Skip to main content

Methods to Evaluate the Effects of Synonymous Variants

  • Chapter
  • First Online:
Single Nucleotide Polymorphisms

Abstract

Degeneracy of the genetic code enables a majority of amino acids to be encoded by multiple codons, called synonymous codons. Synonymous variants are single nucleotide changes that result in synonymous codon substitutions without changing the underlying amino acid sequence. For a long time, synonymous variants were assumed to be functionally neutral and were commonly referred to as “silent variants”. Under the same assumption, it was considered safe to employ synonymous variants in recombinant proteins and gene therapy designs using a technique called codon optimization to primarily improve protein expression. However, there is a critical mass of evidence in the last two decades showing that synonymous variants are not always silent and can affect encoded protein’s expression and quality through multiple mechanisms at both transcriptional and translational level. A large number of synonymous variants are implicated in several diseases as well as altering drug responses. Misclassification of synonymous variants as neutral or silent could result in failure to recognize disease-causing variants or failure to properly regulate recombinant therapeutic products harboring synonymous variants. Therefore, a careful evaluation of the functional effects of synonymous variants is critical. A plethora of in-vitro, ex-vivo and in-silico tools are currently available to assess functional effects of one or more co-occurring synonymous variants. The current chapter reviews methods frequently employed to study functional effects of synonymous variants.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Agarwal V, Bell GW, Nam JW, Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. elife 4:e05005

    Google Scholar 

  • Alexaki A, Kames J, Holcomb DD, Athey J, Santana-Quintero LV, Lam PVN, Hamasaki-Katagiri N, Osipova E, Simonyan V, Bar H et al (2019a) Codon and codon-pair usage tables (CoCoPUTs): facilitating genetic variation analyses and recombinant gene design. J Mol Biol 431:2434–2441

    CAS  Google Scholar 

  • Alexaki A, Hettiarachchi GK, Athey JC, Katneni UK, Simhadri V, Hamasaki-Katagiri N, Nanavaty P, Lin B, Takeda K, Freedberg D et al (2019b) Effects of codon optimization on coagulation factor IX translation and structure: implications for protein and gene therapies. Sci Rep 9:15449

    Google Scholar 

  • Ando H, Miyoshi-Akiyama T, Watanabe S, Kirikae T (2014) A silent mutation in mabA confers isoniazid resistance on Mycobacterium tuberculosis. Mol Microbiol 91:538–547

    CAS  Google Scholar 

  • Apetrei A, Molin A, Gruchy N, Godin M, Bracquemart C, Resbeut A, Rey G, Nadeau G, Richard N (2021) A novel synonymous variant in exon 1 of GNAS gene results in a cryptic splice site and causes pseudohypoparathyroidism type 1A and pseudo-pseudohypoparathyroidism in a French family. Bone Rep 14:101073

    CAS  Google Scholar 

  • Athey J, Alexaki A, Osipova E, Rostovtsev A, Santana-Quintero LV, Katneni U, Simonyan V, Kimchi-Sarfaty C (2017) A new and updated resource for codon usage tables. BMC Bioinform 18:391

    Google Scholar 

  • Aviner R, Geiger T, Elroy-Stein O (2014) Genome-wide identification and quantification of protein synthesis in cultured cells and whole tissues by puromycin-associated nascent chain proteomics (PUNCH-P). Nat Protoc 9:751–760

    CAS  Google Scholar 

  • Babendure JR, Babendure JL, Ding J-H, Tsien RY (2006) Control of mammalian translation by mRNA structure near caps. RNA (New York, NY) 12:851–861

    CAS  Google Scholar 

  • Bahiri-Elitzur S, Tuller T (2021) Codon-based indices for modeling gene expression and transcript evolution. Comput Struct Biotechnol J 19:2646–2663

    CAS  Google Scholar 

  • Bailey SF, Hinz A, Kassen R (2014) Adaptive synonymous mutations in an experimentally evolved Pseudomonas fluorescens population. Nat Commun 5:4076

    CAS  Google Scholar 

  • Bali V, Bebok Z (2015) Decoding mechanisms by which silent codon changes influence protein biogenesis and function. Int J Biochem Cell Biol 64:58–74

    CAS  Google Scholar 

  • Bandyopadhyay S, Mitra R (2009) TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples. Bioinformatics 25:2625–2631

    CAS  Google Scholar 

  • Bandyopadhyay S, Ghosh D, Mitra R, Zhao Z (2015) MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets. Sci Rep 5:8004

    CAS  Google Scholar 

  • Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136:215–233

    CAS  Google Scholar 

  • Bartoszewski RA, Jablonsky M, Bartoszewska S, Stevenson L, Dai Q, Kappes J, Collawn JF, Bebok Z (2010) A synonymous single nucleotide polymorphism in DeltaF508 CFTR alters the secondary structure of the mRNA and the expression of the mutant protein. J Biol Chem 285:28741–28748

    CAS  Google Scholar 

  • Bellaousov S, Mathews DH (2010) ProbKnot: fast prediction of RNA secondary structure including pseudoknots. RNA 16:1870–1880

    CAS  Google Scholar 

  • Ben Or G, Veksler-Lublinsky I (2021) Comprehensive machine-learning-based analysis of microRNA–target interactions reveals variable transferability of interaction rules across species. BMC Bioinform 22:264

    CAS  Google Scholar 

  • Bertalovitz AC, Badhey MLO, McDonald TV (2018) Synonymous nucleotide modification of the KCNH2 gene affects both mRNA characteristics and translation of the encoded hERG ion channel. J Biol Chem 293:12120–12136

    CAS  Google Scholar 

  • Bertolazzi G, Benos PV, Tumminello M, Coronnello C (2020) An improvement of ComiR algorithm for microRNA target prediction by exploiting coding region sequences of mRNAs. BMC Bioinform 21:201

    CAS  Google Scholar 

  • Betel D, Koppal A, Agius P, Sander C, Leslie C (2010) Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol 11:R90

    Google Scholar 

  • Blum JS, Wearsch PA, Cresswell P (2013) Pathways of antigen processing. Annu Rev Immunol 31:443–473

    CAS  Google Scholar 

  • Brest P, Lapaquette P, Souidi M, Lebrigand K, Cesaro A, Vouret-Craviari V, Mari B, Barbry P, Mosnier JF, Hébuterne X et al (2011) A synonymous variant in IRGM alters a binding site for miR-196 and causes deregulation of IRGM-dependent xenophagy in Crohn’s disease. Nat Genet 43:242–245

    CAS  Google Scholar 

  • Buhr F, Jha S, Thommen M, Mittelstaet J, Kutz F, Schwalbe H, Rodnina MV, Komar AA (2016) Synonymous codons direct cotranslational folding toward different protein conformations. Mol Cell 61:341–351

    CAS  Google Scholar 

  • Burge C, Karlin S (1997) Prediction of complete gene structures in human genomic DNA11Edited by F. E. Cohen. J Mol Biol 268:78–94

    CAS  Google Scholar 

  • Buske OJ, Manickaraj A, Mital S, Ray PN, Brudno M (2013) Identification of deleterious synonymous variants in human genomes. Bioinformatics 29:1843–1850

    CAS  Google Scholar 

  • Calonaci N, Jones A, Cuturello F, Sattler M, Bussi G (2020) Machine learning a model for RNA structure prediction. NAR Genom Bioinform 2:lqaa090

    Google Scholar 

  • Chassé H, Boulben S, Costache V, Cormier P, Morales J (2016) Analysis of translation using polysome profiling. Nucleic Acids Res 45:e15–e15

    Google Scholar 

  • Chekulaeva M, Landthaler M (2016) Eyes on translation. Mol Cell 63:918–925

    CAS  Google Scholar 

  • Chen Y, Wang X (2019) miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res 48:D127–D131

    Google Scholar 

  • Chen X, Li Y, Umarov R, Gao X, Song L (2020) RNA secondary structure prediction by learning unrolled algorithms. arXiv preprint arXiv:200205810

    Google Scholar 

  • Chi SW, Zang JB, Mele A, Darnell RB (2009) Argonaute HITS-CLIP decodes microRNA–mRNA interaction maps. Nature 460:479–486

    CAS  Google Scholar 

  • Clarke TFIV, Clark PL (2008) Rare codons cluster. PLoS One 3:e3412

    Google Scholar 

  • Coleman JR, Papamichail D, Skiena S, Futcher B, Wimmer E, Mueller S (2008) Virus attenuation by genome-scale changes in codon pair bias. Science 320:1784–1787

    CAS  Google Scholar 

  • Coronnello C, Benos PV (2013) ComiR: combinatorial microRNA target prediction tool. Nucleic Acids Res 41:W159–W164

    Google Scholar 

  • Cuevas JM, Domingo-Calap P, Sanjuán R (2012) The fitness effects of synonymous mutations in DNA and RNA viruses. Mol Biol Evol 29:17–20

    CAS  Google Scholar 

  • Dermit M, Dodel M, Mardakheh FK (2017) Methods for monitoring and measurement of protein translation in time and space. Mol BioSyst 13:2477–2488

    CAS  Google Scholar 

  • Desmet F-O, Hamroun D, Lalande M, Collod-Béroud G, Claustres M, Béroud C (2009) Human Splicing Finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Res 37:e67–e67

    Google Scholar 

  • Diambra LA (2017) Differential bicodon usage in lowly and highly abundant proteins. PeerJ 5:e3081

    Google Scholar 

  • Dieterich DC, Link AJ, Graumann J, Tirrell DA, Schuman EM (2006) Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT). Proc Natl Acad Sci 103:9482–9487

    CAS  Google Scholar 

  • Dieterich DC, Hodas JJL, Gouzer G, Shadrin IY, Ngo JT, Triller A, Tirrell DA, Schuman EM (2010) In situ visualization and dynamics of newly synthesized proteins in rat hippocampal neurons. Nat Neurosci 13:897–905

    CAS  Google Scholar 

  • Ding Y, Tang Y, Kwok CK, Zhang Y, Bevilacqua PC, Assmann SM (2014) In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features. Nature 505:696–700

    CAS  Google Scholar 

  • Dobrowolski SF, Andersen HS, Doktor TK, Andresen BS (2010) The phenylalanine hydroxylase c.30C>G synonymous variation (p.G10G) creates a common exonic splicing silencer. Mol Genet Metab 100:316–323

    CAS  Google Scholar 

  • Dölken L, Ruzsics Z, Rädle B, Friedel CC, Zimmer R, Mages J, Hoffmann R, Dickinson P, Forster T, Ghazal P et al (2008) High-resolution gene expression profiling for simultaneous kinetic parameter analysis of RNA synthesis and decay. RNA 14:1959–1972

    Google Scholar 

  • Domingo-Calap P, Cuevas JM, Sanjuán R (2009) The fitness effects of random mutations in single-stranded DNA and RNA bacteriophages. PLoS Genet 5:e1000742

    Google Scholar 

  • Duan J, Wainwright MS, Comeron JM, Saitou N, Sanders AR, Gelernter J, Gejman PV (2003) Synonymous mutations in the human dopamine receptor D2 (DRD2) affect mRNA stability and synthesis of the receptor. Hum Mol Genet 12:205–216

    CAS  Google Scholar 

  • Erkelenz S, Theiss S, Otte M, Widera M, Peter JO, Schaal H (2014) Genomic HEXploring allows landscaping of novel potential splicing regulatory elements. Nucleic Acids Res 42:10681–10697

    CAS  Google Scholar 

  • Fairbrother WG, Yeo GW, Yeh R, Goldstein P, Mawson M, Sharp PA, Burge CB (2004) RESCUE-ESE identifies candidate exonic splicing enhancers in vertebrate exons. Nucleic Acids Res 32:W187–W190

    CAS  Google Scholar 

  • Fang Z, Rajewsky N (2011) The impact of miRNA target sites in coding sequences and in 3′ UTRs. PLoS One 6:e18067

    CAS  Google Scholar 

  • Feng Y, De Franceschi G, Kahraman A, Soste M, Melnik A, Boersema PJ, de Laureto PP, Nikolaev Y, Oliveira AP, Picotti P (2014) Global analysis of protein structural changes in complex proteomes. Nat Biotechnol 32:1036–1044

    CAS  Google Scholar 

  • Forman JJ, Coller HA (2010) The code within the code: microRNAs target coding regions. Cell Cycle 9:1533–1541

    CAS  Google Scholar 

  • Forman JJ, Legesse-Miller A, Coller HA (2008) A search for conserved sequences in coding regions reveals that the let-7 microRNA targets Dicer within its coding sequence. Proc Natl Acad Sci 105:14879–14884

    Google Scholar 

  • Fox JM, Erill I (2010) Relative codon adaptation: a generic codon bias index for prediction of gene expression. DNA Res 17:185–196

    CAS  Google Scholar 

  • Friedman Y, Naamati G, Linial M (2010) MiRror: a combinatorial analysis web tool for ensembles of microRNAs and their targets. Bioinformatics 26:1920–1921

    CAS  Google Scholar 

  • Friedrich U, Datta S, Schubert T, Plössl K, Schneider M, Grassmann F, Fuchshofer R, Tiefenbach K-J, Längst G, Weber BHF (2015) Synonymous variants in HTRA1 implicated in AMD susceptibility impair its capacity to regulate TGF-β signaling. Hum Mol Genet 24:6361–6373

    CAS  Google Scholar 

  • Frumkin I, Lajoie MJ, Gregg CJ, Hornung G, Church GM, Pilpel Y (2018) Codon usage of highly expressed genes affects proteome-wide translation efficiency. Proc Natl Acad Sci 115:E4940–E4949

    Google Scholar 

  • Fu J, Murphy KA, Zhou M, Li YH, Lam VH, Tabuloc CA, Chiu JC, Liu Y (2016) Codon usage affects the structure and function of the Drosophila circadian clock protein PERIOD. Genes Dev 30:1761–1775

    CAS  Google Scholar 

  • Gaither JBS, Lammi GE, Li JL, Gordon DM, Kuck HC, Kelly BJ, Fitch JR, White P (2021) Synonymous variants that disrupt messenger RNA structure are significantly constrained in the human population. Gigascience 10:giab023

    Google Scholar 

  • Gao K, Oerlemans R, Groves MR (2020) Theory and applications of differential scanning fluorimetry in early-stage drug discovery. Biophys Rev 12:85–104

    Google Scholar 

  • Gartner JJ, Parker SCJ, Prickett TD, Dutton-Regester K, Stitzel ML, Lin JC, Davis S, Simhadri VL, Jha S, Katagiri N et al (2013) Whole-genome sequencing identifies a recurrent functional synonymous mutation in melanoma. Proc Natl Acad Sci 110:13481–13486

    CAS  Google Scholar 

  • Gebert LFR, MacRae IJ (2019) Regulation of microRNA function in animals. Nat Rev Mol Cell Biol 20:21–37

    CAS  Google Scholar 

  • Gelfman S, Wang Q, McSweeney KM, Ren Z, La Carpia F, Halvorsen M, Schoch K, Ratzon F, Heinzen EL, Boland MJ et al (2017) Annotating pathogenic non-coding variants in genic regions. Nat Commun 8:236–236

    Google Scholar 

  • Ghisaidoobe ABT, Chung SJ (2014) Intrinsic tryptophan fluorescence in the detection and analysis of proteins: a focus on Förster resonance energy transfer techniques. Int J Mol Sci 15:22518–22538

    CAS  Google Scholar 

  • Gill P, Moghadam TT, Ranjbar B (2010) Differential scanning calorimetry techniques: applications in biology and nanoscience. J Biomol Tech 21:167–193

    Google Scholar 

  • Greenfield NJ (2006a) Using circular dichroism spectra to estimate protein secondary structure. Nat Protoc 1:2876–2890

    CAS  Google Scholar 

  • Greenfield NJ (2006b) Using circular dichroism collected as a function of temperature to determine the thermodynamics of protein unfolding and binding interactions. Nat Protoc 1:2527–2535

    CAS  Google Scholar 

  • Griseri P, Bourcier C, Hieblot C, Essafi-Benkhadir K, Chamorey E, Touriol C, Pagès G (2011) A synonymous polymorphism of the Tristetraprolin (TTP) gene, an AU-rich mRNA-binding protein, affects translation efficiency and response to Herceptin treatment in breast cancer patients. Hum Mol Genet 20:4556–4568

    CAS  Google Scholar 

  • Gruber AR, Lorenz R, Bernhart SH, Neuböck R, Hofacker IL (2008) The Vienna RNA websuite. Nucleic Acids Res 36:W70–W74

    CAS  Google Scholar 

  • Gu W, Zhou T, Wilke CO (2010) A universal trend of reduced mRNA stability near the translation-initiation site in prokaryotes and eukaryotes. PLoS Comput Biol 6:e1000664

    Google Scholar 

  • Halstead JM, Lionnet T, Wilbertz JH, Wippich F, Ephrussi A, Singer RH, Chao JA (2015) An RNA biosensor for imaging the first round of translation from single cells to living animals. Science 347:1367–1671

    CAS  Google Scholar 

  • Hamasaki-Katagiri N, Lin BC, Simon J, Hunt RC, Schiller T, Russek-Cohen E, Komar AA, Bar H, Kimchi-Sarfaty C (2017) The importance of mRNA structure in determining the pathogenicity of synonymous and non-synonymous mutations in haemophilia. Haemophilia 23:e8–e17

    CAS  Google Scholar 

  • Hebsgaard SM, Korning PG, Tolstrup N, Engelbrecht J, Rouzé P, Brunak S (1996) Splice site prediction in arabidopsis thaliana pre-mRNA by combining local and global sequence information. Nucleic Acids Res 24:3439–3452

    CAS  Google Scholar 

  • Heiman M, Kulicke R, Fenster RJ, Greengard P, Heintz N (2014) Cell type-specific mRNA purification by translating ribosome affinity purification (TRAP). Nat Protoc 9:1282–1291

    CAS  Google Scholar 

  • Hermeling S, Jiskoot W, Crommelin D, Bornæs C, Schellekens H (2005) Development of a transgenic mouse model immune tolerant for human interferon beta. Pharm Res 22:847–851

    CAS  Google Scholar 

  • Hiard S, Charlier C, Coppieters W, Georges M, Baurain D (2010) Patrocles: a database of polymorphic miRNA-mediated gene regulation in vertebrates. Nucleic Acids Res 38:D640–D651

    CAS  Google Scholar 

  • Honarmand Ebrahimi K, West GM, Flefil R (2014) Mass spectrometry approach and ELISA reveal the effect of codon optimization on N-linked glycosylation of HIV-1 gp120. J Proteome Res 13:5801–5811

    CAS  Google Scholar 

  • Hoover DM, Lubkowski J (2002) DNAWorks: an automated method for designing oligonucleotides for PCR-based gene synthesis. Nucleic Acids Res 30:e43

    Google Scholar 

  • Horton JS, Flanagan LM, Jackson RW, Priest NK, Taylor TB (2021) A mutational hotspot that determines highly repeatable evolution can be built and broken by silent genetic changes. Nat Commun 12:6092

    CAS  Google Scholar 

  • Howden AJM, Geoghegan V, Katsch K, Efstathiou G, Bhushan B, Boutureira O, Thomas B, Trudgian DC, Kessler BM, Dieterich DC et al (2013) QuaNCAT: quantitating proteome dynamics in primary cells. Nat Methods 10:343–346

    CAS  Google Scholar 

  • Hunt RC, Simhadri VL, Iandoli M, Sauna ZE, Kimchi-Sarfaty C (2014) Exposing synonymous mutations. Trends Genet 30:308–321

    CAS  Google Scholar 

  • Hunt R, Hettiarachchi G, Katneni U, Hernandez N, Holcomb D, Kames J, Alnifaidy R, Lin B, Hamasaki-Katagiri N, Wesley A et al (2019) A single synonymous variant (c.354G>A [p.P118P]) in ADAMTS13 confers enhanced specific activity. Int J Mol Sci 20:5734

    CAS  Google Scholar 

  • Ingolia NT, Ghaemmaghami S, Newman JRS, Weissman JS (2009) Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science (New York, NY) 324:218–223

    CAS  Google Scholar 

  • Iwasaki S, Ingolia NT (2017) The growing toolbox for protein synthesis studies. Trends Biochem Sci 42:612–624

    CAS  Google Scholar 

  • Jabbari H, Wark I, Montemagno C, Will S (2018) Knotty: efficient and accurate prediction of complex RNA pseudoknot structures. Bioinformatics 34:3849–3856

    CAS  Google Scholar 

  • Jacobs WM, Shakhnovich EI (2017) Evidence of evolutionary selection for cotranslational folding. Proc Natl Acad Sci 114:11434–11439

    CAS  Google Scholar 

  • Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, Kosmicki JA, Arbelaez J, Cui W, Schwartz GB et al (2019) Predicting splicing from primary sequence with deep learning. Cell 176:535–548.e524

    Google Scholar 

  • Jan CH, Williams CC, Weissman JS (2014) Principles of ER cotranslational translocation revealed by proximity-specific ribosome profiling. Science (New York, NY) 346:1257521–1257521

    Google Scholar 

  • Jankowski W, Park Y, McGill J, Maraskovsky E, Hofmann M, Diego VP, Luu BW, Howard TE, Kellerman R, Key NS et al (2019) Peptides identified on monocyte-derived dendritic cells: a marker for clinical immunogenicity to FVIII products. Blood Adv 3:1429–1440

    CAS  Google Scholar 

  • Jing M, Bowser MT (2011) Methods for measuring aptamer-protein equilibria: a review. Anal Chim Acta 686:9–18

    CAS  Google Scholar 

  • Johnson CM (2013) Differential scanning calorimetry as a tool for protein folding and stability. Arch Biochem Biophys 531:100–109

    CAS  Google Scholar 

  • Jonas S, Izaurralde E (2015) Towards a molecular understanding of microRNA-mediated gene silencing. Nat Rev Genet 16:421–433

    CAS  Google Scholar 

  • Karle AC (2020) Applying MAPPs assays to assess drug immunogenicity. Front Immunol 11 Article 698

    Google Scholar 

  • Katneni UK, Liss A, Holcomb D, Katagiri NH, Hunt R, Bar H, Ismail A, Komar AA, Kimchi-Sarfaty C (2019) Splicing dysregulation contributes to the pathogenicity of several F9 exonic point variants. Mol Genet Genomic Med 7:e840

    Google Scholar 

  • Ke S, Shang S, Kalachikov SM, Morozova I, Yu L, Russo JJ, Ju J, Chasin LA (2011) Quantitative evaluation of all hexamers as exonic splicing elements. Genome Res 21:1360–1374

    CAS  Google Scholar 

  • Keightley PD, Halligan DL (2011) Inference of site frequency spectra from high-throughput sequence data: quantification of selection on nonsynonymous and synonymous sites in humans. Genetics 188:931–940

    Google Scholar 

  • Kelly MS, Price CN (2000) The use of circular dichroism in the investigation of protein structure and function. Curr Protein Pept Sci 1:349–384

    CAS  Google Scholar 

  • Kershner JP, Yu McLoughlin S, Kim J, Morgenthaler A, Ebmeier CC, Old WM, Copley SD (2016) A synonymous mutation upstream of the gene encoding a weak-link enzyme causes an ultrasensitive response in growth rate. J Bacteriol 198:2853–2863

    CAS  Google Scholar 

  • Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39:1278–1284

    CAS  Google Scholar 

  • Kimchi-Sarfaty C, Oh JM, Kim I-W, Sauna ZE, Calcagno AM, Ambudkar SV, Gottesman MM (2007) A “silent” polymorphism in the MDR1 gene changes substrate specificity. Science 315:525–528

    CAS  Google Scholar 

  • Kimchi-Sarfaty C, Simhadri VL, Kopelman D, Friedman A, Edwards N, Javaid A, Okunji C, Komar A, Sauna Z, Katagiri N (2010) The synonymous V107V mutation in factor IX is not so silent and may cause hemophilia B in patients. Blood 116:2197–2197

    Google Scholar 

  • Kirchner S, Cai Z, Rauscher R, Kastelic N, Anding M, Czech A, Kleizen B, Ostedgaard LS, Braakman I, Sheppard DN et al (2017) Alteration of protein function by a silent polymorphism linked to tRNA abundance. PLoS Biol 15:e2000779–e2000779

    Google Scholar 

  • Knöppel A, Näsvall J, Andersson DI (2016) Compensating the fitness costs of synonymous mutations. Mol Biol Evol 33:1461–1477

    Google Scholar 

  • Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M et al (2005) Combinatorial microRNA target predictions. Nat Genet 37:495–500

    CAS  Google Scholar 

  • Kudla G, Murray AW, Tollervey D, Plotkin JB (2009) Coding-sequence determinants of gene expression in Escherichia coli. Science 324:255–258

    CAS  Google Scholar 

  • Kunec D, Osterrieder N (2016) Codon pair bias is a direct consequence of dinucleotide bias. Cell Rep 14:55–67

    CAS  Google Scholar 

  • Lawrie DS, Messer PW, Hershberg R, Petrov DA (2013) Strong purifying selection at synonymous sites in D. melanogaster. PLoS Genet 9:e1003527

    CAS  Google Scholar 

  • Lebeuf-Taylor E, McCloskey N, Bailey SF, Hinz A, Kassen R (2019) The distribution of fitness effects among synonymous mutations in a gene under directional selection. elife 8:1

    Google Scholar 

  • Lee B, Baek J, Park S, Yoon S (2016) deepTarget: end-to-end learning framework for microRNA target prediction using deep recurrent neural networks. In: Proceedings of the 7th ACM international conference on bioinformatics, computational biology, and health informatics (Seattle, WA, USA, Association for Computing Machinery), pp 434–442

    Google Scholar 

  • Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP, Burge CB (2003) Prediction of mammalian microRNA targets. Cell 115:787–798

    CAS  Google Scholar 

  • Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120:15–20

    CAS  Google Scholar 

  • Li Q, Li J, Yu C-p, Chang S, Xie L-l, Wang S (2021) Synonymous mutations that regulate translation speed might play a non-negligible role in liver cancer development. BMC Cancer 21:388

    CAS  Google Scholar 

  • Liu Y (2020) A code within the genetic code: codon usage regulates co-translational protein folding. Cell Commun Signal 18:145

    CAS  Google Scholar 

  • Livingstone M, Folkman L, Yang Y, Zhang P, Mort M, Cooper DN, Liu Y, Stantic B, Zhou Y (2017) Investigating DNA-, RNA-, and protein-based features as a means to discriminate pathogenic synonymous variants. Hum Mutat 38:1336–1347

    CAS  Google Scholar 

  • Lu W, Tang Y, Wu H, Huang H, Fu Q, Qiu J, Li H (2019) Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter. BMC Bioinform 20:684

    CAS  Google Scholar 

  • Lundblad RL (2009) Approaches to the conformational analysis of biopharmaceuticals. Chapman and Hall/CRC, New York

    Google Scholar 

  • Marín RM, Sulc M, Vanícek J (2013) Searching the coding region for microRNA targets. RNA 19:467–474

    Google Scholar 

  • Markham NR, Zuker M (2008) UNAFold: software for nucleic acid folding and hybridization. Methods Mol Biol 453:3–31

    CAS  Google Scholar 

  • Mauro VP, Chappell SA (2014) A critical analysis of codon optimization in human therapeutics. Trends Mol Med 20:604–613

    CAS  Google Scholar 

  • McDermott SP, Eppert K, Lechman ER, Doedens M, Dick JE (2010) Comparison of human cord blood engraftment between immunocompromised mouse strains. Blood 116:193–200

    CAS  Google Scholar 

  • Mordstein C, Savisaar R, Young RS, Bazile J, Talmane L, Luft J, Liss M, Taylor MS, Hurst LD, Kudla G (2020) Codon usage and splicing jointly influence mRNA localization. Cell Syst 10:351–362.e358

    CAS  Google Scholar 

  • Morisaki T, Lyon K, DeLuca KF, DeLuca JG, English BP, Zhang Z, Lavis LD, Grimm JB, Viswanathan S, Looger LL et al (2016) Real-time quantification of single RNA translation dynamics in living cells. Science 352:1425–1429

    CAS  Google Scholar 

  • Mueller WF, Larsen LSZ, Garibaldi A, Hatfield GW, Hertel KJ (2015) The silent sway of splicing by synonymous substitutions*. J Biol Chem 290:27700–27711

    CAS  Google Scholar 

  • Nackley AG, Shabalina SA, Tchivileva IE, Satterfield K, Korchynskyi O, Makarov SS, Maixner W, Diatchenko L (2006) Human catechol-O-methyltransferase haplotypes modulate protein expression by altering mRNA secondary structure. Science 314:1930–1933

    CAS  Google Scholar 

  • Newman ZR, Young JM, Ingolia NT, Barton GM (2016) Differences in codon bias and GC content contribute to the balanced expression of TLR7 and TLR9. Proc Natl Acad Sci U S A 113:E1362–E1371

    CAS  Google Scholar 

  • Niesen FH, Berglund H, Vedadi M (2007) The use of differential scanning fluorimetry to detect ligand interactions that promote protein stability. Nat Protoc 2:2212–2221

    CAS  Google Scholar 

  • Ong S-E, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics*. Mol Cell Proteomics 1:376–386

    CAS  Google Scholar 

  • Oubounyt M, Louadi Z, Tayara H, Chong KT (2019) DeePromoter: robust promoter predictor using deep learning. Front Genet 10:286

    CAS  Google Scholar 

  • Ozohanics O, Ambrus A (2020) Hydrogen-deuterium exchange mass spectrometry: a novel structural biology approach to structure, dynamics and interactions of proteins and their complexes. Life 10:286

    CAS  Google Scholar 

  • Pagani F, Raponi M, Baralle FE (2005) Synonymous mutations in CFTR exon 12 affect splicing and are not neutral in evolution. Proc Natl Acad Sci U S A 102:6368–6372

    CAS  Google Scholar 

  • Paraskevopoulou MD, Georgakilas G, Kostoulas N, Vlachos IS, Vergoulis T, Reczko M, Filippidis C, Dalamagas T, Hatzigeorgiou AG (2013) DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows. Nucleic Acids Res 41:W169–W173

    Google Scholar 

  • Parvathy ST, Udayasuriyan V, Bhadana V (2022) Codon usage bias. Mol Biol Rep 49:539–565

    CAS  Google Scholar 

  • Peris JB, Davis P, Cuevas JM, Nebot MR, Sanjuán R (2010) Distribution of fitness effects caused by single-nucleotide substitutions in bacteriophage f1. Genetics 185:603–609

    CAS  Google Scholar 

  • Peterson J, Li S, Kaltenbrun E, Erdogan O, Counter CM (2020) Expression of transgenes enriched in rare codons is enhanced by the MAPK pathway. Sci Rep 10:22166

    CAS  Google Scholar 

  • Pratt KP (2018) Anti-drug antibodies: emerging approaches to predict, reduce or reverse biotherapeutic immunogenicity. Antibodies (Basel) 7:19

    CAS  Google Scholar 

  • Proctor JR, Meyer IM (2013) COFOLD: an RNA secondary structure prediction method that takes co-transcriptional folding into account. Nucleic Acids Res 41:e102

    CAS  Google Scholar 

  • Puigbò P, Guzmán E, Romeu A, Garcia-Vallvé S (2007) OPTIMIZER: a web server for optimizing the codon usage of DNA sequences. Nucleic Acids Res 35:W126–W131

    Google Scholar 

  • Rahman S, Kosakovsky Pond SL, Webb A, Hey J (2021) Weak selection on synonymous codons substantially inflates dN/dS estimates in bacteria. Proc Natl Acad Sci 118:e2023575118

    CAS  Google Scholar 

  • Raponi M, Kralovicova J, Copson E, Divina P, Eccles D, Johnson P, Baralle D, Vorechovsky I (2011) Prediction of single-nucleotide substitutions that result in exon skipping: identification of a splicing silencer in BRCA1 exon 6. Hum Mutat 32:436–444

    CAS  Google Scholar 

  • Reese MG, Eeckman FH, Kulp D, Haussler D (1997) Improved splice site detection in genie. J Comput Biol 4:311–323

    CAS  Google Scholar 

  • Riffo-Campos ÁL, Riquelme I, Brebi-Mieville P (2016) Tools for sequence-based miRNA target prediction: what to choose? Int J Mol Sci 17:1987

    Google Scholar 

  • Riolo G, Cantara S, Marzocchi C, Ricci C (2020) miRNA targets: from prediction tools to experimental validation. Methods Protoc 4(1)

    Google Scholar 

  • Riolo G, Cantara S, Ricci C (2021) What’s wrong in a jump? Prediction and validation of splice site variants. Methods Protoc 4:62

    CAS  Google Scholar 

  • Rodriguez A, Wright G, Emrich S, Clark PL (2018) %MinMax: a versatile tool for calculating and comparing synonymous codon usage and its impact on protein folding. Protein Sci 27:356–362

    CAS  Google Scholar 

  • Rogozin IB, Milanesi L (1997) Analysis of donor splice sites in different eukaryotic organisms. J Mol Evol 45:50–59

    CAS  Google Scholar 

  • Saetrom O, Snøve O Jr, Saetrom P (2005) Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms. RNA 11:995–1003

    CAS  Google Scholar 

  • Salari R, Kimchi-Sarfaty C, Gottesman MM, Przytycka TM (2013) Sensitive measurement of single-nucleotide polymorphism-induced changes of RNA conformation: application to disease studies. Nucleic Acids Res 41:44–53

    CAS  Google Scholar 

  • Salvat R, Moise L, Bailey-Kellogg C, Griswold KE (2014) A high throughput MHC II binding assay for quantitative analysis of peptide epitopes. J Vis Exp 85:51308

    Google Scholar 

  • Sanavia T, Birolo G, Montanucci L, Turina P, Capriotti E, Fariselli P (2020) Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine. Comput Struct Biotechnol J 18:1968–1979

    CAS  Google Scholar 

  • Sato K, Kato Y (2021) Prediction of RNA secondary structure including pseudoknots for long sequences. Brief Bioinform 23:1–9

    Google Scholar 

  • Sauna ZE, Kimchi-Sarfaty C (2011) Understanding the contribution of synonymous mutations to human disease. Nat Rev Genet 12:683–691

    CAS  Google Scholar 

  • Sauna ZE, Kimchi-Sarfaty C (2013) Synonymous mutations as a cause of human genetic disease. In: eLS. Wiley

    Google Scholar 

  • Sauna ZE, Kimchi-Sarfaty C, Ambudkar SV, Gottesman MM (2007) Silent polymorphisms speak: how they affect pharmacogenomics and the treatment of cancer. Cancer Res 67:9609–9612

    CAS  Google Scholar 

  • Savisaar R, Hurst LD (2018) Exonic splice regulation imposes strong selection at synonymous sites. Genome Res 28:1442–1454

    CAS  Google Scholar 

  • Schnall-Levin M, Zhao Y, Perrimon N, Berger B (2010) Conserved microRNA targeting in Drosophila is as widespread in coding regions as in 3′ UTRs. Proc Natl Acad Sci 107:15751–15756

    CAS  Google Scholar 

  • Seoighe C, Kiniry SJ, Peters A, Baranov PV, Yang H (2020) Selection shapes synonymous stop codon use in mammals. J Mol Evol 88:549–561

    CAS  Google Scholar 

  • Shabalina SA, Spiridonov NA, Kashina A (2013) Sounds of silence: synonymous nucleotides as a key to biological regulation and complexity. Nucleic Acids Res 41:2073–2094

    CAS  Google Scholar 

  • Sharma Y, Miladi M, Dukare S, Boulay K, Caudron-Herger M, Groß M, Backofen R, Diederichs S (2019) A pan-cancer analysis of synonymous mutations. Nat Commun 10:2569

    Google Scholar 

  • Sharp PM, Li WH (1987) The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15:1281–1295

    CAS  Google Scholar 

  • Shi F, Yao Y, Bin Y, Zheng C-H, Xia J (2019) Computational identification of deleterious synonymous variants in human genomes using a feature-based approach. BMC Med Genet 12:12

    Google Scholar 

  • Shin C, Nam JW, Farh KK, Chiang HR, Shkumatava A, Bartel DP (2010) Expanding the microRNA targeting code: functional sites with centered pairing. Mol Cell 38:789–802

    CAS  Google Scholar 

  • Simhadri VL, Hamasaki-Katagiri N, Lin BC, Hunt R, Jha S, Tseng SC, Wu A, Bentley AA, Zichel R, Lu Q et al (2017) Single synonymous mutation in factor IX alters protein properties and underlies haemophilia B. J Med Genet 54:338–345

    CAS  Google Scholar 

  • Singh J, Hanson J, Paliwal K, Zhou Y (2019) RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning. Nat Commun 10:5407

    Google Scholar 

  • Singh J, Paliwal K, Zhang T, Singh J, Litfin T, Zhou Y (2021) Improved RNA secondary structure and tertiary base-pairing prediction using evolutionary profile, mutational coupling and two-dimensional transfer learning. Bioinformatics 37:2589–2600

    Google Scholar 

  • Sloma MF, Mathews DH (2017) Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs. PLoS Comput Biol 13:e1005827

    Google Scholar 

  • Smith PJ, Zhang C, Wang J, Chew SL, Zhang MQ, Krainer AR (2006) An increased specificity score matrix for the prediction of SF2/ASF-specific exonic splicing enhancers. Hum Mol Genet 15:2490–2508

    CAS  Google Scholar 

  • Sønderstrup G, Cope AP, Patel S, Congia M, Hain N, Hall FC, Parry SL, Fugger LH, Michie S, McDevitt HO (1999) HLA class II transgenic mice: models of the human CD4+ T-cell immune response. Immunol Rev 172:335–343

    Google Scholar 

  • Sroubek J, Krishnan Y, McDonald TV (2013) Sequence and structure-specific elements of HERG mRNA determine channel synthesis and trafficking efficiency. FASEB J 27:3039–3053

    CAS  Google Scholar 

  • Stergachis AB, Haugen E, Shafer A, Fu W, Vernot B, Reynolds A, Raubitschek A, Ziegler S, LeProust EM, Akey JM et al (2013) Exonic transcription factor binding directs codon choice and affects protein evolution. Science 342:1367–1372

    CAS  Google Scholar 

  • Šulc M, Marín RM, Robins HS, Vaníček J (2015) PACCMIT/PACCMIT-CDS: identifying microRNA targets in 3′ UTRs and coding sequences. Nucleic Acids Res 43:W474–W479

    Google Scholar 

  • Tang X, Zhang T, Cheng N, Wang H, Zheng C-H, Xia J, Zhang T (2021) usDSM: a novel method for deleterious synonymous mutation prediction using undersampling scheme. Brief Bioinform 22:5416

    Google Scholar 

  • Tinoco I Jr, Uhlenbeck OC, Levine MD (1971) Estimation of secondary structure in ribonucleic acids. Nature 230:362–367

    CAS  Google Scholar 

  • Trabjerg E, Nazari ZE, Rand KD (2018) Conformational analysis of complex protein states by hydrogen/deuterium exchange mass spectrometry (HDX-MS): challenges and emerging solutions. TrAC Trends Anal Chem 106:125–138

    CAS  Google Scholar 

  • Tüfekci KU, Meuwissen RL, Genç S (2014) The role of microRNAs in biological processes. Methods Mol Biol 1107:15–31

    Google Scholar 

  • Turner DH, Mathews DH (2010) NNDB: the nearest neighbor parameter database for predicting stability of nucleic acid secondary structure. Nucleic Acids Res 38:D280–D282

    CAS  Google Scholar 

  • Umarov R, Kuwahara H, Li Y, Gao X, Solovyev V (2019) Promoter analysis and prediction in the human genome using sequence-based deep learning models. Bioinformatics 35:2730–2737

    CAS  Google Scholar 

  • Villalobos A, Ness JE, Gustafsson C, Minshull J, Govindarajan S (2006) Gene designer: a synthetic biology tool for constructing artificial DNA segments. BMC Bioinform 7:1–8

    Google Scholar 

  • Vivian JT, Callis PR (2001) Mechanisms of tryptophan fluorescence shifts in proteins. Biophys J 80:2093–2109

    CAS  Google Scholar 

  • Wai HA, Lord J, Lyon M, Gunning A, Kelly H, Cibin P, Seaby EG, Spiers-Fitzgerald K, Lye J, Ellard S et al (2020) Blood RNA analysis can increase clinical diagnostic rate andresolve variants of uncertain significance. Genet Med 22:1005–1014

    CAS  Google Scholar 

  • Walsh IM, Bowman MA, Soto Santarriaga IF, Rodriguez A, Clark PL (2020) Synonymous codon substitutions perturb cotranslational protein folding in vivo and impair cell fitness. Proc Natl Acad Sci 117:3528–3534

    CAS  Google Scholar 

  • Wang Y, Qiu C, Cui Q (2015) A large-scale analysis of the relationship of synonymous SNPs changing microRNA regulation with functionality and disease. Int J Mol Sci 16:23545–23555

    CAS  Google Scholar 

  • Wang L, Liu Y, Zhong X, Liu H, Lu C, Li C, Zhang H (2019) DMfold: a novel method to predict RNA secondary structure with pseudoknots based on deep learning and improved base pair maximization principle. Front Genet 10:143

    CAS  Google Scholar 

  • Wen M, Cong P, Zhang Z, Lu H, Li T (2018) DeepMirTar: a deep-learning approach for predicting human miRNA targets. Bioinformatics 34:3781–3787

    CAS  Google Scholar 

  • Wong N, Wang X (2014) miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res 43:D146–D152

    Google Scholar 

  • Wu B, Eliscovich C, Yoon YJ, Singer RH (2016) Translation dynamics of single mRNAs in live cells and neurons. Science 352:1430–1435

    CAS  Google Scholar 

  • Wu P, Zhou D, Lin W, Li Y, Wei H, Qian X, Jiang Y, He F (2018) Cell-type-resolved alternative splicing patterns in mouse liver. DNA Res 25:265–275

    CAS  Google Scholar 

  • Wu Q, Medina SG, Kushawah G, DeVore ML, Castellano LA, Hand JM, Wright M, Bazzini AA (2019) Translation affects mRNA stability in a codon-dependent manner in human cells. elife 8:e45396

    Google Scholar 

  • Xayaphoummine A, Bucher T, Isambert H (2005) Kinefold web server for RNA/DNA folding path and structure prediction including pseudoknots and knots. Nucleic Acids Res 33:W605–W610

    CAS  Google Scholar 

  • Yeo G, Burge CB (2004) Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J Comput Biol 11:377–394

    CAS  Google Scholar 

  • Yousef M, Jung S, Kossenkov AV, Showe LC, Showe MK (2007) Naïve Bayes for microRNA target predictions-machine learning for microRNA targets. Bioinformatics 23:2987–2992

    CAS  Google Scholar 

  • Yu C-H, Dang Y, Zhou Z, Wu C, Zhao F, Sachs MS, Liu Y (2015) Codon usage influences the local rate of translation elongation to regulate co-translational protein folding. Mol Cell 59:744–754

    CAS  Google Scholar 

  • Zeng Z, Bromberg Y (2019) Predicting functional effects of synonymous variants: a systematic review and perspectives. Front Genet 10 Article 914

    Google Scholar 

  • Zeng K, Charlesworth B (2009) Estimating selection intensity on synonymous codon usage in a nonequilibrium population. Genetics 183:651–662, 651si–623si

    Google Scholar 

  • Zhang H, Zhang C, Li Z, Li C, Wei X, Zhang B, Liu Y (2019) A new method of RNA secondary structure prediction based on convolutional neural network and dynamic programming. Front Genet 10:467

    Google Scholar 

  • Zhao F, Yu C-H, Liu Y (2017) Codon usage regulates protein structure and function by affecting translation elongation speed in Drosophila cells. Nucleic Acids Res 45:8484–8492

    CAS  Google Scholar 

  • Zhao Q, Zhao Z, Fan X, Yuan Z, Mao Q, Yao Y (2021) Review of machine learning methods for RNA secondary structure prediction. PLoS Comput Biol 17:e1009291

    CAS  Google Scholar 

  • Zhou X, Zhou W, Wang C, Wang L, Jin Y, Jia Z, Liu Z, Zheng B (2021) A comprehensive analysis and splicing characterization of naturally occurring synonymous variants in the ATP7B gene. Front Genet 11:592611–592611

    Google Scholar 

  • Zichel R, Chearwae W, Pandey GS, Golding B, Sauna ZE (2012) Aptamers as a sensitive tool to detect subtle modifications in therapeutic proteins. PLoS One 7:e31948–e31948

    CAS  Google Scholar 

  • zu Siederdissen CH, Bernhart SH, Stadler PF, Hofacker IL (2011) A folding algorithm for extended RNA secondary structures. Bioinformatics 27:i129–i136

    Google Scholar 

  • Zucchelli E, Pema M, Stornaiuolo A, Piovan C, Scavullo C, Giuliani E, Bossi S, Corna S, Asperti C, Bordignon C et al (2017) Codon optimization leads to functional impairment of RD114-TR envelope glycoprotein. Mol Ther Methods Clin Dev 4:102–114

    CAS  Google Scholar 

  • Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31:3406–3415

    CAS  Google Scholar 

  • Zuker M, Stiegler P (1981) Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res 9:133–148

    CAS  Google Scholar 

Download references

Disclaimer

Our comments/our contributions are an informal communication and represent our own best judgement. These comments do not bind or obligate FDA.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Upendra K. Katneni .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Lin, B.C., Jankowska, K.I., Meyer, D., Katneni, U.K. (2022). Methods to Evaluate the Effects of Synonymous Variants. In: Sauna, Z.E., Kimchi-Sarfaty, C. (eds) Single Nucleotide Polymorphisms. Springer, Cham. https://doi.org/10.1007/978-3-031-05616-1_7

Download citation

Publish with us

Policies and ethics