Serine Hydroxymethyltransferase 2 Expression Promotes Tumorigenesis in Rhabdomyosarcoma with 12Q13-Q14 Amplification
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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. -
Complexity of a Small Non-Protein Coding Sequence in Chromosomal Region 22Q11.2: Presence of Specialized DNA Secondary Structures and RNA Exon/Intron Motifs Delihas
Complexity of a small non-protein coding sequence in chromosomal region 22q11.2: presence of specialized DNA secondary structures and RNA exon/intron motifs Delihas Delihas BMC Genomics (2015) 16:785 DOI 10.1186/s12864-015-1958-6 Delihas BMC Genomics (2015) 16:785 DOI 10.1186/s12864-015-1958-6 RESEARCHARTICLE Open Access Complexity of a small non-protein coding sequence in chromosomal region 22q11.2: presence of specialized DNA secondary structures and RNA exon/intron motifs Nicholas Delihas Abstract Background: DiGeorge Syndrome is a genetic abnormality involving ~3 Mb deletion in human chromosome 22, termed 22q.11.2. To better understand the non-coding regions of 22q.11.2, a small 10,000 bp non-protein-coding sequence close to the DiGeorge Critical Region 6 gene (DGCR6) was chosen for analysis and functional entities as the homologous sequence in the chimpanzee genome could be aligned and used for comparisons. Methods: The GenBank database provided genomic sequences. In silico computer programs were used to find homologous DNA sequences in human and chimpanzee genomes, generate random sequences, determine DNA sequence alignments, sequence comparisons and nucleotide repeat copies, and to predicted DNA secondary structures. Results: At its 5′ half, the 10,000 bp sequence has three distinct sections that represent phylogenetically variable sequences. These Variable Regions contain biased mutations with a very high A + T content, multiple copies of the motif TATAATATA and sequences that fold into long A:T-base-paired stem loops. The 3′ half of the 10,000 bp unit, highly conserved between human and chimpanzee, has sequences representing exons of lncRNA genes and segments of introns of protein genes. -
A Different View on DNA Amplifications Indicates Frequent, Highly Complex, and Stable Amplicons on 12Q13-21 in Glioma
A Different View on DNA Amplifications Indicates Frequent, Highly Complex, and Stable Amplicons on 12q13-21 in Glioma Ulrike Fischer,1 Andreas Keller,3 Petra Leidinger,1 Stephanie Deutscher,1 Sabrina Heisel,1 Steffi Urbschat,2 Hans-Peter Lenhof,3 and Eckart Meese1 Departments of 1Human Genetics and 2Neurosurgery, Saarland University, Homburg/Saar, Germany and 3Center for Bioinformatics, Saarland University, Saarbru¨cken, Germany Abstract Introduction To further understand the biological significance of DNA amplification does not occur in normal human cells amplifications for glioma development and recurrencies, but in multidrug-resistant cells and in tumor cells. Numerous we characterized amplicon frequency and size in studies described gene amplification in various human tumors low-grade glioma and amplicon stability in vivo in by cytogenetic and molecular genetic means. The breakage- recurring glioblastoma. We developed a 12q13-21 fusion-bridge cycle (1) is the most popular model to explain amplicon–specific genomic microarray and a intrachromosomal amplifications and many amplified structures bioinformatics amplification prediction tool to analyze like mixed ladders that were found in homogeneously staining amplicon frequency, size, and maintenance in 40 glioma regions (2). The ‘‘episome model’’ proposes that episomes samples including 16 glioblastoma, 10 anaplastic result from excision of small circular DNA that enlarges by astrocytoma, 7 astrocytoma WHO grade 2, and 7 overreplication or recombination until it becomes cytogeneti- pilocytic astrocytoma. Whereas previous studies cally visible as double minute chromosomes (3). Recent results reported two amplified subregions, we found a more of Tanaka et al. (4) showthat large palindromic sequences were complex situation with many amplified subregions. present in human cancers and the location of palindromes in the Analyzing 40 glioma, we found that all analyzed cancer genome serves as a structural platform to support glioblastoma and the majority of pilocytic astrocytoma, subsequent gene amplification. -
A Different View on DNA Amplifications Indicates Frequent, Highly Complex, and Stable Amplicons on 12Q13-21 in Glioma
A Different View on DNA Amplifications Indicates Frequent, Highly Complex, and Stable Amplicons on 12q13-21 in Glioma Ulrike Fischer,1 Andreas Keller,3 Petra Leidinger,1 Stephanie Deutscher,1 Sabrina Heisel,1 Steffi Urbschat,2 Hans-Peter Lenhof,3 and Eckart Meese1 Departments of 1Human Genetics and 2Neurosurgery, Saarland University, Homburg/Saar, Germany and 3Center for Bioinformatics, Saarland University, Saarbru¨cken, Germany Abstract Introduction To further understand the biological significance of DNA amplification does not occur in normal human cells amplifications for glioma development and recurrencies, but in multidrug-resistant cells and in tumor cells. Numerous we characterized amplicon frequency and size in studies described gene amplification in various human tumors low-grade glioma and amplicon stability in vivo in by cytogenetic and molecular genetic means. The breakage- recurring glioblastoma. We developed a 12q13-21 fusion-bridge cycle (1) is the most popular model to explain amplicon–specific genomic microarray and a intrachromosomal amplifications and many amplified structures bioinformatics amplification prediction tool to analyze like mixed ladders that were found in homogeneously staining amplicon frequency, size, and maintenance in 40 glioma regions (2). The ‘‘episome model’’ proposes that episomes samples including 16 glioblastoma, 10 anaplastic result from excision of small circular DNA that enlarges by astrocytoma, 7 astrocytoma WHO grade 2, and 7 overreplication or recombination until it becomes cytogeneti- pilocytic astrocytoma. Whereas previous studies cally visible as double minute chromosomes (3). Recent results reported two amplified subregions, we found a more of Tanaka et al. (4) showthat large palindromic sequences were complex situation with many amplified subregions. present in human cancers and the location of palindromes in the Analyzing 40 glioma, we found that all analyzed cancer genome serves as a structural platform to support glioblastoma and the majority of pilocytic astrocytoma, subsequent gene amplification. -
Variation in Protein Coding Genes Identifies Information Flow
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. 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. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. 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. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
Accurate Prediction of Kinase-Substrate Networks Using
bioRxiv preprint doi: https://doi.org/10.1101/865055; this version posted December 4, 2019. 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 4.0 International license. Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs V´ıtNov´aˇcek1∗+, Gavin McGauran3, David Matallanas3, Adri´anVallejo Blanco3,4, Piero Conca2, Emir Mu~noz1,2, Luca Costabello2, Kamalesh Kanakaraj1, Zeeshan Nawaz1, Sameh K. Mohamed1, Pierre-Yves Vandenbussche2, Colm Ryan3, Walter Kolch3,5,6, Dirk Fey3,6∗ 1Data Science Institute, National University of Ireland Galway, Ireland 2Fujitsu Ireland Ltd., Co. Dublin, Ireland 3Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland 4Department of Oncology, Universidad de Navarra, Pamplona, Spain 5Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland 6School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland ∗ Corresponding authors ([email protected], [email protected]). + Lead author. 1 bioRxiv preprint doi: https://doi.org/10.1101/865055; this version posted December 4, 2019. 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 4.0 International license. Abstract Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular pro- cesses. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. -
Lethal Congenital Contractural Syndrome Type 2 (LCCS2) Is Caused by a Mutation in ERBB3 (Her3), a Modulator of the Phosphatidylinositol-3-Kinase/Akt Pathway
REPORT Lethal Congenital Contractural Syndrome Type 2 (LCCS2) Is Caused by a Mutation in ERBB3 (Her3), a Modulator of the Phosphatidylinositol-3-Kinase/Akt Pathway Ginat Narkis, Rivka Ofir, Esther Manor, Daniella Landau, Khalil Elbedour, and Ohad S. Birk Lethal congenital contractural syndrome type 2 (LCCS2) is an autosomal recessive neurogenic form of arthrogryposis that is associated with atrophy of the anterior horn of the spinal cord. We previously mapped LCCS2 to 6.4 Mb on chromosome 12q13 and have now narrowed the locus to 4.6 Mb. We show that the disease is caused by aberrant splicing of ERBB3, which leads to a predicted truncated protein. ERBB3 (Her3), an activator of the phosphatidylinositol-3-kinase/ Akt pathway—regulating cell survival and vesicle trafficking—is essential for the generation of precursors of Schwann cells that normally accompany peripheral axons of motor neurons. Gain-of-function mutations in members of the epidermal growth-factor tyrosine kinase–receptor family have been associated with predilection to cancer. This is the first report of a human phenotype resulting from loss of function of a member of this group. Lethal congenital contractual syndrome type 2 (LCCS2 shown). The possibility that the disease-associated locus [MIM 607598]), a neonatally lethal form of arthrogryposis is between D12S305 and D12S1072 cannot be ruled out, presenting in two Israeli-Bedouin kindreds1 (families A but it is less likely, since this would require two crossing- and B) (fig. 1), is characterized by multiple joint contrac- over events occurring in individual 61 (fig. 2). tures, anterior horn atrophy in the spinal cord, and a In an attempt to identify the specific molecular defect unique feature of a markedly distended urinary bladder. -
Gnomad Lof Supplement
1 gnomAD supplement gnomAD supplement 1 Data processing 4 Alignment and read processing 4 Variant Calling 4 Coverage information 5 Data processing 5 Sample QC 7 Hard filters 7 Supplementary Table 1 | Sample counts before and after hard and release filters 8 Supplementary Table 2 | Counts by data type and hard filter 9 Platform imputation for exomes 9 Supplementary Table 3 | Exome platform assignments 10 Supplementary Table 4 | Confusion matrix for exome samples with Known platform labels 11 Relatedness filters 11 Supplementary Table 5 | Pair counts by degree of relatedness 12 Supplementary Table 6 | Sample counts by relatedness status 13 Population and subpopulation inference 13 Supplementary Figure 1 | Continental ancestry principal components. 14 Supplementary Table 7 | Population and subpopulation counts 16 Population- and platform-specific filters 16 Supplementary Table 8 | Summary of outliers per population and platform grouping 17 Finalizing samples in the gnomAD v2.1 release 18 Supplementary Table 9 | Sample counts by filtering stage 18 Supplementary Table 10 | Sample counts for genomes and exomes in gnomAD subsets 19 Variant QC 20 Hard filters 20 Random Forest model 20 Features 21 Supplementary Table 11 | Features used in final random forest model 21 Training 22 Supplementary Table 12 | Random forest training examples 22 Evaluation and threshold selection 22 Final variant counts 24 Supplementary Table 13 | Variant counts by filtering status 25 Comparison of whole-exome and whole-genome coverage in coding regions 25 Variant annotation 30 Frequency and context annotation 30 2 Functional annotation 31 Supplementary Table 14 | Variants observed by category in 125,748 exomes 32 Supplementary Figure 5 | Percent observed by methylation. -
Inbred Mouse Strains Expression in Primary Immunocytes Across
Downloaded from http://www.jimmunol.org/ by guest on September 28, 2021 Daphne is online at: average * The Journal of Immunology published online 29 September 2014 from submission to initial decision 4 weeks from acceptance to publication Sara Mostafavi, Adriana Ortiz-Lopez, Molly A. Bogue, Kimie Hattori, Cristina Pop, Daphne Koller, Diane Mathis, Christophe Benoist, The Immunological Genome Consortium, David A. Blair, Michael L. Dustin, Susan A. Shinton, Richard R. Hardy, Tal Shay, Aviv Regev, Nadia Cohen, Patrick Brennan, Michael Brenner, Francis Kim, Tata Nageswara Rao, Amy Wagers, Tracy Heng, Jeffrey Ericson, Katherine Rothamel, Adriana Ortiz-Lopez, Diane Mathis, Christophe Benoist, Taras Kreslavsky, Anne Fletcher, Kutlu Elpek, Angelique Bellemare-Pelletier, Deepali Malhotra, Shannon Turley, Jennifer Miller, Brian Brown, Miriam Merad, Emmanuel L. Gautier, Claudia Jakubzick, Gwendalyn J. Randolph, Paul Monach, Adam J. Best, Jamie Knell, Ananda Goldrath, Vladimir Jojic, J Immunol http://www.jimmunol.org/content/early/2014/09/28/jimmun ol.1401280 Koller, David Laidlaw, Jim Collins, Roi Gazit, Derrick J. Rossi, Nidhi Malhotra, Katelyn Sylvia, Joonsoo Kang, Natalie A. Bezman, Joseph C. Sun, Gundula Min-Oo, Charlie C. Kim and Lewis L. Lanier Variation and Genetic Control of Gene Expression in Primary Immunocytes across Inbred Mouse Strains Submit online. Every submission reviewed by practicing scientists ? is published twice each month by http://jimmunol.org/subscription http://www.jimmunol.org/content/suppl/2014/09/28/jimmunol.140128 0.DCSupplemental Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2014 by The American Association of Immunologists, Inc. -
Annotating Gene Sequence Variation Watch for Multiple Transcripts!
Identifying functionally significant causal variants in Segregation data Functional data Annotating gene sequence variation Frequency Animal in Interpretation Models Controls Shamil Sunyaev Department of Biomedical Informatics Harvard Medical School Bioinformatics Broad Institute of M.I.T. and Harvard Map variants on genomic annotation Nonsense variants One of most significant types of variants usually leading to the complete loss of function. Watch for multiple transcripts! Nonsense variants are enriched in sequencing artifacts Watch for conflicting annotations! Important considerations: i) location along the gene, ii) does the variant cause NMD? iii) is the variant in a commonly skipped exon? Tool: LOFTEE Variants involved in splicing SpliceAI Variants in canonic splicing sites Variants in exonic or intronic splicing enhancers Gain of splicing variants Missense variants: computational Does the mutation fit the pattern of past predictions evolution? This image cannot currently be displayed. human VVSTADLCAPSSTKLDERA dog FVSTSELCAGSTTRLEERA fish FLSTSELCVPSTLKVNEKV Statistical issues: -sequences are related by phylogeny -generally, we have too few sequences Does the mutation fit the pattern of past evolution? Continuous time Markov model • We assume a constant fitness landscape: what is good for fish is good for human! GLY VAL ALA GLY ALA • We can estimate whether the mutation fits the pattern of amino acid changes. • We can also estimate rate of evolution at the amino acid site Continuous time Markov model Protein structure view P – matrix of transition probabilities 푃 푡 = 푒-/ p – stationary distribution • Most of pathogenic mutations are important for stability (good news?). • DDG is difficult to estimate. 푄휋1# 0 • Unfolded protein response pathway has to be taken into account. -
Identification of MLL-AF9 Related Target Genes and Micrornas Involved in Leukemogenesis
Identification of MLL-AF9 related target genes and microRNAs involved in leukemogenesis Dissertation zur Erlangung des Doktorgrades der Naturwissenschaften (Dr. rer. nat.) an der Fakultät für Biologie der Ludwig-Maximilians-Universität München Durchgeführt am Forschungszentrum des Dr. von Haunerschen Kinderspitals der Ludwig-Maximilians-Universität München vorgelegt von Katrin Kristina Fleischmann München, 3. Mai 2012 Erstgutachterin: Frau Prof. Dr. Elisabeth Weiss Zweitgutachter: Herr Prof. Dr. Michael Schleicher Sondergutachter: Herr Prof. Dr. Adelbert Roscher Tag der Abgabe: 03.05.2012 Tag der mündlichen Prüfung: 17.09.2012 What is a scientist after all? It is a curious man looking through a keyhole, the keyhole of nature, trying to know what's going on. Jacques Yves Cousteau Preamble and Acknowledgments How to explain the change from evolutionary ecology to tumor biology? After my diploma thesis in evolutionary ecology and a major in ecology, I was facing some skepticism while being interviewed for PhD positions in the biomedical field. What was my motivation to study biology? Why do I continue to feel this strong enthusiasm for this field? For me, it was always the grand question “How does life work?” that drove me and which may interconnect almost every aspect within natural sciences. Only when we strive to get answers to this grand question we may reach an understanding as to what happens in disease. I am profoundly grateful to my dear colleagues Dr. Julia von Frowein, Dr. Thomas Magg and Dr. Uta Fuchs who supported me to become one of their colleagues at the research center of the Dr. von Haunerschen Kinderspital. Since then, innumerable great and inspiring discussions allied us. -
Table S1. 103 Ferroptosis-Related Genes Retrieved from the Genecards
Table S1. 103 ferroptosis-related genes retrieved from the GeneCards. Gene Symbol Description Category GPX4 Glutathione Peroxidase 4 Protein Coding AIFM2 Apoptosis Inducing Factor Mitochondria Associated 2 Protein Coding TP53 Tumor Protein P53 Protein Coding ACSL4 Acyl-CoA Synthetase Long Chain Family Member 4 Protein Coding SLC7A11 Solute Carrier Family 7 Member 11 Protein Coding VDAC2 Voltage Dependent Anion Channel 2 Protein Coding VDAC3 Voltage Dependent Anion Channel 3 Protein Coding ATG5 Autophagy Related 5 Protein Coding ATG7 Autophagy Related 7 Protein Coding NCOA4 Nuclear Receptor Coactivator 4 Protein Coding HMOX1 Heme Oxygenase 1 Protein Coding SLC3A2 Solute Carrier Family 3 Member 2 Protein Coding ALOX15 Arachidonate 15-Lipoxygenase Protein Coding BECN1 Beclin 1 Protein Coding PRKAA1 Protein Kinase AMP-Activated Catalytic Subunit Alpha 1 Protein Coding SAT1 Spermidine/Spermine N1-Acetyltransferase 1 Protein Coding NF2 Neurofibromin 2 Protein Coding YAP1 Yes1 Associated Transcriptional Regulator Protein Coding FTH1 Ferritin Heavy Chain 1 Protein Coding TF Transferrin Protein Coding TFRC Transferrin Receptor Protein Coding FTL Ferritin Light Chain Protein Coding CYBB Cytochrome B-245 Beta Chain Protein Coding GSS Glutathione Synthetase Protein Coding CP Ceruloplasmin Protein Coding PRNP Prion Protein Protein Coding SLC11A2 Solute Carrier Family 11 Member 2 Protein Coding SLC40A1 Solute Carrier Family 40 Member 1 Protein Coding STEAP3 STEAP3 Metalloreductase Protein Coding ACSL1 Acyl-CoA Synthetase Long Chain Family Member 1 Protein