Current literature describes several methods for the design of efficient siRNAs with 19 perfectly matched base pairs and 2 nt overhangs. Using four independent databases totaling 3336 experimentally verified siRNAs, we compared how well several of these methods predict siRNA cleavage efficiency. According to receiver operating characteristics (ROC) and correlation analyses, the best programs were BioPredsi, ThermoComposition and DSIR. We also studied individual parameters that significantly and consistently correlated with siRNA efficacy in different databases. As a result of this work we developed a new method which utilizes linear regression fitting with local duplex stability, nucleotide position-dependent preferences and total G/C content of siRNA duplexes as input parameters. The new method's discrimination ability of efficient and inefficient siRNAs is comparable with that of the best methods identified, but its parameters are more obviously related to the mechanisms of siRNA action in comparison with BioPredsi. This permits insight to the underlying physical features and relative importance of the parameters. The new method of predicting siRNA efficiency is faster than that of ThermoComposition because it does not employ time-consuming RNA secondary structure calculations and has much less parameters than DSIR. It is available as a web tool called ‘siRNA scales’.

Next Generation Sequencing (NGS) technology is based on cutting DNA into small fragments, and their massive parallel sequencing. The multiple overlapping segments termed “reads” are assembled into a contiguous sequence. To reduce sequencing errors, every genome region should be sequenced several dozen times. This sequencing approach is based on the assumption that genomic DNA breaks are random and sequence-independent. However, previously we showed that for the sonicated restriction DNA fragments the rates of double-stranded breaks depend on the nucleotide sequence. In this work we analyzed genomic reads from NGS data and discovered that fragmentation methods based on the action of the hydrodynamic forces on DNA, produce similar bias. Consideration of this non-random DNA fragmentation may allow one to unravel what factors and to what extent influence the non-uniform coverage of various genomic regions.

Prediction of efficient oligonucleotides for RNA interference presents a serious challenge, especially for the development of genome-wide RNAi libraries which encounter difficulties and limitations due to ambiguities in the results and the requirement for significant computational resources. Here we present a fast and practical algorithm for shRNA design based on the thermodynamic parameters. In order to identify shRNA and siRNA features universally associated with high silencing efficiency, we analyzed structure-activity relationships in thousands of individual RNAi experiments from publicly available databases (ftp://ftp.ncbi.nlm.nih.gov/pub/shabalin/siRNA/si_shRNA_selector/). Using this statistical analysis, we found free energy ranges for the terminal duplex asymmetry and for fully paired duplex stability, such that shRNAs or siRNAs falling in both ranges have a high probability of being efficient. When combined, these two parameters yield a ∼72% success rate on shRNAs from the siRecords database, with the target RNA levels reduced to below 20% of the control. Two other parameters correlate well with silencing efficiency: the stability of target RNA and the antisense strand secondary structure. Both parameters also correlate with the short RNA duplex stability; as a consequence, adding these parameters to our prediction scheme did not substantially improve classification accuracy. To test the validity of our predictions, we designed 83 shRNAs with optimal terminal asymmetry, and experimentally verified that small shifts in duplex stability strongly affected silencing efficiency. We showed that shRNAs with short fully paired stems could be successfully selected by optimizing only two parameters: terminal duplex asymmetry and duplex stability of the hypothetical cleavage product, which also relates to the specificity of mRNA target recognition. Our approach performs at the level of the best currently utilized algorithms that take into account prediction of the secondary structure of the target and antisense RNAs, but at significantly lower computational costs. Based on this study, we created the si-shRNA Selector program that predicts both highly efficient shRNAs and functional siRNAs (ftp://ftp.ncbi.nlm.nih.gov/pub/shabalin/siRNA/si_shRNA_selector/).

COVID-19 has specific characteristics that distinguish this disease from many other infections. We suggest that the pathogenesis of severe forms of COVID-19 can be associated with acidosis. This review article discusses several mechanisms potentially linking the damaging effects of COVID-19 with acidosis and shows the existence of a vicious cycle between the development of hypoxia and acidosis in COVID-19 patients. At the early stages of the disease, inflammation, difficulty in gas exchange in the lungs and thrombosis collectively contribute to the onset of acidosis. In accordance with the Verigo-Bohr effect, a decrease in blood pH leads to a decrease in oxygen saturation, which contributes to the exacerbation of acidosis and results in a deterioration of the patient’s condition. A decrease in pH can also cause conformational changes in the S-protein of the virus and thus lead to a decrease in the affinity and avidity of protective antibodies. Hypoxia and acidosis lead to dysregulation of the immune system and multidirectional pro- and anti-inflammatory reactions, resulting in the development of a “cytokine storm”. In this review, we highlight the potential importance of supporting normal blood pH as an approach to COVID-19 therapy.