Proteomics Analysis Reveals the Implications of Cytoskeleton and Mitochondria in the Response of the Rat Brain to Starvation
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
Rationale for the Development of Alternative Forms of Androgen Deprivation Therapy
248 S Kumari, D Senapati et al. New approaches to inhibit 24:8 R275–R295 Review AR action Rationale for the development of alternative forms of androgen deprivation therapy Sangeeta Kumari1,*, Dhirodatta Senapati1,* and Hannelore V Heemers1,2,3 1Department of Cancer Biology, Cleveland Clinic, Cleveland, Ohio, USA Correspondence 2Department of Urology, Cleveland Clinic, Cleveland, Ohio, USA should be addressed 3Department of Hematology/Medical Oncology, Cleveland Clinic, Cleveland, Ohio, USA to H V Heemers *(S Kumari and D Senapati contributed equally to this work) Email [email protected] Abstract With few exceptions, the almost 30,000 prostate cancer deaths annually in the United Key Words States are due to failure of androgen deprivation therapy. Androgen deprivation f androgen receptor therapy prevents ligand-activation of the androgen receptor. Despite initial remission f prostate cancer after androgen deprivation therapy, prostate cancer almost invariably progresses while f nuclear receptor continuing to rely on androgen receptor action. Androgen receptor’s transcriptional f coregulator output, which ultimately controls prostate cancer behavior, is an alternative therapeutic f transcription target, but its molecular regulation is poorly understood. Recent insights in the molecular f castration mechanisms by which the androgen receptor controls transcription of its target genes f hormonal therapy are uncovering gene specificity as well as context-dependency. Heterogeneity in the Endocrine-Related Cancer Endocrine-Related androgen receptor’s transcriptional output is reflected both in its recruitment to diverse cognate DNA binding motifs and in its preferential interaction with associated pioneering factors, other secondary transcription factors and coregulators at those sites. This variability suggests that multiple, distinct modes of androgen receptor action that regulate diverse aspects of prostate cancer biology and contribute differentially to prostate cancer’s clinical progression are active simultaneously in prostate cancer cells. -
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 -
Epigenetic Services Citations
Active Motif Epigenetic Services Publications The papers below contain data generated by Active Motif’s Epigenetic Services team. To learn more about our services, please give us a call or visit us at www.activemotif.com/services. Technique Target Journal Year Reference Justin C. Boucher et al. CD28 Costimulatory Domain- ATAC-Seq, Cancer Immunol. Targeted Mutations Enhance Chimeric Antigen Receptor — 2021 RNA-Seq Res. T-cell Function. Cancer Immunol. Res. doi: 10.1158/2326- 6066.CIR-20-0253. Satvik Mareedu et al. Sarcolipin haploinsufficiency Am. J. Physiol. prevents dystrophic cardiomyopathy in mdx mice. RNA-Seq — Heart Circ. 2021 Am J Physiol Heart Circ Physiol. doi: 10.1152/ Physiol. ajpheart.00601.2020. Gabi Schutzius et al. BET bromodomain inhibitors regulate Nature Chemical ChIP-Seq BRD4 2021 keratinocyte plasticity. Nat. Chem. Biol. doi: 10.1038/ Biology s41589-020-00716-z. Siyun Wang et al. cMET promotes metastasis and ChIP-qPCR FOXO3 J. Cell Physiol. 2021 epithelial-mesenchymal transition in colorectal carcinoma by repressing RKIP. J. Cell Physiol. doi: 10.1002/jcp.30142. Sonia Iyer et al. Genetically Defined Syngeneic Mouse Models of Ovarian Cancer as Tools for the Discovery of ATAC-Seq — Cancer Discovery 2021 Combination Immunotherapy. Cancer Discov. doi: doi: 10.1158/2159-8290 Vinod Krishna et al. Integration of the Transcriptome and Genome-Wide Landscape of BRD2 and BRD4 Binding BRD2, BRD4, RNA Motifs Identifies Key Superenhancer Genes and Reveals ChIP-Seq J. Immunol. 2021 Pol II the Mechanism of Bet Inhibitor Action in Rheumatoid Arthritis Synovial Fibroblasts. J. Immunol. doi: doi: 10.4049/ jimmunol.2000286. Daniel Haag et al. -
PRICKLE3 Linked to Atpase Biogenesis Manifested Leber's Hereditary Optic Neuropathy
PRICKLE3 linked to ATPase biogenesis manifested Leber’s hereditary optic neuropathy Jialing Yu, … , Pingping Jiang, Min-Xin Guan J Clin Invest. 2020. https://doi.org/10.1172/JCI134965. Research In-Press Preview Genetics Ophthalmology Graphical abstract Find the latest version: https://jci.me/134965/pdf PRICKLE3 linked to ATPase biogenesis manifested Leber’s hereditary optic neuropathy Jialing Yu1,2,3#, Xiaoyang Liang2#, Yanchun Ji1,2 #, Cheng Ai2#, Junxia Liu2, Ling Zhu1,2, Zhipeng Nie2, Xiaofen Jin2,3, Chenghui Wang2, Juanjuan Zhang2,4, Fuxin Zhao4, Shuang Mei2, Xiaoxu Zhao1,2, Xiangtian Zhou4, Minglian Zhang5, Meng Wang1,2, Taosheng Huang6, Pingping Jiang1,2* and Min-Xin Guan1,2,3,7* 1The Children’s Hospital, Division of Medical Genetics and Genomics, Zhejiang University School of Medicine and National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China 2Institute of Genetics and Department of Human Genetics, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. 3Key Laboratory of Reproductive Genetics, Ministry of Education of PRC, Hangzhou, Zhejiang, China 4School of Optometry and Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China 5Department of Ophthalmology, Hebei Provincial Eye Hospital, Xingtai, Hebei, China 6Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA 7Joint Institute of Genetics and Genomic Medicine between Zhejiang University and University of Toronto, Zhejiang University, Hangzhou, Zhejiang, China #The first four authors have equally contributed to the work. *Corresponding authors: Min-Xin Guan, Ph.D., Institute of Genetics, Zhejiang University School of Medicine, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, China; Tel: 86-571-88206916; Fax: 86-571-88982377; E-mail: [email protected]; Pingping Jiang, Ph.D., Department of Human Genetics, Zhejiang University School of Medicine, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, China; Tel: 86-571- 88208328; Fax: 86-571-88982356; E-mail: [email protected]. -
Recurrent Inversion Toggling and Great Ape Genome Evolution
HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Nat Genet Manuscript Author . Author manuscript; Manuscript Author available in PMC 2020 December 15. Published in final edited form as: Nat Genet. 2020 August ; 52(8): 849–858. doi:10.1038/s41588-020-0646-x. Recurrent inversion toggling and great ape genome evolution David Porubsky1,2,9, Ashley D. Sanders3,9, Wolfram Höps3, PingHsun Hsieh1, Arvis Sulovari1, Ruiyang Li1, Ludovica Mercuri4, Melanie Sorensen1, Shwetha C. Murali1,5, David Gordon1,5, Stuart Cantsilieris1,6, Alex A. Pollen7, Mario Ventura4, Francesca Antonacci4, Tobias Marschall8, Jan O. Korbel3, Evan E. Eichler1,5,* 1Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington, USA. 2Max Planck Institute for Informatics, Saarland Informatics Campus E1.4, Saarbrücken, Germany. 3European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany. 4Dipartimento di Biologia, Università degli Studi di Bari “Aldo Moro”, Bari, Italy. 5Howard Hughes Medical Institute, University of Washington, Seattle, Washington, USA. 6Centre for Eye Research Australia, Department of Surgery (Ophthalmology), University of Melbourne, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia. 7Department of Neurology, University of California, San Francisco (UCSF), San Francisco, California, USA. 8Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, Germany. 9These authors contributed equally to this work. Abstract Inversions play an important role in disease and evolution but are difficult to characterize because their breakpoints map to large repeats. We increased by six-fold the number (n = 1,069) of previously reported great ape inversions using Strand-seq and long-read sequencing. We find that the X chromosome is most enriched (2.5-fold) for inversions based on its size and duplication content. -
In-Silico Discovery of Cancer-Specific Peptide-HLA Complexes for Targeted Therapy Ankur Dhanik*, Jessica R
Dhanik et al. BMC Bioinformatics (2016) 17:286 DOI 10.1186/s12859-016-1150-2 RESEARCH ARTICLE Open Access In-silico discovery of cancer-specific peptide-HLA complexes for targeted therapy Ankur Dhanik*, Jessica R. Kirshner, Douglas MacDonald, Gavin Thurston, Hsin C. Lin, Andrew J. Murphy and Wen Zhang Abstract Background: Major Histocompatibility Complex (MHC) or Human Leukocyte Antigen (HLA) Class I molecules bind to peptide fragments of proteins degraded inside the cell and display them on the cell surface. We are interested in peptide-HLA complexes involving peptides that are derived from proteins specifically expressed in cancer cells. Such complexes have been shown to provide an effective means of precisely targeting cancer cells by engineered T-cells and antibodies, which would be an improvement over current chemotherapeutic agents that indiscriminately kill proliferating cells. An important concern with the targeting of peptide-HLA complexes is off-target toxicity that could occur due to the presence of complexes similar to the target complex in cells from essential, normal tissues. Results: We developed a novel computational strategy for identifying potential peptide-HLA cancer targets and evaluating the likelihood of off-target toxicity associated with these targets. Our strategy combines sequence-based and structure-based approaches in a unique way to predict potential off-targets. The focus of our work is on the complexes involving the most frequent HLA class I allele HLA-A*02:01. Using our strategy, we predicted the off-target toxicity observed in past clinical trials. We employed it to perform a first-ever comprehensive exploration of the human peptidome to identify cancer-specific targets utilizing gene expression data from TCGA (The Cancer Genome Atlas) and GTEx (Gene Tissue Expression), and structural data from PDB (Protein Data Bank). -
View a Copy of This Licence, Visit
Robertson et al. BMC Biology (2020) 18:103 https://doi.org/10.1186/s12915-020-00826-z RESEARCH ARTICLE Open Access Large-scale discovery of male reproductive tract-specific genes through analysis of RNA-seq datasets Matthew J. Robertson1,2, Katarzyna Kent3,4,5, Nathan Tharp3,4,5, Kaori Nozawa3,5, Laura Dean3,4,5, Michelle Mathew3,4,5, Sandra L. Grimm2,6, Zhifeng Yu3,5, Christine Légaré7,8, Yoshitaka Fujihara3,5,9,10, Masahito Ikawa9, Robert Sullivan7,8, Cristian Coarfa1,2,6*, Martin M. Matzuk1,3,5,6 and Thomas X. Garcia3,4,5* Abstract Background: The development of a safe, effective, reversible, non-hormonal contraceptive method for men has been an ongoing effort for the past few decades. However, despite significant progress on elucidating the function of key proteins involved in reproduction, understanding male reproductive physiology is limited by incomplete information on the genes expressed in reproductive tissues, and no contraceptive targets have so far reached clinical trials. To advance product development, further identification of novel reproductive tract-specific genes leading to potentially druggable protein targets is imperative. Results: In this study, we expand on previous single tissue, single species studies by integrating analysis of publicly available human and mouse RNA-seq datasets whose initial published purpose was not focused on identifying male reproductive tract-specific targets. We also incorporate analysis of additional newly acquired human and mouse testis and epididymis samples to increase the number of targets identified. We detected a combined total of 1178 genes for which no previous evidence of male reproductive tract-specific expression was annotated, many of which are potentially druggable targets. -
Bioinformatic Pipeline for Whole Exome Sequence (WES) Analysis
SUPPLEMENT Supplement contents: Supplementary Methods Supplementary Figure 1: Bioinformatic pipeline for whole exome sequence (WES) analysis. Supplementary Figure 2: Pedigrees and HomozygosityMapper output for families with single variants identified, in addition to those shown in Figure 2. Supplementary Figure 3: Spatiotemporal expression of ID genes in human development using RNA sequencing data. Supplementary Figure 4: Developmental expression pattern of ID genes in the human prefrontal cortex. Supplementary Table 1: Family statistics Supplementary Table 2: Homozygosity-by-descent/autozygosity shared regions, as defined using HomozygosityMapper, cross-referenced with FSuite. Supplementary Table 3: Mutations identified per family. A. Single homozygous variant identified. B. Two to four variants identified. C. Dominant/de novo mutation identified. Supplementary Table 4: Pathogenic CNVs and variants of unknown significance identified by microarray analysis. Supplementary Table 5: BioGRID protein interaction and gene ontology analysis. (separate Excel file) Supplementary Table 6: Gene Ontology Pathway analysis. Supplementary Table 7: Gene List for anatomic/temporal transcription analyses. Supplementary Table 8: Top anatomical regions for ID gene expression. Supplementary Methods HBD/Autozygosity mapping Both of the below methods and the hg19 version of the genome were used to ensure a consistent and uniform genotyping. Genotyping data was uploaded to the HomozygosityMapper server to determine putative homozygous-by- descent (HBD) regions based on the allele frequencies of the markers uploaded to the server from previous studies. HBD regions were identified by manual curation and only HBD regions larger than 1 Mb shared between all affected members (and not unaffected members) of the family were chosen. These regions were extracted based on SNP RS numbers and these dbSNP identifiers were converted to a genomic position for used to represent genomic regions with NGS data. -
Kidney V-Atpase-Rich Cell Proteome Database
A comprehensive list of the proteins that are expressed in V-ATPase-rich cells harvested from the kidneys based on the isolation by enzymatic digestion and fluorescence-activated cell sorting (FACS) from transgenic B1-EGFP mice, which express EGFP under the control of the promoter of the V-ATPase-B1 subunit. In these mice, type A and B intercalated cells and connecting segment principal cells of the kidney express EGFP. The protein identification was performed by LC-MS/MS using an LTQ tandem mass spectrometer (Thermo Fisher Scientific). For questions or comments please contact Sylvie Breton ([email protected]) or Mark A. Knepper ([email protected]). -
MAGE Proteins and the Regulation of E2F Pathway
Central JSM Clinical Oncology and Research Case Report *Corresponding author Martin Monte, Departamento de Química Bi¬ológica, Facultad de Ciencias Exac¬tas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, MAGE Proteins and the C1428EHA Ciudad de Buenos Aires, Argentina, Tel: 541145763300; Email: Regulation of E2F Pathway Submitted: 04 April 2017 Accepted: 06 April 2017 1,2 1,2 Ladelfa M Fatima and Monte Martin * Published: 08 April 2017 1 Departamento de Química Biológica, Universidad de Buenos Aires, Argentina Copyright 2CONICET – Universidad de Buenos Aires, Instituto de Química Biológica de la Facultad © 2017 Martin et al. de Ciencias Exactas y Naturales (IQUIBICEN), Argentina OPEN ACCESS Abstract Keywords Melanoma Antigens Genes (MAGE) constitutes a mutagenic family divided in two • MAGE subfamilies, MAGE-I and MAGE-II, according to its tissue pattern expression. While • Transcription factors MAGE-I in adult humans are only expressed in testis and tumors tissues, those belonging • E2F1 to MAGE-II subfamily are ubiquitously expressed. During the last decade, functional characterization of MAGE proteins points to a role in transcription regulation. E2F1 is a member of the E2F family and is among the transcription factors reported to be modulated by MAGE proteins. In this article we will focus on reported cases of E2F1 modulation by members of MAGE-I and MAGE-II subfamilies and the resulting biological consequences observed in normal and tumor cells. ABBREVIATIONS MAGE: Melanoma Antigens Genes; CDKs: Cyclin/Cyclin- proteins were, at the beginning, mainly studied as possible Dependent Kinases; AR: Androgen Receptor; E1A: Human antigens for cancer vaccines or as diagnostic and prognostic Adenoviral Early Region Protein E1A; HDM2: Human Double markers of cancer [4-6]. -
Molecular Architecture Underlying Fluid Absorption by the Developing Inner
RESEARCH ARTICLE Molecular architecture underlying fluid absorption by the developing inner ear Keiji Honda1†, Sung Huhn Kim2‡, Michael C Kelly3, Joseph C Burns3§, Laura Constance2, Xiangming Li2#, Fei Zhou2, Michael Hoa4, Matthew W Kelley3, Philine Wangemann2*, Robert J Morell5, Andrew J Griffith1* 1Molecular Biology and Genetics Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, United States; 2Anatomy and Physiology Department, Kansas State University, Manhattan, United States; 3Developmental Neuroscience Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, United States; 4Auditory Development and Restoration Program, National Institute on *For correspondence: Deafness and Other Communication Disorders, National Institutes of Health, [email protected] (PW); Bethesda, United States; 5Genomics and Computational Biology Core, National [email protected] (AJG) Institute on Deafness and Other Communication Disorders, National Institutes of Present address: Health, Bethesda, United States †Otolaryngology Department, Tsuchiura Kyodo General Hospital, Tsuchiura, Japan; ‡Department of Abstract Mutations of SLC26A4 are a common cause of hearing loss associated with Otorhinolaryngology, Head and enlargement of the endolymphatic sac (EES). Slc26a4 expression in the developing mouse Neck Surgery, Yonsei University College of Medicine, Seoul, endolymphatic sac is required for acquisition of normal inner ear structure and function. Here, we Korea; §Decibel Therapeutics, show that the mouse endolymphatic sac absorbs fluid in an SLC26A4-dependent fashion. Fluid Cambridge, United States; absorption was sensitive to ouabain and gadolinium but insensitive to benzamil, bafilomycin and #Technique R and D-Drug S3226. Single-cell RNA-seq analysis of pre- and postnatal endolymphatic sacs demonstrates two Substance, GlaxoSmithKline types of differentiated cells.