Small Open Reading Frames Tiny Treasures of the Non-Coding Genomic Regions
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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. -
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. -
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. -
Designing Lentiviral Vectors for Gene Therapy of Genetic Diseases
viruses Review Designing Lentiviral Vectors for Gene Therapy of Genetic Diseases Valentina Poletti 1,2,3,* and Fulvio Mavilio 4 1 Department of Woman and Child Health, University of Padua, 35128 Padua, Italy 2 Harvard Medical School, Harvard University, Boston, MA 02115, USA 3 Pediatric Research Institute City of Hope, 35128 Padua, Italy 4 Department of Life Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy; [email protected] * Correspondence: [email protected] Abstract: Lentiviral vectors are the most frequently used tool to stably transfer and express genes in the context of gene therapy for monogenic diseases. The vast majority of clinical applications involves an ex vivo modality whereby lentiviral vectors are used to transduce autologous somatic cells, ob- tained from patients and re-delivered to patients after transduction. Examples are hematopoietic stem cells used in gene therapy for hematological or neurometabolic diseases or T cells for immunotherapy of cancer. We review the design and use of lentiviral vectors in gene therapy of monogenic diseases, with a focus on controlling gene expression by transcriptional or post-transcriptional mechanisms in the context of vectors that have already entered a clinical development phase. Keywords: lentiviral vectors; transcriptional regulation; post-transcriptional regulation; miRNA; promoters; retroviral integration; ex vivo gene therapy Citation: Poletti, V.; Mavilio, F. 1. Introduction Designing Lentiviral Vectors for Gene Therapy of Genetic Diseases. -
Analysis of Codon Usage Patterns in Giardia Duodenalis Based on Transcriptome Data from Giardiadb
G C A T T A C G G C A T genes Article Analysis of Codon Usage Patterns in Giardia duodenalis Based on Transcriptome Data from GiardiaDB Xin Li, Xiaocen Wang, Pengtao Gong, Nan Zhang, Xichen Zhang and Jianhua Li * Key Laboratory of Zoonosis Research, Ministry of Education, College of Veterinary Medicine, Jilin University, Changchun 130062, China; [email protected] (X.L.); [email protected] (X.W.); [email protected] (P.G.); [email protected] (N.Z.); [email protected] (X.Z.) * Correspondence: [email protected]; Tel.: +86-431-8783-6172; Fax: +86-431-8798-1351 Abstract: Giardia duodenalis, a flagellated parasitic protozoan, the most common cause of parasite- induced diarrheal diseases worldwide. Codon usage bias (CUB) is an important evolutionary character in most species. However, G. duodenalis CUB remains unclear. Thus, this study analyzes codon usage patterns to assess the restriction factors and obtain useful information in shaping G. duo- denalis CUB. The neutrality analysis result indicates that G. duodenalis has a wide GC3 distribution, which significantly correlates with GC12. ENC-plot result—suggesting that most genes were close to the expected curve with only a few strayed away points. This indicates that mutational pressure and natural selection played an important role in the development of CUB. The Parity Rule 2 plot (PR2) result demonstrates that the usage of GC and AT was out of proportion. Interestingly, we identified 26 optimal codons in the G. duodenalis genome, ending with G or C. In addition, GC content, gene expression, and protein size also influence G. -
Transcription and Open Reading Frame
Transcription The expression of genetic information stored in the DNA sequence starts with synthesis of the RNA copy of the gene in a process called transcription. The RNA copy of the gene is called messenger RNA (mRNA). A special enzyme, RNA polymerase, recognizes a sequence, called promoter, on the DNA double helix upstream from the protein coding sequence. RNA polymerase binds to the promoter and opens it up: separates the complementary strands at about 12-nt-long region of the promoter. Then the enzyme starts the mRNA synthesis using all 4 NTPs. The DNA strand, which is used as a template by RNA polymerase, is called the template or antisense strand. The opposite strand of the gene, which sequence is identical to the sequence of mRNA (except the substitution T U, of course), is called the coding or sense strand. There is also a special sequence after the end of the gene, which signals to RNA polymerase to terminate the mRNA synthesis. Thus synthesized mRNA molecule, which includes the protein coding region flanked by short unrelated sequences from both sides, is either translated by the ribosome into the protein molecule at the spot (in case of prokaryotes) or is transported from the nucleus to the cytoplasm (in case of eukaryotes) and there it is translated by the ribosome. Open Reading Frame (ORF) Since the genetic code is triplet, three reading frames are possible for the same mRNA molecule. For instance, the sequence: …..AUUGCCUAACCCUUAGGG…. can be separated into triplets by three possible ways: ….AUUGCCUAACCCUUAGGG…. ….AUUGCCUAACCCUUAGGG…. ….AUUGCCUAACCCUUAGGG…. The frame, in which no stop codons are encountered, is called the Open Reading Frame (ORF). -
LESSON 4 Using Bioinformatics to Analyze Protein Sequences
LESSON 4 Using Bioinformatics to 4 Analyze Protein Sequences Introduction In this lesson, students perform a paper exercise designed to reinforce the student understanding of the complementary nature of DNA and how that complementarity leads to six potential protein reading frames in any given DNA sequence. They also gain familiarity with the circular format codon table. Students then use the bioinformatics tool ORF Finder to identify the reading frames in their DNA sequence from Lesson Two and Lesson Three, and to select Class Time the proper open reading frame to use in a multiple sequence alignment with 2 class periods (approximately 50 their protein sequences. In Lesson Four, students also learn how biological minutes each). anthropologists might use bioinformatics tools in their career. Prior Knowledge Needed • DNA contains the genetic information Learning Objectives that encodes traits. • DNA is double stranded and At the end of this lesson, students will know that: anti-parallel. • Each DNA molecule is composed of two complementary strands, which are • The beginning of a DNA strand is arranged anti-parallel to one another. called the 5’ (“five prime”) region and • There are three potential reading frames on each strand of DNA, and a total the end of a DNA strand is called the of six potential reading frames for protein translation in any given region of 3’ (“three prime”) region. the DNA molecule (three on each strand). • Proteins are produced through the processes of transcription and At the end of this lesson, students will be able to: translation. • Amino acids are encoded by • Identify the best open reading frame among the six possible reading frames nucleotide triplets called codons. -
Codon Usage Biases Co-Evolve with Transcription Termination Machinery
RESEARCH ARTICLE Codon usage biases co-evolve with transcription termination machinery to suppress premature cleavage and polyadenylation Zhipeng Zhou1†, Yunkun Dang2,3†*, Mian Zhou4, Haiyan Yuan1, Yi Liu1* 1Department of Physiology, The University of Texas Southwestern Medical Center, Dallas, United States; 2State Key Laboratory for Conservation and Utilization of Bio- Resources in Yunnan, Yunnan University, Kunming, China; 3Center for Life Science, School of Life Sciences, Yunnan University, Kunming, China; 4State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China Abstract Codon usage biases are found in all genomes and influence protein expression levels. The codon usage effect on protein expression was thought to be mainly due to its impact on translation. Here, we show that transcription termination is an important driving force for codon usage bias in eukaryotes. Using Neurospora crassa as a model organism, we demonstrated that introduction of rare codons results in premature transcription termination (PTT) within open reading frames and abolishment of full-length mRNA. PTT is a wide-spread phenomenon in Neurospora, and there is a strong negative correlation between codon usage bias and PTT events. Rare codons lead to the formation of putative poly(A) signals and PTT. A similar role for codon *For correspondence: usage bias was also observed in mouse cells. Together, these results suggest that codon usage [email protected] (YD); biases co-evolve with the transcription termination machinery to suppress premature termination of [email protected] (YL) transcription and thus allow for optimal gene expression. †These authors contributed DOI: https://doi.org/10.7554/eLife.33569.001 equally to this work Competing interests: The authors declare that no Introduction competing interests exist. -
Glossary of Terms
GLOSSARY OF TERMS Table of Contents A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z A Amino acids: any of a class of 20 molecules that are combined to form proteins in living things. The sequence of amino acids in a protein and hence protein function are determined by the genetic code. From http://www.geneticalliance.org.uk/glossary.htm#C • The building blocks of proteins, there are 20 different amino acids. From https://www.yourgenome.org/glossary/amino-acid Antisense: Antisense nucleotides are strings of RNA or DNA that are complementary to "sense" strands of nucleotides. They bind to and inactivate these sense strands. They have been used in research, and may become useful for therapy of certain diseases (See Gene silencing). From http://www.encyclopedia.com/topic/Antisense_DNA.aspx. Antisense and RNA interference are referred as gene knockdown technologies: the transcription of the gene is unaffected; however, gene expression, i.e. protein synthesis (translation), is lost because messenger RNA molecules become unstable or inaccessible. Furthermore, RNA interference is based on naturally occurring phenomenon known as Post-Transcriptional Gene Silencing. From http://www.ncbi.nlm.nih.gov/probe/docs/applsilencing/ B Biobank: A biobank is a large, organised collection of samples, usually human, used for research. Biobanks catalogue and store samples using genetic, clinical, and other characteristics such as age, gender, blood type, and ethnicity. Some samples are also categorised according to environmental factors, such as whether the donor had been exposed to some substance that can affect health.