iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers.

BACKGROUND: Inflammatory mediators play havoc in several diseases including the novel Coronavirus disease 2019 (COVID-19) and generally correlate with the severity of the disease. Interleukin-13 (IL-13), is a pleiotropic cytokine that is known to be associated with airway inflammation in asthma and reactive airway diseases, in neoplastic and autoimmune diseases. Interestingly, the recent association of IL-13 with COVID-19 severity has sparked interest in this cytokine. Therefore characterization of new molecules which can regulate IL-13 induction might lead to novel therapeutics. RESULTS: Here, we present an improved prediction of IL-13-inducing peptides. The positive and negative datasets were obtained from a recent study (IL13Pred) and the Pfeature algorithm was used to compute features for the peptides. As compared to the state-of-the-art which used the regularization based feature selection technique (linear support vector classifier with the L1 penalty), we used a multivariate feature selection technique (minimum redundancy maximum relevance) to obtain non-redundant and highly relevant features. In the proposed study (improved IL-13 prediction (iIL13Pred)), the use of the mRMR feature selection method is instrumental in choosing the most discriminatory features of IL-13-inducing peptides with improved performance. We investigated seven common machine learning classifiers including Decision Tree, Gaussian Naïve Bayes, k-Nearest Neighbour, Logistic Regression, Support Vector Machine, Random Forest, and extreme gradient boosting to efficiently classify IL-13-inducing peptides. We report improved AUC, and MCC scores of 0.83 and 0.33 on validation data as compared to the current method. CONCLUSIONS: Extensive benchmarking experiments suggest that the proposed method (iIL13Pred) could provide improved performance metrics in terms of sensitivity, specificity, accuracy, the area under the curve - receiver operating characteristics (AUCROC) and Matthews correlation coefficient (MCC) than the existing state-of-the-art approach (IL13Pred) on the validation dataset and an external dataset comprising of experimentally validated IL-13-inducing peptides. Additionally, the experiments were performed with an increased number of experimentally validated training datasets to obtain a more robust model. A user-friendly web server ( www.soodlab.com/iil13pred ) is also designed to facilitate rapid screening of IL-13-inducing peptides.

In silico analysis of SARS-CoV-2 genomes: Insights from SARS encoded non-coding RNAs.

The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 has resulted in enormous deaths around the world. Clues from genomic sequences of parent and their mutants can be obtained to understand the evolving pathogenesis of this virus. Apart from the viral proteins, virus-encoded microRNAs (miRNAs) have been shown to play a vital role in regulating viral pathogenesis. Thus we sought to investigate the miRNAs encoded by SARS-CoV-2, its mutants, and the host. Here, we present the results obtained using a dual approach i.e (i) identifying host-encoded miRNAs that might regulate viral pathogenesis and (ii) identifying viral-encoded miRNAs that might regulate host cell signaling pathways and aid in viral pathogenesis. Analysis utilizing the first approach resulted in the identification of ten host-encoded miRNAs that could target the SARS, SARS-CoV-2, and its mutants. Interestingly our analysis revealed that there is a significantly higher number of host miRNAs that could target the SARS-CoV-2 genome as compared to the SARS reference genome. Results from the second approach resulted in the identification of a set of virus-encoded miRNAs which might regulate host signaling pathways. Our analysis further identified a similar "GA" rich motif in the SARS-CoV-2 and its mutant genomes that was shown to play a vital role in lung pathogenesis during severe SARS infections. In summary, we have identified human and virus-encoded miRNAs that might regulate the pathogenesis of SARS coronaviruses and describe similar non-coding RNA sequences in SARS-CoV-2 that were shown to regulate SARS-induced lung pathology in mice.

Time Series Analysis of SARS-CoV-2 Genomes and Correlations among Highly Prevalent Mutations.

The efforts of the scientific community to tame the recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seem to have been diluted by the emergence of new viral strains. Therefore, it is imperative to understand the effect of mutations on viral evolution. We performed a time series analysis on 59,541 SARS-CoV-2 genomic sequences from around the world to gain insights into the kinetics of the mutations arising in the viral genomes. These 59,541 genomes were grouped according to month (January 2020 to March 2021) based on the collection date. Meta-analysis of these data led us to identify significant mutations in viral genomes. Pearson correlation of these mutations led us to the identification of 16 comutations. Among these comutations, some of the individual mutations have been shown to contribute to viral replication and fitness, suggesting a possible role of other unexplored mutations in viral evolution. We observed that the mutations 241C>T in the 5' untranslated region (UTR), 3037C>T in nsp3, 14408C>T in the RNA-dependent RNA polymerase (RdRp), and 23403A>G in spike are correlated with each other and were grouped in a single cluster by hierarchical clustering. These mutations have replaced the wild-type nucleotides in SARS-CoV-2 sequences. Additionally, we employed a suite of computational tools to investigate the effects of T85I (1059C>T), P323L (14408C>T), and Q57H (25563G>T) mutations in nsp2, RdRp, and the ORF3a protein of SARS-CoV-2, respectively. We observed that the mutations T85I and Q57H tend to be deleterious and destabilize the respective wild-type protein, whereas P323L in RdRp tends to be neutral and has a stabilizing effect. IMPORTANCE We performed a meta-analysis on SARS-CoV-2 genomes categorized by collection month and identified several significant mutations. Pearson correlation analysis of these significant mutations identified 16 comutations having absolute correlation coefficients of >0.4 and a frequency of >30% in the genomes used in this study. The correlation results were further validated by another statistical tool called hierarchical clustering, where mutations were grouped in clusters on the basis of their similarity. We identified several positive and negative correlations among comutations in SARS-CoV-2 isolates from around the world which might contribute to viral pathogenesis. The negative correlations among some of the mutations in SARS-CoV-2 identified in this study warrant further investigations. Further analysis of mutations such as T85I in nsp2 and Q57H in ORF3a protein revealed that these mutations tend to destabilize the protein relative to the wild type, whereas P323L in RdRp is neutral and has a stabilizing effect. Thus, we have identified several comutations which can be further characterized to gain insights into SARS-CoV-2 evolution.

Novel insights into host responses to Japanese Encephalitis Virus infection: Reanalysis of public transcriptome and microRNAome datasets.

PURPOSE: Japanese encephalitis (JE), caused by the Japanese encephalitis virus (JEV), is the principal cause of viral encephalitis in South-East Asian and Western Pacific countries; accounting for 68,000 cases, and up to 20,400 fatalities, annually across the world. Despite being a high-risk condition, there is no specific treatment for JE. Given rapid additions in genomics databases and the power of data reanalysis in addressing critical medical questions, the present study was designed to identify novel host factors that might have potential roles in JEV infection. METHODS: We extracted microarray and RNA-Seq data sets from NCBI-GEO and compared mock and JEV-infected samples. Raw data from all the studies were re-analyzed to identify host factors associated with JEV replication. RESULTS: We identified several coding and non-coding host factors that had no prior known role in viral infections. Of these, the coding transcripts: Myosin Heavy Chain 10 (MYH10), Progestin and AdipoQ Receptor Family Member 8 (PAQR8), and the microRNAs: hsa-miR-193b-5p, hsa-miR-3714 and hsa-miR-513a-5p were found to be novel host factors deregulated during JEV infection. MYH10 encodes a conventional non-muscle myosin, and mutations in MYH10 have been shown to cause neurological defects. PAQR8 has been associated with epilepsy, which exhibits symptoms similar to JEV infection. JE is a neuro-degenerative disease, and the known involvement of MYH10 and PAQR8 in neurological disorders strongly indicates potential roles of these host factors in JEV infection. Additionally, we observed that MYH10 and PAQR8 had a significant negative correlation with Activating transcription factor 3 (ATF3), which is a previously validated modulator of JEV infection. ATF3 is a transcription factor that binds to the promotors of genes encoding other transcription factors or interferon-stimulated genes and negatively regulates host antiviral responses during JE. CONCLUSION: Our findings demonstrate the significance of data reanalysis in the identification of novel host factors that may become targets for diagnosis/ therapy against viral diseases of major concern, such as, JE. The deregulated coding and non-coding transcripts identified in this study need further experimental analysis for validation.

A novel binary k-mer approach for classification of coding and non-coding RNAs across diverse species.

Classification among coding sequences (CDS) and non-coding RNA (ncRNA) sequences is a challenge and several machine learning models have been developed for the same. Since the frequency of curated CDS is many-folds as compared to that of the ncRNAs, we devised a novel approach to work with the complete datasets from fifteen diverse species. In our proposed binary approach, we replaced all the 'A's and 'T's with '0's and 'G's and 'C's with '1's to obtain a binary form of CDS and ncRNAs. The k-mer analysis of these binary sequences revealed that the frequency of binary patterns among the CDS and ncRNAs can be used as features to distinguish among them. Using insights from these distinguishing frequencies, we used k-nearest neighbor classifier to classify among them. Our strategy is not only time-efficient but leads to significantly increased performance metrics in terms of Matthews Correlation Coefficient (MCC), Accuracy, F1 score, Precision, Recall and AUC-ROC, for species like P. paniscus, M. mulatta, M. lucifugus, G. gallus, C. japonica, C. abingdonii, A. carolinensis, D. melanogaster and C. elegans when compared with the conventional ATGC approach. Additionally, we also show that the performance obtained for diverse species tested on the model based on H. sapiens, correlated with the geological evolutionary timeline, thereby further strengthening our approach. Therefore, we propose that CDS and ncRNAs can be efficiently classified using "2-character" binary frequency as compared to "4-character" frequency of ATGC approach. Thus, our highly efficient binary approach can replace the more complex ATGC approach successfully.

In silico characterization of mutations circulating in SARS-CoV-2 structural proteins.

SARS-CoV-2 has recently emerged as a pandemic that has caused more than 2.4 million deaths worldwide. Since the onset of infections, several full-length sequences of viral genome have been made available which have been used to gain insights into viral dynamics. We utilised a meta-data driven comparative analysis tool for sequences (Meta-CATS) algorithm to identify mutations in 829 SARS-CoV-2 genomes from around the world. The algorithm predicted sixty-one mutations among SARS-CoV-2 genomes. We observed that most of the mutations were concentrated around three protein coding genes viz nsp3 (non-structural protein 3), RdRp (RNA-directed RNA polymerase) and Nucleocapsid (N) proteins of SARS-CoV-2. We used various computational tools including normal mode analysis (NMA), C-α discrete molecular dynamics (DMD) and all-atom molecular dynamic simulations (MD) to study the effect of mutations on functionality, stability and flexibility of SARS-CoV-2 structural proteins including envelope (E), N and spike (S) proteins. PredictSNP predictor suggested that four mutations (L37H in E, R203K and P344S in N and D614G in S) out of seven were predicted to be neutral whilst the remaining ones (P13L, S197L and G204R in N) were predicted to be deleterious in nature thereby impacting protein functionality. NMA, C-α DMD and all-atom MD suggested some mutations to have stabilizing roles (P13L, S197L and R203K in N protein) where remaining ones were predicted to destabilize mutant protein. In summary, we identified significant mutations in SARS-CoV-2 genomes as well as used computational approaches to further characterize the possible effect of highly significant mutations on SARS-CoV-2 structural proteins.Communicated by Ramaswamy H. Sarma.

Replication of Dengue Virus in K562-Megakaryocytes Induces Suppression in the Accumulation of Reactive Oxygen Species.

Dengue virus can infect human megakaryocytes leading to decreased platelet biogenesis. In this article, we report a study of Dengue replication in human K562 cells undergoing PMA-induced differentiation into megakaryocytes. PMA-induced differentiation in these cells recapitulates steps of megakaryopoiesis including gene activation, expression of CD41/61 and CD61 platelet surface markers and accumulation of intracellular reactive oxygen species (ROS). Our results show differentiating megakaryocyte cells to support higher viral replication without any apparent increase in virus entry. Further, Dengue replication suppresses the accumulation of ROS in differentiating cells, probably by only augmenting the activity of the transcription factor NFE2L2 without influencing the expression of the coding gene. Interestingly pharmacological modulation of NFE2L2 activity showed a simultaneous but opposite effect on intracellular ROS and virus replication suggesting the former to have an inhibitory effect on the later. Also cells that differentiated while supporting intracellular virus replication showed reduced level of surface markers compared to uninfected differentiated cells.