Mapping the Spatial Proteome of Metastatic Cells in Colorectal Cancer
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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. -
De Novo EIF2AK1 and EIF2AK2 Variants Are Associated with Developmental Delay, Leukoencephalopathy, and Neurologic Decompensation
bioRxiv preprint doi: https://doi.org/10.1101/757039; this version posted September 16, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. De novo EIF2AK1 and EIF2AK2 variants are associated with developmental delay, leukoencephalopathy, and neurologic decompensation Dongxue Mao1,2, Chloe M. Reuter3,4, Maura R.Z. Ruzhnikov5,6, Anita E. Beck7, Emily G. Farrow8,9,10, Lisa T. Emrick1,11,12,13, Jill A. Rosenfeld12, Katherine M. Mackenzie5, Laurie Robak2,12,13, Matthew T. Wheeler3,14, Lindsay C. Burrage12,13, Mahim Jain15, Pengfei Liu12, Daniel Calame11,13, Sebastien Küry17,18, Martin Sillesen19, Klaus Schmitz-Abe20, Davide Tonduti21, Luigina Spaccini22, Maria Iascone23, Casie A. Genetti20, Madeline Graf16, Alyssa Tran12, Mercedes Alejandro12, Undiagnosed Diseases Network, Brendan H. Lee12,13, Isabelle Thiffault8,9,24, Pankaj B. Agrawal#,20, Jonathan A. Bernstein#,3,25, Hugo J. Bellen#,2,12,26,27,28, Hsiao- Tuan Chao#,1,2,11,12,13,28,27,29 #Correspondence should be addressed: [email protected] (P.A.), [email protected] (J.A.B.), [email protected] (H.J.B.), [email protected] (H.T.C.) 1Department of Pediatrics, Baylor College of Medicine (BCM), Houston, TX 2Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 3Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA 4Stanford Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, -
Serum Albumin OS=Homo Sapiens
Protein Name Cluster of Glial fibrillary acidic protein OS=Homo sapiens GN=GFAP PE=1 SV=1 (P14136) Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 Cluster of Isoform 3 of Plectin OS=Homo sapiens GN=PLEC (Q15149-3) Cluster of Hemoglobin subunit beta OS=Homo sapiens GN=HBB PE=1 SV=2 (P68871) Vimentin OS=Homo sapiens GN=VIM PE=1 SV=4 Cluster of Tubulin beta-3 chain OS=Homo sapiens GN=TUBB3 PE=1 SV=2 (Q13509) Cluster of Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 (P60709) Cluster of Tubulin alpha-1B chain OS=Homo sapiens GN=TUBA1B PE=1 SV=1 (P68363) Cluster of Isoform 2 of Spectrin alpha chain, non-erythrocytic 1 OS=Homo sapiens GN=SPTAN1 (Q13813-2) Hemoglobin subunit alpha OS=Homo sapiens GN=HBA1 PE=1 SV=2 Cluster of Spectrin beta chain, non-erythrocytic 1 OS=Homo sapiens GN=SPTBN1 PE=1 SV=2 (Q01082) Cluster of Pyruvate kinase isozymes M1/M2 OS=Homo sapiens GN=PKM PE=1 SV=4 (P14618) Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 Clathrin heavy chain 1 OS=Homo sapiens GN=CLTC PE=1 SV=5 Filamin-A OS=Homo sapiens GN=FLNA PE=1 SV=4 Cytoplasmic dynein 1 heavy chain 1 OS=Homo sapiens GN=DYNC1H1 PE=1 SV=5 Cluster of ATPase, Na+/K+ transporting, alpha 2 (+) polypeptide OS=Homo sapiens GN=ATP1A2 PE=3 SV=1 (B1AKY9) Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 Dihydropyrimidinase-related protein 2 OS=Homo sapiens GN=DPYSL2 PE=1 SV=1 Cluster of Alpha-actinin-1 OS=Homo sapiens GN=ACTN1 PE=1 SV=2 (P12814) 60 kDa heat shock protein, mitochondrial OS=Homo -
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Table S1. Putative miR-322 target transcripts which is highly expressed in MGCs ________________________________________________________________________________ 0610037L13Rik, 1700037H04Rik, 2310061I04Rik, 2810006K23Rik, Abcc5, Abhd16a, Acbd3, Acox1, Acsbg1, Acsl4, Actr1a, Actr2, Adck5, Adh5, Adrbk1, Aff4, Agk, Ahcyl1, Akap11, Akap7, Akirin1, Alg3, Amfr, Ammecr1, Amotl2, Ankfy1, Ankhd1, Ankrd52, Ap2a1, Ap2b1, Ap3b1, Ap3d1, App, Arcn1, Arf3, Arfgap2, Arhgap12, Arhgap5, Arhgdia, Arhgef11, Arih1, Arl2, Arl3, Arl8b, Armcx6, Asap1, Asnsd1, Atf6, Atg13, Atg4b, Atp13a3, Atp5g1, Atp6v1a, Atxn2, Atxn7l3, Atxn7l3b, AW549877, B4galt1, B4galt7, Bace1, Bag5, Baiap2, Baz2a, BC037034, Bcl2l1, Bcl2l2, Bfar, Bmpr1a, Bptf, Brd2, Brd4, Brpf3, Btbd3, Btg2, Cab39, Cacna2d1, Calm1, Capns1, Caprin1, Capza2, Carm1, Caskin1, Cbfa2t3, Cbx5, Cbx6, Cc2d1b, Ccdc127, Ccdc6, Ccnd2, Ccnt2, Ccnyl1, Cd164, Cd2ap, Cdc25a, Cdc27, Cdc37l1, Cdc42se2, Cdca4, Cdipt, Cdk5rap3, Cdk8, Cdk9, Cdv3, Celf1, Cfl2, Chchd3, Chd6, Chmp1a, Chmp7, Chordc1, Chpf, Chpt1, Chst8, Chtf8, Cisd2, Clasrp, Clcn3, Clstn1, Cmpk1, Cnih2, Cnot1, Cnot2, Col4a3bp, Cope, Cops2, Cops7a, Cops7b, Copz1, Coq6, Cpd, Crebzf, Crim1, Crk, Crkl, Csde1, Cse1l, Ctnnb1, Cul4a, Cul4b, Cxx1a, Cxx1b, Cxx1c, D15Ertd621e, D2hgdh, Dcaf7, Dcbld2, Dcp1a, Dctn5, Ddost, Ddr1, Ddx39, Ddx3x, Ddx6, Dedd, Dhdds, Dhx16, Diap1, Dido1, Dlst, Dmtf1, Dnaja2, Dnajb14, Dnajb2, Dnajc1, Dnajc16, Dnajc25, Dph3, Dpm1, Dpp9, Dpy19l4, Dsel, Dtl, Dvl1, Dync1li2, Dynll2, Dynlt3, Dyrk1a, Dyrk1b, Ebna1bp2, Edc4, Eftud2, Egln2, Eif1a, Eif2b2, Eif2s1, -
Supplementary Table 1
Supplementary Table 1. Large-scale quantitative phosphoproteomic profiling was performed on paired vehicle- and hormone-treated mTAL-enriched suspensions (n=3). A total of 654 unique phosphopeptides corresponding to 374 unique phosphoproteins were identified. The peptide sequence, phosphorylation site(s), and the corresponding protein name, gene symbol, and RefSeq Accession number are reported for each phosphopeptide identified in any one of three experimental pairs. For those 414 phosphopeptides that could be quantified in all three experimental pairs, the mean Hormone:Vehicle abundance ratio and corresponding standard error are also reported. Peptide Sequence column: * = phosphorylated residue Site(s) column: ^ = ambiguously assigned phosphorylation site Log2(H/V) Mean and SE columns: H = hormone-treated, V = vehicle-treated, n/a = peptide not observable in all 3 experimental pairs Sig. column: * = significantly changed Log 2(H/V), p<0.05 Log (H/V) Log (H/V) # Gene Symbol Protein Name Refseq Accession Peptide Sequence Site(s) 2 2 Sig. Mean SE 1 Aak1 AP2-associated protein kinase 1 NP_001166921 VGSLT*PPSS*PK T622^, S626^ 0.24 0.95 PREDICTED: ATP-binding cassette, sub-family A 2 Abca12 (ABC1), member 12 XP_237242 GLVQVLS*FFSQVQQQR S251^ 1.24 2.13 3 Abcc10 multidrug resistance-associated protein 7 NP_001101671 LMT*ELLS*GIRVLK T464, S468 -2.68 2.48 4 Abcf1 ATP-binding cassette sub-family F member 1 NP_001103353 QLSVPAS*DEEDEVPVPVPR S109 n/a n/a 5 Ablim1 actin-binding LIM protein 1 NP_001037859 PGSSIPGS*PGHTIYAK S51 -3.55 1.81 6 Ablim1 actin-binding -
EIF2B2 Gene Eukaryotic Translation Initiation Factor 2B Subunit Beta
EIF2B2 gene eukaryotic translation initiation factor 2B subunit beta Normal Function The EIF2B2 gene provides instructions for making one of five parts of a protein called eIF2B, specifically the beta subunit of this protein. The eIF2B protein helps regulate overall protein production (synthesis) in the cell by interacting with another protein, eIF2. The eIF2 protein is called an initiation factor because it is involved in starting (initiating) protein synthesis. Under some conditions, eIF2B increases protein synthesis by helping to recycle molecules called GTP, which carry energy to the initiation factor. Under other conditions, it slows protein synthesis by binding tightly to the initiation factor, which converts the eIF2B protein into an inactive form and prevents recycling of GTP. Proper regulation of protein synthesis is vital for ensuring that the correct levels of protein are available for the cell to cope with changing conditions. For example, cells must synthesize protein much faster if they are multiplying than if they are in a resting state. Health Conditions Related to Genetic Changes Leukoencephalopathy with vanishing white matter Mutations in the EIF2B2 gene have been identified in a few people with leukoencephalopathy with vanishing white matter, including some affected females with a variant of the disorder in which the neurological features are accompanied by ovarian failure (ovarioleukodystrophy). These mutations cause partial loss of eIF2B function. Impairment of eIF2B function makes it more difficult for the body's cells to regulate protein synthesis and deal with changing conditions and stress. Researchers believe that cells in the white matter (nerve fibers covered by a fatty substance called myelin that insulates and protects nerves) may be particularly affected by an abnormal response to stress, resulting in the signs and symptoms of leukoencephalopathy with vanishing white matter. -
Whole Transcriptomic Expression Differences in EBV Immortalized Versus Primary B-Cells
W&M ScholarWorks Undergraduate Honors Theses Theses, Dissertations, & Master Projects 12-2010 Whole Transcriptomic Expression Differences in EBV Immortalized versus Primary B-Cells Dolores Huberts College of William and Mary Follow this and additional works at: https://scholarworks.wm.edu/honorstheses Part of the Biology Commons Recommended Citation Huberts, Dolores, "Whole Transcriptomic Expression Differences in EBV Immortalized versus Primary B- Cells" (2010). Undergraduate Honors Theses. Paper 347. https://scholarworks.wm.edu/honorstheses/347 This Honors Thesis is brought to you for free and open access by the Theses, Dissertations, & Master Projects at W&M ScholarWorks. It has been accepted for inclusion in Undergraduate Honors Theses by an authorized administrator of W&M ScholarWorks. For more information, please contact [email protected]. Whole Transcriptomic Expression Differences in EBV Immortalized versus Primary B-Cells A thesis submitted in partial fulfillment of the requirement for the degree of Bachelor of Science with Honors in Biology from the College of William and Mary in Virginia By Dolores Huberts Accepted for Honors ________________________________________ Lizabeth A. Allison, Director ________________________________________ Matthew Wawersik ________________________________________ Drew LaMar ________________________________________ Beverly Sher Williamsburg, Virginia December 17, 2010 ABSTRACT The Epstein–Barr Virus (EBV) is a human gamma herpes virus that infects more than 90% of the human population worldwide. It is commonly known in the US as the cause of Infectious Mononucleosis, and around the world as the cause of nasopharyngeal carcinoma and malignant lymphomas such as non-Hodgkin lymphoma, endemic Burkett’s lymphoma and Hodgkin lymphoma. Additionally, the EBV is used to immortalize cells to create cell lines for in-vitro studies. -
1 1 2 Pharmacological Dimerization and Activation of the Exchange
1 2 3 Pharmacological dimerization and activation of the exchange factor eIF2B antagonizes the 4 integrated stress response 5 6 7 *Carmela Sidrauski1,2, *Jordan C. Tsai1,2, Martin Kampmann2,3, Brian R. Hearn4, Punitha 8 Vedantham4, Priyadarshini Jaishankar4 , Masaaki Sokabe5, Aaron S. Mendez1,2, Billy W. 9 Newton6, Edward L. Tang6.7, Erik Verschueren6, Jeffrey R. Johnson6,7, Nevan J. Krogan6,7,, 10 Christopher S. Fraser5, Jonathan S. Weissman2,3, Adam R. Renslo4, and Peter Walter 1,2 11 12 1Department of Biochemistry and Biophysics, University of California, San Francisco, United 13 States 14 2Howard Hughes Medical Institute, University of California, San Francisco, United States 15 3Department of Cellular and Molecular Pharmacology, University of California, San Francisco, 16 United States 17 4Department of Pharmaceutical Chemistry and the Small Molecule Discovery Center, University 18 of California at San Francisco, United States 19 5Department of Molecular and Cellular Biology, College of Biological Sciences, University of 20 California, Davis, United States 21 6QB3, California Institute for Quantitative Biosciences, University of California, San Francisco, 22 United States 23 7Gladstone Institutes, San Francisco, United States 24 25 * Both authors contributed equally to this work 26 27 28 Abstract 29 30 The general translation initiation factor eIF2 is a major translational control point. Multiple 31 signaling pathways in the integrated stress response phosphorylate eIF2 serine-51, inhibiting 32 nucleotide exchange by eIF2B. ISRIB, a potent drug-like small molecule, renders cells 33 insensitive to eIF2α phosphorylation and enhances cognitive function in rodents by blocking 34 long-term depression. ISRIB was identified in a phenotypic cell-based screen, and its mechanism 35 of action remained unknown. -
Relevance of Translation Initiation in Diffuse Glioma Biology and Its
cells Review Relevance of Translation Initiation in Diffuse Glioma Biology and its Therapeutic Potential Digregorio Marina 1, Lombard Arnaud 1,2, Lumapat Paul Noel 1, Scholtes Felix 1,2, Rogister Bernard 1,3 and Coppieters Natacha 1,* 1 Laboratory of Nervous System Disorders and Therapy, GIGA-Neurosciences Research Centre, University of Liège, 4000 Liège, Belgium; [email protected] (D.M.); [email protected] (L.A.); [email protected] (L.P.N.); [email protected] (S.F.); [email protected] (R.B.) 2 Department of Neurosurgery, CHU of Liège, 4000 Liège, Belgium 3 Department of Neurology, CHU of Liège, 4000 Liège, Belgium * Correspondence: [email protected] Received: 18 October 2019; Accepted: 26 November 2019; Published: 29 November 2019 Abstract: Cancer cells are continually exposed to environmental stressors forcing them to adapt their protein production to survive. The translational machinery can be recruited by malignant cells to synthesize proteins required to promote their survival, even in times of high physiological and pathological stress. This phenomenon has been described in several cancers including in gliomas. Abnormal regulation of translation has encouraged the development of new therapeutics targeting the protein synthesis pathway. This approach could be meaningful for glioma given the fact that the median survival following diagnosis of the highest grade of glioma remains short despite current therapy. The identification of new targets for the development of novel therapeutics is therefore needed in order to improve this devastating overall survival rate. This review discusses current literature on translation in gliomas with a focus on the initiation step covering both the cap-dependent and cap-independent modes of initiation. -
Disruption of the Anaphase-Promoting Complex Confers Resistance to TTK Inhibitors in Triple-Negative Breast Cancer
Disruption of the anaphase-promoting complex confers resistance to TTK inhibitors in triple-negative breast cancer K. L. Thua,b, J. Silvestera,b, M. J. Elliotta,b, W. Ba-alawib,c, M. H. Duncana,b, A. C. Eliaa,b, A. S. Merb, P. Smirnovb,c, Z. Safikhanib, B. Haibe-Kainsb,c,d,e, T. W. Maka,b,c,1, and D. W. Cescona,b,f,1 aCampbell Family Institute for Breast Cancer Research, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada M5G 1L7; bPrincess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada M5G 1L7; cDepartment of Medical Biophysics, University of Toronto, Toronto, ON, Canada M5G 1L7; dDepartment of Computer Science, University of Toronto, Toronto, ON, Canada M5G 1L7; eOntario Institute for Cancer Research, Toronto, ON, Canada M5G 0A3; and fDepartment of Medicine, University of Toronto, Toronto, ON, Canada M5G 1L7 Contributed by T. W. Mak, December 27, 2017 (sent for review November 9, 2017; reviewed by Mark E. Burkard and Sabine Elowe) TTK protein kinase (TTK), also known as Monopolar spindle 1 (MPS1), ator of the spindle assembly checkpoint (SAC), which delays is a key regulator of the spindle assembly checkpoint (SAC), which anaphase until all chromosomes are properly attached to the functions to maintain genomic integrity. TTK has emerged as a mitotic spindle, TTK has an integral role in maintaining genomic promising therapeutic target in human cancers, including triple- integrity (6). Because most cancer cells are aneuploid, they are negative breast cancer (TNBC). Several TTK inhibitors (TTKis) are heavily reliant on the SAC to adequately segregate their abnormal being evaluated in clinical trials, and an understanding of karyotypes during mitosis. -
Epigenetic Mechanisms Are Involved in the Oncogenic Properties of ZNF518B in Colorectal Cancer
Epigenetic mechanisms are involved in the oncogenic properties of ZNF518B in colorectal cancer Francisco Gimeno-Valiente, Ángela L. Riffo-Campos, Luis Torres, Noelia Tarazona, Valentina Gambardella, Andrés Cervantes, Gerardo López-Rodas, Luis Franco and Josefa Castillo SUPPLEMENTARY METHODS 1. Selection of genomic sequences for ChIP analysis To select the sequences for ChIP analysis in the five putative target genes, namely, PADI3, ZDHHC2, RGS4, EFNA5 and KAT2B, the genomic region corresponding to the gene was downloaded from Ensembl. Then, zoom was applied to see in detail the promoter, enhancers and regulatory sequences. The details for HCT116 cells were then recovered and the target sequences for factor binding examined. Obviously, there are not data for ZNF518B, but special attention was paid to the target sequences of other zinc-finger containing factors. Finally, the regions that may putatively bind ZNF518B were selected and primers defining amplicons spanning such sequences were searched out. Supplementary Figure S3 gives the location of the amplicons used in each gene. 2. Obtaining the raw data and generating the BAM files for in silico analysis of the effects of EHMT2 and EZH2 silencing The data of siEZH2 (SRR6384524), siG9a (SRR6384526) and siNon-target (SRR6384521) in HCT116 cell line, were downloaded from SRA (Bioproject PRJNA422822, https://www.ncbi. nlm.nih.gov/bioproject/), using SRA-tolkit (https://ncbi.github.io/sra-tools/). All data correspond to RNAseq single end. doBasics = TRUE doAll = FALSE $ fastq-dump -I --split-files SRR6384524 Data quality was checked using the software fastqc (https://www.bioinformatics.babraham. ac.uk /projects/fastqc/). The first low quality removing nucleotides were removed using FASTX- Toolkit (http://hannonlab.cshl.edu/fastxtoolkit/). -
Global Analysis of Trna and Translation Factor Expression Reveals a Dynamic Landscape of Translational Regulation in Human Cancers
ARTICLE https://doi.org/10.1038/s42003-018-0239-8 OPEN Global analysis of tRNA and translation factor expression reveals a dynamic landscape of translational regulation in human cancers Zhao Zhang1, Youqiong Ye1, Jing Gong1, Hang Ruan1, Chun-Jie Liu2, Yu Xiang1, Chunyan Cai3, An-Yuan Guo2, 1234567890():,; Jiqiang Ling4, Lixia Diao5, John N. Weinstein5 & Leng Han 1,6 The protein translational system, including transfer RNAs (tRNAs) and several categories of enzymes, plays a key role in regulating cell proliferation. Translation dysregulation also contributes to cancer development, though relatively little is known about the changes that occur to the translational system in cancer. Here, we present global analyses of tRNAs and three categories of enzymes involved in translational regulation in ~10,000 cancer patients across 31 cancer types from The Cancer Genome Atlas. By analyzing the expression levels of tRNAs at the gene, codon, and amino acid levels, we identified unequal alterations in tRNA expression, likely due to the uneven distribution of tRNAs decoding different codons. We find that overexpression of tRNAs recognizing codons with a low observed-over-expected ratio may overcome the translational bottleneck in tumorigenesis. We further observed overall overexpression and amplification of tRNA modification enzymes, aminoacyl-tRNA synthe- tases, and translation factors, which may play synergistic roles with overexpression of tRNAs to activate the translational systems across multiple cancer types. 1 Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. 2 Department of Bioinformatics and Systems Biology, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology Wuhan, 430074 Hubei, People’s Republic of China.