Influence of mRNA features on siRNA interference efficacy

A Comprehensive Computational Investigation into the Conserved Virulent Proteins of Shigella species Unveils Potential Small-Interfering RNA Candidates as a New Therapeutic Strategy against Shigellosis

Shigella species account for the second-leading cause of deaths due to diarrheal diseases among children of less than 5 years of age. The emergence of multi-drug-resistant Shigella isolates and the lack of availability of Shigella vaccines have led to the pertinence in the efforts made for the development of new therapeutic strategies against shigellosis. Consequently, designing small-interfering RNA (siRNA) candidates against such infectious agents represents a novel approach to propose new therapeutic candidates to curb the rampant rise of anti-microbial resistance in such pathogens. In this study, we analyzed 264 conserved sequences from 15 different conserved virulence genes of Shigella sp., through extensive rational validation using a plethora of first-generation and second-generation computational algorithms for siRNA designing. Fifty-eight siRNA candidates were obtained by using the first-generation algorithms, out of which only 38 siRNA candidates complied with the second-generation rules of siRNA designing. Further computational validation showed that 16 siRNA candidates were found to have a substantial functional efficiency, out of which 11 siRNA candidates were found to be non-immunogenic. Finally, three siRNA candidates exhibited a sterically feasible three-dimensional structure as exhibited by parameters of nucleic acid geometry such as: the probability of wrong sugar puckers, bad backbone confirmations, bad bonds, and bad angles being within the accepted threshold for stable tertiary structure. Although the findings of our study require further wet-lab validation and optimization for therapeutic use in the treatment of shigellosis, the computationally validated siRNA candidates are expected to suppress the expression of the virulence genes, namely: IpgD (siRNA 9) and OspB (siRNA 15 and siRNA 17) and thus act as a prospective tool in the RNA interference (RNAi) pathway. However, the findings of our study require further wet-lab validation and optimization for regular therapeutic use for treatment of shigellosis.

Inhibition of SARS-CoV-2 Replication by a Small Interfering RNA Targeting the Leader Sequence

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected almost 200 million people worldwide and led to approximately 4 million deaths as of August 2021. Despite successful vaccine development, treatment options are limited. A promising strategy to specifically target viral infections is to suppress viral replication through RNA interference (RNAi). Hence, we designed eight small interfering RNAs (siRNAs) targeting the highly conserved 5′-untranslated region (5′-UTR) of SARS-CoV-2. The most promising candidate identified in initial reporter assays, termed siCoV6, targets the leader sequence of the virus, which is present in the genomic as well as in all subgenomic RNAs. In assays with infectious SARS-CoV-2, it reduced replication by two orders of magnitude and prevented the development of a cytopathic effect. Moreover, it retained its activity against the SARS-CoV-2 alpha variant and has perfect homology against all sequences of the delta variant that were analyzed by bioinformatic means. Interestingly, the siRNA was even highly active in virus replication assays with the SARS-CoV-1 family member. This work thus identified a very potent siRNA with a broad activity against various SARS-CoV viruses that represents a promising candidate for the development of new treatment options.

Inhibition of Mycobacterium tuberculosis tRNA-Ligases Using siRNA-Based Gene Silencing Method: A Computational Approach

Tuberculosis (TB) is a major public health problem in several countries. Development of first-line and second-line drug resistance strains of Mycobacterium tuberculosis further complicated the management of the disease. Despite available drugs to treat TB, 1.6 million people died from the disease in 2017. In this study, we designed 10 siRNAs against 8 tRNA ligases of M. tuberculosis and validated their usefulness for inhibition of protein synthesis by using computational approach. We found that the predicted siRNAs efficiently form seed duplex complex against their respective mRNA targets. Other different computational approaches were also undertaken to assess the stability, accessibility, and strength of seed duplex complex of designed siRNA and targeted mRNA. On the basis of the computational approach, we reciprocated that the technique will help in opening a new window in the field of TB control program and could be taken for further clinical studies to find their appropriateness for TB eradication.

Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level

Small interfering RNAs (siRNAs) may induce to targeted gene knockdown, and the gene silencing effectiveness relies on the efficacy of the siRNA. Therefore, the task of this paper is to construct an effective siRNA prediction method. In our work, we try to describe siRNA from both quantitative and qualitative aspects. For quantitative analyses, we form four groups of effective features, including nucleotide frequencies, thermodynamic stability profile, thermodynamic of siRNA-mRNA interaction, and mRNA related features, as a new mixed representation, in which thermodynamic of siRNA-mRNA interaction is introduced to siRNA efficacy prediction for the first time to our best knowledge. And then an F-score based feature selection is employed to investigate the contribution of each feature and remove the weak relevant features. Meanwhile, we encode the siRNA sequence and existed empirical design rules as a qualitative siRNA representation. These two kinds of siRNA representations are combined to predict siRNA efficacy by supported Vector Regression (SVR) at score level. The experimental results indicate that our method may select the features with powerful discriminative ability and make the two kinds of siRNA representations work at full capacity. The prediction results also demonstrate that our method can outperform other popular siRNA efficacy prediction algorithms.

Utilizing Selected Di- and Trinucleotides of siRNA to Predict RNAi Activity

Small interfering RNAs (siRNAs) induce posttranscriptional gene silencing in various organisms. siRNAs targeted to different positions of the same gene show different effectiveness; hence, predicting siRNA activity is a crucial step. In this paper, we developed and evaluated a powerful tool named “siRNApred” with a new mixed feature set to predict siRNA activity. To improve the prediction accuracy, we proposed 2-3NTs as our new features. A Random Forest siRNA activity prediction model was constructed using the feature set selected by our proposed Binary Search Feature Selection (BSFS) algorithm. Experimental data demonstrated that the binding site of the Argonaute protein correlates with siRNA activity. “siRNApred” is effective for selecting active siRNAs, and the prediction results demonstrate that our method can outperform other current siRNA activity prediction methods in terms of prediction accuracy.

Design of Potential RNAi (miRNA and siRNA) Molecules for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Gene Silencing by Computational Method

The Middle East respiratory syndrome coronavirus (MERS-CoV) is a virus that manifests itself in viral infection with fever, cough, shortness of breath, renal failure and severe acute pneumonia, which often result in a fatal outcome. MERS-CoV has been shown to spread between people who are in close contact. Transmission from infected patients to healthcare personnel has also been observed and is irredeemable with present technology. Genetic studies on MERS-CoV have shown that ORF1ab encodes replicase polyproteins and play a foremost role in viral infection. Therefore, ORF1ab replicase polyprotein may be used as a suitable target for disease control. Viral activity can be controlled by RNA interference (RNAi) technology, a leading method for post transcriptional gene silencing in a sequence-specific manner. However, there is a genetic inconsistency in different viral isolates; it is a great challenge to design potential RNAi (miRNA and siRNA) molecules which can silence the respective target genes rather than any other viral gene simultaneously. In the current study, four effective miRNA and five siRNA molecules for silencing of nine different strains of MERS-CoV were rationally designed and corroborated using computational methods, which might lead to knockdown the activity of virus. siRNA and miRNA molecules were predicted against ORF1ab gene of different strains of MERS-CoV as effective candidate using computational methods. Thus, this method may provide an insight for the chemical synthesis of antiviral RNA molecule for the treatment of MERS-CoV, at genomic level.

Bioethical conflicts of gene therapy: a brief critical review

Methods and techniques employed in gene therapy are reviewed in parallel with pertinent ethical conflicts. Clinical interventions based on gene therapy techniques preferentially use vectors for the transportation of therapeutic genes, however little is known about the potential risks and damages to the patient. Thus, attending carefully to the clinical complications arising as well as to security is essential. Despite the scientific and technological advances, there are still many uncertainties about the side effects of gene therapy. Moreover, there is a need, above all, to understand the principles of bioethics as both science and ethics, in accordance with its socioecological responsibility, in order to prioritize the health and welfare of man and nature, using properly natural resources and technology. Therefore, it is hard to determine objective results and to which extent the insertion of genes can affect the organism, as well as the ethical implication.

Design of potential RNAi (miRNA and siRNA) molecules for Middle East respiratory syndrome coronavirus (MERS-CoV) gene silencing by computational method

The Middle East respiratory syndrome coronavirus (MERS-CoV) is a virus that manifests itself in viral infection with fever, cough, shortness of breath, renal failure and severe acute pneumonia, which often result in a fatal outcome. MERS-CoV has been shown to spread between people who are in close contact. Transmission from infected patients to healthcare personnel has also been observed and is irredeemable with present technology. Genetic studies on MERS-CoV have shown that ORF 1ab encodes replicase polyproteins and play a foremost role in viral infection. Therefore, ORF 1ab replicase polyprotein may be used as suitable target for disease control. Viral activity can be controlled by RNA interference (RNAi) technology, a leading method for post transcriptional gene silencing in a sequence specific manner. However, there is a genetic inconsistency in different viral isolates; it is a great challenge to design potential RNAi (miRNA and siRNA) molecules which can silence the respective target genes rather than any other viral gene simultaneously. In current study four effective miRNA and five siRNA molecules for silencing of nine different strains of MERS-CoV were rationally designed and corroborated using computational methods, which might lead to knockdown the activity of virus. siRNA and miRNA molecules were predicted against ORF1ab gene of different strains of MERS-CoV as effective candidate using computational methods. Thus, this method may provide an insight for the chemical synthesis of antiviral RNA molecule for the treatment of MERS-CoV, at genomic level.

Design of potential RNAi (miRNA and siRNA) molecules for Middle East respiratory syndrome coronavirus (MERS-CoV) gene silencing by computational method

The Middle East respiratory syndrome coronavirus (MERS-CoV) is a virus that manifests itself in viral infection with fever, cough, shortness of breath, renal failure and severe acute pneumonia, which often result in a fatal outcome. MERS-CoV has been shown to spread between people who are in close contact. Transmission from infected patients to healthcare personnel has also been observed and is irredeemable with present technology. Genetic studies on MERS-CoV have shown that ORF 1ab encodes replicase polyproteins and play a foremost role in viral infection. Therefore, ORF 1ab replicase polyprotein may be used as suitable target for disease control. Viral activity can be controlled by RNA interference (RNAi) technology, a leading method for post transcriptional gene silencing in a sequence specific manner. However, there is a genetic inconsistency in different viral isolates; it is a great challenge to design potential RNAi (miRNA and siRNA) molecules which can silence the respective target genes rather than any other viral gene simultaneously. In current study four effective miRNA and five siRNA molecules for silencing of nine different strains of MERS-CoV were rationally designed and corroborated using computational methods, which might lead to knockdown the activity of virus. siRNA and miRNA molecules were predicted against ORF1ab gene of different strains of MERS-CoV as effective candidate using computational methods. Thus, this method may provide an insight for the chemical synthesis of antiviral RNA molecule for the treatment of MERS-CoV, at genomic level.