Discovering New Protein-Coding Genes in Non-Canonical Open-Reading Frames
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Insights Into Comparative Genomics, Codon Usage Bias, And
plants Article Insights into Comparative Genomics, Codon Usage Bias, and Phylogenetic Relationship of Species from Biebersteiniaceae and Nitrariaceae Based on Complete Chloroplast Genomes Xiaofeng Chi 1,2 , Faqi Zhang 1,2 , Qi Dong 1,* and Shilong Chen 1,2,* 1 Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China; [email protected] (X.C.); [email protected] (F.Z.) 2 Qinghai Provincial Key Laboratory of Crop Molecular Breeding, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China * Correspondence: [email protected] (Q.D.); [email protected] (S.C.) Received: 29 October 2020; Accepted: 17 November 2020; Published: 18 November 2020 Abstract: Biebersteiniaceae and Nitrariaceae, two small families, were classified in Sapindales recently. Taxonomic and phylogenetic relationships within Sapindales are still poorly resolved and controversial. In current study, we compared the chloroplast genomes of five species (Biebersteinia heterostemon, Peganum harmala, Nitraria roborowskii, Nitraria sibirica, and Nitraria tangutorum) from Biebersteiniaceae and Nitrariaceae. High similarity was detected in the gene order, content and orientation of the five chloroplast genomes; 13 highly variable regions were identified among the five species. An accelerated substitution rate was found in the protein-coding genes, especially clpP. The effective number of codons (ENC), parity rule 2 (PR2), and neutrality plots together revealed that the codon usage bias is affected by mutation and selection. The phylogenetic analysis strongly supported (Nitrariaceae (Biebersteiniaceae + The Rest)) relationships in Sapindales. Our findings can provide useful information for analyzing phylogeny and molecular evolution within Biebersteiniaceae and Nitrariaceae. -
1. a 6-Frame Translation Map of a Segment of DNA Is Shown, with Three Open Reading Frames (A, B, and C). Orfs a and B Are Known to Be in Separate Genes
1. A 6-frame translation map of a segment of DNA is shown, with three open reading frames (A, B, and C). ORFs A and B are known to be in separate genes. 1a. Two transcription bubbles are shown, one in ORF A and one in ORF B. In the transcription bubble diagram, mark the following: • the location of RNA polymerase on the appropriate strand in each bubble • the RNA transcripts to show the relative lengths of RNA made by those two polymerases 1b. Are the promoters for ORFs A and/or B present in the DNA region shown in this diagram? Promoter for A: Present? Yes No (circle one) If present, mark its location and label it. Promoter for B: Present? Yes No (circle one) If present, mark its location and label it. 1c. Electron microscopy experiments failed to show RNA polymerases over the ORF "C" region of DNA. State whether each of the three explanations listed below is valid or not, explaining as necessary: Explanation If valid, just write “Valid.” If invalid, BRIEFLY explain why. _______________________________________________________________________ ORFs B and C are exons of the same gene and a splicing error causes ORF C to be left out. Splicing occurs after transcription. Incorrect splicing doesn't explain why transcription didn't happen. ORF C has a mutation in its start (ATG) codon, preventing transcription. The start codon is used for translation, not transcription... whether or not the start codon is intact, transcription could still happen. ORF C has a promoter mutation preventing transcription. VALID. (A promoter mutation is consistent with failure to transcribe the gene.) 2. -
Mutation Bias Shapes Gene Evolution in Arabidopsis Thaliana
bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156752; this version posted June 18, 2020. 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. Mutation bias shapes gene evolution in Arabidopsis thaliana 1,2† 1 1 3,4 Monroe, J. Grey , Srikant, Thanvi , Carbonell-Bejerano, Pablo , Exposito-Alonso, Moises , 5 6 7 1† Weng, Mao-Lun , Rutter, Matthew T. , Fenster, Charles B. , Weigel, Detlef 1 Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany 2 Department of Plant Sciences, University of California Davis, Davis, CA 95616, USA 3 Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94305, USA 4 Department of Biology, Stanford University, Stanford, CA 94305, USA 5 Department of Biology, Westfield State University, Westfield, MA 01086, USA 6 Department of Biology, College of Charleston, SC 29401, USA 7 Department of Biology and Microbiology, South Dakota State University, Brookings, SD 57007, USA † corresponding authors: [email protected], [email protected] Classical evolutionary theory maintains that mutation rate variation between genes should be random with respect to fitness 1–4 and evolutionary optimization of genic 3,5 mutation rates remains controversial . However, it has now become known that cytogenetic (DNA sequence + epigenomic) features influence local mutation probabilities 6 , which is predicted by more recent theory to be a prerequisite for beneficial mutation 7 rates between different classes of genes to readily evolve . To test this possibility, we used de novo mutations in Arabidopsis thaliana to create a high resolution predictive model of mutation rates as a function of cytogenetic features across the genome. -
Polysome-Profiling in Small Tissue Samples
bioRxiv preprint doi: https://doi.org/10.1101/104596; this version posted February 1, 2017. 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. Polysome-profiling in small tissue samples Shuo Liang1,#, Hermano Bellato2,#, Julie Lorent1,#, Fernanda Lupinacci2, Vincent Van Hoef1, Laia Masvidal1,*, Glaucia Hajj2,* and Ola Larsson1,* 1. Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 171 77, Sweden 2. International Research Center, A.C. Camargo Cancer Center, São Paulo, Brazil # Equally contributing authors * Correspondence: Laia Masvidal ([email protected]), Glaucia Hajj ([email protected]) and Ola Larsson ([email protected]) ABSTRACT Polysome-profiling is commonly used to study genome wide patterns of translational efficiency, i.e. the translatome. The standard approach for collecting efficiently translated polysome-associated RNA results in laborious extraction of RNA from a large volume spread across multiple fractions. This property makes polysome-profiling inconvenient for larger experimental designs or samples with low RNA amounts such as primary cells or frozen tissues. To address this we optimized a non-linear sucrose gradient which reproducibly enriches for mRNAs associated with >3 ribosomes in only one or two fractions, thereby reducing sample handling 5-10 fold. The technique can be applied to cells and frozen tissue samples from biobanks, and generates RNA with a quality reflecting the starting material. When coupled with smart-seq2, a single-cell RNA sequencing technique, translatomes from small tissue samples can be obtained. Translatomes acquired using optimized non-linear gradients are very similar to those obtained when applying linear gradients. -
Using Ribosome Profiling to Quantify Differences in Protein Expression
bioRxiv preprint doi: https://doi.org/10.1101/501478; this version posted December 19, 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 4.0 International license. 1 Using ribosome profiling to quantify differences in protein expression: 2 a case study in Saccharomyces cerevisiae oxidative stress conditions 3 4 5 William R. Blevins1,#, Teresa Tavella1,#, Simone G. Moro1, Bernat Blasco-Moreno2, 6 Adrià Closa-Mosquera2, Juana Díez2, Lucas B. Carey2, M. Mar Albà1,2,3,* 7 8 1Evolutionary Genomics Groups, Research Programme on Biomedical Informatics (GRIB), Hospital del 9 Mar Research Institute (IMIM), Barcelona, Spain 10 2Health and Experimental Sciences Department, Universitat Pompeu Fabra(UPF), Barcelona, Spain 11 3Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain. 12 #Shared first co-authorship 13 *To whom correspondence should be addressed. 14 Running title: differential gene translation 15 Keywords: differential gene expression, ribosome profiling, RNA-Seq, translation, oxidative stress 1 bioRxiv preprint doi: https://doi.org/10.1101/501478; this version posted December 19, 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 4.0 International license. 16 Abstract 17 18 Cells respond to changes in the environment by modifying the concentration of specific 19 proteins. Paradoxically, the cellular response is usually examined by measuring variations 20 in transcript abundance by high throughput RNA sequencing (RNA-Seq), instead of 21 directly measuring protein concentrations. -
Chapter 3. the Beginnings of Genomic Biology – Molecular
Chapter 3. The Beginnings of Genomic Biology – Molecular Genetics Contents 3. The beginnings of Genomic Biology – molecular genetics 3.1. DNA is the Genetic Material 3.6.5. Translation initiation, elongation, and termnation 3.2. Watson & Crick – The structure of DNA 3.6.6. Protein Sorting in Eukaryotes 3.3. Chromosome structure 3.7. Regulation of Eukaryotic Gene Expression 3.3.1. Prokaryotic chromosome structure 3.7.1. Transcriptional Control 3.3.2. Eukaryotic chromosome structure 3.7.2. Pre-mRNA Processing Control 3.3.3. Heterochromatin & Euchromatin 3.4. DNA Replication 3.7.3. mRNA Transport from the Nucleus 3.4.1. DNA replication is semiconservative 3.7.4. Translational Control 3.4.2. DNA polymerases 3.7.5. Protein Processing Control 3.4.3. Initiation of replication 3.7.6. Degradation of mRNA Control 3.4.4. DNA replication is semidiscontinuous 3.7.7. Protein Degradation Control 3.4.5. DNA replication in Eukaryotes. 3.8. Signaling and Signal Transduction 3.4.6. Replicating ends of chromosomes 3.8.1. Types of Cellular Signals 3.5. Transcription 3.8.2. Signal Recognition – Sensing the Environment 3.5.1. Cellular RNAs are transcribed from DNA 3.8.3. Signal transduction – Responding to the Environment 3.5.2. RNA polymerases catalyze transcription 3.5.3. Transcription in Prokaryotes 3.5.4. Transcription in Prokaryotes - Polycistronic mRNAs are produced from operons 3.5.5. Beyond Operons – Modification of expression in Prokaryotes 3.5.6. Transcriptions in Eukaryotes 3.5.7. Processing primary transcripts into mature mRNA 3.6. Translation 3.6.1. -
Super-Resolution Ribosome Profiling Reveals Unannotated Translation Events in Arabidopsis
Super-resolution ribosome profiling reveals unannotated translation events in Arabidopsis Polly Yingshan Hsua, Lorenzo Calviellob,c, Hsin-Yen Larry Wud,1, Fay-Wei Lia,e,f,1, Carl J. Rothfelse,f, Uwe Ohlerb,c, and Philip N. Benfeya,g,2 aDepartment of Biology, Duke University, Durham, NC 27708; bBerlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany; cDepartment of Biology, Humboldt Universität zu Berlin, 10099 Berlin, Germany; dBioinformatics Research Center and Department of Statistics, North Carolina State University, Raleigh, NC 27695; eUniversity Herbarium, University of California, Berkeley, CA 94720; fDepartment of Integrative Biology, University of California, Berkeley, CA 94720; and gHoward Hughes Medical Institute, Duke University, Durham, NC 27708 Contributed by Philip N. Benfey, September 13, 2016 (sent for review June 30, 2016; reviewed by Pam J. Green and Albrecht G. von Arnim) Deep sequencing of ribosome footprints (ribosome profiling) maps and contaminants. Several metrics associated with translation have and quantifies mRNA translation. Because ribosomes decode mRNA been exploited (11), for example, the following: (i)ribosomesre- every 3 nt, the periodic property of ribosome footprints could be lease after encountering a stop codon (9), (ii) local enrichment of used to identify novel translated ORFs. However, due to the limited footprints within the predicted ORF (4, 13), (iii) ribosome footprint resolution of existing methods, the 3-nt periodicity is observed length distribution (7), and (iv) 3-nt periodicity displayed by trans- mostly in a global analysis, but not in individual transcripts. Here, we lating ribosomes (2, 6, 10, 14, 15). Among these features, some work report a protocol applied to Arabidopsis that maps over 90% of the well in distinguishing groups of coding vs. -
Integrated Analyses of Translatome and Proteome Identify the Rules of Translation Selectivity in RPS14-Deficient Cells
ARTICLE Red Cell Biology and its Disorders Integrated analyses of translatome and Ferrata Storti Foundation proteome identify the rules of translation selectivity in RPS14-deficient cells Ismael Boussaid,1 Salomé Le Goff,1,2,* Célia Floquet,1,* Emilie-Fleur Gautier,1,3 Anna Raimbault,1 Pierre-Julien Viailly,4 Dina Al Dulaimi,1 Barbara Burroni,5 Isabelle Dusanter-Fourt,1 Isabelle Hatin,6 Patrick Mayeux,1,2,3,# Bertrand Cosson7,# and Michaela Fontenay1,2,3,4,8 Haematologica 2021 1 Université de Paris, Institut Cochin, CNRS UMR 8104, INSERM U1016, Paris; Volume 106(3):746-758 2Laboratoire d’Excellence du Globule Rouge GR-Ex, Université de Paris, Paris; 3Proteomic Platform 3P5, Université de Paris, Paris; 4Centre Henri-Becquerel, Institut de Recherche et d’Innovation Biomedicale de Haute Normandie, INSERM U1245, Rouen; 5Assistance Publique-Hôpitaux de Paris, Centre-Université de Paris - Cochin, Service de Pathologie, Paris; 6Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université de Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette Cedex; 7Université de Paris, Epigenetics and Cell Fate, CNRS UMR 7216, Paris and 8Assistance Publique- Hôpitaux de Paris, Centre-Université de Paris - Hôpital Cochin, Service d’Hématologie Biologique, Paris, France *SLG and CF contributed equally to this work. #PM and BC contributed equally as co-senior authors. ABSTRACT n ribosomopathies, the Diamond-Blackfan anemia (DBA) or 5q- syn- drome, ribosomal protein (RP) genes are affected by mutation or deletion, Iresulting in bone marrow erythroid hypoplasia. Unbalanced production of ribosomal subunits leading to a limited ribosome cellular content regu- lates translation at the expense of the master erythroid transcription factor GATA1. -
Minireview: Novel Micropeptide Discovery by Proteomics and Deep Sequencing Methods
fgene-12-651485 May 6, 2021 Time: 11:28 # 1 MINI REVIEW published: 06 May 2021 doi: 10.3389/fgene.2021.651485 Minireview: Novel Micropeptide Discovery by Proteomics and Deep Sequencing Methods Ravi Tharakan1* and Akira Sawa2,3 1 National Institute on Aging, National Institutes of Health, Baltimore, MD, United States, 2 Departments of Psychiatry, Neuroscience, Biomedical Engineering, and Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 3 Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States A novel class of small proteins, called micropeptides, has recently been discovered in the genome. These proteins, which have been found to play important roles in many physiological and cellular systems, are shorter than 100 amino acids and were overlooked during previous genome annotations. Discovery and characterization of more micropeptides has been ongoing, often using -omics methods such as proteomics, RNA sequencing, and ribosome profiling. In this review, we survey the recent advances in the micropeptides field and describe the methodological and Edited by: conceptual challenges facing future micropeptide endeavors. Liangliang Sun, Keywords: micropeptides, miniproteins, proteogenomics, sORF, ribosome profiling, proteomics, genomics, RNA Michigan State University, sequencing United States Reviewed by: Yanbao Yu, INTRODUCTION J. Craig Venter Institute (Rockville), United States The sequencing and publication of complete genomic sequences of many organisms have aided Hongqiang Qin, the medical sciences greatly, allowing advances in both human genetics and the biology of human Dalian Institute of Chemical Physics, Chinese Academy of Sciences, China disease, as well as a greater understanding of the biology of human pathogens (Firth and Lipkin, 2013). -
Classification and Function of Small Open-Reading Frames Abstract
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Sussex Research Online 1 Classification and function of small open-reading frames Juan-Pablo Couso1,2* and Pedro Patraquim2 1Centro Andaluz de Biologia del Desarrollo, CSIC-UPO, Sevilla, Spain and 2Brighton and Sussex Medical School, University of Sussex, Brighton, United Kingdom. *Author for correspondence: [email protected] Abstract Small open-reading frames (smORFs or sORFs) of 100 codons or less are usually - if arbitrarily - excluded from canonical proteome annotations. Despite this, the genomes of a wide range of metazoans, including humans, contain hundreds of smORFs, some of which fulfil key physiological functions. Recently, ribosomal profiling has been employed to show that the transcriptome of the model organism Drosophila melanogaster contains thousands of smORFs of different classes actively undergoing translation which produces peptides of mostly unknown function. Here we present a comprehensive analysis of the smORF repertoire in flies, mice and humans. We propose the existence of several classes of smORFs with different functions, from inert DNA sequences to transcribed and translated cis- regulators of translation, and finally to expression of functional peptides with a propensity to act as regulators of canonical membrane-associated proteins, or as components of ancestral protein complexes in the cytoplasm. We suggest that the different smORF classes could represent steps during the evolution of novel peptide and protein sequences. Our analysis introduces a distinction between different peptide-coding classes in animal genomes, and highlights the role of Drosophila melanogaster as a model organism for the study of small peptide biology in the context of development, physiology and human disease. -
"The" Genetic Code?
Evolutionary Anthropology 14:6–11 (2005) CROTCHETS & QUIDDITIES “The” Genetic Code? KENNETH M. WEISS AND ANNE V. BUCHANAN The DNA-based code for protein through messenger and transfer RNA is widely themselves, that carry the informa- regarded as the code of life. But genomes are littered with other kinds of coding tion. elements as well, and all of them probably came after a supercode for the tRNA Your life depends on the fidelity of system itself. these many codes. Aberrant codes re- lated to cell behavior can lead to dys- genesis or various metabolic diseases. Evolution and the diversification of Everyone knows of “the” genetic Anomalous cell-surface proteins can organisms are made possible by code, by which nucleotide triplets in cause autoimmune destruction, and vi- codes, or arbitrary assignments of DNA in the nucleus of cells specify the ruses are the Alan Turings of life that “meaning,” in multiple ways. Many amino acid (aa) sequence of proteins. evolve ways to break their receptor are not widely appreciated. Codes al- This is the code described in text- codes to gain illicit entry into cells (Fig. low the same system of components books as the heart of the genetic the- 1). to be used for multiple purposes. ory of life and its evolution. Discover- But there is an additional code, a These can be open-ended, the way the ies in recent years have made things code of codes, that makes all of this alphabet and vocabulary make this more complicated by showing that ge- possible, including “the” genetic code column possible, but the flexibility of nomes are littered with all sorts of itself, and may be the oldest and most a code can become constrained once a other kinds of coding elements. -
Efficient Analysis of Mammalian Polysomes in Cells and Tissues
TOOLS AND RESOURCES Efficient analysis of mammalian polysomes in cells and tissues using Ribo Mega-SEC Harunori Yoshikawa1†, Mark Larance1,2†, Dylan J Harney2, Ramasubramanian Sundaramoorthy1, Tony Ly1,3, Tom Owen-Hughes1, Angus I Lamond1* 1Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, United Kingdom; 2Charles Perkins Centre, School of Life and Environmental Sciences, University of Sydney, Sydney, Australia; 3Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, United Kingdom Abstract We describe Ribo Mega-SEC, a powerful approach for the separation and biochemical analysis of mammalian polysomes and ribosomal subunits using Size Exclusion Chromatography and uHPLC. Using extracts from either cells, or tissues, polysomes can be separated within 15 min from sample injection to fraction collection. Ribo Mega-SEC shows translating ribosomes exist predominantly in polysome complexes in human cell lines and mouse liver tissue. Changes in polysomes are easily quantified between treatments, such as the cellular response to amino acid starvation. Ribo Mega-SEC is shown to provide an efficient, convenient and highly reproducible method for studying functional translation complexes. We show that Ribo Mega-SEC is readily combined with high-throughput MS-based proteomics to characterize proteins associated with polysomes and ribosomal subunits. It also facilitates isolation of complexes for electron microscopy and structural studies. *For correspondence: DOI: https://doi.org/10.7554/eLife.36530.001 [email protected] †These authors contributed equally to this work Introduction Competing interests: The The ribosome is a large RNA-protein complex, comprising four ribosomal RNAs (rRNAs) and >80 authors declare that no ribosomal proteins (RPs), which coordinates mRNA-templated protein synthesis.