Bioinformatic Analysis of Chicken Chemokines
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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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. -
FK506-Binding Protein 12.6/1B, a Negative Regulator of [Ca2+], Rescues Memory and Restores Genomic Regulation in the Hippocampus of Aging Rats
This Accepted Manuscript has not been copyedited and formatted. The final version may differ from this version. A link to any extended data will be provided when the final version is posted online. Research Articles: Neurobiology of Disease FK506-Binding Protein 12.6/1b, a negative regulator of [Ca2+], rescues memory and restores genomic regulation in the hippocampus of aging rats John C. Gant1, Eric M. Blalock1, Kuey-Chu Chen1, Inga Kadish2, Olivier Thibault1, Nada M. Porter1 and Philip W. Landfield1 1Department of Pharmacology & Nutritional Sciences, University of Kentucky, Lexington, KY 40536 2Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL 35294 DOI: 10.1523/JNEUROSCI.2234-17.2017 Received: 7 August 2017 Revised: 10 October 2017 Accepted: 24 November 2017 Published: 18 December 2017 Author contributions: J.C.G. and P.W.L. designed research; J.C.G., E.M.B., K.-c.C., and I.K. performed research; J.C.G., E.M.B., K.-c.C., I.K., and P.W.L. analyzed data; J.C.G., E.M.B., O.T., N.M.P., and P.W.L. wrote the paper. Conflict of Interest: The authors declare no competing financial interests. NIH grants AG004542, AG033649, AG052050, AG037868 and McAlpine Foundation for Neuroscience Research Corresponding author: Philip W. Landfield, [email protected], Department of Pharmacology & Nutritional Sciences, University of Kentucky, 800 Rose Street, UKMC MS 307, Lexington, KY 40536 Cite as: J. Neurosci ; 10.1523/JNEUROSCI.2234-17.2017 Alerts: Sign up at www.jneurosci.org/cgi/alerts to receive customized email alerts when the fully formatted version of this article is published. -
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. -
Location Analysis of Estrogen Receptor Target Promoters Reveals That
Location analysis of estrogen receptor ␣ target promoters reveals that FOXA1 defines a domain of the estrogen response Jose´ e Laganie` re*†, Genevie` ve Deblois*, Ce´ line Lefebvre*, Alain R. Bataille‡, Franc¸ois Robert‡, and Vincent Gigue` re*†§ *Molecular Oncology Group, Departments of Medicine and Oncology, McGill University Health Centre, Montreal, QC, Canada H3A 1A1; †Department of Biochemistry, McGill University, Montreal, QC, Canada H3G 1Y6; and ‡Laboratory of Chromatin and Genomic Expression, Institut de Recherches Cliniques de Montre´al, Montreal, QC, Canada H2W 1R7 Communicated by Ronald M. Evans, The Salk Institute for Biological Studies, La Jolla, CA, July 1, 2005 (received for review June 3, 2005) Nuclear receptors can activate diverse biological pathways within general absence of large scale functional data linking these putative a target cell in response to their cognate ligands, but how this binding sites with gene expression in specific cell types. compartmentalization is achieved at the level of gene regulation is Recently, chromatin immunoprecipitation (ChIP) has been used poorly understood. We used a genome-wide analysis of promoter in combination with promoter or genomic DNA microarrays to occupancy by the estrogen receptor ␣ (ER␣) in MCF-7 cells to identify loci recognized by transcription factors in a genome-wide investigate the molecular mechanisms underlying the action of manner in mammalian cells (20–24). This technology, termed 17-estradiol (E2) in controlling the growth of breast cancer cells. ChIP-on-chip or location analysis, can therefore be used to deter- We identified 153 promoters bound by ER␣ in the presence of E2. mine the global gene expression program that characterize the Motif-finding algorithms demonstrated that the estrogen re- action of a nuclear receptor in response to its natural ligand. -
RT² Profiler PCR Array (96-Well Format and 384-Well [4 X 96] Format)
RT² Profiler PCR Array (96-Well Format and 384-Well [4 x 96] Format) Human Mitochondria Cat. no. 330231 PAHS-087ZA For pathway expression analysis Format For use with the following real-time cyclers RT² Profiler PCR Array, Applied Biosystems® models 5700, 7000, 7300, 7500, Format A 7700, 7900HT, ViiA™ 7 (96-well block); Bio-Rad® models iCycler®, iQ™5, MyiQ™, MyiQ2; Bio-Rad/MJ Research Chromo4™; Eppendorf® Mastercycler® ep realplex models 2, 2s, 4, 4s; Stratagene® models Mx3005P®, Mx3000P®; Takara TP-800 RT² Profiler PCR Array, Applied Biosystems models 7500 (Fast block), 7900HT (Fast Format C block), StepOnePlus™, ViiA 7 (Fast block) RT² Profiler PCR Array, Bio-Rad CFX96™; Bio-Rad/MJ Research models DNA Format D Engine Opticon®, DNA Engine Opticon 2; Stratagene Mx4000® RT² Profiler PCR Array, Applied Biosystems models 7900HT (384-well block), ViiA 7 Format E (384-well block); Bio-Rad CFX384™ RT² Profiler PCR Array, Roche® LightCycler® 480 (96-well block) Format F RT² Profiler PCR Array, Roche LightCycler 480 (384-well block) Format G RT² Profiler PCR Array, Fluidigm® BioMark™ Format H Sample & Assay Technologies Description The Human Mitochondria RT² Profiler PCR Array profiles the expression of 84 genes involved in the biogenesis and function of the cell's powerhouse organelle. The genes monitored by this array include regulators and mediators of mitochondrial molecular transport of not only the metabolites needed for the electron transport chain and oxidative phosphorylation, but also the ions required for maintaining the mitochondrial membrane polarization and potential important for ATP synthesis. Metabolism and energy production are not the only functions of mitochondria. -
Xenopus Piwi Proteins Interact with a Broad Proportion of the Oocyte Transcriptome
Downloaded from rnajournal.cshlp.org on September 27, 2021 - Published by Cold Spring Harbor Laboratory Press Xenopus Piwi proteins interact with a broad proportion of the oocyte transcriptome JAMES A. TOOMBS,1,2,4 YULIYA A. SYTNIKOVA,3,5 GUNG-WEI CHIRN,3,6 IGNATIUS ANG,3,7 NELSON C. LAU,3 and MICHAEL D. BLOWER1,2 1Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts 02114, USA 2Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA 3Department of Biology and Rosenstiel Basic Medical Science Research Center, Brandeis University, Waltham, Massachusetts 02454, USA ABSTRACT Piwi proteins utilize small RNAs (piRNAs) to recognize target transcripts such as transposable elements (TE). However, extensive piRNA sequence diversity also suggests that Piwi/piRNA complexes interact with many transcripts beyond TEs. To determine Piwi target RNAs, we used ribonucleoprotein-immunoprecipitation (RIP) and cross-linking and immunoprecipitation (CLIP) to identify thousands of transcripts associated with the Piwi proteins XIWI and XILI (Piwi-protein-associated transcripts, PATs) from early stage oocytes of X. laevis and X. tropicalis. Most PATs associate with both XIWI and XILI and include transcripts of developmentally important proteins in oogenesis and embryogenesis. Only a minor fraction of PATs in both frog species displayed near perfect matches to piRNAs. Since predicting imperfect pairing between all piRNAs and target RNAs remains intractable, we instead determined that PAT read counts correlate well with the lengths and expression levels of transcripts, features that have also been observed for oocyte mRNAs associated with Drosophila Piwi proteins. We used an in vitro assay with exogenous RNA to confirm that XIWI associates with RNAs in a length- and concentration-dependent manner. -
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 -
Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement. -
Mitochondrial Carriers Regulating Insulin Secretion Profiled in Human Islets Upon Metabolic Stress
Jiménez-Sánchez et al. Supplementary files Mitochondrial carriers regulating insulin secretion profiled in human islets upon metabolic stress Supplementary Table S1: Clinical Data of the human donors of pancreatic islets, type of analyses performed and tested conditions. Supplementary Table S2: Quantitative data related to the transcriptomic profiles of mitochondrial solute carriers and associated genes in human islets upon metabolic stress. NA: Not applicable, ND: not detected. Supplementary Table S3: Quantitative data related to the transcriptomic profiles of the electron transport chain machinery and related mitochondrial carriers in human islets upon metabolic stress.NA: Not applicable, ND: not detected. Supplementary Table S4: Quantitative data related to the transcriptomic profiles of the outer and inner mitochondrial membrane translocases TOM/TIM machinery in human islets upon metabolic stress.NA: Not applicable, ND: not detected. Supplementary Table S5: Quantitative data related to the transcriptomic profiles of mitochondrial iron transport genes in human islets under metabolic stress. NA: Not applicable, ND: not detected. Supplementary Table S6: Quantitative data related to the transcriptomic profiles of mitochondrial calcium transport genes in human islets upon metabolic stress. NA: Not applicable, ND: not detected. Supplementary Table S7: Primers used for quantitative RT-PCR analysis Supplementary Figure S1: Functional interaction network of human (a) mitochondrial calcium transport genes; (b) outer and inner mitochondrial membrane translocases TOM/TIM machinery; (c) electron transport chain machinery and related carriers; (d) mitochondrial iron transport genes. Nodes were connected using the STRING interaction knowledgebase with a confidence score >0.4. Supplementary Figure S2: Effects of high 25 mM glucose (G25) and 0.4 mM oleate (Olea) or palmitate (Palm) on the transcriptional regulation of the electron transport chain machinery. -
Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A. -
Proteomics of Lipid Accumulation and DGAT Inhibition in Hepg2 Liver Carcinoma Cells
Proteomics of lipid accumulation and DGAT inhibition in HepG2 liver carcinoma cells. By Bhumika Bhatt-Wessel A thesis submitted to Victoria University of Wellington in fulfilment of the requirement for the degree of Doctor of Philosophy In Cell and Molecular Biology. Victoria University of Wellington 2017. i ABSTRACT Non-alcoholic fatty liver disease (NAFLD) is a manifestation of the metabolic syndrome in the liver. It is marked by hepatocyte accumulation of triacylglycerol (TAG) rich lipid droplets. In some patients, the disease progresses to non-alcoholic steatohepatitis (NASH), characterized by cellular damage, inflammation and fibrosis. In some cases, cirrhosis and liver failure may occur. However, the pathogenesis of NAFLD is still unclear. The present project is based on the hypothesis that hepatocytes are equipped with mechanisms that allow them to manage lipid accumulation to a certain extent. Continued or increased lipid accumulation beyond this triggers molecular mechanisms such as oxidative stress, lipid peroxidation and cell death that aggravate the condition and cause disease progression. The aim of this project is to study the effects of lipid accumulation on the cells using proteomics approach to identify proteins involved in the disease progression. A cell culture model was used in the study. HepG2 cells, a human liver carcinoma cell line, were treated with a mixture of fatty acids (FA) to induce lipid accumulation. The lipid accumulation in HepG2 cells was measured with Oil red O assay and the effect of lipid accumulation on the proliferation of the cells was measured using an MTT cell proliferation assay. HepG2 cells treated with 1 mM FA mixture for 6 hours induced lipid accumulation 1.4 times of control with 90% of cell proliferation capacity of the control cells. -
The Hominoid-Specific Gene TBC1D3 Promotes Generation of Basal Neural
1 The hominoid-specific gene TBC1D3 promotes generation of basal neural 2 progenitors and induces cortical folding in mice 3 4 Xiang-Chun Ju1,3,9, Qiong-Qiong Hou1,3,9, Ai-Li Sheng1, Kong-Yan Wu1, Yang Zhou8, 5 Ying Jin8, Tieqiao Wen7, Zhengang Yang6, Xiaoqun Wang2,5, Zhen-Ge Luo1,2,3,4 6 7 1Institute of Neuroscience, State Key Laboratory of Neuroscience, Shanghai Institutes for 8 Biological Sciences, Chinese Academy of Sciences, Shanghai, China. 9 2CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, 10 China. 11 3Chinese Academy of Sciences University, Beijing, China. 12 4ShanghaiTech University, Shanghai, China 13 5Institute of Biophysics, Chinese Academy of Sciences, Beijing, China. 14 6Institutes of Brain Science, State Key Laboratory of Medical Neurobiology, Fudan 15 University, Shanghai, China. 16 7School of Life Sciences, Shanghai University, Shanghai, China. 17 8The Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese 18 Academy of Sciences, Shanghai, China. 19 9Co-first author 20 Correspondence should be addressed to Z.G.L ([email protected]) 21 22 Author contributions 23 X.-C.J and Q.-Q. H performed most experiments, analyzed data and wrote the paper. 24 A.-L.S helped with in situ hybridization. K.-Y.W assisted with imaging analysis. T.W 25 helped with the construction of nestin promoter construct. Y. Z and Y. J helped with 26 ReNeuron cell culture and analysis. Z.Y provided human fetal samples and assisted with 1 27 immunohistochemistry analysis. X.W provided help with live-imaging analysis. Z.-G.L 28 supervised the whole study, designed the research, analyzed data and wrote the paper.