Design of siRNAs Against Immune-Implicated Atherosclerosis Genes: Computational Study

Year 2024, , 12 - 18, 21.05.2024

Haitham Al-madhagi

https://doi.org/10.33435/tcandtc.1246320

Abstract

Atherosclerosis is a chronic, immune-implicated, disease with high numbers of mortality globally. The aim of the current study is to target these genes by specific siRNA utilizing bioinformatics tools. Eight siRNAs were designed via RNAxs from C1QA and ITBG2 gene sequences retrieved from NCBI database. GC% and Tm of siRNAs were calculated through OligoCalc web interface. In addition, hybridization energy of siRNAs with the corresponding target sequences as well as docking to argonaute 2 protein were performed using DuplexFold and HDock. The designed siRNAs exhibited acceptable GC content and Tm values. Besides, the hybridization energy and docking scores were highly significant to block the expression of the mentioned genes. In conclusion, the designed siRNAs are superior candidates for silencing immune-mediated atherosclerotic genes which deserve further consideration.

Keywords

Atherosclerosis, siRNA, gene silencing, RNA interference, docking.

Thanks

I would thank Hisham Al-Ward from Tianjin University for reviewing this manuscript.

References

• [1] Dorsett Y, Tuschl T. siRNAs: applications in functional genomics and potential as therapeutics. Nat Rev Drug Discov 2004;3:318–29. https://doi.org/10.1038/nrd1345.
• [2] Friedrich M, Aigner A. Therapeutic siRNA: State-of-the-Art and Future Perspectives. BioDrugs 2022;36:549–71. https://doi.org/10.1007/s40259-022-00549-3.
• [3] Stephan MT. Empowering patients from within: Emerging nanomedicines for in vivo immune cell reprogramming. Seminars in Immunology 2021;56:101537. https://doi.org/10.1016/j.smim.2021.101537.
• [4] Yang H, Che D, Gu Y, Cao D. Prognostic and immune-related value of complement C1Q (C1QA, C1QB, and C1QC) in skin cutaneous melanoma. Front Genet 2022;13:940306. https://doi.org/10.3389/fgene.2022.940306.
• [5] Zu L, He J, Zhou N, Zeng J, Zhu Y, Tang Q, et al. The Profile and Clinical Significance of ITGB2 Expression in Non-Small-Cell Lung Cancer. Journal of Clinical Medicine 2022;11:6421. https://doi.org/10.3390/jcm11216421.
• [6] Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Ostell J, Pruitt KD, et al. GenBank. Nucleic Acids Research 2018;46:D41–7.
• [7] Tafer H, Ameres SL, Obernosterer G, Gebeshuber CA, Schroeder R, Martinez J, et al. The impact of target site accessibility on the design of effective siRNAs. Nature Biotechnology 2008;26:578–83.
• [8] Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics 2009;10:1–9.
• [9] Kibbe WA. OligoCalc: an online oligonucleotide properties calculator. Nucleic Acids Research 2007;35:W43–6.
• [10] Bellaousov S, Reuter JS, Seetin MG, Mathews DH. RNAstructure: web servers for RNA secondary structure prediction and analysis. Nucleic Acids Research 2013;41:W471–4.
• [11] Yan Y, Zhang D, Zhou P, Li B, Huang S-Y. HDOCK: a web server for protein–protein and protein–DNA/RNA docking based on a hybrid strategy. Nucleic Acids Research 2017;45:W365–73.
• [12] Liu F, Tøstesen E, Sundet JK, Jenssen T-K, Bock C, Jerstad GI, et al. The Human Genomic Melting Map. PLoS Comput Biol 2007;3:e93. https://doi.org/10.1371/journal.pcbi.0030093.
• [13] Reynolds A, Leake D, Boese Q, Scaringe S, Marshall WS, Khvorova A. Rational siRNA design for RNA interference. Nature Biotechnology 2004;22:326–30.
• [14] Amarzguioui M, Prydz H. An algorithm for selection of functional siRNA sequences. Biochemical and Biophysical Research Communications 2004;316:1050–8.
• [15] Ishizuka A, Siomi MC, Siomi H. A Drosophila fragile X protein interacts with components of RNAi and ribosomal proteins. Genes & Development 2002;16:2497–508.
• [16] Sun Y, Gui J, Chen Z. The Influence of RNA Secondary Structure on Efficiency of siRNA Silencing. In: Long M, editor. World Congress on Medical Physics and Biomedical Engineering May 26-31, 2012, Beijing, China, Berlin, Heidelberg: Springer; 2013, p. 398–401. https://doi.org/10.1007/978-3-642-29305-4_106.
• [17] Xu J, Chen C, Yang Y. Identification and Validation of Candidate Gene Module Along With Immune Cells Infiltration Patterns in Atherosclerosis Progression to Plaque Rupture via Transcriptome Analysis. Frontiers in Cardiovascular Medicine 2022;9. https://doi.org/10.3389/fcvm.2022.894879.
• [18] Soehnlein O, Libby P. Targeting inflammation in atherosclerosis — from experimental insights to the clinic. Nat Rev Drug Discov 2021;20:589–610. https://doi.org/10.1038/s41573-021-00198-1.