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Synonymous Codon Variant Analysis for Autophagic Genes Dysregulated in Neurodegeneration

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Abstract

Neurodegenerative disorders are often a culmination of the accumulation of abnormally folded proteins and defective organelles. Autophagy is a process of removing these defective proteins, organelles, and harmful substances from the body, and it works to maintain homeostasis. If autophagic removal of defective proteins has interfered, it affects neuronal health. Some of the autophagic genes are specifically found to be associated with neurodegenerative phenotypes. Non-functional, mutated, or gene copies having silent mutations, often termed synonymous variants, might explain this. However, these synonymous variant which codes for exactly similar proteins have different translation rates, stability, and gene expression profiling. Hence, it would be interesting to study the pattern of synonymous variant usage. In the study, synonymous variant usage in various transcripts of autophagic genes ATG5, ATG7, ATG8A, ATG16, and ATG17/FIP200 reported to cause neurodegeneration (if dysregulated) is studied. These genes were analyzed for their synonymous variant usage; nucleotide composition; any possible nucleotide skew in a gene; physical properties of autophagic protein including GRAVY and AROMA; hydropathicity; instability index; and frequency of acidic, basic, neutral amino acids; and gene expression level. The study will help understand various evolutionary forces acting on these genes and the possible augmentation of a gene if showing unusual behavior.

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References

  1. Mizushima N, Yoshimori T, Ohsumi Y (2011) The role of Atg proteins in autophagosome formation. Annu Rev Cell Dev Biol 27:107–132

    CAS  PubMed  Google Scholar 

  2. Jiang Q, Zhao L, Dai J, Wu Q (2012) Analysis of autophagy genes in microalgae: chlorella as a potential model to study mechanism of autophagy. PLoS ONE 7(7):e41826

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Guo F, Liu X, Cai H, Le W (2018) Autophagy in neurodegenerative diseases: pathogenesis and therapy. Brain Pathol 28(1):3–13

    CAS  PubMed  Google Scholar 

  4. Park H, Kang JH, Lee S (2020) Autophagy in neurodegenerative diseases: a hunter for aggregates. Int J Mol Sci 21(9):E3369

    Google Scholar 

  5. Yuk JM, Yoshimori T, Jo EK (2012) Autophagy and bacterial infectious diseases. Exp Mol Med 44(2):99–108

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Ahmad L, Mostowy S, Sancho-Shimizu V (2018) Autophagy-virus interplay: from cell biology to human disease. Front Cell Dev Biol 6:155

    PubMed  PubMed Central  Google Scholar 

  7. Yun CW, Lee SH (2018) The roles of autophagy in cancer. Int J Mol Sci 19(11):E3466

    Google Scholar 

  8. Mulcahy Levy JM, Thorburn A (2020) Autophagy in cancer: moving from understanding mechanism to improving therapy responses in patients. Cell Death Differ 27(3):843–857

    PubMed  Google Scholar 

  9. Bravo-San Pedro JM, Kroemer G, Galluzzi L (2017) Autophagy and mitophagy in cardiovascular disease. Circ Res 120(11):1812–1824

    CAS  PubMed  Google Scholar 

  10. Schiattarella GG, Hill JA (2016) Therapeutic targeting of autophagy in cardiovascular disease. J Mol Cell Cardiol 95:86–93

    CAS  PubMed  Google Scholar 

  11. Zhang Y, Sowers JR, Ren J (2018) Targeting autophagy in obesity: from pathophysiology to management. Nat Rev Endocrinol 14(6):356–376

    CAS  PubMed  Google Scholar 

  12. Namkoong S, Cho CS, Semple I, Lee JH (2018) Autophagy dysregulation and obesity-associated pathologies. Mol Cells 41(1):3–10

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Choi AMK, Ryter SW, Levine B (2013) Autophagy in human health and disease. N Engl J Med 368(7):651–662

    CAS  PubMed  Google Scholar 

  14. Levine B, Mizushima N, Virgin HW (2011) Autophagy in immunity and inflammation. Nature 469(7330):323–335

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Murrow L, Debnath J (2013) Autophagy as a stress-response and quality-control mechanism: implications for cell injury and human disease. Annu Rev Pathol 24(8):105–137

    Google Scholar 

  16. Cherra SJ, Chu CT (2008) Autophagy in neuroprotection and neurodegeneration: a question of balance. Future Neurol 3(3):309–323

    PubMed  PubMed Central  Google Scholar 

  17. Ravikumar B, Vacher C, Berger Z, Davies JE, Luo S, Oroz LG et al (2004) Inhibition of mTOR induces autophagy and reduces toxicity of polyglutamine expansions in fly and mouse models of Huntington disease. Nat Genet 36(6):585–595

    CAS  PubMed  Google Scholar 

  18. Hara T, Nakamura K, Matsui M, Yamamoto A, Nakahara Y, Suzuki-Migishima R et al (2006) Suppression of basal autophagy in neural cells causes neurodegenerative disease in mice. Nature 441(7095):885–889

    CAS  PubMed  Google Scholar 

  19. Komatsu M, Waguri S, Chiba T, Murata S, Iwata JI, Tanida I et al (2006) Loss of autophagy in the central nervous system causes neurodegeneration in mice. Nature 441(7095):880–4

    CAS  PubMed  Google Scholar 

  20. Liang CC, Wang C, Peng X, Gan B, Guan JL (2010) Neural-specific deletion of FIP200 leads to cerebellar degeneration caused by increased neuronal death and axon degeneration. J Biol Chem 285(5):3499–3509

    CAS  PubMed  Google Scholar 

  21. Heckmann BL, Teubner BJW, Boada-Romero E, Tummers B, Guy C, Fitzgerald P et al (2020) Noncanonical function of an autophagy protein prevents spontaneous Alzheimer’s disease. Sci Adv 6(33):eabb9036

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Lipinski MM, Zheng B, Lu T, Yan Z, Py BF, Ng A et al (2010) Genome-wide analysis reveals mechanisms modulating autophagy in normal brain aging and in Alzheimer’s disease. Proc Natl Acad Sci U S A 107(32):14164–14169

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Simonsen A, Cumming RC, Brech A, Isakson P, Schubert DR, Finley KD (2008) Promoting basal levels of autophagy in the nervous system enhances longevity and oxidant resistance in adult Drosophila. Autophagy 4(2):176–184

    CAS  PubMed  Google Scholar 

  24. Quax TEF, Claassens NJ, Söll D, van der Oost J (2015) Codon bias as a means to fine-tune gene expression. Mol Cell 59(2):149–161

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Tuller T, Carmi A, Vestsigian K, Navon S, Dorfan Y, Zaborske J et al (2010) An evolutionarily conserved mechanism for controlling the efficiency of protein translation. Cell 141(2):344–354

    CAS  PubMed  Google Scholar 

  26. Purvis IJ, Bettany AJ, Santiago TC, Coggins JR, Duncan K, Eason R et al (1987) The efficiency of folding of some proteins is increased by controlled rates of translation in vivo. A hypothesis J Mol Biol 193(2):413–417

    CAS  PubMed  Google Scholar 

  27. Pechmann S, Frydman J (2013) Evolutionary conservation of codon optimality reveals hidden signatures of cotranslational folding. Nat Struct Mol Biol 20(2):237–243

    CAS  PubMed  Google Scholar 

  28. Gustafsson C, Govindarajan S, Minshull J (2004) Codon bias and heterologous protein expression. Trends Biotechnol 22(7):346–353

    CAS  PubMed  Google Scholar 

  29. Gingold H, Tehler D, Christoffersen NR, Nielsen MM, Asmar F, Kooistra SM et al (2014) A dual program for translation regulation in cellular proliferation and differentiation. Cell 158(6):1281–1292

    CAS  PubMed  Google Scholar 

  30. Neafsey DE, Galagan JE (2007) Positive selection for unpreferred codon usage in eukaryotic genomes. BMC Evol Biol 18(7):119

    Google Scholar 

  31. Arella D, Dilucca M, Giansanti A (2021) Codon usage bias and environmental adaptation in microbial organisms. Mol Genet Genomics 296(3):751–762

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Galtier N, Roux C, Rousselle M, Romiguier J, Figuet E, Glémin S et al (2018) Codon usage bias in animals: disentangling the effects of natural selection, effective population size, and GC-biased gene conversion. Mol Biol Evol 35(5):1092–1103

    CAS  PubMed  Google Scholar 

  33. Plotkin JB, Robins H, Levine AJ (2004) Tissue-specific codon usage and the expression of human genes. Proc Natl Acad Sci U S A 101(34):12588–12591

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Payne BL, Alvarez-Ponce D (2019) Codon usage differences among genes expressed in different tissues of Drosophila melanogaster. Genome Biol Evol 11(4):1054–1065

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Allen SR, Stewart RK, Rogers M, Ruiz IJ, Cohen E, Laederach A et al (2022) Distinct responses to rare codons in select Drosophila tissues. Elife 6(11):e76893

    Google Scholar 

  36. Goodman DB, Church GM, Kosuri S (2013) Causes and effects of N-terminal codon bias in bacterial genes. Science 342(6157):475–479

    CAS  PubMed  Google Scholar 

  37. Miller JB, Brase LR, Ridge PG (2019) ExtRamp: a novel algorithm for extracting the ramp sequence based on the tRNA adaptation index or relative codon adaptiveness. Nucleic Acids Res 47(3):1123–1131

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Esposito E, Weidemann DE, Rogers JM, Morton CM, Baybay EK, Chen J et al (2022) Mitotic checkpoint gene expression is tuned by codon usage bias. EMBO J 41(15):e107896

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Fu H, Liang Y, Zhong X, Pan Z, Huang L, Zhang H et al (2020) Codon optimization with deep learning to enhance protein expression. Sci Rep 10(1):17617

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Lorenzo MM, Nogales A, Chiem K, Blasco R, Martínez-Sobrido L (2022) Vaccinia virus attenuation by codon deoptimization of the A24R gene for vaccine development. Microbiol Spectr 10(3):e0027222

    PubMed  Google Scholar 

  41. Ullah S, Ross TM (2022) Next generation live-attenuated influenza vaccine platforms. Expert Rev Vaccines 21(8):1097–1110

    CAS  PubMed  Google Scholar 

  42. Khandia R, Ali Khan A, Alexiou A, Povetkin SN, Verevkina MN (2022) Codon usage analysis of pro-apoptotic Bim gene isoforms. J Alzheimers Dis 86(4):1711–1725. https://doi.org/10.3233/JAD-215691

    Article  CAS  PubMed  Google Scholar 

  43. Ingusci S, Verlengia G, Soukupova M, Zucchini S, Simonato M (2019) Gene therapy tools for brain diseases. Front Pharmacol 10:724

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Karlin S, Mrázek J, Campbell AM (1998) Codon usages in different gene classes of the Escherichia coli genome. Mol Microbiol 29(6):1341–1355

    CAS  PubMed  Google Scholar 

  45. Camiolo S, Sablok G, Porceddu A (2017) The evolutionary basis of translational accuracy in plants. G3 Bethesda 7(7):2363–73

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 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(3):1281–1295

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Munjal A, Khandia R, Shende KK, Das J (2020) Mycobacterium lepromatosis genome exhibits unusually high CpG dinucleotide content and selection is key force in shaping codon usage. Infect Genet Evol 84:104399

    CAS  PubMed  Google Scholar 

  48. Yang X, Luo X, Cai X (2014) Analysis of codon usage pattern in Taenia saginata based on a transcriptome dataset. Parasit Vectors 2(7):527

    Google Scholar 

  49. Mirsafian H, Mat Ripen A, Singh A, Teo PH, Merican AF, Mohamad SB (2014) A comparative analysis of synonymous codon usage bias pattern in human albumin superfamily. ScientificWorldJournal 2014:639682

    PubMed  PubMed Central  Google Scholar 

  50. Wright F (1990) The, “effective number of codons” used in a gene. Gene 87(1):23–29

    CAS  PubMed  Google Scholar 

  51. Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157(1):105–132

    CAS  PubMed  Google Scholar 

  52. Khandia R, Alqahtani T, Alqahtani AM (2021) Genes common in primary immunodeficiencies and cancer display overrepresentation of codon CTG and dominant role of selection pressure in shaping codon usage. Biomedicines 9(8):1001

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, et al (2005) Protein identification and analysis tools on the ExPASy Server. In: Walker JM, editor. The Proteomics Protocols Handbook [Internet]. Totowa, NJ: Humana Press; 2005 [cited 2022 Apr 13]. p. 571–607. (Springer Protocols Handbooks). Available from: https://doi.org/10.1385/1-59259-890-0:571

  54. Hammer O, Harper DAT, Ryan PD. PAST: paleontological statistics software package for education and data analysis. :9.

  55. Piszter G, Kertész K, Bálint Z, Biró LP (2020) Stability and selective vapor sensing of structurally colored lepidopteran wings under humid conditions. Sensors (Basel) 20(11):E3258

    Google Scholar 

  56. Hernández-Barreto DF, Giraldo L, Moreno-Piraján JC (2020) Dataset on adsorption of phenol onto activated carbons: equilibrium, kinetics and mechanism of adsorption. Data Brief 32:106312

    PubMed  PubMed Central  Google Scholar 

  57. Moura G, Pinheiro M, Arrais J, Gomes AC, Carreto L, Freitas A et al (2007) Large scale comparative codon-pair context analysis unveils general rules that fine-tune evolution of mRNA primary structure. PLoS ONE 2(9):e847

    PubMed  PubMed Central  Google Scholar 

  58. Kim DJ, Kim J, Lee DH, Lee J, Woo HM (2022) DeepTESR: a deep learning framework to predict the degree of translational elongation short ramp for gene expression control. ACS Synth Biol 11(5):1719–1726

    CAS  PubMed  Google Scholar 

  59. Miller JB, Meurs TE, Hodgman MW, Song B, Miller KN, Ebbert MTW et al (2022) The ramp atlas: facilitating tissue and cell-specific ramp sequence analyses through an intuitive web interface. NAR Genom Bioinform 4(2):Iqac039

    Google Scholar 

  60. Zahdeh F, Carmel L (2019) Nucleotide composition affects codon usage toward the 3’-end. PLoS ONE 14(12):e0225633

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Zhang J, Wang M, Liu WQ, Zhou JH, Chen HT, Ma LN et al (2011) Analysis of codon usage and nucleotide composition bias in polioviruses. Virol J 8:146

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Deka H, Chakraborty S (2014) Compositional constraint is the key force in shaping codon usage bias in hemagglutinin gene in H1N1 subtype of influenza A virus. Int J Genomics 2014:349139

    PubMed  PubMed Central  Google Scholar 

  63. Di Giallonardo F, Schlub TE, Shi M, Holmes EC (2017) Dinucleotide composition in animal RNA viruses is shaped more by virus family than by host species. J Virol 91(8):e02381-e2416

    PubMed  PubMed Central  Google Scholar 

  64. Alqahtani T, Khandia R, Puranik N, Alqahtani AM, Almikhlafi MA, Algahtany MA (2021) Leucine encoding codon TTG shows an inverse relationship with GC content in genes involved in neurodegeneration with iron accumulation. J Integr Neurosci 20(4):905–918

    PubMed  Google Scholar 

  65. Barbhuiya PA, Uddin A, Chakraborty S (2019) Compositional properties and codon usage of TP73 gene family. Gene 30(683):159–168

    Google Scholar 

  66. Elhaik E (2022) Principal component analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated. Sci Rep 12(1):14683

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Lazaridis I, Nadel D, Rollefson G, Merrett DC, Rohland N, Mallick S et al (2016) Genomic insights into the origin of farming in the ancient Near East. Nature 536(7617):419–424

    CAS  PubMed  PubMed Central  Google Scholar 

  68. de Freire CCM, Palmisano G, Braconi CT, Cugola FR, Russo FB, Beltrão-Braga PC et al (2018) NS1 codon usage adaptation to humans in pandemic Zika virus. Mem Inst Oswaldo Cruz 113(5):e170385

    PubMed  PubMed Central  Google Scholar 

  69. Hassan S, Mahalingam V, Kumar V (2009) Synonymous codon usage analysis of thirty two mycobacteriophage genomes. Adv Bioinformatics 2009:316936

    PubMed  Google Scholar 

  70. Mazumder TH, Chakraborty S (2015) Gaining insights into the codon usage patterns of TP53 gene across eight mammalian species. PLoS ONE 10(3):e0121709

    PubMed  PubMed Central  Google Scholar 

  71. Wang L, Xing H, Yuan Y, Wang X, Saeed M, Tao J et al (2018) Genome-wide analysis of codon usage bias in four sequenced cotton species. PLoS ONE 13(3):e0194372

    PubMed  PubMed Central  Google Scholar 

  72. Zhou Z, Dang Y, Zhou M, Li L, Yu CH, Fu J et al (2016) Codon usage is an important determinant of gene expression levels largely through its effects on transcription. Proc Natl Acad Sci U S A 113(41):E6117–E6125

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Sahoo S, Das SS, Rakshit R (2019) Codon usage pattern and predicted gene expression in Arabidopsis thaliana. Gene 721S:100012

    PubMed  Google Scholar 

  74. Yannai A, Katz S, Hershberg R (2018) The codon usage of lowly expressed genes is subject to natural selection. Genome Biol Evol 10(5):1237–1246

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Deb B, Uddin A, Chakraborty S (2020) Codon usage pattern and its influencing factors in different genomes of hepadnaviruses. Arch Virol 165(3):557–570

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Butt AM, Nasrullah I, Qamar R, Tong Y (2016) Evolution of codon usage in Zika virus genomes is host and vector specific. Emerg Microbes Infect 5(10):e107

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Forcelloni S, Giansanti A (2020) Evolutionary forces and codon bias in different flavors of intrinsic disorder in the human proteome. J Mol Evol 88(2):164–178

    CAS  PubMed  Google Scholar 

  78. Majeed A, Kaur H, Bhardwaj P (2020) Selection constraints determine preference for A/U-ending codons in Taxus contorta. Genome 63(4):215–224

    CAS  PubMed  Google Scholar 

  79. Yap CC, Winckler B (2012) Harnessing the power of the endosome to regulate neural development. Neuron 74(3):440–451

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Guo T, Nan Z, Miao C, Jin X, Yang W, Wang Z et al (2019) The autophagy-related gene Atg101 in Drosophila regulates both neuron and midgut homeostasis. J Biol Chem 294(14):5666–5676

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Lehtonen Š, Sonninen TM, Wojciechowski S, Goldsteins G, Koistinaho J (2019) Dysfunction of cellular proteostasis in Parkinson’s disease. Front Neurosci 13:457

    PubMed  PubMed Central  Google Scholar 

  82. Djajadikerta A, Keshri S, Pavel M, Prestil R, Ryan L, Rubinsztein DC (2020) Autophagy induction as a therapeutic strategy for neurodegenerative diseases. J Mol Biol 432(8):2799–2821

    CAS  PubMed  Google Scholar 

  83. Grishkevich V, Yanai I (2014) Gene length and expression level shape genomic novelties. Genome Res 24(9):1497–1503

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Norkiene M, Gedvilaite A (2012) Influence of codon bias on heterologous production of human papillomavirus type 16 major structural protein L1 in yeast. ScientificWorldJournal 2012:979218

    PubMed  PubMed Central  Google Scholar 

  85. Chamary JV, Hurst LD (2005) Evidence for selection on synonymous mutations affecting stability of mRNA secondary structure in mammals. Genome Biol 6(9):R75

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Shen X, Song S, Li C, Zhang J (2022) Synonymous mutations in representative yeast genes are mostly strongly non-neutral. Nature 606(7915):725–731

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Plotkin JB, Kudla G (2011) Synonymous but not the same: the causes and consequences of codon bias. Nat Rev Genet 12(1):32–42

    CAS  PubMed  Google Scholar 

  88. Davis JJ, Olsen GJ (2010) Modal codon usage: assessing the typical codon usage of a genome. Mol Biol Evol 27(4):800–810

    CAS  PubMed  Google Scholar 

  89. Beutler E, Gelbart T, Han JH, Koziol JA, Beutler B (1989) Evolution of the genome and the genetic code: selection at the dinucleotide level by methylation and polyribonucleotide cleavage. Proc Natl Acad Sci U S A 86(1):192–196

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Peifer M, Karro JE, von Grünberg HH (2008) Is there an acceleration of the CpG transition rate during the mammalian radiation? Bioinformatics 24(19):2157–2164

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Blake RD, Hess ST, Nicholson-Tuell J (1992) The influence of nearest neighbors on the rate and pattern of spontaneous point mutations. J Mol Evol 34(3):189–200

    CAS  PubMed  Google Scholar 

  92. Hodgman MW, Miller JB, Meurs TE, Kauwe JSK (2020) CUBAP: an interactive web portal for analyzing codon usage biases across populations. Nucleic Acids Res 48(19):11030–11039

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Miller JB, McKinnon LM, Whiting MF, Kauwe JSK, Ridge PG (2020) Codon pairs are phylogenetically conserved: a comprehensive analysis of codon pairing conservation across the tree of life. PLoS ONE 15(5):e0232260

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Irwin B, Heck JD, Hatfield GW (1995) Codon pair utilization biases influence translational elongation step times. J Biol Chem 270(39):22801–22806

    CAS  PubMed  Google Scholar 

  95. Huang Y, Lin T, Lu L, Cai F, Lin J, Jiang YE et al (2021) Codon pair optimization (CPO): a software tool for synthetic gene design based on codon pair bias to improve the expression of recombinant proteins in Pichia pastoris. Microb Cell Fact 20(1):209

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Kunec D, Osterrieder N, Trimpert J (2022) Synthetically recoded virus sCPD9 - a tool to accelerate SARS-CoV-2 research under biosafety level 2 conditions. Comput Struct Biotechnol J 20:4376–4380

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Groenke N, Trimpert J, Merz S, Conradie AM, Wyler E, Zhang H et al (2020) Mechanism of virus attenuation by codon pair deoptimization. Cell Rep 31(4):107586

    CAS  PubMed  Google Scholar 

  98. Miller JB, Hippen AA, Belyeu JR, Whiting MF, Ridge PG (2017) Missing something? Codon aversion as a new character system in phylogenetics. Cladistics 33(5):545–556

    PubMed  Google Scholar 

  99. Brest P, Lapaquette P, Souidi M, Lebrigand K, Cesaro A, Vouret-Craviari V 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(3):242–245

    CAS  PubMed  Google Scholar 

  100. Behura SK, Severson DW (2012) Comparative analysis of codon usage bias and codon context patterns between dipteran and hymenopteran sequenced genomes. PLoS ONE 7(8):e43111

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Ahmed W, Gupta S, Mukherjee I, Babu V, Singh R (2022) Comparative studies of codon usage profile of Anisakis simplex (Nematoda) and Carassius gibelio (Prussian carp). J Environ Biol 7(43):123–132

    Google Scholar 

  102. Wang P, Mao Y, Su Y, Wang J (2020) Comparative analysis of the codon usage patterns in two closely related Marsupenaeus species based on comparative transcriptomics

  103. Fei YJ, Stoming TA, Kutlar A, Huisman TH, Stamatoyannopoulos G (1989) One form of inclusion body beta-thalassemia is due to a GAA––TAA mutation at codon 121 of the beta chain. Blood 73(4):1075–1077

    CAS  PubMed  Google Scholar 

  104. Sørensen MA, Pedersen S (1991) Absolute in vivo translation rates of individual codons in Escherichia coli. The two glutamic acid codons GAA and GAG are translated with a threefold difference in rate. J Mol Biol 222(2):265–80

    PubMed  Google Scholar 

  105. Malakar AK, Halder B, Paul P, Chakraborty S (2016) Cytochrome P450 genes in coronary artery diseases: codon usage analysis reveals genomic GC adaptation. Gene 590(1):35–43

    CAS  PubMed  Google Scholar 

  106. Nath Choudhury M, Uddin A, Chakraborty S (2017) Codon usage bias and its influencing factors for Y-linked genes in human. Comput Biol Chem 69:77–86

    CAS  PubMed  Google Scholar 

  107. Gupta SK, Ghosh TC (2001) Gene expressivity is the main factor in dictating the codon usage variation among the genes in Pseudomonas aeruginosa. Gene 273(1):63–70

    CAS  PubMed  Google Scholar 

  108. Hou ZC, Yang N (2003) Factors affecting codon usage in Yersinia pestis. Sheng Wu Hua Xue Yu Sheng Wu Wu Li Xue Bao (Shanghai) 35(6):580–586

    CAS  PubMed  Google Scholar 

  109. Moriyama EN, Powell JR (1998) Gene length and codon usage bias in Drosophila melanogaster, Saccharomyces cerevisiae and Escherichia coli. Nucleic Acids Res 26(13):3188–3193

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Eyre-Walker A (1996) Synonymous codon bias is related to gene length in Escherichia coli: selection for translational accuracy? Mol Biol Evol 13(6):864–872

    CAS  PubMed  Google Scholar 

  111. Puigbò P, Bravo IG, Garcia-Vallve S (2008) CAIcal: a combined set of tools to assess codon usage adaptation. Biol Direct 16(3):38

    Google Scholar 

  112. Trotta E (2013) Selection on codon bias in yeast: a transcriptional hypothesis. Nucleic Acids Res 41(20):9382–9395

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Zhou T, Weems M, Wilke CO (2009) Translationally optimal codons associate with structurally sensitive sites in proteins. Mol Biol Evol 26(7):1571–1580

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Uddin A, Mazumder TH, Barbhuiya PA, Chakraborty S (2020) Similarities and dissimilarities of codon usage in mitochondrial ATP genes among fishes, aves, and mammals. IUBMB Life 72(5):899–914

    CAS  PubMed  Google Scholar 

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Acknowledgements

We acknowledge the respective universities and institutes for providing support for the study.

Funding

This work was funded by the researchers supporting project number (RSP-2023R339) King Saud University, Riyadh, Saudi Arabia.

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Authors and Affiliations

  1. Department of Biochemistry and Genetics, Barkatullah University, Bhopal, 462026, India

    Rekha Khandia

  2. Department of Translational Medicine, All India Institute of Medical Sciences, Bhopal, 462020, India

    Megha Katare Pandey

  3. Medical and Biological Faculty, North Caucasus Federal University, Stavropol, Russia

    Igor Vladimirovich Rzhepakovsky

  4. Pharmaceutical Biotechnology Laboratory, Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh, 11451, Saudi Arabia

    Azmat Ali Khan

  5. Novel Global Community Educational Foundation, Hebersham, Australia

    Athanasios Alexiou

  6. AFNP Med, Wien, Austria

    Athanasios Alexiou

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  1. Rekha Khandia
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  2. Megha Katare Pandey
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  4. Azmat Ali Khan
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Contributions

The manuscript was written through the contributions of all authors. All authors have approved the final version of the manuscript. RK, conception and supervision; RK, MP, AAK, and AA, writing—reviewing and editing; RK and AAK, interpretation of data and data curation; RK and MP, writing—reviewing and editing. IVR contributed significantly during revisions. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript and take responsibility of the work.

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Correspondence to Rekha Khandia or Azmat Ali Khan.

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Khandia, R., Pandey, M.K., Rzhepakovsky, I.V. et al. Synonymous Codon Variant Analysis for Autophagic Genes Dysregulated in Neurodegeneration. Mol Neurobiol 60, 2252–2267 (2023). https://doi.org/10.1007/s12035-022-03081-1

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