Wet Lab Validation Research Articles

CIRCprimerXL: Convenient and High-Throughput PCR Primer Design for Circular RNA Quantification.

Circular RNA (circRNA) is a class of endogenous non-coding RNA characterized by a back-splice junction (BSJ). In general, large-scale circRNA BSJ detection is performed based on RNA sequencing data, followed by the selection and validation of circRNAs of interest using RT-qPCR with circRNA-specific PCR primers. Such a primer pair is convergent and functional on the circRNA template but divergent and non-functional on the linear host gene. Although a few circRNA primer design pipelines have been published, none of them offer large-scale, easy-to-use circRNA primer design. Other limitations are that these tools generally do not take into account assay specificity, secondary structures, and SNPs in the primer annealing regions. Furthermore, these tools are limited to circRNA primer design for humans (no other organisms possible), and no wet-lab validation is demonstrated. Here, we present CIRCprimerXL, a circRNA RT-qPCR assay design pipeline based on the primer design framework primerXL. CIRCprimerXL takes a circRNA BSJ position as input, and designs BSJ-spanning primers using Primer3. The user can choose to use the unspliced or spliced circRNA sequence as template. Prior to primer design, sequence regions with secondary structures and common SNPs are flagged. Next, the primers are filtered based on predicted specificity and the absence of secondary structures of the amplicon to select a suitable primer pair. Our tool is both available as a user-friendly web tool and as a stand-alone pipeline based on Docker and Nextflow, allowing users to run the pipeline on a wide range of computer infrastructures. The CIRCprimerXL Nextflow pipeline can be used to design circRNA primers for any species by providing the appropriate reference genome. The CIRCprimerXL web tool supports circRNA primer design for human, mouse, rat, zebrafish, Xenopus tropicalis, and C. elegans. The design process can easily be scaled up for the qPCR assay design of tens of thousands of circRNAs within a couple of hours. We show how CIRCprimerXL has been successfully used to design qPCR assays for over 15,000 human circRNAs of which 20 were empirically validated. CIRCprimerXL software, documentation, and test data can be found at: https://github.com/OncoRNALab/CIRCprimerXL. CIRCprimerXL is also implemented as a webtool at: https://circprimerxl.cmgg.be.

Chemi-Informatic Approach to Investigate Putative Pharmacoactive Agents of Plant Origin to Eradicate COVID-19

Background:: The scientific community has supported from the medicinal flora of ancient as well as modern times in extracting chemicals, which holds therapeutic potential. In many previous studies, it was discovered Amentoflavone as an anti-viral agent and its presence as a bioactive constituent in many plants of different families Selaginellaceae, Euphorbiaceae, and Calophyllaceae etc. Withania somnifera (Ashwagandha) is already considered significant anti-viral agent in traditional medicine, and it is the main source of Somniferine-A, Withanolide-B. Objective:: In this study phytochemicals such as Withanolide-B, Somniferine-A, Stigmasterol, Amentoflavone, and Chavicine were analyzed to screen protein inhibitors out of them; such proteins are involved in SARS-Cov-2's internalization and interaction with human cytological domains. This will help in developing check point for SARS-Cov-2 internalization. Material and methods:: Chemi-informatic tools like basic local alignment search tool (BLAST), AutoDock-vina, SwissADME, MDWeb, Molsoft, ProTox-II, and LigPlot were deployed to examine the action of pharmacoactive agents against SARS-Cov-2. The many tools deployed in the study were based on finest algorithms like Artificial neural networking, Machine Learning, and Artificial intelligence. Results:: On the basis of binding energies less than equal to -8.5 kcal/mol. Amentoflavone, Stigmasterol, and Somniferine-A were found to be most effective against COVID-19 disease as these chemical agents exhibit hydrogen bond interactions and competitively inhibit major proteins (SARS-Cov-2 Spike, Human ACE-2 receptor, Human Furin protease, SARS-Cov-2 RNA binding protein) that are found to involved in its infection and pathogenesis. Simulation analysis provides more validity to the selection of drug candidate Amentoflavone. ADMET properties were found to be in feasible range for putative drug candidates. Conclusion:: Computational analysis was successfully deployed in searching pharmacoactive phytochemicals Like Amentoflavone, Somniferine-A, and Stigmasterol that can bring control over COVID-19 expansion. This new methodology was found to be efficient, as reduces monetary expenditures and time consumption, aftermath molecular wet-lab validations will provide better approval for finalizing our selected drug model for controlling COVID-19 pandemic.

Integrative approaches for studying the role of noncoding RNAs in influencing drug efficacy and toxicity

Introduction Drug efficacy and toxicity are important factors for evaluation in drug development. Drug metabolizing enzymes and transporters (DMETs) play an essential role in drug efficacy and toxicity. Noncoding RNAs (ncRNAs) have been implicated to influence inter-individual variations in drug efficacy and safety by regulating DMETs. An efficient strategy is urgently needed to identify and functionally characterize ncRNAs that mediate drug efficacy and toxicity through regulating DMETs. Areas covered : We outline an integrative strategy to identify ncRNAs that modulate DMETs. We include reliable tools and databases for computational prediction of ncRNA targets with regards to their advantages and limitations. Various biochemical, molecular, and cellular assays are discussed for in vitro experimental verification of the regulatory function of ncRNAs. In vivo approaches for association of ncRNAs with drug treatment and toxicity are also reviewed. Expert opinion : A streamlined integration of computational prediction and wet-lab validation is important to elucidate mechanisms of ncRNAs in the regulation of DMETs related to drug efficacy and safety. Bioinformatic analyses using open-access tools and databases serve as a powerful booster for ncRNA research in toxicology. Further refinement of computational algorithms and experimental technologies is needed to improve accuracy and efficiency in ncRNA target identification and characterization.

Computational screening and biochemical analysis of Pistacia integerrima and Pandanus odorifer plants to find effective inhibitors against Receptor-Binding domain (RBD) of the spike protein of SARS-Cov-2.

Although World Health Organization-approved emergency vaccines are available in many countries, the mortality rate from COVID-19 remains high due to the fourth or fifth wave and the delta variant of the coronavirus. Thus, an effective mechanistic investigation in treating this disease is urgently needed. In this work, we extracted phytochemicals from two mangrove plants, Pistacia integerrima and Pandanus odorifer, assessing their potential actions against the receptor-binding domain (RBD) of the spike protein of SARS-CoV-2. The antioxidant activities of Pistacia integerrima leaves and fruits were 142.10 and 97.13µg/mL, respectively, whereas Pandanus odorifer leaves and fruits were 112.50 and 292.71µg/mL, respectively. Furthermore, leaf extracts from both plants had lower cytotoxicity against Artemia salina than fruit extracts. Gas chromatography-mass spectrometry analysis revealed a total of 145 potential phytochemicals from these extracts. Three phytochemicals, 28-demethyl-beta-amyrone, 24-Noroleana-3,12-diene, and stigmasterol, displayed binding free energy values of-8.3, -7.5, and-8.1 Kcal/mol, respectively, in complexes with the spike protein of SARS-CoV-2. The root-mean-square deviation, solvent-accessible surface area, radius of gyration, root-mean-square fluctuations, and hydrogen bonds were used to ensure the binding stability of the docked complexes in the atomistic simulation. Thus, wet-lab validations are necessary to support these findings.

Determining chromosomal arms 1p/19q co-deletion status in low graded glioma by cross correlation-periodogram pattern analysis

Prediction of mutational status of different graded glioma is extremely crucial for its diagnosis and treatment planning. Currently FISH and the surgical biopsy techniques are the ‘gold standard’ in the field of diagnostics; the analyses of which helps to decide appropriate treatment regime. In this study we proposed a novel approach to analyze structural MRI image signature pattern for predicting 1p/19q co-deletion status non-invasively. A total of 159 patients with grade-II and grade-III glioma were included in the analysis. These patients earlier underwent biopsy; the report of which confirmed 57 cases with no 1p/19q co-deletion and 102 cases with 1p/19q co-deletion. Tumor tissue heterogeneity was investigated by variance of cross correlation (VoCC). Significant differences in the pattern of VoCC between two classes was quantified using Lomb-Scargle (LS) periodogram. Energy and the cut-off frequency of LS power spectral density were derived and utilized as the features for classification. RUSBoost classifier was used that yield highest classification accuracy of 84% for G-II and 87% for G-III glioma respectively in classifying 1p/19q co-deleted and 1p/19q non-deleted glioma. In clinical practice the proposed technique can be utilized as a non-invasive pre-confirmatory test of glioma mutation, before wet-lab validation.

An ECHO of Cartilage: In Silico Prediction of Combinatorial Treatments to Switch Between Transient and Permanent Cartilage Phenotypes With Ex Vivo Validation.

A fundamental question in cartilage biology is: what determines the switch between permanent cartilage found in the articular joints and transient hypertrophic cartilage that functions as a template for bone? This switch is observed both in a subset of OA patients that develop osteophytes, as well as in cell-based tissue engineering strategies for joint repair. A thorough understanding of the mechanisms regulating cell fate provides opportunities for treatment of cartilage disease and tissue engineering strategies. The objective of this study was to understand the mechanisms that regulate the switch between permanent and transient cartilage using a computational model of chondrocytes, ECHO. To investigate large signaling networks that regulate cell fate decisions, we developed the software tool ANIMO, Analysis of Networks with interactive Modeling. In ANIMO, we generated an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 proteins with 200 interactions. We called this model ECHO, for executable chondrocyte. Previously, we showed that ECHO could be used to characterize mechanisms of cell fate decisions. ECHO was first developed based on a Boolean model of growth plate. Here, we show how the growth plate Boolean model was translated to ANIMO and how we adapted the topology and parameters to generate an articular cartilage model. In ANIMO, many combinations of overactivation/knockout were tested that result in a switch between permanent cartilage (SOX9+) and transient, hypertrophic cartilage (RUNX2+). We used model checking to prioritize combination treatments for wet-lab validation. Three combinatorial treatments were chosen and tested on metatarsals from 1-day old rat pups that were treated for 6 days. We found that a combination of IGF1 with inhibition of ERK1/2 had a positive effect on cartilage formation and growth, whereas activation of DLX5 combined with inhibition of PKA had a negative effect on cartilage formation and growth and resulted in increased cartilage hypertrophy. We show that our model describes cartilage formation, and that model checking can aid in choosing and prioritizing combinatorial treatments that interfere with normal cartilage development. Here we show that combinatorial treatments induce changes in the zonal distribution of cartilage, indication possible switches in cell fate. This indicates that simulations in ECHO aid in describing pathologies in which switches between cell fates are observed, such as OA.

Improved supervised prediction of aging-related genes via weighted dynamic network analysis

BackgroundThis study focuses on the task of supervised prediction of aging-related genes from -omics data. Unlike gene expression methods for this task that capture aging-specific information but ignore interactions between genes (i.e., their protein products), or protein–protein interaction (PPI) network methods for this task that account for PPIs but the PPIs are context-unspecific, we recently integrated the two data types into an aging-specific PPI subnetwork, which yielded more accurate aging-related gene predictions. However, a dynamic aging-specific subnetwork did not improve prediction performance compared to a static aging-specific subnetwork, despite the aging process being dynamic. This could be because the dynamic subnetwork was inferred using a naive Induced subgraph approach. Instead, we recently inferred a dynamic aging-specific subnetwork using a methodologically more advanced notion of network propagation (NP), which improved upon Induced dynamic aging-specific subnetwork in a different task, that of unsupervised analyses of the aging process.ResultsHere, we evaluate whether our existing NP-based dynamic subnetwork will improve upon the dynamic as well as static subnetwork constructed by the Induced approach in the considered task of supervised prediction of aging-related genes. The existing NP-based subnetwork is unweighted, i.e., it gives equal importance to each of the aging-specific PPIs. Because accounting for aging-specific edge weights might be important, we additionally propose a weighted NP-based dynamic aging-specific subnetwork. We demonstrate that a predictive machine learning model trained and tested on the weighted subnetwork yields higher accuracy when predicting aging-related genes than predictive models run on the existing unweighted dynamic or static subnetworks, regardless of whether the existing subnetworks were inferred using NP or the Induced approach.ConclusionsOur proposed weighted dynamic aging-specific subnetwork and its corresponding predictive model could guide with higher confidence than the existing data and models the discovery of novel aging-related gene candidates for future wet lab validation.

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations.

Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefore, computational screening has become an important way to prioritize drug combinations. Graph neural network has recently shown remarkable performance in the prediction of compound-protein interactions, but it has not been applied to the screening of drug combinations. In this paper, we proposed a deep learning model based on graph neural network and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The feature embeddings of drug molecule structure and gene expression profiles were taken as input to multilayer feedforward neural network to identify the synergistic drug combinations. We compared DeepDDS (Deep Learning for Drug-Drug Synergy prediction) with classical machine learning methods and other deep learning-based methods on benchmark data set, and the leave-one-out experimental results showed that DeepDDS achieved better performance than competitive methods. Also, on an independent test set released by well-known pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive methods by more than 16% predictive precision. Furthermore, we explored the interpretability of the graph attention network and found the correlation matrix of atomic features revealed important chemical substructures of drugs. We believed that DeepDDS is an effective tool that prioritized synergistic drug combinations for further wet-lab experiment validation. Source code and data are available at https://github.com/Sinwang404/DeepDDS/tree/master.

One common enemy, a pandemic, uniting interdisciplinary teams

One common enemy, a pandemic, uniting interdisciplinary teams

IRGvalid: A Robust in silico Method for Optimal Reference Gene Validation.

BackgroundAppropriate reference genes are critical to accurately quantifying relative gene expression in research and clinical applications. Numerous efforts have been made to select the most stable reference gene(s), but a consensus has yet to be achieved. In this report, we propose an in silico reference gene validation method, iRGvalid, that can be used as a universal tool to validate the reference genes recommended from different resources so as to identify the best ones without a need for any wet lab validation tests.MethodsiRGvalid takes advantage of high throughput gene expression data and is built on a double-normalization strategy. First, the expression level of each individual gene is normalized against the total gene expression level of each sample, followed by a target gene normalization to the candidate reference gene(s). Linear regression analysis is then performed between the pre- and post- normalized target gene across the whole sample set to evaluate the stability of the reference gene(s), which is positively associated with the Pearson correlation coefficient, Rt. The higher the Rt value, the more stable the reference gene. We applied iRGvalid to 14 candidate reference genes to validate and identify the most stable reference genes in four cancer types: lung adenocarcinoma, breast cancer, colon adenocarcinoma, and nasopharyngeal cancer. The stability of the reference gene is evaluated both individually and in groups of all possible combinations.ResultsHighly stable reference genes resulted in high Rt values regardless of the target gene used. The highest stability was achieved with a specific combination of 3 to 6 reference genes. A few genes were among the best reference genes across the cancer types studied here.ConclusioniRGvalid provides an easy and robust method to validate and identify the most stable reference gene or genes from a pool of candidate reference genes. The inclusivity of large expression data sets as well as the direct comparison of candidate reference genes makes it possible to identify reference genes with universal quality. This method can be used in any other gene expression studies when large cohorts of expression data are available.

Reconstruction of the miR-506-Quaking axis in Idiopathic Pulmonary Fibrosis using integrative multi-source bioinformatics

The family of RNA-binding proteins (RBP) functions as a crucial regulator of multiple biological processes and diseases. However, RBP function in the clinical setting of idiopathic pulmonary fibrosis (IPF) is still unknown. We developed a practical in silico screening approach for the characterization of RBPs using multi-sources data information and comparative molecular network bioinformatics followed by wet-lab validation studies. Data mining of bulk RNA-Sequencing data of tissues of patients with IPF identified Quaking (QKI) as a significant downregulated RBP. Cell-type specific expression was confirmed by single-cell RNA-Sequencing analysis of IPF patient data. We systematically analyzed the molecular interaction network around QKI and its functional interplay with microRNAs (miRs) in human lung fibroblasts and discovered a novel regulatory miR-506-QKI axis contributing to the pathogenesis of IPF. The in silico results were validated by in-house experiments applying model systems of miR and lung biology. This study supports an understanding of the intrinsic molecular mechanisms of IPF regulated by the miR-506-QKI axis. Initially applied to human lung disease, the herein presented integrative in silico data mining approach can be adapted to other disease entities, underlining its practical relevance in RBP research.

Modulating Target Protein Biology Through the Re-mapping of Conformational Distributions Using Small Molecules

Over the last 10 years considerable progress has been made in the application of small molecules to modulating protein-protein interactions (PPIs), and the navigation from “undruggable” to a host of candidate molecules in clinical trials has been well-charted in recent, comprehensive reviews. Structure-based design has played an important role in this scientific journey, with three dimensional structures guiding medicinal chemistry efforts. However, the importance of two additional dimensions: movement and time is only now being realised, as increasing computing power, closely aligned with wet lab validation, is applied to the challenge. Protein dynamics are fundamental to biology and disease, and application to PPI drug discovery has massively widened the scope for new chemical entities to influence function from allosteric, and previously unreported, sites. In this forward-looking perspective we highlight exciting, new opportunities for small molecules to modulate disease biology, by adjusting the frequency profile of natural conformational sampling, through the stabilisation of clinically desired conformers of target proteins.

An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires an urgent need to find effective therapeutics for the treatment of coronavirus disease 2019 (COVID-19). In this study, we developed an integrative drug repositioning framework, which fully takes advantage of machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 can interact with the nucleocapsid (N) protein of SARS-CoV-2 and is able to suppress the LPS-induced production of several inflammatory cytokines that are highly relevant to the prevention of immunopathology induced by SARS-CoV-2 infection.

In-silico Studies on Virulence Factors of Cryptococcus Species: Phylogenetic Analysis and B-cell Epitope Prediction

Virulence proteins ensure the survival of Cryptococcus in its host. The epitopes present in these virulence factors can modulate the host's immune system and contribute tocryptococcosis's pathobiology significantly. The amino acid sequences of virulence factors (glucuronoxylomannan (GXM), superoxide dismutase (SOD), mannoprotein (MP), urease, CAP binding protein, galactoxylomannan (GalXM), phospholipase-B, and laccase) of C. neoformans, C. n. grubii, and C. gattii were retrieved from NCBI. Analyses of the phylogenetic relationship between virulence factors were performed by using PhyML software and JMP 13.1 software. Further, ABCpred, BCPred, BcePred web servers were employed for the prediction of linear B-cell epitopes in amino acid sequences of said virulence factors. In all the three Cryptococcus species, laccase, CAP binding protein, and mannoprotein were highly conserved compared to GalXM, GXM, and SOD virulence factors. Superoxide dismutase (SOD) with the lowest gamma distribution value is considered to be highly adaptable. Further, the maximum number of B-cell epitopes was observed on the urease of C. n. grubii. In due course of time, Cu, Zn Superoxide dismutase (SOD) might play the main role in Cryptococcus species' pathogenicity due to its highly variable nature. Additionally, urease could be used to design epitope-based anti-cryptococcal drugs. Nonetheless, the results of this in-silico study need wet lab validation.

DeepCNV: a deep learning approach for authenticating copy number variations.

Copy number variations (CNVs) are an important class of variations contributing to the pathogenesis of many disease phenotypes. Detecting CNVs from genomic data remains difficult, and the most currently applied methods suffer from an unacceptably high false positive rate. A common practice is to have human experts manually review original CNV calls for filtering false positives before further downstream analysis or experimental validation. Here, we propose DeepCNV, a deep learning-based tool, intended to replace human experts when validating CNV calls, focusing on the calls made by one of the most accurate CNV callers, PennCNV. The sophistication of the deep neural network algorithm is enriched with over 10000 expert-scored samples that are split into training and testing sets. Variant confidence, especially for CNVs, is a main roadblock impeding the progress of linking CNVs with the disease. We show that DeepCNV adds to the confidence of the CNV calls with an optimal area under the receiver operating characteristic curve of 0.909, exceeding other machine learning methods. The superiority of DeepCNV was also benchmarked and confirmed using an experimental wet-lab validation dataset. We conclude that the improvement obtained by DeepCNV results in significantly fewer false positive results and failures to replicate the CNV association results.

In silico identification and experimental validation of cellular uptake by a new cell penetrating peptide P1 derived from MARCKS

Viral vectors for vaccine delivery are challenged by recently reported safety issues like immunogenicity and risk for cancer development, and thus there is a growing need for the development of non-viral vectors. Cell penetrating peptides (CPPs) are non-viral vectors that can enter plasma membranes efficiently and deliver a broad range of cargoes. Our bioinformatic prediction and wet-lab validation data suggested that peptide P1 derived from MARCKS protein phosphorylation site domain is a new potential CPP candidate. We found that peptide P1 can efficiently internalize into various cell lines in a concentration-dependent manner. Receptor-mediated endocytosis pathway is the major mechanism of P1 penetration, although P1 also directly penetrates the plasma membrane. We also found that peptide P1 has low cytotoxicity in cultured cell lines as well as mouse red blood cells. Furthermore, peptide P1 not only can enter into cultured cells itself, but it also can interact with plasmid DNA and mediate the functional delivery of plasmid DNA into cultured cells, even in hard-to-transfect cells. Combined, these findings indicate that P1 may be a promising vector for efficient intracellular delivery of bioactive cargos.

In Silico Studies Reveal Antiviral Effects of Traditional Indian Spices on COVID-19.

The global health emergency due to SARS-CoV-2 causing the COVID-19 pandemic emphasized the scientific community to intensify their research work for its therapeutic solution. In this study, Indian traditional spices owing to various medicinal properties were tested in silico for their inhibitory activity against SARS-CoV-2 proteins. SARS-CoV-2 spike proteins (SP) and main proteases (Mpro) play a significant role in infection development were considered as potential drug targets. A total of 75 phytochemicals present in traditional Indian spices retrieved from the published literature and Dr. Duke's Phytochemical and Ethnobotanical Database, were docked with Mpro (PDB IDs: 6YNQ), and the SP (PDB IDs: 6LXT and 6YOR). Through the screening process, 75 retrieved phytochemicals were docked with spike protein (PDB IDs: 6LXT and 6YOR) and main protease (PDB ID: 6YNQ) of SARS-CoV-2. Among them, myricetin, a flavonoid (rank score: 6LXT: -11.72383; 6YOR: -9.87943; 6YNQ: -11.68164) from Allium sativum L and Isovitexin, an example of flavone (rank score: 6LXT: -12.14922; 6YOR: -10.19443; 6YNQ: -12.60603) from Pimpinella anisumL were the most potent ligands against SP and Mpro of SARS-CoV-2. Whereas, Astragalin from Crocus sativus L.; Rutin from Illicium verum, Oxyguttiferone from Garcinia cambogia; Scopolin from Apium graveolens L, Luteolin from Salvia officinalis, Emodin, Aloe-emodin from Cinnamomum zeylanicium and Apigenin from Allium sativum L showed better inhibition against Mpro than SP of SARS-CoV-2. The amino acid residues like SER, LYS, ASP and TYR were found playing important role in protein-ligand interactions via hydrogen bonding and Vander Waals forces. Optimal use of traditional spices in our daily meals may help fight against COVID-19. This study also paves the path for herbal drug formulation against SARS-CoV-2 after wet lab validation.

Computational insights into RNAi-based therapeutics for foot and mouth disease of Bostaurus

Foot-and-mouth disease (FMD) endangers a large number of livestock populations across the globe being a highly contagious viral infection in wild and domestic cloven-hoofed animals. It adversely affects the socioeconomic status of millions of households. Vaccination has been used to protect animals against FMD virus (FMDV) to some extent but the effectiveness of available vaccines has been decreased due to high genetic variability in the FMDV genome. Another key aspect that the current vaccines are not favored is they do not provide the ability to differentiate between infected and vaccinated animals. Thus, RNA interference (RNAi) being a potential strategy to control virus replication, has opened up a new avenue for controlling the viral transmission. Hence, an attempt has been made here to establish the role of RNAi in therapeutic developments for FMD by computationally identifying (i) microRNA (miRNA) targets in FMDV using target prediction algorithms, (ii) targetable genomic regions in FMDV based on their dissimilarity with the host genome and, (iii) plausible anti-FMDV miRNA-like simulated nucleotide sequences (SNSs). The results revealed 12 mature host miRNAs that have 284 targets in 98 distinct FMDV genomic sequences. Wet-lab validation for anti-FMDV properties of 8 host miRNAs was carried out and all were observed to confer variable magnitude of antiviral effect. In addition, 14 miRBase miRNAs were found with better target accessibility in FMDV than that of Bostaurus. Further, 8 putative targetable regions having sense strand properties of siRNAs were identified on FMDV genes that are highly dissimilar with the host genome. A total of 16 SNSs having > 90% identity with mature miRNAs were also identified that have targets in FMDV genes. The information generated from this study is populated at http://bioinformatics.iasri.res.in/fmdisc/ to cater the needs of biologists, veterinarians and animal scientists working on FMD.

Molecular docking and simulation investigation: effect of beta-sesquiphellandrene with ionic integration on SARS-CoV2 and SFTS viruses

BackgroundAt present, viral diseases become major concern for the world. SARS-CoV2 and SFTS viruses are deadly in nature, and there is a need for developing best treatments for them. Modern in silico approaches were found to be very handy in determining putative drug molecules. In this study, we analyze interaction of beta-sesquiphellandrene (compound belongs to ginger) with spike protein (Sp) and membrane glycoprotein polyprotein (MPp). ResultsOur molecular docking and simulation study reveals the perfect binding pocket of Sp and MPp holding beta-sesquiphellandrene (bS). Binding energies for MPp-bS and Sp-bS were found to be − 9.5 kcal/mol and − 10.3 kcal/mol respectively. RMSD and RMSF values for docked complexes were found to be in selectable range, i.e., 1 to 3 Å and 1 to 8 Å respectively. Modern computational tools were used here to make this investigation fast and effective. Further, ADME analysis reveals the therapeutic validations for beta-sesquiphellandrene to act as a useful pharmacoactive compound. Beta-sesquiphellandrene provides not only inhibitory effect on spike protein of SARS-CoV2 but also similar inhibitory effects on membrane glycoprotein polyprotein complex of SFTS virus, which hampers the pathological initiation of the diseases caused by both the viruses, i.e., COVID-19 and severe fever with thrombocytopenia syndrome. ConclusionThis method of computational analysis was found to be rapid and effective, and opens new doors in the domain of in silico drug discovery. Beta-sesquiphellandrene can be used as effective medicine to control these harmful pathogens after wet lab validations.

Codon bias analyses on thyroid carcinoma genes.

Thyroid carcinoma is one of the most common cancers in the world. Although the genetics of thyroid carcinoma was intensively studied, new mechanisms could be involved in its development as the codon bias. In this paper, we studied the codon bias of thyroid-cancer genes, considering not only the sequences but also the synonymous mutations. Different measures and statistical analyses were employed to characterize the thyroid-cancer genes. We considered classical measures as RSCU and ENC, the compositional and protein characteristics, but also the codon bias landscape via the %MinMax algorithm. The compositional analyses highlighted two groups of thyroid cancer genes according to the GC% and GC3% content. The ENC did not show a clear codon bias in the genes. Differently, the RSCU analyses showed interesting codons that could play an important role in the development of thyroid cancer as the codon Ser-tcG. Furthermore, interesting synonymous mutations were detected that could affect the codon bias. The codon bias landscape detected genes enriched in rare codons as AKAP9 and KTN1. A cluster analysis based on %MinMax classified the thyroid cancer genes in four different groups according to the distribution of rare/frequent codons in the sequence. This is the first study that analyzed the codon bias in thyroid cancer genes based also on synonymous mutations. This study provided different hints that should be further investigated by wet-lab validation and that it could open new scenarios in the understanding the molecular mechanisms involved in thyroid cancer development based on codon bias.