, and Differential Netwo
Washington University School of Medicine Digital Commons@Becker Open Access Publications 2014 An animal model with a cardiomyocyte-specific deletion of estrogen receptor alpha: Functional, metabolic, and differential network analysis Sriram Devanathan Washington University School of Medicine in St. Louis Timothy Whitehead Washington University School of Medicine in St. Louis George G. Schweitzer Washington University School of Medicine in St. Louis Nicole Fettig Washington University School of Medicine in St. Louis Attila Kovacs Washington University School of Medicine in St. Louis See next page for additional authors Follow this and additional works at: https://digitalcommons.wustl.edu/open_access_pubs Recommended Citation Devanathan, Sriram; Whitehead, Timothy; Schweitzer, George G.; Fettig, Nicole; Kovacs, Attila; Korach, Kenneth S.; Finck, Brian N.; and Shoghi, Kooresh I., ,"An animal model with a cardiomyocyte-specific deletion of estrogen receptor alpha: Functional, metabolic, and differential network analysis." PLoS One.9,7. e101900. (2014). https://digitalcommons.wustl.edu/open_access_pubs/3326 This Open Access Publication is brought to you for free and open access by Digital Commons@Becker. It has been accepted for inclusion in Open Access Publications by an authorized administrator of Digital Commons@Becker. For more information, please contact [email protected]. Authors Sriram Devanathan, Timothy Whitehead, George G. Schweitzer, Nicole Fettig, Attila Kovacs, Kenneth S. Korach, Brian N. Finck, and Kooresh I. Shoghi This open access publication is available at Digital Commons@Becker: https://digitalcommons.wustl.edu/open_access_pubs/3326 An Animal Model with a Cardiomyocyte-Specific Deletion of Estrogen Receptor Alpha: Functional, Metabolic, and Differential Network Analysis Sriram Devanathan1, Timothy Whitehead1, George G. Schweitzer2, Nicole Fettig1, Attila Kovacs3, Kenneth S. -
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 -
Angiogenic Patterning by STEEL, an Endothelial-Enriched Long
Angiogenic patterning by STEEL, an endothelial- enriched long noncoding RNA H. S. Jeffrey Mana,b, Aravin N. Sukumara,b, Gabrielle C. Lamc,d, Paul J. Turgeonb,e, Matthew S. Yanb,f, Kyung Ha Kub,e, Michelle K. Dubinskya,b, J. J. David Hob,f, Jenny Jing Wangb,e, Sunit Dasg,h, Nora Mitchelli, Peter Oettgeni, Michael V. Seftonc,d,j, and Philip A. Marsdena,b,e,f,1 aInstitute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada; bKeenan Research Centre for Biomedical Science in the Li Ka Shing Knowledge Institute, St. Michael’s Hospital, University of Toronto, Toronto, ON M5B 1T8, Canada; cDonnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E2, Canada; dInstitute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada; eDepartment of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada; fDepartment of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada; gArthur and Sonia Labatt Brain Tumour Research Institute, Hospital for SickKids, University of Toronto, Toronto, ON M5G 1X8, Canada; hDivision of Neurosurgery and Keenan Research Centre for Biomedical Science, St. Michael’s Hospital, University of Toronto, Toronto, ON M5B 1W8, Canada; iDepartment of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115; and jDepartment of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada Edited by Napoleone Ferrara, University of California, San Diego, La Jolla, CA, and approved January 24, 2018 (received for review August 28, 2017) Endothelial cell (EC)-enriched protein coding genes, such as endothelial formation in vitro and blood vessel formation in vivo. -
Exome Sequencing Reveals Cubilin Mutation As a Single-Gene Cause of Proteinuria
BRIEF COMMUNICATION www.jasn.org Exome Sequencing Reveals Cubilin Mutation as a Single-Gene Cause of Proteinuria Bugsu Ovunc,*† Edgar A. Otto,* Virginia Vega-Warner,* Pawaree Saisawat,* Shazia Ashraf,* Gokul Ramaswami,* Hanan M. Fathy,‡ Dominik Schoeb,* Gil Chernin,* Robert H. Lyons,§ ʈ Engin Yilmaz,† and Friedhelm Hildebrandt* ¶ ʈ Departments of *Pediatrics and Human Genetics, §Department of Biological Chemistry and DNA Sequencing Core, and ¶Howard Hughes Medical Institute, University of Michigan, Ann Arbor, Michigan; †Department of Medical Biology, Hacettepe University, Ankara, Turkey; and ‡The Pediatric Nephrology Unit, Alexandria University, Alexandria, Egypt ABSTRACT In two siblings of consanguineous parents with intermittent nephrotic-range pro- tion is still unknown.7 This forbids the use of teinuria, we identified a homozygous deleterious frameshift mutation in the gene cohort studies for gene identification and ne- CUBN, which encodes cubulin, using exome capture and massively parallel re- cessitates the ability to identify disease-caus- sequencing. The mutation segregated with affected members of this family and ing genes in single families. We therefore was absent from 92 healthy individuals, thereby identifying a recessive mutation in combined whole genome homozygosity CUBN as the single-gene cause of proteinuria in this sibship. Cubulin mutations mapping with consecutive whole human ex- cause a hereditary form of megaloblastic anemia secondary to vitamin B12 defi- ome capture (WHEC) and massively par- ciency, and proteinuria occurs in 50% of cases since cubilin is coreceptor for both allel re-sequencing to overcome this lim- 6 the intestinal vitamin B12-intrinsic factor complex and the tubular reabsorption of itation. In this way we here identify a protein in the proximal tubule. -
Association of Gene Ontology Categories with Decay Rate for Hepg2 Experiments These Tables Show Details for All Gene Ontology Categories
Supplementary Table 1: Association of Gene Ontology Categories with Decay Rate for HepG2 Experiments These tables show details for all Gene Ontology categories. Inferences for manual classification scheme shown at the bottom. Those categories used in Figure 1A are highlighted in bold. Standard Deviations are shown in parentheses. P-values less than 1E-20 are indicated with a "0". Rate r (hour^-1) Half-life < 2hr. Decay % GO Number Category Name Probe Sets Group Non-Group Distribution p-value In-Group Non-Group Representation p-value GO:0006350 transcription 1523 0.221 (0.009) 0.127 (0.002) FASTER 0 13.1 (0.4) 4.5 (0.1) OVER 0 GO:0006351 transcription, DNA-dependent 1498 0.220 (0.009) 0.127 (0.002) FASTER 0 13.0 (0.4) 4.5 (0.1) OVER 0 GO:0006355 regulation of transcription, DNA-dependent 1163 0.230 (0.011) 0.128 (0.002) FASTER 5.00E-21 14.2 (0.5) 4.6 (0.1) OVER 0 GO:0006366 transcription from Pol II promoter 845 0.225 (0.012) 0.130 (0.002) FASTER 1.88E-14 13.0 (0.5) 4.8 (0.1) OVER 0 GO:0006139 nucleobase, nucleoside, nucleotide and nucleic acid metabolism3004 0.173 (0.006) 0.127 (0.002) FASTER 1.28E-12 8.4 (0.2) 4.5 (0.1) OVER 0 GO:0006357 regulation of transcription from Pol II promoter 487 0.231 (0.016) 0.132 (0.002) FASTER 6.05E-10 13.5 (0.6) 4.9 (0.1) OVER 0 GO:0008283 cell proliferation 625 0.189 (0.014) 0.132 (0.002) FASTER 1.95E-05 10.1 (0.6) 5.0 (0.1) OVER 1.50E-20 GO:0006513 monoubiquitination 36 0.305 (0.049) 0.134 (0.002) FASTER 2.69E-04 25.4 (4.4) 5.1 (0.1) OVER 2.04E-06 GO:0007050 cell cycle arrest 57 0.311 (0.054) 0.133 (0.002) -
Structural Basis of Sterol Recognition and Nonvesicular Transport by Lipid
Structural basis of sterol recognition and nonvesicular PNAS PLUS transport by lipid transfer proteins anchored at membrane contact sites Junsen Tonga, Mohammad Kawsar Manika, and Young Jun Ima,1 aCollege of Pharmacy, Chonnam National University, Bukgu, Gwangju, 61186, Republic of Korea Edited by David W. Russell, University of Texas Southwestern Medical Center, Dallas, TX, and approved December 18, 2017 (received for review November 11, 2017) Membrane contact sites (MCSs) in eukaryotic cells are hotspots for roidogenic acute regulatory protein-related lipid transfer), PITP lipid exchange, which is essential for many biological functions, (phosphatidylinositol/phosphatidylcholine transfer protein), Bet_v1 including regulation of membrane properties and protein trafficking. (major pollen allergen from birch Betula verrucosa), PRELI (pro- Lipid transfer proteins anchored at membrane contact sites (LAMs) teins of relevant evolutionary and lymphoid interest), and LAMs contain sterol-specific lipid transfer domains [StARkin domain (SD)] (LTPs anchored at membrane contact sites) (9). and multiple targeting modules to specific membrane organelles. Membrane contact sites (MCSs) are closely apposed regions in Elucidating the structural mechanisms of targeting and ligand which two organellar membranes are in close proximity, typically recognition by LAMs is important for understanding the interorga- within a distance of 30 nm (7). The ER, a major site of lipid bio- nelle communication and exchange at MCSs. Here, we determined synthesis, makes contact with almost all types of subcellular or- the crystal structures of the yeast Lam6 pleckstrin homology (PH)-like ganelles (10). Oxysterol-binding proteins, which are conserved domain and the SDs of Lam2 and Lam4 in the apo form and in from yeast to humans, are suggested to have a role in the di- complex with ergosterol.