Biology, Medicine, & Natural Product Chemistry ISSN 2089-6514 (paper) Volume 14, Number 2, October 2025 | Pages: 921-926 | DOI: 10.14421/biomedich.2025.142.921-926 ISSN 2540-9328 (online) siRNAs Targeting icaD Gene of Staphylococcus aureus to Inhibit Biofilm Formation: Structural Analysis and Efficacy Dinda Ananda Sulistina1, Rian Ka Praja2*, Margaretha Yayu Indah Anugerahny3, Hanasia2, Ysrafil4 1Undergraduate Program of Medicine, Faculty of Medicine, Universitas Palangka Raya, Indonesia. 2Department of Microbiology, Faculty of Medicine, Universitas Palangka Raya, Indonesia. 3Department of Clinical Medicine, Faculty of Medicine, Universitas Palangka Raya, Indonesia. 4Department of Pharmacotherapy, Faculty of Medicine, Universitas Palangka Raya, Indonesia. Corresponding author* riankapraja@med.upr.ac.id Abstract Antibiotic resistance in Staphylococcus aureus infections, especially those involving biofilm formation, is a global health issue. Biofilm protects bacteria from the immune system and antibiotic treatment, making them 10 to 1000 times more resistant. The icaD gene, part of the ica operon, is crucial for biofilm synthesis by enhancing the enzymes responsible for forming the biofilm matrix. The icaD gene sequence of Staphylococcus aureus was obtained from the GenBank NCBI database with the accession code CP140612.1, with a gene sequence length of 306 bp and employed several bioinformatics methods, including siDirect for designing and evaluating effective siRNA sequences to select the most promising candidates. Additionally, siRNA Scales, MaxExpect, Duplex Fold, and siPred were employed to analyze the siRNA sequence length, secondary structure, binding energy, and efficacy predictions of siRNAs targeting the icaD gene. The study found that out of 54 siRNA candidates, siRNA22, siRNA50, and siRNA25 achieved inhibition rates of 93.69%, 92.82%, and 92.52%, respectively. These results bioinformatically demonstrated their potential to suppress the expression of the icaD gene and highlight their promise as siRNA-based antibacterial therapies to combat biofilm-related infections. The designed siRNA computationally shows potential as an innovative therapy to combat biofilm infections caused by Staphylococcus aureus. Keywords: Staphylococcus aureus; Biofilm; icaD Gene; siRNA; Antibiotic Resistance. INTRODUCTION Antimicrobial resistance (AMR) is one of the top global to public health and development. It is estimated that bacterial AMR was directly responsible for 1.27 million global deaths in 2019 and contributed to 4.95 million deaths (Murray et al., 2022). Staphylococcus aureus (S. aureus), which often forms biofilms, is becoming increasingly difficult to treat, especially strains resistant to methicillin (MRSA). Bacterial biofilms protect them from the immune system and antibiotics, making them 10-1000 times more resistant (Tran et al., 2023). Biofilm formation in S. aureus is encoded by several genes, including icaA, icaB, icaC, and icaD, which are involved in the biofilm matrix formation (Peng et al., 2023). One promising therapeutic approach is the use of small interfering RNA (siRNA) to inhibit the expression of the icaD gene, which plays a role in biofilm formation. siRNA can regulate gene expression through the RNA interference (RNAi) mechanism, which targets mRNA and inhibits the translation of specific proteins. With the advancement of bioinformatics technology, the analysis of siRNA structure and efficacy has become easier. Previously, siRNA has been used for antiviral therapy, such as for hepatitis C and Zika virus infections (Perez- Mendez et al., 2020). This study aimed to explore, analyze the structure, and predict the efficacy of siRNA to suppress the expression of the icaD gene in S. aureus as a strategy to combat infections caused by bacterial biofilms. MATERIALS AND METHODS Study area The study took place in Faculty of Medicine Palangka Raya University between June and Desember 2024. Procedures Retrieval of the icaD gene sequence The downloaded FASTA sequence of the icaD gene from Staphylococcus aureus (NCBI Accession Number: CP140612.1) was renamed accordingly and stored in a dedicated folder for further analysis. The sequence was then utilized to perform several computational analyses, including the design of siRNA molecules targeting the icaD gene, analysis of mRNA length percentage, prediction of the secondary structure of the designed Manuscript received: 08 June, 2025. Revision accepted: 26 October, 2025. Published: 28 October, 2025. https://doi.org/10.14421/biomedich.2025.142.921-926 mailto:riankapraja@med.upr.ac.id 922 Biology, Medicine, & Natural Product Chemistry 14 (2), 2025: 921-926 siRNAs, calculation of binding energies between siRNA and target mRNA, and prediction of siRNA efficacy in silencing the icaD transcript. These analyses were conducted using appropriate bioinformatics tools and databases to ensure the accuracy and reliability of the results. Design of Gene-Silencing siRNA Molecules The icaD gene sequence of Staphylococcus aureus was obtained from the GenBank NCBI database with the accession code CP140612.1, The gene sequence is 306 bp long and employs several bioinformatics methods. The design of siRNA to silence the S. aureus icaD gene was performed using siDirect (http://siDirect2.RNAi.jp/). Percentage Analysis of mRNA Length Percentage analysis of mRNA length was performed using siRNA Scales, a software that predicts the location of the remaining mRNA sequence in the cell after siRNA cleavage (http://gesteland.genetics.utah.edu/siRNA_scales/). Analysis of the Secondary Structure of siRNA Secondary structure analysis of siRNA was done using MaxExpect and could be accessed through a website https://rna.urmc.rochester.edu/RNAstructureWeb/Servers /MaxExpect/MaxExpect.html. Subsequently, the 21nt guide RNA oligo sequences (5′→3′) obtained from siRNA design analysis using siDirect were submitted. Analysis of the Binding Energy Between siRNA and Its Target mRNA Analysis of the binding energy between siRNA and the S. aureus icaD gene target was done using DuplexFold (https://rna.urmc.rochester.edu/RNAstructureWeb/Server s/DuplexFold/DuplexFold.html). The software showed the analysis results, including the binding energy and a predicted secondary RNA structure diagram. Efficacy Prediction of siRNA Molecules Prediction of siRNA efficacy against the icaD target in S. aureus was performed using siPRED (http://predictor.nchu.edu.tw/siPRED/), then the software displayed predicted position and inhibitory effects of the siRNA strand on the icaD gene. RESULTS AND DISCUSSION Results and Discussion should be written as a series of connecting sentences, however, for manuscript with long discussion should be divided into subtitles. Results should be clear and concise. Result of the E xploration of siRNA Targeting the icaD Antigen of Staphylococcus aureus Fifty-four siRNA candidates were successfully designed based on the complete genome of the icaD gene from S. aureus (NCBI Accession Number: CP140612.1). The analysis of target locations, target region length, binding, and inhibition of the antigen sequences in these fifty-four siRNA candidates targeting the icaD antigen of Staphylococcus aureus resulted in data as shown in Table 1 below. Table 1. The analysis of target siRNA. No Target Position RNA oligo sequences 21nt guide (5′→3′) RNA oligo sequences 21nt passenger (5′→3′) siRNA Scales Max Expect Duplex Fold siRNA Efficacy 1 10-32 UGGGUAUUCCCUCUGUCUGGG CAGACAGAGGGAAUACCCAAC 14 1.4 -38.2 74.17 2 16-38 UAGCGUUGGGUAUUCCCUCUG GAGGGAAUACCCAACGCUAAA 7 1.9 -37.4 74.54 3 18-40 UUUAGCGUUGGGUAUUCCCUC GGGAAUACCCAACGCUAAAAU 11 2.0 -34.7 None 4 19-41 UUUUAGCGUUGGGUAUUCCCU GGAAUACCCAACGCUAAAAUC 11 2.0 -31.7 None 5 20-42 AUUUUAGCGUUGGGUAUUCCC GAAUACCCAACGCUAAAAUCA 25 1.8 -29.0 None 6 30-52 UUUAGCGAUGAUUUUAGCGUU CGCUAAAAUCAUCGCUAAACA 6 1.5 -28.5 75.9 7 32-54 UGUUUAGCGAUGAUUUUAGCG CUAAAAUCAUCGCUAAACAUU 17 1.9 -26.9 70.01 8 42-64 UCUCUUAUAAUGUUUAGCGAU CGCUAAACAUUAUAAGAGAAA 10 1.6 -28.2 85.79 9 43-65 UUCUCUUAUAAUGUUUAGCGA GCUAAACAUUAUAAGAGAAAC 2 1.7 -27.3 85.79 10 44-66 UUUCUCUUAUAAUGUUUAGCG CUAAACAUUAUAAGAGAAACA 14 1.8 -23.2 None 11 49-71 UGCUGUUUCUCUUAUAAUGUU CAUUAUAAGAGAAACAGCACU 12 1.7 -27.9 86.47 12 59-81 UAGCGAUAAGUGCUGUUUCUC GAAACAGCACUUAUCGCUAUA 8 1.9 -32.5 82.54 13 65-87 ACGAUAUAGCGAUAAGUGCUG GCACUUAUCGCUAUAUCGUGU 14 1.9 -32.5 80.71 14 73-95 AAAGACACACGAUAUAGCGAU CGCUAUAUCGUGUGUCUUUUG 14 1.9 -31.2 83.94 15 74-96 AAAAGACACACGAUAUAGCGA GCUAUAUCGUGUGUCUUUUGG 9 1.9 -30.9 83.94 16 81-103 UAUAUCCAAAAGACACACGAU CGUGUGUCUUUUGGAUAUAUU 5 1.8 -30.1 80.02 17 82-104 AUAUAUCCAAAAGACACACGA GUGUGUCUUUUGGAUAUAUUG 14 1.8 -28.9 80.02 18 86-108 AACAAUAUAUCCAAAAGACAC GUCUUUUGGAUAUAUUGUUUA 26 1.9 -26.4 87.55 19 88-110 UAAACAAUAUAUCCAAAAGAC CUUUUGGAUAUAUUGUUUAGU 15 1.9 -24.1 80.08 20 93-115 ACAACUAAACAAUAUAUCCAA GGAUAUAUUGUUUAGUUGUUC 19 1.8 -26.6 85.78 21 94-116 AACAACUAAACAAUAUAUCCA GAUAUAUUGUUUAGUUGUUCU 11 1.8 -23.3 84.44 22 102-124 ACGAGUAGAACAACUAAACAA GUUUAGUUGUUCUACUCGUUU 20 1.7 -30.0 93.69 23 107-129 UAUAAACGAGUAGAACAACUA GUUGUUCUACUCGUUUAUAUU 12 1.8 -28.1 83.92 http://sidirect2.rnai.jp/ http://gesteland.genetics.utah.edu/siRNA_scales/ https://rna.urmc.rochester.edu/RNAstructureWeb/Servers/MaxExpect/MaxExpect.html https://rna.urmc.rochester.edu/RNAstructureWeb/Servers/MaxExpect/MaxExpect.html https://rna.urmc.rochester.edu/RNAstructureWeb/Servers/DuplexFold/DuplexFold.html https://rna.urmc.rochester.edu/RNAstructureWeb/Servers/DuplexFold/DuplexFold.html http://predictor.nchu.edu.tw/siPRED/ Sulistina et al. – siRNAs Targeting icaD Gene of Staphylococcus aureus to Inhibit Biofilm 923 No Target Position RNA oligo sequences 21nt guide (5′→3′) RNA oligo sequences 21nt passenger (5′→3′) siRNA Scales Max Expect Duplex Fold siRNA Efficacy 24 113-135 UACCAAUAUAAACGAGUAGAA CUACUCGUUUAUAUUGGUACU 16 1.8 -28.1 92.07 25 116-138 UAGUACCAAUAUAAACGAGUA CUCGUUUAUAUUGGUACUAUA 15 1.8 -27.7 92.52 26 118-140 UAUAGUACCAAUAUAAACGAG CGUUUAUAUUGGUACUAUAUU -3 1.7 -26.1 82.05 27 119-141 AUAUAGUACCAAUAUAAACGA GUUUAUAUUGGUACUAUAUUU 14 1.6 -24.9 82.05 28 129-151 UGAAUUUCAAAUAUAGUACCA GUACUAUAUUUGAAAUUCAUG 15 1.8 -24.0 77.03 29 132-154 UCAUGAAUUUCAAAUAUAGUA CUAUAUUUGAAAUUCAUGACG 16 1.8 -23.6 85.43 30 140-162 UACUUUCGUCAUGAAUUUCAA GAAAUUCAUGACGAAAGUAUC 14 1.6 -28.4 91.93 31 146-168 UAUUGAUACUUUCGUCAUGAA CAUGACGAAAGUAUCAAUACA 12 1.8 -28.0 82.7 32 149-171 UUGUAUUGAUACUUUCGUCAU GACGAAAGUAUCAAUACAAUA 8 1.8 -28.6 88.71 33 151-173 UAUUGUAUUGAUACUUUCGUC CGAAAGUAUCAAUACAAUACG 9 1.6 -25.2 None 34 156-178 ACACGUAUUGUAUUGAUACUU GUAUCAAUACAAUACGUGUUG 23 1.9 -27.8 83.19 35 160-162 AGCAACACGUAUUGUAUUGAU CAAUACAAUACGUGUUGCUUU 26 1.4 -29.2 88.89 36 165-187 UUUAAAGCAACACGUAUUGUA CAAUACGUGUUGCUUUAAACA 5 1.8 -26.4 80.67 37 171-193 UCAAUGUUUAAAGCAACACGU GUGUUGCUUUAAACAUUGAAA 9 1.5 -28.6 74.8 38 173-195 UUUCAAUGUUUAAAGCAACAC GUUGCUUUAAACAUUGAAAAU 17 1.5 -26.1 73.85 39 176-198 UAUUUUCAAUGUUUAAAGCAA GCUUUAAACAUUGAAAAUACU 9 1.8 -23.9 84.08 40 184-206 AAUUUCAGUAUUUUCAAUGUU CAUUGAAAAUACUGAAAUUUU 6 1.8 -22.4 73.05 41 195-217 AAUAUAUCUAAAAUUUCAGUA CUGAAAUUUUAGAUAUAUUUG 17 1.8 -20.9 71.95 42 224-246 UGAUAAUCGCGAAAAUGCCCA GGCAUUUUCGCGAUUAUCAUU 16 1.9 -31.3 None 43 225-247 AUGAUAAUCGCGAAAAUGCCC GCAUUUUCGCGAUUAUCAUUU 16 1.9 -28.6 None 44 226-248 AAUGAUAAUCGCGAAAAUGCC CAUUUUCGCGAUUAUCAUUUU 18 1.9 -25.7 None 45 232-254 AACAAAAAUGAUAAUCGCGAA CGCGAUUAUCAUUUUUGUUUU 18 1.8 -26.0 87.89 46 233-255 AAACAAAAAUGAUAAUCGCGA GCGAUUAUCAUUUUUGUUUUU 12 1.8 -25.1 82.24 47 234-256 AAAACAAAAAUGAUAAUCGCG CGAUUAUCAUUUUUGUUUUUU 4 1.8 -21.3 None 48 235-257 AAAAACAAAAAUGAUAAUCGC GAUUAUCAUUUUUGUUUUUUU 14 1.8 -21.1 None 49 241-263 UGUAAAAAAAACAAAAAUGAU CAUUUUUGUUUUUUUUACAAU 14 1.8 -20.3 85.58 50 248-270 UGCUAAUUGUAAAAAAAACAA GUUUUUUUUACAAUUAGCAUA 21 1.6 -23.6 92.82 51 258-280 UGAAUCAAUAUGCUAAUUGUA CAAUUAGCAUAUUGAUUCAAA 5 1.5 -26.1 84.7 52 271-293 UUUCUGCCAUUUUUGAAUCAA GAUUCAAAAAUGGCAGAAAGG 9 1.7 -28.7 79.92 53 275-297 UUCCUUUCUGCCAUUUUUGAA CAAAAAUGGCAGAAAGGAAGA 10 1.8 -30.0 87.94 54 284-306 ACGAUUCUCUUCCUUUCUGCC CAGAAAGGAAGAGAAUCGUGA 28 1.8 -31.6 71.35 *Top 3 siRNAs with highest efficacy was highlighted with bold font. Results of the Structural Analysis of the Optimal icaD siRNA This structure was selected based on the parameters of siRNA scales, MaxExpert, Duplex fold, and siRNA Efficacy according to the optimal criteria. The 3D visualization of the best siRNA structure provides a detailed overview of the domain arrangement and the interactions between the siRNA binding and the target RNA, which in this case is icaD from the S. Aureus biofilm, as shown in Figure 1 below. Figure 1. (A) The structure of siRNA22 (B) The structure of siRNA50 (C) The structure of siRNA25 (D) The structure of siRNA22 (E) The structure of siRNA50 (F) The structure of siRNA25. siRNA candidates 22, 50, and 25 are some of the best targets for gene silencing based on binding strength and the highest predicted efficacy among all siRNA candidates, with potential to exert a suppressive effect on the icaD RNA. Discussion The siRNA molecules were designed using siDirect with a multi-step bioinformatics screening approach. siDirect provides functional siRNA designs by considering the relationship between siRNA sequences and RNAi activity, and it also calculates potential gene candidates that do not match the target. The rapid and sensitive homology search, with an updated algorithm, significantly reduces off-target silencing. The analysis results were implemented to select potential icaD gene siRNA candidates targeting the icaD RNA of S. aureus. Based on the exploration of siRNA design for icaD using 924 Biology, Medicine, & Natural Product Chemistry 14 (2), 2025: 921-926 siDirect, 54 RNA oligo sequences were obtained, including 21nt guide (5′→3′) and 21nt passenger (5′→3′) RNA oligo sequences. The siRNA candidates were evaluated using siRNA scales to show the analysis results in the form of the percentage of mRNA remaining in the cells after siRNA-mediated cleavage. The range of predicted efficiency values represents the percentage of mRNA remaining after cleavage by siRNA, which refers to the gene knockdown efficiency by siRNA. This value is usually expressed as the percentage of mRNA that was not successfully degraded (remaining in the cell). SiRNAs with less than 30% remaining mRNA are considered efficient in suppressing the target gene expression. In this study, siRNAs with ≤10% remaining mRNA were categorized as highly effective because they resulted in nearly perfect knockdown. Meanwhile, siRNAs with 10–30% remaining mRNA are still quite effective for certain biological applications. On the other hand, siRNAs with >30% remaining mRNA are considered to have low knockdown efficiency and are typically avoided as candidates. The smaller the percentage of remaining mRNA, the higher the effectiveness of the siRNA in working according to its target without indirectly affecting other genes or processes (Angart et al., 2013; Dana et al., 2017). Based on guidelines from siRNA design, such as those from biotechnology companies (Thermo Fisher Scientific, Qiagen, or Dharmacon), high doses (>30%) may increase the risk of nonspecific effects, such as activation of the interferon pathway or off-target effects. A low percentage of remaining mRNA indicates a more effective mRNA cleavage by the RISC complex, thereby enhancing the knockdown efficiency of the target gene (Bartel & Sharp., 2004). The candidate siRNA was further analyzed using MaxExpect and DuplexFold to calculate the free binding energy of the siRNA and the free binding energy between the guide strand and the target. MaxExpect was tested on a database of siRNA sequences for the icaD antigen of S. aureus with known secondary structures. MaxExpect predicts the optimal structure (with the highest expected strand accuracy) and suboptimal structures as alternative hypotheses for that structure. The optimal structure, the maximum expected accuracy structure, is predicted and compared with the known structure in the database, and the prediction accuracy is reported as sensitivity and PPV (positive predictive value) (Lu et al., 2009). Target mRNA regions with MaxExpect values >1.5 indicate high accessibility to siRNA, reflecting greater sensitivity in identifying open target areas. This increases the chances of successful siRNA knockdown. Furthermore, higher predicted PPV supports the effectiveness of siRNA in reducing target gene expression during experimental tests. Therefore, MaxExpect values >1.5 can be considered optimal, especially when supported by other parameters such as knockdown prediction results showing remaining mRNA ≤30%, as analyzed through previous siRNA scales (Pan et al., 2011; Mysara & Garibaldi, 2011; Filhol et al., 2012). The DuplexFold analysis is conducted to find the structure with the Gibbs free energy (ΔG), which is a measure used to calculate the maximum work that can be done in a thermodynamic system when temperature and pressure are kept constant, indicating the most stable conformation under specific thermodynamic conditions. DuplexFold predicts the lowest free energy conformation of RNA hybrid duplexes based on intermolecular base pairing, while targetRNA identifies the complementarity of base strands and calculates the RRI score using the mean forecast error (MFE) model for RNA duplexes (Lybarger & Sandkvist, 2004). Two siRNA candidates that did not meet the criteria, with DuplexFold results ranging from - 36.1 to -20.5 kcal/mol, which is the optimal binding free energy value generated for siRNA prediction, were eliminated. In the context of selecting the most effective siRNA, a lower (more negative) ΔG value typically indicates a stronger and more stable interaction between the siRNA and the target mRNA, which leads to more effective gene silencing. This more stable interaction is crucial because it allows the siRNA to more effectively guide the RNA interference (RNAi) process to reduce the expression of the target gene. The lower the ΔG value, the stronger the binding between the siRNA and mRNA, which increases the likelihood of forming a functional RISC complex, an essential factor in the effectiveness of gene silencing (Kajino et al., 2022). The inhibition ability of the selected remaining siRNA is predicted using siPred. Accurate predictions will facilitate the design of optimized siRNA by maximizing the success of the knockout of the siRNA candidate on the target (high sensitivity) and minimizing off-target effects (high specificity) (Chuai et al., 2018; Chuai et al., 2017). The 22nd, 50th, and 25th siRNA candidates are among the best targets for gene silencing based on binding strength and the highest efficacy predictions of all siRNA candidates, showing potential to suppress the icaD RNA of S. aureus effectively. SiRNA with a 90% inhibition rate demonstrates exceptional ability to reduce target gene expression efficiently. This ensures that the target mRNA is almost completely inhibited, which is crucial in therapeutic applications or research requiring highly effective gene knockdown (Liu et al., 2012; Caffrey et al., 2011). With high efficiency, the dose of siRNA required to achieve therapeutic effects can be minimized. This reduction in dose can decrease the risk of side effects, such as immune response activation or off-target effects, which are often associated with higher siRNA doses (Caffrey et al., 2011). SiRNA with high inhibition levels typically have a strong binding affinity to the target mRNA. This strong affinity ensures that, even with a smaller amount of siRNA, binding to the target remains efficient, allowing for optimal gene knockdown. In a clinical context, the ability to achieve near-perfect knockdown provides greater confidence in the Sulistina et al. – siRNAs Targeting icaD Gene of Staphylococcus aureus to Inhibit Biofilm 925 effectiveness of siRNA as a therapy. This is especially important for diseases that require drastic inhibition of the target gene, such as cancer or chronic infections (Liu et al., 2012). The application of gene silencing based on siRNA technology is a powerful strategy to limit bacterial infections by targeting and degrading bacterial mRNA, ensuring that its sequence matches the target (Hartawan et al., 2022). Bacteria like S. aureus have undergone many mutations over time. Therefore, siRNA for bacteria must be targeted at genes that are considered to have an impact on infection. The icaD gene is a promising target for gene silencing because it is located within the biofilm structure of S. aureus, which plays an important role in the virulence factors of an infectious disease. CONCLUSIONS Based on the results of the bioinformatics study on the exploration, structure, and efficacy prediction of siRNA icaD interacting with icaD RNA in S. aureus bacteria, this study found the binding energy and inhibition capability of siRNA in silencing the icaD gene. The best results were obtained with siRNA strands 22, 50, and 25, which resulted in inhibition of 93.69%, 92.82%, and 92.52%, respectively. SiRNA with high inhibition levels typically has a strong binding affinity to the target mRNA. This strong affinity ensures that, even with a smaller amount of siRNA, binding to the target remains efficient, leading to optimal gene knockdown. These three potential siRNA molecules can be used as an siRNA-based antibacterial therapy to suppress infections caused by Staphylococcus aureus with biofilms. However, the siRNA predictions in this study are important to validate through laboratory experiments. Acknowledgements: The authors would like to thank to the Faculty of Medical, Palangka Raya University, Central Kalimantan, for providing valuable resources that facilitated a deeper understanding of siRNA for therapeutic purposes. Authors’ Contributions: Study concept and design D.A.S., R.K.P., and M.Y.I.A.; Data acquisition D.A.S. and R.K.P; Analysis and interpretation of data D.A.S; Drafting of the manuscript D.A.S., Critical revision of the manuscript D.A.S., R.K.P., M.Y.I.A., H., and Y.; Administrative, technical, and material support R.K.P.; Study supervision R.K.P., M.Y.I.A., H, and Y. Competing Interests: The authors declare that there are no competing interests. 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