A Metabolic Interplay Coordinated by HLX Regulates Myeloid Differentiation and AML Through Partly Overlapping Pathways
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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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. -
1 Evidence for Gliadin Antibodies As Causative Agents in Schizophrenia
1 Evidence for gliadin antibodies as causative agents in schizophrenia. C.J.Carter PolygenicPathways, 20 Upper Maze Hill, Saint-Leonard’s on Sea, East Sussex, TN37 0LG [email protected] Tel: 0044 (0)1424 422201 I have no fax Abstract Antibodies to gliadin, a component of gluten, have frequently been reported in schizophrenia patients, and in some cases remission has been noted following the instigation of a gluten free diet. Gliadin is a highly immunogenic protein, and B cell epitopes along its entire immunogenic length are homologous to the products of numerous proteins relevant to schizophrenia (p = 0.012 to 3e-25). These include members of the DISC1 interactome, of glutamate, dopamine and neuregulin signalling networks, and of pathways involved in plasticity, dendritic growth or myelination. Antibodies to gliadin are likely to cross react with these key proteins, as has already been observed with synapsin 1 and calreticulin. Gliadin may thus be a causative agent in schizophrenia, under certain genetic and immunological conditions, producing its effects via antibody mediated knockdown of multiple proteins relevant to the disease process. Because of such homology, an autoimmune response may be sustained by the human antigens that resemble gliadin itself, a scenario supported by many reports of immune activation both in the brain and in lymphocytes in schizophrenia. Gluten free diets and removal of such antibodies may be of therapeutic benefit in certain cases of schizophrenia. 2 Introduction A number of studies from China, Norway, and the USA have reported the presence of gliadin antibodies in schizophrenia 1-5. Gliadin is a component of gluten, intolerance to which is implicated in coeliac disease 6. -
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 -
An Investigation Into the Genetic Architecture of Multiple System Atrophy and Familial Parkinson's Disease
An investigation into the genetic architecture of multiple system atrophy and familial Parkinson’s disease By Monica Federoff A thesis submitted to University College London for the degree of Doctor of Philosophy Laboratory of Neurogenetics, Department of Molecular Neuroscience, Institute of Neurology, University College London (UCL) 2 I, Monica Federoff, confirm that the work presented in this thesis is my own. Information derived from other sources and collaborative work have been indicated appropriately. Signature: Date: 09/06/2016 3 Acknowledgements: When I first joined the Laboratory of Neurogenetics (LNG), NIA, NIH as a summer intern in 2008, I had minimal experience working in a laboratory and was both excited and anxious at the prospect of it. From my very first day, Dr. Andrew Singleton was incredibly welcoming and introduced me to my first mentor, Dr. Javier Simon- Sanchez. Within just ten weeks working in the lab, both Dr. Singleton and Dr. Simon- Sanchez taught me the fundamental skills in an encouraging and supportive environment. I quickly got to know others in the lab, some of whom are still here today, and I sincerely appreciate their help with my assimilation into the LNG. After returning for an additional summer and one year as an IRTA postbac, I was honored to pursue a PhD in such an intellectually stimulating and comfortable environment. I am so grateful that Dr. Singleton has been such a wonderful mentor, as he is not only a brilliant scientist, but also extremely personable and approachable. If I inquire about meeting with him, he always manages to make time in his busy schedule and provides excellent guidance and mentorship. -
Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1. -
Mitochondrial Protein Quality Control Mechanisms
G C A T T A C G G C A T genes Review Mitochondrial Protein Quality Control Mechanisms Pooja Jadiya * and Dhanendra Tomar * Center for Translational Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, PA 19140, USA * Correspondence: [email protected] (P.J.); [email protected] (D.T.); Tel.: +1-215-707-9144 (D.T.) Received: 29 April 2020; Accepted: 15 May 2020; Published: 18 May 2020 Abstract: Mitochondria serve as a hub for many cellular processes, including bioenergetics, metabolism, cellular signaling, redox balance, calcium homeostasis, and cell death. The mitochondrial proteome includes over a thousand proteins, encoded by both the mitochondrial and nuclear genomes. The majority (~99%) of proteins are nuclear encoded that are synthesized in the cytosol and subsequently imported into the mitochondria. Within the mitochondria, polypeptides fold and assemble into their native functional form. Mitochondria health and integrity depend on correct protein import, folding, and regulated turnover termed as mitochondrial protein quality control (MPQC). Failure to maintain these processes can cause mitochondrial dysfunction that leads to various pathophysiological outcomes and the commencement of diseases. Here, we summarize the current knowledge about the role of different MPQC regulatory systems such as mitochondrial chaperones, proteases, the ubiquitin-proteasome system, mitochondrial unfolded protein response, mitophagy, and mitochondria-derived vesicles in the maintenance of mitochondrial proteome and health. The proper understanding of mitochondrial protein quality control mechanisms will provide relevant insights to treat multiple human diseases. Keywords: mitochondria; proteome; ubiquitin; proteasome; chaperones; protease; mitophagy; mitochondrial protein quality control; mitochondria-associated degradation; mitochondrial unfolded protein response 1. Introduction Mitochondria are double membrane, dynamic, and semiautonomous organelles which have several critical cellular functions. -
Large Meta-Analysis of Genome-Wide Association Studies
medRxiv preprint doi: https://doi.org/10.1101/2020.10.01.20200659; this version posted October 4, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Large meta-analysis of genome-wide association studies expands knowledge of the genetic etiology of Alzheimer’s disease and highlights potential translational opportunities Céline Bellenguez1,*,#, Fahri Küçükali2,3,4*, Iris Jansen5,6*, Victor Andrade7,8*, Sonia Morenau- Grau9,10,*, Najaf Amin11,12, Benjamin Grenier-Boley1, Anne Boland13, Luca Kleineidam7,8, Peter Holmans14, Pablo Garcia9,10, Rafael Campos Martin7, Adam Naj15,16, Yang Qiong17, Joshua C. Bis18, Vincent Damotte1, Sven Van der Lee5,6,19, Marcos Costa1, Julien Chapuis1, Vilmentas Giedraitis20, María Jesús Bullido10,21, Adolfo López de Munáin10,22, Jordi Pérez- Tur10,23, Pascual Sánchez-Juan10,24, Raquel Sánchez-Valle25, Victoria Álvarez26, Pau Pastor27, Miguel Medina10,28, Jasper Van Dongen2,3,4, Christine Van Broeckhoven2,3,4, Rik Vandenberghe29,30, Sebastiaan Engelborghs31,32, Gael Nicolas33, Florence Pasquier34, Olivier Hanon35, Carole Dufouil36, Claudine Berr37, Stéphanie Debette36, Jean-François Dartigues36, Gianfranco Spalletta38, Benedetta Nacmias39,40, Vincenzo Solfrezzi41, Barbara Borroni42, Lucio Tremolizzo43, Davide Seripa44, Paolo Caffarra45, Antonio Daniele46,47, Daniela Galimberti48,49, Innocenzo Rainero50, Luisa Benussi51, Alesio Squassina52, Patrizia Mecoci53, Lucilla Parnetti54, Carlo Masullo55, Beatrice Arosio56, John Hardy57, Simon Mead58, Kevin Morgan59, Clive Holmes60, Patrick Kehoe61, Bob Woods62, EADB, Charge, ADGC, Jin Sha15,16, Yi Zhao15,63, Chien-Yueh Lee15,63, Pavel P. -
Meta-Analysis of Genetic Association with Diagnosed Alzheimer's Disease Identifies Novel Risk Loci and Implicates Abeta, Tau, Immunity and Lipid Processing
bioRxiv preprint doi: https://doi.org/10.1101/294629; this version posted April 4, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meta-analysis of genetic association with diagnosed Alzheimer's disease identifies novel risk loci and implicates Abeta, Tau, immunity and lipid processing Kunkle BW+*1, Grenier-Boley B+2,3,4, Sims R5, Bis JC6, Naj AC7, Boland A8, Vronskaya M5, van der Lee SJ9, Amlie- Wolf A10, Bellenguez C2,3,4, Frizatti A5, Chouraki V2,11, Martin ER1,12, Sleegers K13,14, Badarinarayan N5, Jakobsdottir J15, Hamilton-Nelson KL1, Aloso R8, Raybould R5, Chen Y10, Kuzma AB10, Hiltunen M17,18, Morgan T5, Ahmad S9, Vardarajan BN19-21, Epelbaum J22, Hoffmann P23,24,25, Boada M26, Beecham GW1,12, Garnier JG8, Harold D27, Fitzpatrick AL28,29, Valladares O10, Moutet ML8, Gerrish A5, Smith AV30,31, Qu L10, Bacq D8, Denning N5, Jian X32, Zhao Y10, Zompo MD33, Fox NC34, Grove ML23, Choi SH16, Mateo I35, Hughes JT36, Adams HH9, Malamon J10, Garcia FS36, Patel Y37, Brody JA6, Dombroski B10, Naranjo MCD36, Daniilidou M38, Eiriksdottir G15, Mukherjee S39, Wallon D40,41, Uphill J42, Aspelund T15,43, Cantwell LB10, Garzia F8, Galimberti D44, Hofer E45,46, Butkiewics M47, Fin B8, Scarpini E44, Sarnowski C16, Bush W47, Meslage S8, Kornhuber J48, White CC49, Song Y47, Barber RC50, Engelborghs S51,52, Pichler S53, Voijnovic D9, Adams PM54, Vandenberghe -
The Landscape of Genomic Imprinting Across Diverse Adult Human Tissues
Downloaded from genome.cshlp.org on September 30, 2021 - Published by Cold Spring Harbor Laboratory Press Research The landscape of genomic imprinting across diverse adult human tissues Yael Baran,1 Meena Subramaniam,2 Anne Biton,2 Taru Tukiainen,3,4 Emily K. Tsang,5,6 Manuel A. Rivas,7 Matti Pirinen,8 Maria Gutierrez-Arcelus,9 Kevin S. Smith,5,10 Kim R. Kukurba,5,10 Rui Zhang,10 Celeste Eng,2 Dara G. Torgerson,2 Cydney Urbanek,11 the GTEx Consortium, Jin Billy Li,10 Jose R. Rodriguez-Santana,12 Esteban G. Burchard,2,13 Max A. Seibold,11,14,15 Daniel G. MacArthur,3,4,16 Stephen B. Montgomery,5,10 Noah A. Zaitlen,2,19 and Tuuli Lappalainen17,18,19 1The Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv 69978, Israel; 2Department of Medicine, University of California San Francisco, San Francisco, California 94158, USA; 3Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA; 4Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA; 5Department of Pathology, Stanford University, Stanford, California 94305, USA; 6Biomedical Informatics Program, Stanford University, Stanford, California 94305, USA; 7Wellcome Trust Center for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7BN, United Kingdom; 8Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland; 9Department of Genetic Medicine and Development, University of Geneva, 1211 Geneva, Switzerland; -
SUPPLEMENTARY MATERIAL Bone Morphogenetic Protein 4 Promotes
www.intjdevbiol.com doi: 10.1387/ijdb.160040mk SUPPLEMENTARY MATERIAL corresponding to: Bone morphogenetic protein 4 promotes craniofacial neural crest induction from human pluripotent stem cells SUMIYO MIMURA, MIKA SUGA, KAORI OKADA, MASAKI KINEHARA, HIROKI NIKAWA and MIHO K. FURUE* *Address correspondence to: Miho Kusuda Furue. Laboratory of Stem Cell Cultures, National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8, Saito-Asagi, Ibaraki, Osaka 567-0085, Japan. Tel: 81-72-641-9819. Fax: 81-72-641-9812. E-mail: [email protected] Full text for this paper is available at: http://dx.doi.org/10.1387/ijdb.160040mk TABLE S1 PRIMER LIST FOR QRT-PCR Gene forward reverse AP2α AATTTCTCAACCGACAACATT ATCTGTTTTGTAGCCAGGAGC CDX2 CTGGAGCTGGAGAAGGAGTTTC ATTTTAACCTGCCTCTCAGAGAGC DLX1 AGTTTGCAGTTGCAGGCTTT CCCTGCTTCATCAGCTTCTT FOXD3 CAGCGGTTCGGCGGGAGG TGAGTGAGAGGTTGTGGCGGATG GAPDH CAAAGTTGTCATGGATGACC CCATGGAGAAGGCTGGGG MSX1 GGATCAGACTTCGGAGAGTGAACT GCCTTCCCTTTAACCCTCACA NANOG TGAACCTCAGCTACAAACAG TGGTGGTAGGAAGAGTAAAG OCT4 GACAGGGGGAGGGGAGGAGCTAGG CTTCCCTCCAACCAGTTGCCCCAAA PAX3 TTGCAATGGCCTCTCAC AGGGGAGAGCGCGTAATC PAX6 GTCCATCTTTGCTTGGGAAA TAGCCAGGTTGCGAAGAACT p75 TCATCCCTGTCTATTGCTCCA TGTTCTGCTTGCAGCTGTTC SOX9 AATGGAGCAGCGAAATCAAC CAGAGAGATTTAGCACACTGATC SOX10 GACCAGTACCCGCACCTG CGCTTGTCACTTTCGTTCAG Suppl. Fig. S1. Comparison of the gene expression profiles of the ES cells and the cells induced by NC and NC-B condition. Scatter plots compares the normalized expression of every gene on the array (refer to Table S3). The central line -
NDUFAB1 Protects Heart by Coordinating Mitochondrial Respiratory Complex
bioRxiv preprint doi: https://doi.org/10.1101/302281; this version posted April 16, 2018. 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. NDUFAB1 Protects Heart by Coordinating Mitochondrial Respiratory Complex and Supercomplex Assembly Running title: Hou et al. Cardiac Protection by NDUFAB1 Tingting Hou 1; Rufeng Zhang 1; Chongshu Jian 1; Wanqiu Ding 1; Yanru Wang 1; Qi Ma 1; Xinli Hu 1; Heping Cheng 1,†; Xianhua Wang 1,† 1State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking-Tsinghua Center for Life Sciences, Institute of Molecular Medicine, Peking University, China. † Corresponding author. Xianhua Wang Tel: 86-10-62754605 Email: [email protected] Heping Cheng Tel: 86-10-62765957 Email: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/302281; this version posted April 16, 2018. 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 The impairment of mitochondrial bioenergetics, often coupled with exaggerated reactive oxygen species (ROS) production, is emerging as a common mechanism in diseases of organs with a high demand for energy, such as the heart. Building a more robust cellular powerhouse holds promise for protecting these organs in stressful conditions. Here, we demonstrate that NDUFAB1 (NADH:ubiquinone oxidoreductase subunit AB1), acts as a powerful cardio-protector by enhancing mitochondrial energy biogenesis. In particular, NDUFAB1 coordinates the assembly of respiratory complexes I, II, and III and supercomplexes, conferring greater capacity and efficiency of mitochondrial energy metabolism. -
Dynamic Gene Expression by Putative Hair-Cell Progenitors During
Dynamic gene expression by putative hair-cell PNAS PLUS progenitors during regeneration in the zebrafish lateral line Aaron B. Steiner1, Taeryn Kim, Victoria Cabot2, and A. J. Hudspeth Howard Hughes Medical Institute and Laboratory of Sensory Neuroscience, The Rockefeller University, New York, NY 10065 Edited* by Yuh Nung Jan, Howard Hughes Medical Institute, University of California, San Francisco, CA, and approved February 25, 2014 (received for review October 2, 2013) Hearing loss is most commonly caused by the destruction of been identified, however, and even fewer have been confirmed as mechanosensory hair cells in the ear. This condition is usually mantle cell-specific (15–17). permanent: Despite the presence of putative hair-cell progenitors Although previous transcriptomic screens have sought genes in the cochlea, hair cells are not naturally replenished in adult expressed in the lateral line, none has focused on mantle cells mammals. Unlike those of the mammalian ear, the progenitor cells (18–20). The results of such studies reflect gene expression in of nonmammalian vertebrates can regenerate hair cells through- several cell types, a complication that might mask gene expres- out life. The basis of this difference remains largely unexplored sion in progenitors. One factor impeding the isolation and char- but may lie in molecular dissimilarities that affect how progenitors acterization of progenitor cells has been the lack of a transgenic respond to hair-cell death. To approach this issue, we analyzed line in which these cells are inclusively and specifically labeled, gene expression in hair-cell progenitors of the lateral-line system. allowing their separation by cell sorting.