The pssRNAit web server designs functional small interfering RNAs for precise gene silencing in plants and provides a path to effectively study gene functions a

Background: SARS-CoV-2 causes ongoing pandemic coronavirus disease of 2019 (COVID-19), infects the cells of the lower respiratory tract that leads to a cytokine storm in a significant number of patients resulting in severe pneumonia, shortness of breathing, respiratory and organ failure. Extensive studies suggested the role of Vitamin D in suppressing cytokine storm in COVID-19 and reducing viral infection; however, the precise molecular mechanism is not clearly known. In this work, bioinformatics and systems biology approaches were used to understand SARS-CoV-2 induced cytokine pathways and the potential mechanism of Vitamin D in suppressing cytokine storm and enhancing antiviral response.Results: This study used transcriptome data and identified 108 differentially expressed host genes (DEHGs) in SARS-CoV-2 infected normal human bronchial epithelial (NHBE) cells compared to control. Then, the DEHGs was integrated with the human protein-protein interaction data to generate a SARS-CoV-2 induced host gene regulatory network (SiHgrn). Analysis of SiHgrn identified a sub-network “Cluster 1” with the highest MCODE score, 31 up-regulated genes, and predominantly associated immune and inflammatory response. Interestingly, the iRegulone tool identified that “Cluster 1” is under the regulation of transcription factors STAT1, STAT2, STAT3, POU2F2, and NFkB1, collectively referred to as “host response signature network”. Functional enrichment analysis with NDEx revealed that the “host response signature network” is predominantly associated with critical pathways, including “cytokines and inflammatory response”, “non-genomic action of Vitamin D”, “the human immune response to tuberculosis”, and “lung fibrosis”. Finally, in-depth analysis and literature mining revealed that Vitamin D binds with its receptor and could work through two different pathways: (i) it inhibits the expression of pro-inflammatory cytokines through blocking the TNF induced NFkB1 signaling pathway; and (ii) it initiates the expression of interferon-stimulating genes (ISGs) for antiviral defense program through activating the IFN-α induced Jak-STAT signaling pathway.Conclusion: This comprehensive study identified the pathways associated with cytokine storm in SARS-CoV-2 infection. The proposed underlying mechanism of Vitamin D could be promising in suppressing the cytokine storm and inducing a robust antiviral response in severe COVID-19 patients. The finding in this study urgently needs further experimental validations for the suitability of Vitamin D in combination with IFN-α to control severe COVID-19.

Background: Loss of control on cell division is an important factor for the development of non-small cell lung cancer (NSCLC), however, its molecular mechanism and gene regulatory network are not clearly understood. This study utilized the systems bioinformatics approach to reveal the “driver-network” involve in tumorigenic processes in NSCLC. Methods: A meta-analysis of gene expression data of NSCLC was integrated with protein-protein interaction (PPI) data to construct an NSCLC network. MCODE and iRegulone were used to identify the local clusters and its upstream transcription regulators involve in NSCLC. Pair-wise gene expression correlation was performed using GEPIA. The survival analysis was performed by the Kaplan-Meier plot. Results: This study identified a local “driver-network” with highest MCODE score having 26 up-regulated genes involved in the process of cell proliferation in NSCLC. Interestingly, the “driver-network” is under the regulation of TFs FOXM1 and MYBL2 as well as miRNAs. Furthermore, the overexpression of member genes in “driver-network” and the TFs are associated with poor overall survival (OS) in NSCLC patients. Conclusion: This study identified a local “driver-network” and its upstream regulators responsible for the cell proliferation in NSCLC, which could be promising biomarkers and therapeutic targets for NSCLC treatment.

In past, numerous methods have been developed for predicting efficacy of short interfering RNA (siRNA). However these methods have been developed for predicting efficacy of fully complementary siRNA against a gene. Best of author's knowledge no method has been developed for predicting efficacy of mismatch siRNA against a gene. In this study, a systematic attempt has been made to identify highly effective complementary as well as mismatch siRNAs for silencing a gene. Support vector machine (SVM) based models have been developed for predicting efficacy of siRNAs using composition, binary and hybrid pattern siRNAs. We achieved maximum correlation 0.67 between predicted and actual efficacy of siRNAs using hybrid model. All models were trained and tested on a dataset of 2182 siRNAs and performance was evaluated using five-fold cross validation techniques. The performance of our method desiRm is comparable to other well-known methods. In this study, first time attempt has been made to design mutant siRNAs (mismatch siRNAs). In this approach we mutated a given siRNA on all possible sites/positions with all possible nucleotides. Efficacy of each mutated siRNA is predicted using our method desiRm. It is well known from literature that mismatches between siRNA and target affects the silencing efficacy. Thus we have incorporated the rules derived from base mismatches experimental data to find out over all efficacy of mutated or mismatch siRNAs. Finally we developed a webserver, desiRm (http://www.imtech.res.in/raghava/desirm/) for designing highly effective siRNA for silencing a gene. This tool will be helpful to design siRNA to degrade disease isoform of heterozygous single nucleotide polymorphism gene without depleting the wild type protein.

Dicer, an RNase III enzyme, plays a vital role in the processing of pre-miRNAs for generating the miRNAs. The structural and sequence features on pre-miRNA which can facilitate position and efficiency of cleavage are not well known. A precise cleavage by Dicer is crucial because an inaccurate processing can produce miRNA with different seed regions which can alter the repertoire of target genes.

Background: MicroRNAs (miRNAs) are produced by the sequential processing of a long hairpin RNA transcript by Drosha and Dicer, an RNase III enzymes, and form transitory small RNA duplexes. One strand of the duplex, which incorporates into RNA-induced silencing complex (RISC) and silences the gene expression is called guide strand, or miRNA; while the other strand of duplex is degraded and called the passenger strand, or miRNA*. Predicting the guide strand of miRNA is important for better understanding the RNA interference pathways.Results: This paper describes support vector machine (SVM) models developed for predicting the guide strands of miRNAs. All models were trained and tested on a dataset consisting of 329 miRNA and 329 miRNA* pairs using five fold cross validation technique. Firstly, models were developed using mono-, di-, and tri-nucleotide composition of miRNA strands and achieved the highest accuracies of 0.588, 0.638 and 0.596 respectively. Secondly, models were developed using split nucleotide composition and achieved maximum accuracies of 0.553, 0.641 and 0.602 for mono-, di-, and tri-nucleotide respectively. Thirdly, models were developed using binary pattern and achieved the highest accuracy of 0.708. Furthermore, when integrating the secondary structure features with binary pattern, an accuracy of 0.719 was seen. Finally, hybrid models were developed by combining various features and achieved maximum accuracy of 0.799 with sensitivity 0.781 and specificity 0.818. Moreover, the performance of this model was tested on an independent dataset that achieved an accuracy of 0.80. In addition, we also compared the performance of our method with various siRNA-designing methods on miRNA and siRNA datasets.Conclusion: In this study, first time a method has been developed to predict guide miRNA strands, of miRNA duplex. This study demonstrates that guide and passenger strand of miRNA precursors can be distinguished using their nucleotide sequence and secondary structure. This method will be useful in understanding microRNA processing and can be implemented in RNA silencing technology to improve the biological and clinical research. A web server has been developed based on SVM models described in this studyhttp://crdd.osdd.net:8081/RISCbinder/.